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January 17, 2014 17:55
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| { | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "cell_type": "heading", | |
| "source": "Using R SEM library \"Lavaan\" in ipython notebook", | |
| "level": 1 | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "Notes and examples of using Lavaan from the notebook. " | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "I am new to Lavaan, so my knowledge of what is more/less important in SEM research applications is changing rapidly. What I've done here is work out what seem to be the best and/or easiest ways of moving the key data inputs for a lavaan SEM analysis into R from the notebook, and moving the key output data structures from R into the notebook. \n\n\nA key part of this is some neat use of the various '%R' line and cell magics (which make use of the rpy2 python library) for exchanging data between python and R. These operations should be generically useful for any R package. \n\n\nAlso important and of general applicability is the use of the pandas python library for flexible manipulation of data structures and plotting (via its in-built integration with matplotlib). \n\n\nThe statistical clout of R, the general all round excellence of python, and the graphical capabilities of the notebook platform are a formidable combination. " | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "\n\n<div align=\"right\"> John Griffiths (Cambridge/Toronto)</div>\n<div align=\"right\"> email: j.davidgriffiths@gmail.com</div>" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "---\n\n*View this notebook online*\n\n - as a [github gist](https://gist.github.com/JohnGriffiths/8478146)\n - as a [notebook](http://nbviewer.ipython.org/gist/JohnGriffiths/8478146) \n - as a slideshow (selected content; + c.f. caveats below!)\n - via [slideviewer](http://slideviewer.herokuapp.com/8478146?theme=sky)\n - via [gh-pages](http://johngriffiths.github.io/Discussions/using_R_SEM_library_lavaan_in_ipynb__TutorialExamples.slides.html?theme=sky)\n " | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": " \n \n*(note re: the slideshow - this is an add-on after writing the notebook. Some of the texty descriptions and long dataframe outputs don't lend themselves v. well to slideshow format. So they are eithe included with this caveat, or skipped in the slideshow)\n\n*(Some general words of advice on slideshows: they are a really great functionality of notebooks. I am still getting the hang of how best to use them. In general, I would suggest thinking about how notebooks might be used as slideshows **as you write them**. Split them up into sections with large text headings as you would do a slideshow. Have short bits of text or bullet point lists describing main points, and then also have longer bits of text elaborating. The longer bits would be targeted at notebook usage / consumption, the shorter bits for slideshow presentation. Similarly, have short bits of text describing figures and other code outputs. With slides you can't easily have long trains of steps without regular reminders of what you're doing, or complte summaries at the final step. All obvious points, really. \n\nDamian avila has been the source of much that is great about ipyuthon slideshows. See his [blog](http://www.damian.oquanta.info/categories/slideshow.html)\n\n\n ---" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "cell_type": "heading", | |
| "source": "Contents", | |
| "level": 2 | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "-" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "[Prelims](#Prelims) \n[Notebook Setup](#NotebookSetup) \n[Moving data between Python and R](#MovingData) \n[PoliticalDemocracy tutorial dataset example](#TutorialExample) \n[Beyond the tutorial](#Beyond) \n[Appendix 1: Lavaan syntax for mediation and multiple mediation](#Appendix1) \n[Appendix 2: Using R magics within python loops](#Appendix2) \n[Appendix 3: Some useful statmodels plotting options](#Appendix3)" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "<a name=\"Prelims\">\n\n[back to top](#Contents)" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "cell_type": "heading", | |
| "source": "Prelims", | |
| "level": 2 | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "***R magics***" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "The ipython R line and cell magics are what make this worth bothering with. They not only allow you to call R from notebook cells, but also to exchange certain types of data between R and the notebook very smoothly. \n\nFor more info on this google it. If I really had to tell you that you probably shouldn't be doing whatever it is you're doing. Everything is succinctly and pragmatically summarized in the tutorial notebook" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "([see here](http://nbviewer.ipython.org/urls/raw.github.com/ipython/ipython/3607712653c66d63e0d7f13f073bde8c0f209ba8/docs/examples/notebooks/rmagic_extension.ipynb))" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "***Pandas***" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "Pandas is awesome and a must for anyone using ipython notebooks. Why? Two reasons: i) flexible and powerful basic tools for reading, writing, and manipulating blocks of numbers+other information. ii) very nice integration with matplotlib. Also in the current context, iii) the pandas dataframes are explicitly based on R dataframes, so are the perfect partner in the python side of the relationship we will be nurturing. And also iv) whilst there are still some wrinkles (all the tools used here are all very new), the pandas-R combo seems pretty likely to be well maintained and developed in the coming years. \n\nFor more info on pandas, you know what to do. There are a particularly nice set of ipynb pandas tutorials available - see this and much much more at the [ipynb portal of glory](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks). The popular coursea data analysis course uses pandas as together with the python library [statsmodels](http://statsmodels.sourceforge.net/devel/examples/index.html) very effectively - and the materials are all in [notebooks](https://github.com/herrfz/dataanalysis). Statsmodels is basically the principal python alternative to R (better than scipy.stats imho, and more tuned to conventional linear modelling than scikits-learn, although less powerful in other areas). It's very R flavoured, although substantially less developed. It's pretty hard for a stats package to be more developed than R 'ecosystem' though, to be fair. At present I am using R and statsmodels pretty interchangeably for basic stuff. The procedures in this notebook show how to do this. They each have their pros and cons." | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "---\n\n**UPDATE** \n\n*There is now a pandas-rpy2 interface that isn't mentioned below but can provide some superior functionality to %Rpush and %Rpull; I would recommend checking out after going through this notebook and see which method works best for you. See http://pandas.pydata.org/pandas-docs/stable/r_interface.html\n\n\n---" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "***Lavaan***" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "In case you didn't know, Lavaan is an R library for latent variable modelling, which includes structural equation modeling (SEM), confirmatory factor analysis (CFA), and latent growth curve (LGC) models. It is also straightforward to use it for mediation and multiple mediation analyses. \n\n\nThe content of this notebook focuses on \nYou should go through the tutorials and documentation on the lavaan [website](http://lavaan.ugent.be/) before this notebook, which focuses on one of the example datasets. Another useful resource I have come across is the series of tutorials [here](http://jarrettbyrnes.info/ubc_sem/) ." | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "**Let's get cracking then...**\n" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "<a name=\"NotebookSetup\">\n\n[back to top](#Contents)" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "cell_type": "heading", | |
| "source": "Notebook Setup", | |
| "level": 2 | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "Importage" | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "-" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "%matplotlib inline\nimport matplotlib\nfrom matplotlib import pyplot as plt\n\nfrom os.path import join\n\nimport glob, json\n\nfrom datetime import datetime\n\nfrom IPython.display import HTML, SVG, Image\n\nimport pandas as pd\n\nimport warnings\n\nfrom statsmodels.graphics.plot_grids import scatter_ellipse\nfrom statsmodels.graphics.regressionplots import plot_partregress", | |
| "prompt_number": 1, | |
| "outputs": [], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "Load R magic, and lavaan and semPlot R libraries" | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "%load_ext rmagic", | |
| "prompt_number": 2, | |
| "outputs": [], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "%R library(lavaan);", | |
| "prompt_number": 3, | |
| "outputs": [ | |
| { | |
| "text": "This is lavaan 0.5-15\nlavaan is BETA software! Please report any bugs.\n", | |
| "output_type": "display_data", | |
| "metadata": {} | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "semPlot is a very nice tool that is goes hand-in-glove with Lavaan, and indeed many other SEM packages. Check out the examples here. It's very customizable. \n" | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "%R library(semPlot);", | |
| "prompt_number": 4, | |
| "outputs": [ | |
| { | |
| "text": "This is semPlot 0.3.3\nsemPlot is BETA software! Please report any bugs.\n", | |
| "output_type": "display_data", | |
| "metadata": {} | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "Notebook formatting" | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "#(read this simple handy hint here: http://stackoverflow.com/questions/18380168/center-output-plots-in-the-notebook)\ncss_dir = '../../DoctoralThesis/styles'\n#css_file = join(css_dir, 'custom.css')\ncss_file = join(css_dir, 'tmp.css') # 'custom_css_from_BayesianMethodsForHackersBook.css')\nmatplotlibrc_file = join(css_dir, 'bmh_matplotlibrc.json')\n\ndef css_styling():\n styles = open(css_file, \"r\").read() #or edit path to custom.css # \"custom.css\",\n return HTML(styles)\ncss_styling()\n\ns = json.load( open(matplotlibrc_file)) # \"bmh_matplotlibrc.json\") ) #edit path to json file\nmatplotlib.rcParams.update(s)\n\n#Some more figure config stuff\n\n#matplotlib.rcParams['savefig.dpi'] = 120\n#matplotlib.rcParams['figure.figsize'] = (12,9)\n#matplotlib.rcParams['font.size'] = 6\n#pd.set_option('display.max_rows', 200) # Forces IPython notebook to show large tables\n#pd.set_option('display.max_columns', 10)\n\n\n# This can be handy if you get lots of ugly ipython notebook warnings about deprecations etc, \n# and you're comfortable they're not immediately probelmatic\nwarnings.simplefilter(action = \"ignore\", category = DeprecationWarning)", | |
| "prompt_number": 6, | |
| "outputs": [], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "<a name=\"MovingData\"> \n\n[back to top](#Contents)\n" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "cell_type": "heading", | |
| "source": "Moving data between Python and R", | |
| "level": 2 | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "Pandas is absolutely the dogs bollocks for data manipulation and management, and it is well worth taking a little time to get the hang of things like concateting dataframes, indexes, multi-indexes, pivot tables, etc. But that's not essential to get started. In the notebook dataframes play ball very nicely with matplotlib. \n\nSo first thing I always do is put my data in dataframes. This often involves reading from .csv or .xls(x) files, which is a cinch in pandas. If your data is in .mat files then you may want use scipy.io.loadmat to read in as a numpy array, and then make a pandas dataframe from that. " | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "The Lavaan tutorial data is already in R, but for the sake of this example let's pull it out to python as if it were a dataset you've read in and prepared for a Lavaan analysis. This also gives us the opportunity to point out a few useful things to know about pulling and pushing dataframes between python and R. " | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "Rpush/pull" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "```python\n%Rpush <object>\n%Rpull <object>\n```" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "'-i' and '-o' with R line and cell magics\n" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "```python\n%R -i <object> -o <object> <code>\n\n%%R -i -o <object>\n<code>\n```" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "If the object is relatively simple (e.g. a string, or a simpler array), then the result will be the same each time. However for some more complex objects like 'named arrays', I have found that it is necessary to use Rpush/Rpull with the '-d flag. " | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "So to give a concrete example, let's pull the Lavaan 'PoliticalDemocracy' dataset into python, and push it back into R to continue with the tutorial" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "This is what R thinks it looks like (note the column names)" | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "%R print(PoliticalDemocracy)", | |
| "prompt_number": 7, | |
| "outputs": [ | |
| { | |
| "text": " y1 y2 y3 y4 y5 y6 y7 y8\n1 2.50 0.000000 3.333333 0.000000 1.250000 0.000000 3.726360 3.333333\n2 1.25 0.000000 3.333333 0.000000 6.250000 1.100000 6.666666 0.736999\n3 7.50 8.800000 9.999998 9.199991 8.750000 8.094061 9.999998 8.211809\n4 8.90 8.800000 9.999998 9.199991 8.907948 8.127979 9.999998 4.615086\n5 10.00 3.333333 9.999998 6.666666 7.500000 3.333333 9.999998 6.666666\n6 7.50 3.333333 6.666666 6.666666 6.250000 1.100000 6.666666 0.368500\n7 7.50 3.333333 6.666666 6.666666 5.000000 2.233333 8.271257 1.485166\n8 7.50 2.233333 9.999998 1.496333 6.250000 3.333333 9.999998 6.666666\n9 2.50 3.333333 3.333333 3.333333 6.250000 3.333333 3.333333 3.333333\n10 10.00 6.666666 9.999998 8.899991 8.750000 6.666666 9.999998 10.000000\n11 7.50 3.333333 9.999998 6.666666 8.750000 3.333333 9.999998 6.666666\n12 7.50 3.333333 6.666666 6.666666 8.750000 3.333333 6.666666 6.666666\n13 7.50 3.333333 9.999998 6.666666 7.500000 3.333333 6.666666 10.000000\n14 7.50 7.766664 9.999998 6.666666 7.500000 0.000000 9.999998 0.000000\n15 7.50 9.999998 3.333333 10.000000 7.500000 6.666666 9.999998 10.000000\n16 7.50 9.999998 9.999998 7.766666 7.500000 1.100000 6.666666 6.666666\n17 2.50 3.333333 6.666666 6.666666 5.000000 1.100000 6.666666 0.368500\n18 1.25 0.000000 3.333333 3.333333 1.250000 3.333333 3.333333 3.333333\n19 10.00 9.999998 9.999998 10.000000 8.750000 9.999998 9.999998 10.000000\n20 7.50 3.333299 3.333333 6.666666 7.500000 2.233299 6.666666 2.948164\n21 10.00 9.999998 9.999998 10.000000 10.000000 9.999998 9.999998 10.000000\n22 1.25 0.000000 0.000000 0.000000 2.500000 0.000000 0.000000 0.000000\n23 2.50 0.000000 3.333333 3.333333 2.500000 0.000000 3.333333 3.333333\n24 7.50 6.666666 9.999998 10.000000 7.500000 6.666666 9.999998 7.766666\n25 8.50 9.999998 6.666666 6.666666 8.750000 9.999998 7.351018 6.666666\n26 6.10 0.000000 5.400000 3.333333 0.000000 0.000000 4.696028 3.333333\n27 3.30 0.000000 6.666666 3.333333 6.250000 0.000000 6.666666 3.333333\n28 2.90 3.333333 6.666666 3.333333 2.385559 0.000000 3.177568 1.116666\n29 9.20 0.000000 9.900000 3.333333 7.609660 0.000000 8.118828 3.333333\n30 6.90 0.000000 6.666666 3.333333 4.226033 0.000000 0.000000 0.000000\n31 2.90 0.000000 3.333333 3.333333 5.000000 0.000000 3.333333 3.333333\n32 2.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000\n33 5.00 0.000000 3.333333 3.333333 5.000000 0.000000 3.333333 3.333333\n34 5.00 0.000000 9.999998 3.333333 0.000000 0.000000 3.333333 0.744370\n35 4.10 9.999998 4.700000 6.666666 3.750000 0.000000 7.827667 6.666666\n36 6.30 9.999998 9.999998 6.666666 6.250000 2.233333 6.666666 2.955702\n37 5.20 4.999998 6.600000 3.333333 3.633403 1.100000 3.314128 3.333333\n38 5.00 3.333333 6.400000 6.666666 2.844997 0.000000 4.429657 1.485166\n39 3.10 4.999998 4.200000 5.000000 3.750000 0.000000 6.164304 3.333333\n40 4.10 9.999998 6.666666 3.333333 5.000000 0.000000 4.938089 2.233333\n41 5.00 9.999998 6.666666 1.666666 5.000000 0.000000 6.666666 0.368500\n42 5.00 7.700000 6.666666 8.399997 6.250000 4.358243 9.999998 4.141377\n43 5.00 6.200000 9.999998 6.060997 5.000000 2.782771 6.666666 4.974739\n44 5.60 4.900000 0.000000 0.000000 6.555647 4.055463 6.666666 3.821796\n45 5.70 4.800000 0.000000 0.000000 6.555647 4.055463 0.000000 0.000000\n46 7.50 9.999998 7.900000 6.666666 3.750000 9.999998 7.631891 6.666666\n47 2.50 0.000000 6.666666 3.333333 2.500000 0.000000 0.000000 0.000000\n48 8.90 9.999998 9.700000 6.666666 5.000000 9.999998 9.556024 6.666666\n49 7.60 0.000000 10.000000 0.000000 5.000000 1.100000 6.666666 1.099999\n50 7.80 9.999998 6.666666 6.666666 5.000000 3.333333 6.666666 6.666666\n51 2.50 0.000000 6.666666 3.333333 5.000000 0.000000 6.666666 3.333333\n52 3.80 0.000000 5.100000 0.000000 3.750000 0.000000 6.666666 1.485166\n53 5.00 3.333333 3.333333 2.233333 5.000000 3.333333 6.666666 5.566663\n54 6.25 3.333333 9.999998 2.955702 6.250000 5.566663 9.999998 6.666666\n55 1.25 0.000000 3.333333 0.000000 2.500000 0.000000 0.000000 0.000000\n56 1.25 0.000000 4.700000 0.736999 2.500000 0.000000 3.333333 3.333333\n57 1.25 0.000000 6.666666 0.000000 2.500000 0.000000 5.228375 0.000000\n58 7.50 7.766664 9.999998 6.666666 7.500000 3.333333 9.999998 6.666666\n59 2.50 0.000000 6.666666 4.433333 5.000000 0.000000 6.666666 1.485166\n60 7.50 9.999998 9.999998 10.000000 8.750000 9.999998 9.999998 10.000000\n61 1.25 0.000000 0.000000 0.000000 1.250000 0.000000 0.000000 0.000000\n62 1.25 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000\n63 2.50 0.000000 0.000000 0.000000 0.000000 0.000000 6.666666 2.948164\n64 6.25 2.233299 6.666666 2.970332 3.750000 3.333299 6.666666 3.333333\n65 7.50 9.999998 9.999998 10.000000 7.500000 9.999998 9.999998 10.000000\n66 5.00 0.000000 6.100000 0.000000 5.000000 3.333333 9.999998 3.333333\n67 7.50 9.999998 9.999998 10.000000 3.750000 9.999998 9.999998 10.000000\n68 4.90 2.233333 9.999998 0.000000 5.000000 0.000000 3.621989 3.333333\n69 5.00 0.000000 8.200000 0.000000 5.000000 0.000000 0.000000 0.000000\n70 2.90 3.333333 6.666666 3.333333 2.500000 3.333333 6.666666 3.333333\n71 5.40 9.999998 6.666666 3.333333 3.750000 6.666666 6.666666 1.485166\n72 7.50 8.800000 9.999998 6.066666 7.500000 6.666666 9.999998 6.666666\n73 7.50 7.000000 9.999998 6.852998 7.500000 6.348340 6.666666 7.508044\n74 10.00 6.666666 9.999998 10.000000 10.000000 6.666666 9.999998 10.000000\n75 3.75 3.333333 0.000000 0.000000 1.250000 3.333333 0.000000 0.000000\n x1 x2 x3\n1 4.442651 3.637586 2.557615\n2 5.384495 5.062595 3.568079\n3 5.961005 6.255750 5.224433\n4 6.285998 7.567863 6.267495\n5 5.863631 6.818924 4.573679\n6 5.533389 5.135798 3.892270\n7 5.308268 5.075174 3.316213\n8 5.347108 4.852030 4.263183\n9 5.521461 5.241747 4.115168\n10 5.828946 5.370638 4.446216\n11 5.916202 6.423247 3.791545\n12 5.398163 6.246107 4.535708\n13 6.622736 7.872074 4.906154\n14 5.204007 5.225747 4.561047\n15 5.509388 6.202536 4.586286\n16 5.262690 5.820083 3.948911\n17 4.700480 5.023881 4.394491\n18 5.209486 4.465908 4.510268\n19 5.916202 6.732211 5.829084\n20 6.523562 6.992096 6.424591\n21 6.238325 6.746412 5.741711\n22 5.976351 6.712956 5.948168\n23 5.631212 5.937536 5.686755\n24 6.033086 6.093570 4.611429\n25 6.196444 6.704414 5.475261\n26 4.248495 2.708050 1.740830\n27 5.141664 4.564348 2.255134\n28 4.174387 3.688879 3.046927\n29 4.382027 2.890372 1.711279\n30 4.290459 1.609438 1.001674\n31 4.934474 4.234107 1.418971\n32 3.850148 1.945910 2.345229\n33 5.181784 4.394449 3.167167\n34 5.062595 4.595120 3.834970\n35 4.691348 4.143135 2.255134\n36 4.248495 3.367296 3.217506\n37 5.564520 5.236442 2.677633\n38 4.727388 3.610918 1.418971\n39 4.143135 2.302585 1.418971\n40 4.317488 4.955827 4.249888\n41 5.141664 4.430817 3.046927\n42 4.488636 3.465736 2.013579\n43 4.615121 4.941642 2.255134\n44 3.850148 2.397895 1.740830\n45 3.970292 2.397895 1.050741\n46 3.784190 3.091042 2.113313\n47 3.806662 2.079442 2.137561\n48 4.532599 3.610918 1.587802\n49 5.117994 4.934474 3.834970\n50 5.049856 5.111988 4.381490\n51 5.393628 5.638355 4.169451\n52 4.477337 3.931826 2.474671\n53 5.257495 5.840642 5.001796\n54 5.379897 5.505332 3.299937\n55 5.298317 6.274762 4.381490\n56 4.859812 5.669881 3.537416\n57 4.969813 5.564520 4.510268\n58 6.011267 6.253829 5.001796\n59 5.075174 5.252273 5.350708\n60 6.736967 7.125283 6.330518\n61 5.225747 5.451038 3.167167\n62 4.025352 1.791759 2.657972\n63 4.234107 2.708050 2.474671\n64 4.644391 5.564520 3.046927\n65 4.418841 4.941642 3.380653\n66 4.262680 4.219508 4.368462\n67 4.875197 4.700480 3.834970\n68 4.189655 1.386294 1.418971\n69 4.521789 4.127134 2.113313\n70 4.653960 3.555348 1.881917\n71 4.477337 3.091042 1.987909\n72 5.337538 5.631212 3.491004\n73 6.129050 6.403574 5.001796\n74 5.003946 4.962845 3.976994\n75 4.488636 4.897840 2.867566\n", | |
| "output_type": "display_data", | |
| "metadata": {} | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "If we just use %Rpull, we lose the column names" | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "%Rpull PoliticalDemocracy\nPD1 = PoliticalDemocracy", | |
| "prompt_number": 8, | |
| "outputs": [], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "PD1", | |
| "prompt_number": 9, | |
| "outputs": [ | |
| { | |
| "text": "array([[ 2.5 , 1.25 , 7.5 , 8.9 , 10. ,\n 7.5 , 7.5 , 7.5 , 2.5 , 10. ,\n 7.5 , 7.5 , 7.5 , 7.5 , 7.5 ,\n 7.5 , 2.5 , 1.25 , 10. , 7.5 ,\n 10. , 1.25 , 2.5 , 7.5 , 8.5 ,\n 6.1 , 3.3 , 2.9 , 9.2 , 6.9 ,\n 2.9 , 2. , 5. , 5. , 4.1 ,\n 6.3 , 5.2 , 5. , 3.1 , 4.1 ,\n 5. , 5. , 5. , 5.6 , 5.7 ,\n 7.5 , 2.5 , 8.9 , 7.6 , 7.8 ,\n 2.5 , 3.8 , 5. , 6.25 , 1.25 ,\n 1.25 , 1.25 , 7.5 , 2.5 , 7.5 ,\n 1.25 , 1.25 , 2.5 , 6.25 , 7.5 ,\n 5. , 7.5 , 4.9 , 5. , 2.9 ,\n 5.4 , 7.5 , 7.5 , 10. , 3.75 ],\n [ 0. , 0. , 8.8 , 8.8 , 3.333333,\n 3.333333, 3.333333, 2.233333, 3.333333, 6.666666,\n 3.333333, 3.333333, 3.333333, 7.766664, 9.999998,\n 9.999998, 3.333333, 0. , 9.999998, 3.333299,\n 9.999998, 0. , 0. , 6.666666, 9.999998,\n 0. , 0. , 3.333333, 0. , 0. ,\n 0. , 0. , 0. , 0. , 9.999998,\n 9.999998, 4.999998, 3.333333, 4.999998, 9.999998,\n 9.999998, 7.7 , 6.2 , 4.9 , 4.8 ,\n 9.999998, 0. , 9.999998, 0. , 9.999998,\n 0. , 0. , 3.333333, 3.333333, 0. ,\n 0. , 0. , 7.766664, 0. , 9.999998,\n 0. , 0. , 0. , 2.233299, 9.999998,\n 0. , 9.999998, 2.233333, 0. , 3.333333,\n 9.999998, 8.8 , 7. , 6.666666, 3.333333],\n [ 3.333333, 3.333333, 9.999998, 9.999998, 9.999998,\n 6.666666, 6.666666, 9.999998, 3.333333, 9.999998,\n 9.999998, 6.666666, 9.999998, 9.999998, 3.333333,\n 9.999998, 6.666666, 3.333333, 9.999998, 3.333333,\n 9.999998, 0. , 3.333333, 9.999998, 6.666666,\n 5.4 , 6.666666, 6.666666, 9.9 , 6.666666,\n 3.333333, 0. , 3.333333, 9.999998, 4.7 ,\n 9.999998, 6.6 , 6.4 , 4.2 , 6.666666,\n 6.666666, 6.666666, 9.999998, 0. , 0. ,\n 7.9 , 6.666666, 9.7 , 10. , 6.666666,\n 6.666666, 5.1 , 3.333333, 9.999998, 3.333333,\n 4.7 , 6.666666, 9.999998, 6.666666, 9.999998,\n 0. , 0. , 0. , 6.666666, 9.999998,\n 6.1 , 9.999998, 9.999998, 8.2 , 6.666666,\n 6.666666, 9.999998, 9.999998, 9.999998, 0. ],\n [ 0. , 0. , 9.199991, 9.199991, 6.666666,\n 6.666666, 6.666666, 1.496333, 3.333333, 8.899991,\n 6.666666, 6.666666, 6.666666, 6.666666, 10. ,\n 7.766666, 6.666666, 3.333333, 10. , 6.666666,\n 10. , 0. , 3.333333, 10. , 6.666666,\n 3.333333, 3.333333, 3.333333, 3.333333, 3.333333,\n 3.333333, 0. , 3.333333, 3.333333, 6.666666,\n 6.666666, 3.333333, 6.666666, 5. , 3.333333,\n 1.666666, 8.399997, 6.060997, 0. , 0. ,\n 6.666666, 3.333333, 6.666666, 0. , 6.666666,\n 3.333333, 0. , 2.233333, 2.955702, 0. ,\n 0.736999, 0. , 6.666666, 4.433333, 10. ,\n 0. , 0. , 0. , 2.970332, 10. ,\n 0. , 10. , 0. , 0. , 3.333333,\n 3.333333, 6.066666, 6.852998, 10. , 0. ],\n [ 1.25 , 6.25 , 8.75 , 8.907948, 7.5 ,\n 6.25 , 5. , 6.25 , 6.25 , 8.75 ,\n 8.75 , 8.75 , 7.5 , 7.5 , 7.5 ,\n 7.5 , 5. , 1.25 , 8.75 , 7.5 ,\n 10. , 2.5 , 2.5 , 7.5 , 8.75 ,\n 0. , 6.25 , 2.385559, 7.60966 , 4.226033,\n 5. , 0. , 5. , 0. , 3.75 ,\n 6.25 , 3.633403, 2.844997, 3.75 , 5. ,\n 5. , 6.25 , 5. , 6.555647, 6.555647,\n 3.75 , 2.5 , 5. , 5. , 5. ,\n 5. , 3.75 , 5. , 6.25 , 2.5 ,\n 2.5 , 2.5 , 7.5 , 5. , 8.75 ,\n 1.25 , 0. , 0. , 3.75 , 7.5 ,\n 5. , 3.75 , 5. , 5. , 2.5 ,\n 3.75 , 7.5 , 7.5 , 10. , 1.25 ],\n [ 0. , 1.1 , 8.094061, 8.127979, 3.333333,\n 1.1 , 2.233333, 3.333333, 3.333333, 6.666666,\n 3.333333, 3.333333, 3.333333, 0. , 6.666666,\n 1.1 , 1.1 , 3.333333, 9.999998, 2.233299,\n 9.999998, 0. , 0. , 6.666666, 9.999998,\n 0. , 0. , 0. , 0. , 0. ,\n 0. , 0. , 0. , 0. , 0. ,\n 2.233333, 1.1 , 0. , 0. , 0. ,\n 0. , 4.358243, 2.782771, 4.055463, 4.055463,\n 9.999998, 0. , 9.999998, 1.1 , 3.333333,\n 0. , 0. , 3.333333, 5.566663, 0. ,\n 0. , 0. , 3.333333, 0. , 9.999998,\n 0. , 0. , 0. , 3.333299, 9.999998,\n 3.333333, 9.999998, 0. , 0. , 3.333333,\n 6.666666, 6.666666, 6.34834 , 6.666666, 3.333333],\n [ 3.72636 , 6.666666, 9.999998, 9.999998, 9.999998,\n 6.666666, 8.271257, 9.999998, 3.333333, 9.999998,\n 9.999998, 6.666666, 6.666666, 9.999998, 9.999998,\n 6.666666, 6.666666, 3.333333, 9.999998, 6.666666,\n 9.999998, 0. , 3.333333, 9.999998, 7.351018,\n 4.696028, 6.666666, 3.177568, 8.118828, 0. ,\n 3.333333, 0. , 3.333333, 3.333333, 7.827667,\n 6.666666, 3.314128, 4.429657, 6.164304, 4.938089,\n 6.666666, 9.999998, 6.666666, 6.666666, 0. ,\n 7.631891, 0. , 9.556024, 6.666666, 6.666666,\n 6.666666, 6.666666, 6.666666, 9.999998, 0. ,\n 3.333333, 5.228375, 9.999998, 6.666666, 9.999998,\n 0. , 0. , 6.666666, 6.666666, 9.999998,\n 9.999998, 9.999998, 3.621989, 0. , 6.666666,\n 6.666666, 9.999998, 6.666666, 9.999998, 0. ],\n [ 3.333333, 0.736999, 8.211809, 4.615086, 6.666666,\n 0.3685 , 1.485166, 6.666666, 3.333333, 10. ,\n 6.666666, 6.666666, 10. , 0. , 10. ,\n 6.666666, 0.3685 , 3.333333, 10. , 2.948164,\n 10. , 0. , 3.333333, 7.766666, 6.666666,\n 3.333333, 3.333333, 1.116666, 3.333333, 0. ,\n 3.333333, 0. , 3.333333, 0.74437 , 6.666666,\n 2.955702, 3.333333, 1.485166, 3.333333, 2.233333,\n 0.3685 , 4.141377, 4.974739, 3.821796, 0. ,\n 6.666666, 0. , 6.666666, 1.099999, 6.666666,\n 3.333333, 1.485166, 5.566663, 6.666666, 0. ,\n 3.333333, 0. , 6.666666, 1.485166, 10. ,\n 0. , 0. , 2.948164, 3.333333, 10. ,\n 3.333333, 10. , 3.333333, 0. , 3.333333,\n 1.485166, 6.666666, 7.508044, 10. , 0. ],\n [ 4.442651, 5.384495, 5.961005, 6.285998, 5.863631,\n 5.533389, 5.308268, 5.347108, 5.521461, 5.828946,\n 5.916202, 5.398163, 6.622736, 5.204007, 5.509388,\n 5.26269 , 4.70048 , 5.209486, 5.916202, 6.523562,\n 6.238325, 5.976351, 5.631212, 6.033086, 6.196444,\n 4.248495, 5.141664, 4.174387, 4.382027, 4.290459,\n 4.934474, 3.850148, 5.181784, 5.062595, 4.691348,\n 4.248495, 5.56452 , 4.727388, 4.143135, 4.317488,\n 5.141664, 4.488636, 4.615121, 3.850148, 3.970292,\n 3.78419 , 3.806662, 4.532599, 5.117994, 5.049856,\n 5.393628, 4.477337, 5.257495, 5.379897, 5.298317,\n 4.859812, 4.969813, 6.011267, 5.075174, 6.736967,\n 5.225747, 4.025352, 4.234107, 4.644391, 4.418841,\n 4.26268 , 4.875197, 4.189655, 4.521789, 4.65396 ,\n 4.477337, 5.337538, 6.12905 , 5.003946, 4.488636],\n [ 3.637586, 5.062595, 6.25575 , 7.567863, 6.818924,\n 5.135798, 5.075174, 4.85203 , 5.241747, 5.370638,\n 6.423247, 6.246107, 7.872074, 5.225747, 6.202536,\n 5.820083, 5.023881, 4.465908, 6.732211, 6.992096,\n 6.746412, 6.712956, 5.937536, 6.09357 , 6.704414,\n 2.70805 , 4.564348, 3.688879, 2.890372, 1.609438,\n 4.234107, 1.94591 , 4.394449, 4.59512 , 4.143135,\n 3.367296, 5.236442, 3.610918, 2.302585, 4.955827,\n 4.430817, 3.465736, 4.941642, 2.397895, 2.397895,\n 3.091042, 2.079442, 3.610918, 4.934474, 5.111988,\n 5.638355, 3.931826, 5.840642, 5.505332, 6.274762,\n 5.669881, 5.56452 , 6.253829, 5.252273, 7.125283,\n 5.451038, 1.791759, 2.70805 , 5.56452 , 4.941642,\n 4.219508, 4.70048 , 1.386294, 4.127134, 3.555348,\n 3.091042, 5.631212, 6.403574, 4.962845, 4.89784 ],\n [ 2.557615, 3.568079, 5.224433, 6.267495, 4.573679,\n 3.89227 , 3.316213, 4.263183, 4.115168, 4.446216,\n 3.791545, 4.535708, 4.906154, 4.561047, 4.586286,\n 3.948911, 4.394491, 4.510268, 5.829084, 6.424591,\n 5.741711, 5.948168, 5.686755, 4.611429, 5.475261,\n 1.74083 , 2.255134, 3.046927, 1.711279, 1.001674,\n 1.418971, 2.345229, 3.167167, 3.83497 , 2.255134,\n 3.217506, 2.677633, 1.418971, 1.418971, 4.249888,\n 3.046927, 2.013579, 2.255134, 1.74083 , 1.050741,\n 2.113313, 2.137561, 1.587802, 3.83497 , 4.38149 ,\n 4.169451, 2.474671, 5.001796, 3.299937, 4.38149 ,\n 3.537416, 4.510268, 5.001796, 5.350708, 6.330518,\n 3.167167, 2.657972, 2.474671, 3.046927, 3.380653,\n 4.368462, 3.83497 , 1.418971, 2.113313, 1.881917,\n 1.987909, 3.491004, 5.001796, 3.976994, 2.867566]])", | |
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| { | |
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| "slide_type": "skip" | |
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| }, | |
| "cell_type": "code", | |
| "input": "PD1_df = pd.DataFrame(PD1)\nPD1_df", | |
| "prompt_number": 10, | |
| "outputs": [ | |
| { | |
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3.333333</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 1.100000</td>\n <td> 1.100000</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 2.233299</td>\n <td>...</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> 3.726360</td>\n <td> 6.666666</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 8.271257</td>\n <td> 9.999998</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td>...</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> 3.333333</td>\n <td> 0.736999</td>\n <td> 8.211809</td>\n <td> 4.615086</td>\n <td> 6.666666</td>\n <td> 0.368500</td>\n <td> 1.485166</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 10.000000</td>\n <td> 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5.225747</td>\n <td> 6.202536</td>\n <td> 5.820083</td>\n <td> 5.023881</td>\n <td> 4.465908</td>\n <td> 6.732211</td>\n <td> 6.992096</td>\n <td>...</td>\n </tr>\n <tr>\n <th>10</th>\n <td> 2.557615</td>\n <td> 3.568079</td>\n <td> 5.224433</td>\n <td> 6.267495</td>\n <td> 4.573679</td>\n <td> 3.892270</td>\n <td> 3.316213</td>\n <td> 4.263183</td>\n <td> 4.115168</td>\n <td> 4.446216</td>\n <td> 3.791545</td>\n <td> 4.535708</td>\n <td> 4.906154</td>\n <td> 4.561047</td>\n <td> 4.586286</td>\n <td> 3.948911</td>\n <td> 4.394491</td>\n <td> 4.510268</td>\n <td> 5.829084</td>\n <td> 6.424591</td>\n <td>...</td>\n </tr>\n </tbody>\n</table>\n<p>11 rows × 75 columns</p>\n</div>", | |
| "output_type": "pyout", | |
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| "prompt_number": 10 | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "To get a pandas dataframe looking the same as the R dataframe above, we need to use the '-d' flag:" | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "%Rpull -d PoliticalDemocracy\nPD2 = PoliticalDemocracy", | |
| "prompt_number": 11, | |
| "outputs": [], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "PD2", | |
| "prompt_number": 12, | |
| "outputs": [ | |
| { | |
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6.992096, 6.424591),\n (10.0, 9.999998, 9.999998, 10.0, 10.0, 9.999998, 9.999998, 10.0, 6.238325, 6.746412, 5.741711),\n (1.25, 0.0, 0.0, 0.0, 2.5, 0.0, 0.0, 0.0, 5.976351, 6.712956, 5.948168),\n (2.5, 0.0, 3.333333, 3.333333, 2.5, 0.0, 3.333333, 3.333333, 5.631212, 5.937536, 5.686755),\n (7.5, 6.666666, 9.999998, 10.0, 7.5, 6.666666, 9.999998, 7.766666, 6.033086, 6.09357, 4.611429),\n (8.5, 9.999998, 6.666666, 6.666666, 8.75, 9.999998, 7.351018, 6.666666, 6.196444, 6.704414, 5.475261),\n (6.1, 0.0, 5.4, 3.333333, 0.0, 0.0, 4.696028, 3.333333, 4.248495, 2.70805, 1.74083),\n (3.3, 0.0, 6.666666, 3.333333, 6.25, 0.0, 6.666666, 3.333333, 5.141664, 4.564348, 2.255134),\n (2.9, 3.333333, 6.666666, 3.333333, 2.385559, 0.0, 3.177568, 1.116666, 4.174387, 3.688879, 3.046927),\n (9.2, 0.0, 9.9, 3.333333, 7.60966, 0.0, 8.118828, 3.333333, 4.382027, 2.890372, 1.711279),\n (6.9, 0.0, 6.666666, 3.333333, 4.226033, 0.0, 0.0, 0.0, 4.290459, 1.609438, 1.001674),\n (2.9, 0.0, 3.333333, 3.333333, 5.0, 0.0, 3.333333, 3.333333, 4.934474, 4.234107, 1.418971),\n (2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.850148, 1.94591, 2.345229),\n (5.0, 0.0, 3.333333, 3.333333, 5.0, 0.0, 3.333333, 3.333333, 5.181784, 4.394449, 3.167167),\n (5.0, 0.0, 9.999998, 3.333333, 0.0, 0.0, 3.333333, 0.74437, 5.062595, 4.59512, 3.83497),\n (4.1, 9.999998, 4.7, 6.666666, 3.75, 0.0, 7.827667, 6.666666, 4.691348, 4.143135, 2.255134),\n (6.3, 9.999998, 9.999998, 6.666666, 6.25, 2.233333, 6.666666, 2.955702, 4.248495, 3.367296, 3.217506),\n (5.2, 4.999998, 6.6, 3.333333, 3.633403, 1.1, 3.314128, 3.333333, 5.56452, 5.236442, 2.677633),\n (5.0, 3.333333, 6.4, 6.666666, 2.844997, 0.0, 4.429657, 1.485166, 4.727388, 3.610918, 1.418971),\n (3.1, 4.999998, 4.2, 5.0, 3.75, 0.0, 6.164304, 3.333333, 4.143135, 2.302585, 1.418971),\n (4.1, 9.999998, 6.666666, 3.333333, 5.0, 0.0, 4.938089, 2.233333, 4.317488, 4.955827, 4.249888),\n (5.0, 9.999998, 6.666666, 1.666666, 5.0, 0.0, 6.666666, 0.3685, 5.141664, 4.430817, 3.046927),\n (5.0, 7.7, 6.666666, 8.399997, 6.25, 4.358243, 9.999998, 4.141377, 4.488636, 3.465736, 2.013579),\n (5.0, 6.2, 9.999998, 6.060997, 5.0, 2.782771, 6.666666, 4.974739, 4.615121, 4.941642, 2.255134),\n (5.6, 4.9, 0.0, 0.0, 6.555647, 4.055463, 6.666666, 3.821796, 3.850148, 2.397895, 1.74083),\n (5.7, 4.8, 0.0, 0.0, 6.555647, 4.055463, 0.0, 0.0, 3.970292, 2.397895, 1.050741),\n (7.5, 9.999998, 7.9, 6.666666, 3.75, 9.999998, 7.631891, 6.666666, 3.78419, 3.091042, 2.113313),\n (2.5, 0.0, 6.666666, 3.333333, 2.5, 0.0, 0.0, 0.0, 3.806662, 2.079442, 2.137561),\n (8.9, 9.999998, 9.7, 6.666666, 5.0, 9.999998, 9.556024, 6.666666, 4.532599, 3.610918, 1.587802),\n (7.6, 0.0, 10.0, 0.0, 5.0, 1.1, 6.666666, 1.099999, 5.117994, 4.934474, 3.83497),\n (7.8, 9.999998, 6.666666, 6.666666, 5.0, 3.333333, 6.666666, 6.666666, 5.049856, 5.111988, 4.38149),\n (2.5, 0.0, 6.666666, 3.333333, 5.0, 0.0, 6.666666, 3.333333, 5.393628, 5.638355, 4.169451),\n (3.8, 0.0, 5.1, 0.0, 3.75, 0.0, 6.666666, 1.485166, 4.477337, 3.931826, 2.474671),\n (5.0, 3.333333, 3.333333, 2.233333, 5.0, 3.333333, 6.666666, 5.566663, 5.257495, 5.840642, 5.001796),\n (6.25, 3.333333, 9.999998, 2.955702, 6.25, 5.566663, 9.999998, 6.666666, 5.379897, 5.505332, 3.299937),\n (1.25, 0.0, 3.333333, 0.0, 2.5, 0.0, 0.0, 0.0, 5.298317, 6.274762, 4.38149),\n (1.25, 0.0, 4.7, 0.736999, 2.5, 0.0, 3.333333, 3.333333, 4.859812, 5.669881, 3.537416),\n (1.25, 0.0, 6.666666, 0.0, 2.5, 0.0, 5.228375, 0.0, 4.969813, 5.56452, 4.510268),\n (7.5, 7.766664, 9.999998, 6.666666, 7.5, 3.333333, 9.999998, 6.666666, 6.011267, 6.253829, 5.001796),\n (2.5, 0.0, 6.666666, 4.433333, 5.0, 0.0, 6.666666, 1.485166, 5.075174, 5.252273, 5.350708),\n (7.5, 9.999998, 9.999998, 10.0, 8.75, 9.999998, 9.999998, 10.0, 6.736967, 7.125283, 6.330518),\n (1.25, 0.0, 0.0, 0.0, 1.25, 0.0, 0.0, 0.0, 5.225747, 5.451038, 3.167167),\n (1.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.025352, 1.791759, 2.657972),\n (2.5, 0.0, 0.0, 0.0, 0.0, 0.0, 6.666666, 2.948164, 4.234107, 2.70805, 2.474671),\n (6.25, 2.233299, 6.666666, 2.970332, 3.75, 3.333299, 6.666666, 3.333333, 4.644391, 5.56452, 3.046927),\n (7.5, 9.999998, 9.999998, 10.0, 7.5, 9.999998, 9.999998, 10.0, 4.418841, 4.941642, 3.380653),\n (5.0, 0.0, 6.1, 0.0, 5.0, 3.333333, 9.999998, 3.333333, 4.26268, 4.219508, 4.368462),\n (7.5, 9.999998, 9.999998, 10.0, 3.75, 9.999998, 9.999998, 10.0, 4.875197, 4.70048, 3.83497),\n (4.9, 2.233333, 9.999998, 0.0, 5.0, 0.0, 3.621989, 3.333333, 4.189655, 1.386294, 1.418971),\n (5.0, 0.0, 8.2, 0.0, 5.0, 0.0, 0.0, 0.0, 4.521789, 4.127134, 2.113313),\n (2.9, 3.333333, 6.666666, 3.333333, 2.5, 3.333333, 6.666666, 3.333333, 4.65396, 3.555348, 1.881917),\n (5.4, 9.999998, 6.666666, 3.333333, 3.75, 6.666666, 6.666666, 1.485166, 4.477337, 3.091042, 1.987909),\n (7.5, 8.8, 9.999998, 6.066666, 7.5, 6.666666, 9.999998, 6.666666, 5.337538, 5.631212, 3.491004),\n (7.5, 7.0, 9.999998, 6.852998, 7.5, 6.34834, 6.666666, 7.508044, 6.12905, 6.403574, 5.001796),\n (10.0, 6.666666, 9.999998, 10.0, 10.0, 6.666666, 9.999998, 10.0, 5.003946, 4.962845, 3.976994),\n (3.75, 3.333333, 0.0, 0.0, 1.25, 3.333333, 0.0, 0.0, 4.488636, 4.89784, 2.867566)], \n dtype=[('y1', '<f8'), ('y2', '<f8'), ('y3', '<f8'), ('y4', '<f8'), ('y5', '<f8'), ('y6', '<f8'), ('y7', '<f8'), ('y8', '<f8'), ('x1', '<f8'), ('x2', '<f8'), ('x3', '<f8')])", | |
| "output_type": "pyout", | |
| "metadata": {}, | |
| "prompt_number": 12 | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "PD2_df = pd.DataFrame(PD2)\nPD2_df", | |
| "prompt_number": 13, | |
| "outputs": [ | |
| { | |
| "text": " y1 y2 y3 y4 y5 y6 y7 \\\n0 2.50 0.000000 3.333333 0.000000 1.250000 0.000000 3.726360 \n1 1.25 0.000000 3.333333 0.000000 6.250000 1.100000 6.666666 \n2 7.50 8.800000 9.999998 9.199991 8.750000 8.094061 9.999998 \n3 8.90 8.800000 9.999998 9.199991 8.907948 8.127979 9.999998 \n4 10.00 3.333333 9.999998 6.666666 7.500000 3.333333 9.999998 \n5 7.50 3.333333 6.666666 6.666666 6.250000 1.100000 6.666666 \n6 7.50 3.333333 6.666666 6.666666 5.000000 2.233333 8.271257 \n7 7.50 2.233333 9.999998 1.496333 6.250000 3.333333 9.999998 \n8 2.50 3.333333 3.333333 3.333333 6.250000 3.333333 3.333333 \n9 10.00 6.666666 9.999998 8.899991 8.750000 6.666666 9.999998 \n10 7.50 3.333333 9.999998 6.666666 8.750000 3.333333 9.999998 \n11 7.50 3.333333 6.666666 6.666666 8.750000 3.333333 6.666666 \n12 7.50 3.333333 9.999998 6.666666 7.500000 3.333333 6.666666 \n13 7.50 7.766664 9.999998 6.666666 7.500000 0.000000 9.999998 \n14 7.50 9.999998 3.333333 10.000000 7.500000 6.666666 9.999998 \n15 7.50 9.999998 9.999998 7.766666 7.500000 1.100000 6.666666 \n16 2.50 3.333333 6.666666 6.666666 5.000000 1.100000 6.666666 \n17 1.25 0.000000 3.333333 3.333333 1.250000 3.333333 3.333333 \n18 10.00 9.999998 9.999998 10.000000 8.750000 9.999998 9.999998 \n19 7.50 3.333299 3.333333 6.666666 7.500000 2.233299 6.666666 \n20 10.00 9.999998 9.999998 10.000000 10.000000 9.999998 9.999998 \n21 1.25 0.000000 0.000000 0.000000 2.500000 0.000000 0.000000 \n22 2.50 0.000000 3.333333 3.333333 2.500000 0.000000 3.333333 \n23 7.50 6.666666 9.999998 10.000000 7.500000 6.666666 9.999998 \n24 8.50 9.999998 6.666666 6.666666 8.750000 9.999998 7.351018 \n25 6.10 0.000000 5.400000 3.333333 0.000000 0.000000 4.696028 \n26 3.30 0.000000 6.666666 3.333333 6.250000 0.000000 6.666666 \n27 2.90 3.333333 6.666666 3.333333 2.385559 0.000000 3.177568 \n28 9.20 0.000000 9.900000 3.333333 7.609660 0.000000 8.118828 \n29 6.90 0.000000 6.666666 3.333333 4.226033 0.000000 0.000000 \n30 2.90 0.000000 3.333333 3.333333 5.000000 0.000000 3.333333 \n31 2.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n32 5.00 0.000000 3.333333 3.333333 5.000000 0.000000 3.333333 \n33 5.00 0.000000 9.999998 3.333333 0.000000 0.000000 3.333333 \n34 4.10 9.999998 4.700000 6.666666 3.750000 0.000000 7.827667 \n35 6.30 9.999998 9.999998 6.666666 6.250000 2.233333 6.666666 \n36 5.20 4.999998 6.600000 3.333333 3.633403 1.100000 3.314128 \n37 5.00 3.333333 6.400000 6.666666 2.844997 0.000000 4.429657 \n38 3.10 4.999998 4.200000 5.000000 3.750000 0.000000 6.164304 \n39 4.10 9.999998 6.666666 3.333333 5.000000 0.000000 4.938089 \n40 5.00 9.999998 6.666666 1.666666 5.000000 0.000000 6.666666 \n41 5.00 7.700000 6.666666 8.399997 6.250000 4.358243 9.999998 \n42 5.00 6.200000 9.999998 6.060997 5.000000 2.782771 6.666666 \n43 5.60 4.900000 0.000000 0.000000 6.555647 4.055463 6.666666 \n44 5.70 4.800000 0.000000 0.000000 6.555647 4.055463 0.000000 \n45 7.50 9.999998 7.900000 6.666666 3.750000 9.999998 7.631891 \n46 2.50 0.000000 6.666666 3.333333 2.500000 0.000000 0.000000 \n47 8.90 9.999998 9.700000 6.666666 5.000000 9.999998 9.556024 \n48 7.60 0.000000 10.000000 0.000000 5.000000 1.100000 6.666666 \n49 7.80 9.999998 6.666666 6.666666 5.000000 3.333333 6.666666 \n50 2.50 0.000000 6.666666 3.333333 5.000000 0.000000 6.666666 \n51 3.80 0.000000 5.100000 0.000000 3.750000 0.000000 6.666666 \n52 5.00 3.333333 3.333333 2.233333 5.000000 3.333333 6.666666 \n53 6.25 3.333333 9.999998 2.955702 6.250000 5.566663 9.999998 \n54 1.25 0.000000 3.333333 0.000000 2.500000 0.000000 0.000000 \n55 1.25 0.000000 4.700000 0.736999 2.500000 0.000000 3.333333 \n56 1.25 0.000000 6.666666 0.000000 2.500000 0.000000 5.228375 \n57 7.50 7.766664 9.999998 6.666666 7.500000 3.333333 9.999998 \n58 2.50 0.000000 6.666666 4.433333 5.000000 0.000000 6.666666 \n59 7.50 9.999998 9.999998 10.000000 8.750000 9.999998 9.999998 \n ... ... ... ... ... ... ... \n\n y8 x1 x2 x3 \n0 3.333333 4.442651 3.637586 2.557615 \n1 0.736999 5.384495 5.062595 3.568079 \n2 8.211809 5.961005 6.255750 5.224433 \n3 4.615086 6.285998 7.567863 6.267495 \n4 6.666666 5.863631 6.818924 4.573679 \n5 0.368500 5.533389 5.135798 3.892270 \n6 1.485166 5.308268 5.075174 3.316213 \n7 6.666666 5.347108 4.852030 4.263183 \n8 3.333333 5.521461 5.241747 4.115168 \n9 10.000000 5.828946 5.370638 4.446216 \n10 6.666666 5.916202 6.423247 3.791545 \n11 6.666666 5.398163 6.246107 4.535708 \n12 10.000000 6.622736 7.872074 4.906154 \n13 0.000000 5.204007 5.225747 4.561047 \n14 10.000000 5.509388 6.202536 4.586286 \n15 6.666666 5.262690 5.820083 3.948911 \n16 0.368500 4.700480 5.023881 4.394491 \n17 3.333333 5.209486 4.465908 4.510268 \n18 10.000000 5.916202 6.732211 5.829084 \n19 2.948164 6.523562 6.992096 6.424591 \n20 10.000000 6.238325 6.746412 5.741711 \n21 0.000000 5.976351 6.712956 5.948168 \n22 3.333333 5.631212 5.937536 5.686755 \n23 7.766666 6.033086 6.093570 4.611429 \n24 6.666666 6.196444 6.704414 5.475261 \n25 3.333333 4.248495 2.708050 1.740830 \n26 3.333333 5.141664 4.564348 2.255134 \n27 1.116666 4.174387 3.688879 3.046927 \n28 3.333333 4.382027 2.890372 1.711279 \n29 0.000000 4.290459 1.609438 1.001674 \n30 3.333333 4.934474 4.234107 1.418971 \n31 0.000000 3.850148 1.945910 2.345229 \n32 3.333333 5.181784 4.394449 3.167167 \n33 0.744370 5.062595 4.595120 3.834970 \n34 6.666666 4.691348 4.143135 2.255134 \n35 2.955702 4.248495 3.367296 3.217506 \n36 3.333333 5.564520 5.236442 2.677633 \n37 1.485166 4.727388 3.610918 1.418971 \n38 3.333333 4.143135 2.302585 1.418971 \n39 2.233333 4.317488 4.955827 4.249888 \n40 0.368500 5.141664 4.430817 3.046927 \n41 4.141377 4.488636 3.465736 2.013579 \n42 4.974739 4.615121 4.941642 2.255134 \n43 3.821796 3.850148 2.397895 1.740830 \n44 0.000000 3.970292 2.397895 1.050741 \n45 6.666666 3.784190 3.091042 2.113313 \n46 0.000000 3.806662 2.079442 2.137561 \n47 6.666666 4.532599 3.610918 1.587802 \n48 1.099999 5.117994 4.934474 3.834970 \n49 6.666666 5.049856 5.111988 4.381490 \n50 3.333333 5.393628 5.638355 4.169451 \n51 1.485166 4.477337 3.931826 2.474671 \n52 5.566663 5.257495 5.840642 5.001796 \n53 6.666666 5.379897 5.505332 3.299937 \n54 0.000000 5.298317 6.274762 4.381490 \n55 3.333333 4.859812 5.669881 3.537416 \n56 0.000000 4.969813 5.564520 4.510268 \n57 6.666666 6.011267 6.253829 5.001796 \n58 1.485166 5.075174 5.252273 5.350708 \n59 10.000000 6.736967 7.125283 6.330518 \n ... ... ... ... \n\n[75 rows x 11 columns]", | |
| "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>y1</th>\n <th>y2</th>\n <th>y3</th>\n <th>y4</th>\n <th>y5</th>\n <th>y6</th>\n <th>y7</th>\n <th>y8</th>\n <th>x1</th>\n <th>x2</th>\n <th>x3</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0 </th>\n <td> 2.50</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 0.000000</td>\n <td> 1.250000</td>\n <td> 0.000000</td>\n <td> 3.726360</td>\n <td> 3.333333</td>\n <td> 4.442651</td>\n <td> 3.637586</td>\n <td> 2.557615</td>\n </tr>\n <tr>\n <th>1 </th>\n <td> 1.25</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 0.000000</td>\n <td> 6.250000</td>\n <td> 1.100000</td>\n <td> 6.666666</td>\n <td> 0.736999</td>\n <td> 5.384495</td>\n <td> 5.062595</td>\n <td> 3.568079</td>\n </tr>\n <tr>\n <th>2 </th>\n <td> 7.50</td>\n <td> 8.800000</td>\n <td> 9.999998</td>\n <td> 9.199991</td>\n <td> 8.750000</td>\n <td> 8.094061</td>\n <td> 9.999998</td>\n <td> 8.211809</td>\n <td> 5.961005</td>\n <td> 6.255750</td>\n <td> 5.224433</td>\n </tr>\n <tr>\n <th>3 </th>\n <td> 8.90</td>\n <td> 8.800000</td>\n <td> 9.999998</td>\n <td> 9.199991</td>\n <td> 8.907948</td>\n <td> 8.127979</td>\n <td> 9.999998</td>\n <td> 4.615086</td>\n <td> 6.285998</td>\n <td> 7.567863</td>\n <td> 6.267495</td>\n </tr>\n <tr>\n <th>4 </th>\n <td> 10.00</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 7.500000</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 5.863631</td>\n <td> 6.818924</td>\n <td> 4.573679</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> 7.50</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 6.250000</td>\n <td> 1.100000</td>\n <td> 6.666666</td>\n <td> 0.368500</td>\n <td> 5.533389</td>\n <td> 5.135798</td>\n <td> 3.892270</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> 7.50</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 5.000000</td>\n <td> 2.233333</td>\n <td> 8.271257</td>\n <td> 1.485166</td>\n <td> 5.308268</td>\n <td> 5.075174</td>\n <td> 3.316213</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> 7.50</td>\n <td> 2.233333</td>\n <td> 9.999998</td>\n <td> 1.496333</td>\n <td> 6.250000</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 5.347108</td>\n <td> 4.852030</td>\n <td> 4.263183</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> 2.50</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 6.250000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 5.521461</td>\n <td> 5.241747</td>\n <td> 4.115168</td>\n </tr>\n <tr>\n <th>9 </th>\n <td> 10.00</td>\n <td> 6.666666</td>\n <td> 9.999998</td>\n <td> 8.899991</td>\n <td> 8.750000</td>\n <td> 6.666666</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 5.828946</td>\n <td> 5.370638</td>\n <td> 4.446216</td>\n </tr>\n <tr>\n <th>10</th>\n <td> 7.50</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 8.750000</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 5.916202</td>\n <td> 6.423247</td>\n <td> 3.791545</td>\n </tr>\n <tr>\n <th>11</th>\n <td> 7.50</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 8.750000</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 5.398163</td>\n <td> 6.246107</td>\n <td> 4.535708</td>\n </tr>\n <tr>\n <th>12</th>\n <td> 7.50</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 7.500000</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 10.000000</td>\n <td> 6.622736</td>\n <td> 7.872074</td>\n <td> 4.906154</td>\n </tr>\n <tr>\n <th>13</th>\n <td> 7.50</td>\n <td> 7.766664</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 7.500000</td>\n <td> 0.000000</td>\n <td> 9.999998</td>\n <td> 0.000000</td>\n <td> 5.204007</td>\n <td> 5.225747</td>\n <td> 4.561047</td>\n </tr>\n <tr>\n <th>14</th>\n <td> 7.50</td>\n <td> 9.999998</td>\n <td> 3.333333</td>\n <td> 10.000000</td>\n <td> 7.500000</td>\n <td> 6.666666</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 5.509388</td>\n <td> 6.202536</td>\n <td> 4.586286</td>\n </tr>\n <tr>\n <th>15</th>\n <td> 7.50</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 7.766666</td>\n <td> 7.500000</td>\n <td> 1.100000</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 5.262690</td>\n <td> 5.820083</td>\n <td> 3.948911</td>\n </tr>\n <tr>\n <th>16</th>\n <td> 2.50</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 5.000000</td>\n <td> 1.100000</td>\n <td> 6.666666</td>\n <td> 0.368500</td>\n <td> 4.700480</td>\n <td> 5.023881</td>\n <td> 4.394491</td>\n </tr>\n <tr>\n <th>17</th>\n <td> 1.25</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 1.250000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 5.209486</td>\n <td> 4.465908</td>\n <td> 4.510268</td>\n </tr>\n <tr>\n <th>18</th>\n <td> 10.00</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 8.750000</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 5.916202</td>\n <td> 6.732211</td>\n <td> 5.829084</td>\n </tr>\n <tr>\n <th>19</th>\n <td> 7.50</td>\n <td> 3.333299</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 7.500000</td>\n <td> 2.233299</td>\n <td> 6.666666</td>\n <td> 2.948164</td>\n <td> 6.523562</td>\n <td> 6.992096</td>\n <td> 6.424591</td>\n </tr>\n <tr>\n <th>20</th>\n <td> 10.00</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 10.000000</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 6.238325</td>\n <td> 6.746412</td>\n <td> 5.741711</td>\n </tr>\n <tr>\n <th>21</th>\n <td> 1.25</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 2.500000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 5.976351</td>\n <td> 6.712956</td>\n <td> 5.948168</td>\n </tr>\n <tr>\n <th>22</th>\n <td> 2.50</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 2.500000</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 5.631212</td>\n <td> 5.937536</td>\n <td> 5.686755</td>\n </tr>\n <tr>\n <th>23</th>\n <td> 7.50</td>\n <td> 6.666666</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 7.500000</td>\n <td> 6.666666</td>\n <td> 9.999998</td>\n <td> 7.766666</td>\n <td> 6.033086</td>\n <td> 6.093570</td>\n <td> 4.611429</td>\n </tr>\n <tr>\n <th>24</th>\n <td> 8.50</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 8.750000</td>\n <td> 9.999998</td>\n <td> 7.351018</td>\n <td> 6.666666</td>\n <td> 6.196444</td>\n <td> 6.704414</td>\n <td> 5.475261</td>\n </tr>\n <tr>\n <th>25</th>\n <td> 6.10</td>\n <td> 0.000000</td>\n <td> 5.400000</td>\n <td> 3.333333</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 4.696028</td>\n <td> 3.333333</td>\n <td> 4.248495</td>\n <td> 2.708050</td>\n <td> 1.740830</td>\n </tr>\n <tr>\n <th>26</th>\n <td> 3.30</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 6.250000</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 5.141664</td>\n <td> 4.564348</td>\n <td> 2.255134</td>\n </tr>\n <tr>\n <th>27</th>\n <td> 2.90</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 2.385559</td>\n <td> 0.000000</td>\n <td> 3.177568</td>\n <td> 1.116666</td>\n <td> 4.174387</td>\n <td> 3.688879</td>\n <td> 3.046927</td>\n </tr>\n <tr>\n <th>28</th>\n <td> 9.20</td>\n <td> 0.000000</td>\n <td> 9.900000</td>\n <td> 3.333333</td>\n <td> 7.609660</td>\n <td> 0.000000</td>\n <td> 8.118828</td>\n <td> 3.333333</td>\n <td> 4.382027</td>\n <td> 2.890372</td>\n <td> 1.711279</td>\n </tr>\n <tr>\n <th>29</th>\n <td> 6.90</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 4.226033</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 4.290459</td>\n <td> 1.609438</td>\n <td> 1.001674</td>\n </tr>\n <tr>\n <th>30</th>\n <td> 2.90</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 5.000000</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 4.934474</td>\n <td> 4.234107</td>\n <td> 1.418971</td>\n </tr>\n <tr>\n <th>31</th>\n <td> 2.00</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 3.850148</td>\n <td> 1.945910</td>\n <td> 2.345229</td>\n </tr>\n <tr>\n <th>32</th>\n <td> 5.00</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 5.000000</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 5.181784</td>\n <td> 4.394449</td>\n <td> 3.167167</td>\n </tr>\n <tr>\n <th>33</th>\n <td> 5.00</td>\n <td> 0.000000</td>\n <td> 9.999998</td>\n <td> 3.333333</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 0.744370</td>\n <td> 5.062595</td>\n <td> 4.595120</td>\n <td> 3.834970</td>\n </tr>\n <tr>\n <th>34</th>\n <td> 4.10</td>\n <td> 9.999998</td>\n <td> 4.700000</td>\n <td> 6.666666</td>\n <td> 3.750000</td>\n <td> 0.000000</td>\n <td> 7.827667</td>\n <td> 6.666666</td>\n <td> 4.691348</td>\n <td> 4.143135</td>\n <td> 2.255134</td>\n </tr>\n <tr>\n <th>35</th>\n <td> 6.30</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 6.250000</td>\n <td> 2.233333</td>\n <td> 6.666666</td>\n <td> 2.955702</td>\n <td> 4.248495</td>\n <td> 3.367296</td>\n <td> 3.217506</td>\n </tr>\n <tr>\n <th>36</th>\n <td> 5.20</td>\n <td> 4.999998</td>\n <td> 6.600000</td>\n <td> 3.333333</td>\n <td> 3.633403</td>\n <td> 1.100000</td>\n <td> 3.314128</td>\n <td> 3.333333</td>\n <td> 5.564520</td>\n <td> 5.236442</td>\n <td> 2.677633</td>\n </tr>\n <tr>\n <th>37</th>\n <td> 5.00</td>\n <td> 3.333333</td>\n <td> 6.400000</td>\n <td> 6.666666</td>\n <td> 2.844997</td>\n <td> 0.000000</td>\n <td> 4.429657</td>\n <td> 1.485166</td>\n <td> 4.727388</td>\n <td> 3.610918</td>\n <td> 1.418971</td>\n </tr>\n <tr>\n <th>38</th>\n <td> 3.10</td>\n <td> 4.999998</td>\n <td> 4.200000</td>\n <td> 5.000000</td>\n <td> 3.750000</td>\n <td> 0.000000</td>\n <td> 6.164304</td>\n <td> 3.333333</td>\n <td> 4.143135</td>\n <td> 2.302585</td>\n <td> 1.418971</td>\n </tr>\n <tr>\n <th>39</th>\n <td> 4.10</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 5.000000</td>\n <td> 0.000000</td>\n <td> 4.938089</td>\n <td> 2.233333</td>\n <td> 4.317488</td>\n <td> 4.955827</td>\n <td> 4.249888</td>\n </tr>\n <tr>\n <th>40</th>\n <td> 5.00</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 1.666666</td>\n <td> 5.000000</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 0.368500</td>\n <td> 5.141664</td>\n <td> 4.430817</td>\n <td> 3.046927</td>\n </tr>\n <tr>\n <th>41</th>\n <td> 5.00</td>\n <td> 7.700000</td>\n <td> 6.666666</td>\n <td> 8.399997</td>\n <td> 6.250000</td>\n <td> 4.358243</td>\n <td> 9.999998</td>\n <td> 4.141377</td>\n <td> 4.488636</td>\n <td> 3.465736</td>\n <td> 2.013579</td>\n </tr>\n <tr>\n <th>42</th>\n <td> 5.00</td>\n <td> 6.200000</td>\n <td> 9.999998</td>\n <td> 6.060997</td>\n <td> 5.000000</td>\n <td> 2.782771</td>\n <td> 6.666666</td>\n <td> 4.974739</td>\n <td> 4.615121</td>\n <td> 4.941642</td>\n <td> 2.255134</td>\n </tr>\n <tr>\n <th>43</th>\n <td> 5.60</td>\n <td> 4.900000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 6.555647</td>\n <td> 4.055463</td>\n <td> 6.666666</td>\n <td> 3.821796</td>\n <td> 3.850148</td>\n <td> 2.397895</td>\n <td> 1.740830</td>\n </tr>\n <tr>\n <th>44</th>\n <td> 5.70</td>\n <td> 4.800000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 6.555647</td>\n <td> 4.055463</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 3.970292</td>\n <td> 2.397895</td>\n <td> 1.050741</td>\n </tr>\n <tr>\n <th>45</th>\n <td> 7.50</td>\n <td> 9.999998</td>\n <td> 7.900000</td>\n <td> 6.666666</td>\n <td> 3.750000</td>\n <td> 9.999998</td>\n <td> 7.631891</td>\n <td> 6.666666</td>\n <td> 3.784190</td>\n <td> 3.091042</td>\n <td> 2.113313</td>\n </tr>\n <tr>\n <th>46</th>\n <td> 2.50</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 2.500000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 3.806662</td>\n <td> 2.079442</td>\n <td> 2.137561</td>\n </tr>\n <tr>\n <th>47</th>\n <td> 8.90</td>\n <td> 9.999998</td>\n <td> 9.700000</td>\n <td> 6.666666</td>\n <td> 5.000000</td>\n <td> 9.999998</td>\n <td> 9.556024</td>\n <td> 6.666666</td>\n <td> 4.532599</td>\n <td> 3.610918</td>\n <td> 1.587802</td>\n </tr>\n <tr>\n <th>48</th>\n <td> 7.60</td>\n <td> 0.000000</td>\n <td> 10.000000</td>\n <td> 0.000000</td>\n <td> 5.000000</td>\n <td> 1.100000</td>\n <td> 6.666666</td>\n <td> 1.099999</td>\n <td> 5.117994</td>\n <td> 4.934474</td>\n <td> 3.834970</td>\n </tr>\n <tr>\n <th>49</th>\n <td> 7.80</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 5.000000</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 5.049856</td>\n <td> 5.111988</td>\n <td> 4.381490</td>\n </tr>\n <tr>\n <th>50</th>\n <td> 2.50</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 5.000000</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 5.393628</td>\n <td> 5.638355</td>\n <td> 4.169451</td>\n </tr>\n <tr>\n <th>51</th>\n <td> 3.80</td>\n <td> 0.000000</td>\n <td> 5.100000</td>\n <td> 0.000000</td>\n <td> 3.750000</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 1.485166</td>\n <td> 4.477337</td>\n <td> 3.931826</td>\n <td> 2.474671</td>\n </tr>\n <tr>\n <th>52</th>\n <td> 5.00</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 2.233333</td>\n <td> 5.000000</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 5.566663</td>\n <td> 5.257495</td>\n <td> 5.840642</td>\n <td> 5.001796</td>\n </tr>\n <tr>\n <th>53</th>\n <td> 6.25</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 2.955702</td>\n <td> 6.250000</td>\n <td> 5.566663</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 5.379897</td>\n <td> 5.505332</td>\n <td> 3.299937</td>\n </tr>\n <tr>\n <th>54</th>\n <td> 1.25</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 0.000000</td>\n <td> 2.500000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 5.298317</td>\n <td> 6.274762</td>\n <td> 4.381490</td>\n </tr>\n <tr>\n <th>55</th>\n <td> 1.25</td>\n <td> 0.000000</td>\n <td> 4.700000</td>\n <td> 0.736999</td>\n <td> 2.500000</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 4.859812</td>\n <td> 5.669881</td>\n <td> 3.537416</td>\n </tr>\n <tr>\n <th>56</th>\n <td> 1.25</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 0.000000</td>\n <td> 2.500000</td>\n <td> 0.000000</td>\n <td> 5.228375</td>\n <td> 0.000000</td>\n <td> 4.969813</td>\n <td> 5.564520</td>\n <td> 4.510268</td>\n </tr>\n <tr>\n <th>57</th>\n <td> 7.50</td>\n <td> 7.766664</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 7.500000</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 6.011267</td>\n <td> 6.253829</td>\n <td> 5.001796</td>\n </tr>\n <tr>\n <th>58</th>\n <td> 2.50</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 4.433333</td>\n <td> 5.000000</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 1.485166</td>\n <td> 5.075174</td>\n <td> 5.252273</td>\n <td> 5.350708</td>\n </tr>\n <tr>\n <th>59</th>\n <td> 7.50</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 8.750000</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 6.736967</td>\n <td> 7.125283</td>\n <td> 6.330518</td>\n </tr>\n <tr>\n <th></th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n </tbody>\n</table>\n<p>75 rows × 11 columns</p>\n</div>", | |
| "output_type": "pyout", | |
| "metadata": {}, | |
| "prompt_number": 13 | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "Little side-note on visualizing dataframes: \n\nWhen a dataframe is above a certain size, pandas prefers to show summary rather than the whole thing;" | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "PD2_df", | |
| "prompt_number": 14, | |
| "outputs": [ | |
| { | |
| "text": " y1 y2 y3 y4 y5 y6 y7 \\\n0 2.50 0.000000 3.333333 0.000000 1.250000 0.000000 3.726360 \n1 1.25 0.000000 3.333333 0.000000 6.250000 1.100000 6.666666 \n2 7.50 8.800000 9.999998 9.199991 8.750000 8.094061 9.999998 \n3 8.90 8.800000 9.999998 9.199991 8.907948 8.127979 9.999998 \n4 10.00 3.333333 9.999998 6.666666 7.500000 3.333333 9.999998 \n5 7.50 3.333333 6.666666 6.666666 6.250000 1.100000 6.666666 \n6 7.50 3.333333 6.666666 6.666666 5.000000 2.233333 8.271257 \n7 7.50 2.233333 9.999998 1.496333 6.250000 3.333333 9.999998 \n8 2.50 3.333333 3.333333 3.333333 6.250000 3.333333 3.333333 \n9 10.00 6.666666 9.999998 8.899991 8.750000 6.666666 9.999998 \n10 7.50 3.333333 9.999998 6.666666 8.750000 3.333333 9.999998 \n11 7.50 3.333333 6.666666 6.666666 8.750000 3.333333 6.666666 \n12 7.50 3.333333 9.999998 6.666666 7.500000 3.333333 6.666666 \n13 7.50 7.766664 9.999998 6.666666 7.500000 0.000000 9.999998 \n14 7.50 9.999998 3.333333 10.000000 7.500000 6.666666 9.999998 \n15 7.50 9.999998 9.999998 7.766666 7.500000 1.100000 6.666666 \n16 2.50 3.333333 6.666666 6.666666 5.000000 1.100000 6.666666 \n17 1.25 0.000000 3.333333 3.333333 1.250000 3.333333 3.333333 \n18 10.00 9.999998 9.999998 10.000000 8.750000 9.999998 9.999998 \n19 7.50 3.333299 3.333333 6.666666 7.500000 2.233299 6.666666 \n20 10.00 9.999998 9.999998 10.000000 10.000000 9.999998 9.999998 \n21 1.25 0.000000 0.000000 0.000000 2.500000 0.000000 0.000000 \n22 2.50 0.000000 3.333333 3.333333 2.500000 0.000000 3.333333 \n23 7.50 6.666666 9.999998 10.000000 7.500000 6.666666 9.999998 \n24 8.50 9.999998 6.666666 6.666666 8.750000 9.999998 7.351018 \n25 6.10 0.000000 5.400000 3.333333 0.000000 0.000000 4.696028 \n26 3.30 0.000000 6.666666 3.333333 6.250000 0.000000 6.666666 \n27 2.90 3.333333 6.666666 3.333333 2.385559 0.000000 3.177568 \n28 9.20 0.000000 9.900000 3.333333 7.609660 0.000000 8.118828 \n29 6.90 0.000000 6.666666 3.333333 4.226033 0.000000 0.000000 \n30 2.90 0.000000 3.333333 3.333333 5.000000 0.000000 3.333333 \n31 2.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n32 5.00 0.000000 3.333333 3.333333 5.000000 0.000000 3.333333 \n33 5.00 0.000000 9.999998 3.333333 0.000000 0.000000 3.333333 \n34 4.10 9.999998 4.700000 6.666666 3.750000 0.000000 7.827667 \n35 6.30 9.999998 9.999998 6.666666 6.250000 2.233333 6.666666 \n36 5.20 4.999998 6.600000 3.333333 3.633403 1.100000 3.314128 \n37 5.00 3.333333 6.400000 6.666666 2.844997 0.000000 4.429657 \n38 3.10 4.999998 4.200000 5.000000 3.750000 0.000000 6.164304 \n39 4.10 9.999998 6.666666 3.333333 5.000000 0.000000 4.938089 \n40 5.00 9.999998 6.666666 1.666666 5.000000 0.000000 6.666666 \n41 5.00 7.700000 6.666666 8.399997 6.250000 4.358243 9.999998 \n42 5.00 6.200000 9.999998 6.060997 5.000000 2.782771 6.666666 \n43 5.60 4.900000 0.000000 0.000000 6.555647 4.055463 6.666666 \n44 5.70 4.800000 0.000000 0.000000 6.555647 4.055463 0.000000 \n45 7.50 9.999998 7.900000 6.666666 3.750000 9.999998 7.631891 \n46 2.50 0.000000 6.666666 3.333333 2.500000 0.000000 0.000000 \n47 8.90 9.999998 9.700000 6.666666 5.000000 9.999998 9.556024 \n48 7.60 0.000000 10.000000 0.000000 5.000000 1.100000 6.666666 \n49 7.80 9.999998 6.666666 6.666666 5.000000 3.333333 6.666666 \n50 2.50 0.000000 6.666666 3.333333 5.000000 0.000000 6.666666 \n51 3.80 0.000000 5.100000 0.000000 3.750000 0.000000 6.666666 \n52 5.00 3.333333 3.333333 2.233333 5.000000 3.333333 6.666666 \n53 6.25 3.333333 9.999998 2.955702 6.250000 5.566663 9.999998 \n54 1.25 0.000000 3.333333 0.000000 2.500000 0.000000 0.000000 \n55 1.25 0.000000 4.700000 0.736999 2.500000 0.000000 3.333333 \n56 1.25 0.000000 6.666666 0.000000 2.500000 0.000000 5.228375 \n57 7.50 7.766664 9.999998 6.666666 7.500000 3.333333 9.999998 \n58 2.50 0.000000 6.666666 4.433333 5.000000 0.000000 6.666666 \n59 7.50 9.999998 9.999998 10.000000 8.750000 9.999998 9.999998 \n ... ... ... ... ... ... ... \n\n y8 x1 x2 x3 \n0 3.333333 4.442651 3.637586 2.557615 \n1 0.736999 5.384495 5.062595 3.568079 \n2 8.211809 5.961005 6.255750 5.224433 \n3 4.615086 6.285998 7.567863 6.267495 \n4 6.666666 5.863631 6.818924 4.573679 \n5 0.368500 5.533389 5.135798 3.892270 \n6 1.485166 5.308268 5.075174 3.316213 \n7 6.666666 5.347108 4.852030 4.263183 \n8 3.333333 5.521461 5.241747 4.115168 \n9 10.000000 5.828946 5.370638 4.446216 \n10 6.666666 5.916202 6.423247 3.791545 \n11 6.666666 5.398163 6.246107 4.535708 \n12 10.000000 6.622736 7.872074 4.906154 \n13 0.000000 5.204007 5.225747 4.561047 \n14 10.000000 5.509388 6.202536 4.586286 \n15 6.666666 5.262690 5.820083 3.948911 \n16 0.368500 4.700480 5.023881 4.394491 \n17 3.333333 5.209486 4.465908 4.510268 \n18 10.000000 5.916202 6.732211 5.829084 \n19 2.948164 6.523562 6.992096 6.424591 \n20 10.000000 6.238325 6.746412 5.741711 \n21 0.000000 5.976351 6.712956 5.948168 \n22 3.333333 5.631212 5.937536 5.686755 \n23 7.766666 6.033086 6.093570 4.611429 \n24 6.666666 6.196444 6.704414 5.475261 \n25 3.333333 4.248495 2.708050 1.740830 \n26 3.333333 5.141664 4.564348 2.255134 \n27 1.116666 4.174387 3.688879 3.046927 \n28 3.333333 4.382027 2.890372 1.711279 \n29 0.000000 4.290459 1.609438 1.001674 \n30 3.333333 4.934474 4.234107 1.418971 \n31 0.000000 3.850148 1.945910 2.345229 \n32 3.333333 5.181784 4.394449 3.167167 \n33 0.744370 5.062595 4.595120 3.834970 \n34 6.666666 4.691348 4.143135 2.255134 \n35 2.955702 4.248495 3.367296 3.217506 \n36 3.333333 5.564520 5.236442 2.677633 \n37 1.485166 4.727388 3.610918 1.418971 \n38 3.333333 4.143135 2.302585 1.418971 \n39 2.233333 4.317488 4.955827 4.249888 \n40 0.368500 5.141664 4.430817 3.046927 \n41 4.141377 4.488636 3.465736 2.013579 \n42 4.974739 4.615121 4.941642 2.255134 \n43 3.821796 3.850148 2.397895 1.740830 \n44 0.000000 3.970292 2.397895 1.050741 \n45 6.666666 3.784190 3.091042 2.113313 \n46 0.000000 3.806662 2.079442 2.137561 \n47 6.666666 4.532599 3.610918 1.587802 \n48 1.099999 5.117994 4.934474 3.834970 \n49 6.666666 5.049856 5.111988 4.381490 \n50 3.333333 5.393628 5.638355 4.169451 \n51 1.485166 4.477337 3.931826 2.474671 \n52 5.566663 5.257495 5.840642 5.001796 \n53 6.666666 5.379897 5.505332 3.299937 \n54 0.000000 5.298317 6.274762 4.381490 \n55 3.333333 4.859812 5.669881 3.537416 \n56 0.000000 4.969813 5.564520 4.510268 \n57 6.666666 6.011267 6.253829 5.001796 \n58 1.485166 5.075174 5.252273 5.350708 \n59 10.000000 6.736967 7.125283 6.330518 \n ... ... ... ... \n\n[75 rows x 11 columns]", | |
| "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>y1</th>\n <th>y2</th>\n <th>y3</th>\n <th>y4</th>\n <th>y5</th>\n <th>y6</th>\n <th>y7</th>\n <th>y8</th>\n <th>x1</th>\n <th>x2</th>\n <th>x3</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0 </th>\n <td> 2.50</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 0.000000</td>\n <td> 1.250000</td>\n <td> 0.000000</td>\n <td> 3.726360</td>\n <td> 3.333333</td>\n <td> 4.442651</td>\n <td> 3.637586</td>\n <td> 2.557615</td>\n </tr>\n <tr>\n <th>1 </th>\n <td> 1.25</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 0.000000</td>\n <td> 6.250000</td>\n <td> 1.100000</td>\n <td> 6.666666</td>\n <td> 0.736999</td>\n <td> 5.384495</td>\n <td> 5.062595</td>\n <td> 3.568079</td>\n </tr>\n <tr>\n <th>2 </th>\n <td> 7.50</td>\n <td> 8.800000</td>\n <td> 9.999998</td>\n <td> 9.199991</td>\n <td> 8.750000</td>\n <td> 8.094061</td>\n <td> 9.999998</td>\n <td> 8.211809</td>\n <td> 5.961005</td>\n <td> 6.255750</td>\n <td> 5.224433</td>\n </tr>\n <tr>\n <th>3 </th>\n <td> 8.90</td>\n <td> 8.800000</td>\n <td> 9.999998</td>\n <td> 9.199991</td>\n <td> 8.907948</td>\n <td> 8.127979</td>\n <td> 9.999998</td>\n <td> 4.615086</td>\n <td> 6.285998</td>\n <td> 7.567863</td>\n <td> 6.267495</td>\n </tr>\n <tr>\n <th>4 </th>\n <td> 10.00</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 7.500000</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 5.863631</td>\n <td> 6.818924</td>\n <td> 4.573679</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> 7.50</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 6.250000</td>\n <td> 1.100000</td>\n <td> 6.666666</td>\n <td> 0.368500</td>\n <td> 5.533389</td>\n <td> 5.135798</td>\n <td> 3.892270</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> 7.50</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 5.000000</td>\n <td> 2.233333</td>\n <td> 8.271257</td>\n <td> 1.485166</td>\n <td> 5.308268</td>\n <td> 5.075174</td>\n <td> 3.316213</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> 7.50</td>\n <td> 2.233333</td>\n <td> 9.999998</td>\n <td> 1.496333</td>\n <td> 6.250000</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 5.347108</td>\n <td> 4.852030</td>\n <td> 4.263183</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> 2.50</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 6.250000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 5.521461</td>\n <td> 5.241747</td>\n <td> 4.115168</td>\n </tr>\n <tr>\n <th>9 </th>\n <td> 10.00</td>\n <td> 6.666666</td>\n <td> 9.999998</td>\n <td> 8.899991</td>\n <td> 8.750000</td>\n <td> 6.666666</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 5.828946</td>\n <td> 5.370638</td>\n <td> 4.446216</td>\n </tr>\n <tr>\n <th>10</th>\n <td> 7.50</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 8.750000</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 5.916202</td>\n <td> 6.423247</td>\n <td> 3.791545</td>\n </tr>\n <tr>\n <th>11</th>\n <td> 7.50</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 8.750000</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 5.398163</td>\n <td> 6.246107</td>\n <td> 4.535708</td>\n </tr>\n <tr>\n <th>12</th>\n <td> 7.50</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 7.500000</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 10.000000</td>\n <td> 6.622736</td>\n <td> 7.872074</td>\n <td> 4.906154</td>\n </tr>\n <tr>\n <th>13</th>\n <td> 7.50</td>\n <td> 7.766664</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 7.500000</td>\n <td> 0.000000</td>\n <td> 9.999998</td>\n <td> 0.000000</td>\n <td> 5.204007</td>\n <td> 5.225747</td>\n <td> 4.561047</td>\n </tr>\n <tr>\n <th>14</th>\n <td> 7.50</td>\n <td> 9.999998</td>\n <td> 3.333333</td>\n <td> 10.000000</td>\n <td> 7.500000</td>\n <td> 6.666666</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 5.509388</td>\n <td> 6.202536</td>\n <td> 4.586286</td>\n </tr>\n <tr>\n <th>15</th>\n <td> 7.50</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 7.766666</td>\n <td> 7.500000</td>\n <td> 1.100000</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 5.262690</td>\n <td> 5.820083</td>\n <td> 3.948911</td>\n </tr>\n <tr>\n <th>16</th>\n <td> 2.50</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 5.000000</td>\n <td> 1.100000</td>\n <td> 6.666666</td>\n <td> 0.368500</td>\n <td> 4.700480</td>\n <td> 5.023881</td>\n <td> 4.394491</td>\n </tr>\n <tr>\n <th>17</th>\n <td> 1.25</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 1.250000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 5.209486</td>\n <td> 4.465908</td>\n <td> 4.510268</td>\n </tr>\n <tr>\n <th>18</th>\n <td> 10.00</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 8.750000</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 5.916202</td>\n <td> 6.732211</td>\n <td> 5.829084</td>\n </tr>\n <tr>\n <th>19</th>\n <td> 7.50</td>\n <td> 3.333299</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 7.500000</td>\n <td> 2.233299</td>\n <td> 6.666666</td>\n <td> 2.948164</td>\n <td> 6.523562</td>\n <td> 6.992096</td>\n <td> 6.424591</td>\n </tr>\n <tr>\n <th>20</th>\n <td> 10.00</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 10.000000</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 6.238325</td>\n <td> 6.746412</td>\n <td> 5.741711</td>\n </tr>\n <tr>\n <th>21</th>\n <td> 1.25</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 2.500000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 5.976351</td>\n <td> 6.712956</td>\n <td> 5.948168</td>\n </tr>\n <tr>\n <th>22</th>\n <td> 2.50</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 2.500000</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 5.631212</td>\n <td> 5.937536</td>\n <td> 5.686755</td>\n </tr>\n <tr>\n <th>23</th>\n <td> 7.50</td>\n <td> 6.666666</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 7.500000</td>\n <td> 6.666666</td>\n <td> 9.999998</td>\n <td> 7.766666</td>\n <td> 6.033086</td>\n <td> 6.093570</td>\n <td> 4.611429</td>\n </tr>\n <tr>\n <th>24</th>\n <td> 8.50</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 8.750000</td>\n <td> 9.999998</td>\n <td> 7.351018</td>\n <td> 6.666666</td>\n <td> 6.196444</td>\n <td> 6.704414</td>\n <td> 5.475261</td>\n </tr>\n <tr>\n <th>25</th>\n <td> 6.10</td>\n <td> 0.000000</td>\n <td> 5.400000</td>\n <td> 3.333333</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 4.696028</td>\n <td> 3.333333</td>\n <td> 4.248495</td>\n <td> 2.708050</td>\n <td> 1.740830</td>\n </tr>\n <tr>\n <th>26</th>\n <td> 3.30</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 6.250000</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 5.141664</td>\n <td> 4.564348</td>\n <td> 2.255134</td>\n </tr>\n <tr>\n <th>27</th>\n <td> 2.90</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 2.385559</td>\n <td> 0.000000</td>\n <td> 3.177568</td>\n <td> 1.116666</td>\n <td> 4.174387</td>\n <td> 3.688879</td>\n <td> 3.046927</td>\n </tr>\n <tr>\n <th>28</th>\n <td> 9.20</td>\n <td> 0.000000</td>\n <td> 9.900000</td>\n <td> 3.333333</td>\n <td> 7.609660</td>\n <td> 0.000000</td>\n <td> 8.118828</td>\n <td> 3.333333</td>\n <td> 4.382027</td>\n <td> 2.890372</td>\n <td> 1.711279</td>\n </tr>\n <tr>\n <th>29</th>\n <td> 6.90</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 4.226033</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 4.290459</td>\n <td> 1.609438</td>\n <td> 1.001674</td>\n </tr>\n <tr>\n <th>30</th>\n <td> 2.90</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 5.000000</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 4.934474</td>\n <td> 4.234107</td>\n <td> 1.418971</td>\n </tr>\n <tr>\n <th>31</th>\n <td> 2.00</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 3.850148</td>\n <td> 1.945910</td>\n <td> 2.345229</td>\n </tr>\n <tr>\n <th>32</th>\n <td> 5.00</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 5.000000</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 5.181784</td>\n <td> 4.394449</td>\n <td> 3.167167</td>\n </tr>\n <tr>\n <th>33</th>\n <td> 5.00</td>\n <td> 0.000000</td>\n <td> 9.999998</td>\n <td> 3.333333</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 0.744370</td>\n <td> 5.062595</td>\n <td> 4.595120</td>\n <td> 3.834970</td>\n </tr>\n <tr>\n <th>34</th>\n <td> 4.10</td>\n <td> 9.999998</td>\n <td> 4.700000</td>\n <td> 6.666666</td>\n <td> 3.750000</td>\n <td> 0.000000</td>\n <td> 7.827667</td>\n <td> 6.666666</td>\n <td> 4.691348</td>\n <td> 4.143135</td>\n <td> 2.255134</td>\n </tr>\n <tr>\n <th>35</th>\n <td> 6.30</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 6.250000</td>\n <td> 2.233333</td>\n <td> 6.666666</td>\n <td> 2.955702</td>\n <td> 4.248495</td>\n <td> 3.367296</td>\n <td> 3.217506</td>\n </tr>\n <tr>\n <th>36</th>\n <td> 5.20</td>\n <td> 4.999998</td>\n <td> 6.600000</td>\n <td> 3.333333</td>\n <td> 3.633403</td>\n <td> 1.100000</td>\n <td> 3.314128</td>\n <td> 3.333333</td>\n <td> 5.564520</td>\n <td> 5.236442</td>\n <td> 2.677633</td>\n </tr>\n <tr>\n <th>37</th>\n <td> 5.00</td>\n <td> 3.333333</td>\n <td> 6.400000</td>\n <td> 6.666666</td>\n <td> 2.844997</td>\n <td> 0.000000</td>\n <td> 4.429657</td>\n <td> 1.485166</td>\n <td> 4.727388</td>\n <td> 3.610918</td>\n <td> 1.418971</td>\n </tr>\n <tr>\n <th>38</th>\n <td> 3.10</td>\n <td> 4.999998</td>\n <td> 4.200000</td>\n <td> 5.000000</td>\n <td> 3.750000</td>\n <td> 0.000000</td>\n <td> 6.164304</td>\n <td> 3.333333</td>\n <td> 4.143135</td>\n <td> 2.302585</td>\n <td> 1.418971</td>\n </tr>\n <tr>\n <th>39</th>\n <td> 4.10</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 5.000000</td>\n <td> 0.000000</td>\n <td> 4.938089</td>\n <td> 2.233333</td>\n <td> 4.317488</td>\n <td> 4.955827</td>\n <td> 4.249888</td>\n </tr>\n <tr>\n <th>40</th>\n <td> 5.00</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 1.666666</td>\n <td> 5.000000</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 0.368500</td>\n <td> 5.141664</td>\n <td> 4.430817</td>\n <td> 3.046927</td>\n </tr>\n <tr>\n <th>41</th>\n <td> 5.00</td>\n <td> 7.700000</td>\n <td> 6.666666</td>\n <td> 8.399997</td>\n <td> 6.250000</td>\n <td> 4.358243</td>\n <td> 9.999998</td>\n <td> 4.141377</td>\n <td> 4.488636</td>\n <td> 3.465736</td>\n <td> 2.013579</td>\n </tr>\n <tr>\n <th>42</th>\n <td> 5.00</td>\n <td> 6.200000</td>\n <td> 9.999998</td>\n <td> 6.060997</td>\n <td> 5.000000</td>\n <td> 2.782771</td>\n <td> 6.666666</td>\n <td> 4.974739</td>\n <td> 4.615121</td>\n <td> 4.941642</td>\n <td> 2.255134</td>\n </tr>\n <tr>\n <th>43</th>\n <td> 5.60</td>\n <td> 4.900000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 6.555647</td>\n <td> 4.055463</td>\n <td> 6.666666</td>\n <td> 3.821796</td>\n <td> 3.850148</td>\n <td> 2.397895</td>\n <td> 1.740830</td>\n </tr>\n <tr>\n <th>44</th>\n <td> 5.70</td>\n <td> 4.800000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 6.555647</td>\n <td> 4.055463</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 3.970292</td>\n <td> 2.397895</td>\n <td> 1.050741</td>\n </tr>\n <tr>\n <th>45</th>\n <td> 7.50</td>\n <td> 9.999998</td>\n <td> 7.900000</td>\n <td> 6.666666</td>\n <td> 3.750000</td>\n <td> 9.999998</td>\n <td> 7.631891</td>\n <td> 6.666666</td>\n <td> 3.784190</td>\n <td> 3.091042</td>\n <td> 2.113313</td>\n </tr>\n <tr>\n <th>46</th>\n <td> 2.50</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 2.500000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 3.806662</td>\n <td> 2.079442</td>\n <td> 2.137561</td>\n </tr>\n <tr>\n <th>47</th>\n <td> 8.90</td>\n <td> 9.999998</td>\n <td> 9.700000</td>\n <td> 6.666666</td>\n <td> 5.000000</td>\n <td> 9.999998</td>\n <td> 9.556024</td>\n <td> 6.666666</td>\n <td> 4.532599</td>\n <td> 3.610918</td>\n <td> 1.587802</td>\n </tr>\n <tr>\n <th>48</th>\n <td> 7.60</td>\n <td> 0.000000</td>\n <td> 10.000000</td>\n <td> 0.000000</td>\n <td> 5.000000</td>\n <td> 1.100000</td>\n <td> 6.666666</td>\n <td> 1.099999</td>\n <td> 5.117994</td>\n <td> 4.934474</td>\n <td> 3.834970</td>\n </tr>\n <tr>\n <th>49</th>\n <td> 7.80</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 5.000000</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 5.049856</td>\n <td> 5.111988</td>\n <td> 4.381490</td>\n </tr>\n <tr>\n <th>50</th>\n <td> 2.50</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 5.000000</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 5.393628</td>\n <td> 5.638355</td>\n <td> 4.169451</td>\n </tr>\n <tr>\n <th>51</th>\n <td> 3.80</td>\n <td> 0.000000</td>\n <td> 5.100000</td>\n <td> 0.000000</td>\n <td> 3.750000</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 1.485166</td>\n <td> 4.477337</td>\n <td> 3.931826</td>\n <td> 2.474671</td>\n </tr>\n <tr>\n <th>52</th>\n <td> 5.00</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 2.233333</td>\n <td> 5.000000</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 5.566663</td>\n <td> 5.257495</td>\n <td> 5.840642</td>\n <td> 5.001796</td>\n </tr>\n <tr>\n <th>53</th>\n <td> 6.25</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 2.955702</td>\n <td> 6.250000</td>\n <td> 5.566663</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 5.379897</td>\n <td> 5.505332</td>\n <td> 3.299937</td>\n </tr>\n <tr>\n <th>54</th>\n <td> 1.25</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 0.000000</td>\n <td> 2.500000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 5.298317</td>\n <td> 6.274762</td>\n <td> 4.381490</td>\n </tr>\n <tr>\n <th>55</th>\n <td> 1.25</td>\n <td> 0.000000</td>\n <td> 4.700000</td>\n <td> 0.736999</td>\n <td> 2.500000</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 4.859812</td>\n <td> 5.669881</td>\n <td> 3.537416</td>\n </tr>\n <tr>\n <th>56</th>\n <td> 1.25</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 0.000000</td>\n <td> 2.500000</td>\n <td> 0.000000</td>\n <td> 5.228375</td>\n <td> 0.000000</td>\n <td> 4.969813</td>\n <td> 5.564520</td>\n <td> 4.510268</td>\n </tr>\n <tr>\n <th>57</th>\n <td> 7.50</td>\n <td> 7.766664</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 7.500000</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 6.011267</td>\n <td> 6.253829</td>\n <td> 5.001796</td>\n </tr>\n <tr>\n <th>58</th>\n <td> 2.50</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 4.433333</td>\n <td> 5.000000</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 1.485166</td>\n <td> 5.075174</td>\n <td> 5.252273</td>\n <td> 5.350708</td>\n </tr>\n <tr>\n <th>59</th>\n <td> 7.50</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 8.750000</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 6.736967</td>\n <td> 7.125283</td>\n <td> 6.330518</td>\n </tr>\n <tr>\n <th></th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n </tbody>\n</table>\n<p>75 rows × 11 columns</p>\n</div>", | |
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| } | |
| ], | |
| "language": "python", | |
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| "cell_type": "code", | |
| "input": "PD2_df[0:3]", | |
| "prompt_number": 15, | |
| "outputs": [ | |
| { | |
| "text": " y1 y2 y3 y4 y5 y6 y7 y8 \\\n0 2.50 0.0 3.333333 0.000000 1.25 0.000000 3.726360 3.333333 \n1 1.25 0.0 3.333333 0.000000 6.25 1.100000 6.666666 0.736999 \n2 7.50 8.8 9.999998 9.199991 8.75 8.094061 9.999998 8.211809 \n\n x1 x2 x3 \n0 4.442651 3.637586 2.557615 \n1 5.384495 5.062595 3.568079 \n2 5.961005 6.255750 5.224433 \n\n[3 rows x 11 columns]", | |
| "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>y1</th>\n <th>y2</th>\n <th>y3</th>\n <th>y4</th>\n <th>y5</th>\n <th>y6</th>\n <th>y7</th>\n <th>y8</th>\n <th>x1</th>\n <th>x2</th>\n <th>x3</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td> 2.50</td>\n <td> 0.0</td>\n <td> 3.333333</td>\n <td> 0.000000</td>\n <td> 1.25</td>\n <td> 0.000000</td>\n <td> 3.726360</td>\n <td> 3.333333</td>\n <td> 4.442651</td>\n <td> 3.637586</td>\n <td> 2.557615</td>\n </tr>\n <tr>\n <th>1</th>\n <td> 1.25</td>\n <td> 0.0</td>\n <td> 3.333333</td>\n <td> 0.000000</td>\n <td> 6.25</td>\n <td> 1.100000</td>\n <td> 6.666666</td>\n <td> 0.736999</td>\n <td> 5.384495</td>\n <td> 5.062595</td>\n <td> 3.568079</td>\n </tr>\n <tr>\n <th>2</th>\n <td> 7.50</td>\n <td> 8.8</td>\n <td> 9.999998</td>\n <td> 9.199991</td>\n <td> 8.75</td>\n <td> 8.094061</td>\n <td> 9.999998</td>\n <td> 8.211809</td>\n <td> 5.961005</td>\n <td> 6.255750</td>\n <td> 5.224433</td>\n </tr>\n </tbody>\n</table>\n<p>3 rows × 11 columns</p>\n</div>", | |
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| "cell_type": "markdown", | |
| "source": " ...If you want to see the whole of a large array, you have two options:" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "1: Use the dataframe's 'to_html()' method together with IPython's HTML display method" | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "from IPython.display import HTML", | |
| "prompt_number": 16, | |
| "outputs": [], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "HTML(PD2_df.to_html())", | |
| "prompt_number": 17, | |
| "outputs": [ | |
| { | |
| "text": "<IPython.core.display.HTML at 0x7384c50>", | |
| "html": "<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>y1</th>\n <th>y2</th>\n <th>y3</th>\n <th>y4</th>\n <th>y5</th>\n <th>y6</th>\n <th>y7</th>\n <th>y8</th>\n <th>x1</th>\n <th>x2</th>\n <th>x3</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0 </th>\n <td> 2.50</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 0.000000</td>\n <td> 1.250000</td>\n <td> 0.000000</td>\n <td> 3.726360</td>\n <td> 3.333333</td>\n <td> 4.442651</td>\n <td> 3.637586</td>\n <td> 2.557615</td>\n </tr>\n <tr>\n <th>1 </th>\n <td> 1.25</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 0.000000</td>\n <td> 6.250000</td>\n <td> 1.100000</td>\n <td> 6.666666</td>\n <td> 0.736999</td>\n <td> 5.384495</td>\n <td> 5.062595</td>\n <td> 3.568079</td>\n </tr>\n <tr>\n <th>2 </th>\n <td> 7.50</td>\n <td> 8.800000</td>\n <td> 9.999998</td>\n <td> 9.199991</td>\n <td> 8.750000</td>\n <td> 8.094061</td>\n <td> 9.999998</td>\n <td> 8.211809</td>\n <td> 5.961005</td>\n <td> 6.255750</td>\n <td> 5.224433</td>\n </tr>\n <tr>\n <th>3 </th>\n <td> 8.90</td>\n <td> 8.800000</td>\n <td> 9.999998</td>\n <td> 9.199991</td>\n <td> 8.907948</td>\n <td> 8.127979</td>\n <td> 9.999998</td>\n <td> 4.615086</td>\n <td> 6.285998</td>\n <td> 7.567863</td>\n <td> 6.267495</td>\n </tr>\n <tr>\n <th>4 </th>\n <td> 10.00</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 7.500000</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 5.863631</td>\n <td> 6.818924</td>\n <td> 4.573679</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> 7.50</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 6.250000</td>\n <td> 1.100000</td>\n <td> 6.666666</td>\n <td> 0.368500</td>\n <td> 5.533389</td>\n <td> 5.135798</td>\n <td> 3.892270</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> 7.50</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 5.000000</td>\n <td> 2.233333</td>\n <td> 8.271257</td>\n <td> 1.485166</td>\n <td> 5.308268</td>\n <td> 5.075174</td>\n <td> 3.316213</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> 7.50</td>\n <td> 2.233333</td>\n <td> 9.999998</td>\n <td> 1.496333</td>\n <td> 6.250000</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 5.347108</td>\n <td> 4.852030</td>\n <td> 4.263183</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> 2.50</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 6.250000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 5.521461</td>\n <td> 5.241747</td>\n <td> 4.115168</td>\n </tr>\n <tr>\n <th>9 </th>\n <td> 10.00</td>\n <td> 6.666666</td>\n <td> 9.999998</td>\n <td> 8.899991</td>\n <td> 8.750000</td>\n <td> 6.666666</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 5.828946</td>\n <td> 5.370638</td>\n <td> 4.446216</td>\n </tr>\n <tr>\n <th>10</th>\n <td> 7.50</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 8.750000</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 5.916202</td>\n <td> 6.423247</td>\n <td> 3.791545</td>\n </tr>\n <tr>\n <th>11</th>\n <td> 7.50</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 8.750000</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 5.398163</td>\n <td> 6.246107</td>\n <td> 4.535708</td>\n </tr>\n <tr>\n <th>12</th>\n <td> 7.50</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 7.500000</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 10.000000</td>\n <td> 6.622736</td>\n <td> 7.872074</td>\n <td> 4.906154</td>\n </tr>\n <tr>\n <th>13</th>\n <td> 7.50</td>\n <td> 7.766664</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 7.500000</td>\n <td> 0.000000</td>\n <td> 9.999998</td>\n <td> 0.000000</td>\n <td> 5.204007</td>\n <td> 5.225747</td>\n <td> 4.561047</td>\n </tr>\n <tr>\n <th>14</th>\n <td> 7.50</td>\n <td> 9.999998</td>\n <td> 3.333333</td>\n <td> 10.000000</td>\n <td> 7.500000</td>\n <td> 6.666666</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 5.509388</td>\n <td> 6.202536</td>\n <td> 4.586286</td>\n </tr>\n <tr>\n <th>15</th>\n <td> 7.50</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 7.766666</td>\n <td> 7.500000</td>\n <td> 1.100000</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 5.262690</td>\n <td> 5.820083</td>\n <td> 3.948911</td>\n </tr>\n <tr>\n <th>16</th>\n <td> 2.50</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 5.000000</td>\n <td> 1.100000</td>\n <td> 6.666666</td>\n <td> 0.368500</td>\n <td> 4.700480</td>\n <td> 5.023881</td>\n <td> 4.394491</td>\n </tr>\n <tr>\n <th>17</th>\n <td> 1.25</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 1.250000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 5.209486</td>\n <td> 4.465908</td>\n <td> 4.510268</td>\n </tr>\n <tr>\n <th>18</th>\n <td> 10.00</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 8.750000</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 5.916202</td>\n <td> 6.732211</td>\n <td> 5.829084</td>\n </tr>\n <tr>\n <th>19</th>\n <td> 7.50</td>\n <td> 3.333299</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 7.500000</td>\n <td> 2.233299</td>\n <td> 6.666666</td>\n <td> 2.948164</td>\n <td> 6.523562</td>\n <td> 6.992096</td>\n <td> 6.424591</td>\n </tr>\n <tr>\n <th>20</th>\n <td> 10.00</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 10.000000</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 6.238325</td>\n <td> 6.746412</td>\n <td> 5.741711</td>\n </tr>\n <tr>\n <th>21</th>\n <td> 1.25</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 2.500000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 5.976351</td>\n <td> 6.712956</td>\n <td> 5.948168</td>\n </tr>\n <tr>\n <th>22</th>\n <td> 2.50</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 2.500000</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 5.631212</td>\n <td> 5.937536</td>\n <td> 5.686755</td>\n </tr>\n <tr>\n <th>23</th>\n <td> 7.50</td>\n <td> 6.666666</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 7.500000</td>\n <td> 6.666666</td>\n <td> 9.999998</td>\n <td> 7.766666</td>\n <td> 6.033086</td>\n <td> 6.093570</td>\n <td> 4.611429</td>\n </tr>\n <tr>\n <th>24</th>\n <td> 8.50</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 8.750000</td>\n <td> 9.999998</td>\n <td> 7.351018</td>\n <td> 6.666666</td>\n <td> 6.196444</td>\n <td> 6.704414</td>\n <td> 5.475261</td>\n </tr>\n <tr>\n <th>25</th>\n <td> 6.10</td>\n <td> 0.000000</td>\n <td> 5.400000</td>\n <td> 3.333333</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 4.696028</td>\n <td> 3.333333</td>\n <td> 4.248495</td>\n <td> 2.708050</td>\n <td> 1.740830</td>\n </tr>\n <tr>\n <th>26</th>\n <td> 3.30</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 6.250000</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 5.141664</td>\n <td> 4.564348</td>\n <td> 2.255134</td>\n </tr>\n <tr>\n <th>27</th>\n <td> 2.90</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 2.385559</td>\n <td> 0.000000</td>\n <td> 3.177568</td>\n <td> 1.116666</td>\n <td> 4.174387</td>\n <td> 3.688879</td>\n <td> 3.046927</td>\n </tr>\n <tr>\n <th>28</th>\n <td> 9.20</td>\n <td> 0.000000</td>\n <td> 9.900000</td>\n <td> 3.333333</td>\n <td> 7.609660</td>\n <td> 0.000000</td>\n <td> 8.118828</td>\n <td> 3.333333</td>\n <td> 4.382027</td>\n <td> 2.890372</td>\n <td> 1.711279</td>\n </tr>\n <tr>\n <th>29</th>\n <td> 6.90</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 4.226033</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 4.290459</td>\n <td> 1.609438</td>\n <td> 1.001674</td>\n </tr>\n <tr>\n <th>30</th>\n <td> 2.90</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 5.000000</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 4.934474</td>\n <td> 4.234107</td>\n <td> 1.418971</td>\n </tr>\n <tr>\n <th>31</th>\n <td> 2.00</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 3.850148</td>\n <td> 1.945910</td>\n <td> 2.345229</td>\n </tr>\n <tr>\n <th>32</th>\n <td> 5.00</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 5.000000</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 5.181784</td>\n <td> 4.394449</td>\n <td> 3.167167</td>\n </tr>\n <tr>\n <th>33</th>\n <td> 5.00</td>\n <td> 0.000000</td>\n <td> 9.999998</td>\n <td> 3.333333</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 0.744370</td>\n <td> 5.062595</td>\n <td> 4.595120</td>\n <td> 3.834970</td>\n </tr>\n <tr>\n <th>34</th>\n <td> 4.10</td>\n <td> 9.999998</td>\n <td> 4.700000</td>\n <td> 6.666666</td>\n <td> 3.750000</td>\n <td> 0.000000</td>\n <td> 7.827667</td>\n <td> 6.666666</td>\n <td> 4.691348</td>\n <td> 4.143135</td>\n <td> 2.255134</td>\n </tr>\n <tr>\n <th>35</th>\n <td> 6.30</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 6.250000</td>\n <td> 2.233333</td>\n <td> 6.666666</td>\n <td> 2.955702</td>\n <td> 4.248495</td>\n <td> 3.367296</td>\n <td> 3.217506</td>\n </tr>\n <tr>\n <th>36</th>\n <td> 5.20</td>\n <td> 4.999998</td>\n <td> 6.600000</td>\n <td> 3.333333</td>\n <td> 3.633403</td>\n <td> 1.100000</td>\n <td> 3.314128</td>\n <td> 3.333333</td>\n <td> 5.564520</td>\n <td> 5.236442</td>\n <td> 2.677633</td>\n </tr>\n <tr>\n <th>37</th>\n <td> 5.00</td>\n <td> 3.333333</td>\n <td> 6.400000</td>\n <td> 6.666666</td>\n <td> 2.844997</td>\n <td> 0.000000</td>\n <td> 4.429657</td>\n <td> 1.485166</td>\n <td> 4.727388</td>\n <td> 3.610918</td>\n <td> 1.418971</td>\n </tr>\n <tr>\n <th>38</th>\n <td> 3.10</td>\n <td> 4.999998</td>\n <td> 4.200000</td>\n <td> 5.000000</td>\n <td> 3.750000</td>\n <td> 0.000000</td>\n <td> 6.164304</td>\n <td> 3.333333</td>\n <td> 4.143135</td>\n <td> 2.302585</td>\n <td> 1.418971</td>\n </tr>\n <tr>\n <th>39</th>\n <td> 4.10</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 5.000000</td>\n <td> 0.000000</td>\n <td> 4.938089</td>\n <td> 2.233333</td>\n <td> 4.317488</td>\n <td> 4.955827</td>\n <td> 4.249888</td>\n </tr>\n <tr>\n <th>40</th>\n <td> 5.00</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 1.666666</td>\n <td> 5.000000</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 0.368500</td>\n <td> 5.141664</td>\n <td> 4.430817</td>\n <td> 3.046927</td>\n </tr>\n <tr>\n <th>41</th>\n <td> 5.00</td>\n <td> 7.700000</td>\n <td> 6.666666</td>\n <td> 8.399997</td>\n <td> 6.250000</td>\n <td> 4.358243</td>\n <td> 9.999998</td>\n <td> 4.141377</td>\n <td> 4.488636</td>\n <td> 3.465736</td>\n <td> 2.013579</td>\n </tr>\n <tr>\n <th>42</th>\n <td> 5.00</td>\n <td> 6.200000</td>\n <td> 9.999998</td>\n <td> 6.060997</td>\n <td> 5.000000</td>\n <td> 2.782771</td>\n <td> 6.666666</td>\n <td> 4.974739</td>\n <td> 4.615121</td>\n <td> 4.941642</td>\n <td> 2.255134</td>\n </tr>\n <tr>\n <th>43</th>\n <td> 5.60</td>\n <td> 4.900000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 6.555647</td>\n <td> 4.055463</td>\n <td> 6.666666</td>\n <td> 3.821796</td>\n <td> 3.850148</td>\n <td> 2.397895</td>\n <td> 1.740830</td>\n </tr>\n <tr>\n <th>44</th>\n <td> 5.70</td>\n <td> 4.800000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 6.555647</td>\n <td> 4.055463</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 3.970292</td>\n <td> 2.397895</td>\n <td> 1.050741</td>\n </tr>\n <tr>\n <th>45</th>\n <td> 7.50</td>\n <td> 9.999998</td>\n <td> 7.900000</td>\n <td> 6.666666</td>\n <td> 3.750000</td>\n <td> 9.999998</td>\n <td> 7.631891</td>\n <td> 6.666666</td>\n <td> 3.784190</td>\n <td> 3.091042</td>\n <td> 2.113313</td>\n </tr>\n <tr>\n <th>46</th>\n <td> 2.50</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 2.500000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 3.806662</td>\n <td> 2.079442</td>\n <td> 2.137561</td>\n </tr>\n <tr>\n <th>47</th>\n <td> 8.90</td>\n <td> 9.999998</td>\n <td> 9.700000</td>\n <td> 6.666666</td>\n <td> 5.000000</td>\n <td> 9.999998</td>\n <td> 9.556024</td>\n <td> 6.666666</td>\n <td> 4.532599</td>\n <td> 3.610918</td>\n <td> 1.587802</td>\n </tr>\n <tr>\n <th>48</th>\n <td> 7.60</td>\n <td> 0.000000</td>\n <td> 10.000000</td>\n <td> 0.000000</td>\n <td> 5.000000</td>\n <td> 1.100000</td>\n <td> 6.666666</td>\n <td> 1.099999</td>\n <td> 5.117994</td>\n <td> 4.934474</td>\n <td> 3.834970</td>\n </tr>\n <tr>\n <th>49</th>\n <td> 7.80</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 5.000000</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 5.049856</td>\n <td> 5.111988</td>\n <td> 4.381490</td>\n </tr>\n <tr>\n <th>50</th>\n <td> 2.50</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 5.000000</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 5.393628</td>\n <td> 5.638355</td>\n <td> 4.169451</td>\n </tr>\n <tr>\n <th>51</th>\n <td> 3.80</td>\n <td> 0.000000</td>\n <td> 5.100000</td>\n <td> 0.000000</td>\n <td> 3.750000</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 1.485166</td>\n <td> 4.477337</td>\n <td> 3.931826</td>\n <td> 2.474671</td>\n </tr>\n <tr>\n <th>52</th>\n <td> 5.00</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 2.233333</td>\n <td> 5.000000</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 5.566663</td>\n <td> 5.257495</td>\n <td> 5.840642</td>\n <td> 5.001796</td>\n </tr>\n <tr>\n <th>53</th>\n <td> 6.25</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 2.955702</td>\n <td> 6.250000</td>\n <td> 5.566663</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 5.379897</td>\n <td> 5.505332</td>\n <td> 3.299937</td>\n </tr>\n <tr>\n <th>54</th>\n <td> 1.25</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 0.000000</td>\n <td> 2.500000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 5.298317</td>\n <td> 6.274762</td>\n <td> 4.381490</td>\n </tr>\n <tr>\n <th>55</th>\n <td> 1.25</td>\n <td> 0.000000</td>\n <td> 4.700000</td>\n <td> 0.736999</td>\n <td> 2.500000</td>\n <td> 0.000000</td>\n <td> 3.333333</td>\n <td> 3.333333</td>\n <td> 4.859812</td>\n <td> 5.669881</td>\n <td> 3.537416</td>\n </tr>\n <tr>\n <th>56</th>\n <td> 1.25</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 0.000000</td>\n <td> 2.500000</td>\n <td> 0.000000</td>\n <td> 5.228375</td>\n <td> 0.000000</td>\n <td> 4.969813</td>\n <td> 5.564520</td>\n <td> 4.510268</td>\n </tr>\n <tr>\n <th>57</th>\n <td> 7.50</td>\n <td> 7.766664</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 7.500000</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 6.011267</td>\n <td> 6.253829</td>\n <td> 5.001796</td>\n </tr>\n <tr>\n <th>58</th>\n <td> 2.50</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 4.433333</td>\n <td> 5.000000</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 1.485166</td>\n <td> 5.075174</td>\n <td> 5.252273</td>\n <td> 5.350708</td>\n </tr>\n <tr>\n <th>59</th>\n <td> 7.50</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 8.750000</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 6.736967</td>\n <td> 7.125283</td>\n <td> 6.330518</td>\n </tr>\n <tr>\n <th>60</th>\n <td> 1.25</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 1.250000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 5.225747</td>\n <td> 5.451038</td>\n <td> 3.167167</td>\n </tr>\n <tr>\n <th>61</th>\n <td> 1.25</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 4.025352</td>\n <td> 1.791759</td>\n <td> 2.657972</td>\n </tr>\n <tr>\n <th>62</th>\n <td> 2.50</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 6.666666</td>\n <td> 2.948164</td>\n <td> 4.234107</td>\n <td> 2.708050</td>\n <td> 2.474671</td>\n </tr>\n <tr>\n <th>63</th>\n <td> 6.25</td>\n <td> 2.233299</td>\n <td> 6.666666</td>\n <td> 2.970332</td>\n <td> 3.750000</td>\n <td> 3.333299</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 4.644391</td>\n <td> 5.564520</td>\n <td> 3.046927</td>\n </tr>\n <tr>\n <th>64</th>\n <td> 7.50</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 7.500000</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 4.418841</td>\n <td> 4.941642</td>\n <td> 3.380653</td>\n </tr>\n <tr>\n <th>65</th>\n <td> 5.00</td>\n <td> 0.000000</td>\n <td> 6.100000</td>\n <td> 0.000000</td>\n <td> 5.000000</td>\n <td> 3.333333</td>\n <td> 9.999998</td>\n <td> 3.333333</td>\n <td> 4.262680</td>\n <td> 4.219508</td>\n <td> 4.368462</td>\n </tr>\n <tr>\n <th>66</th>\n <td> 7.50</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 3.750000</td>\n <td> 9.999998</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 4.875197</td>\n <td> 4.700480</td>\n <td> 3.834970</td>\n </tr>\n <tr>\n <th>67</th>\n <td> 4.90</td>\n <td> 2.233333</td>\n <td> 9.999998</td>\n <td> 0.000000</td>\n <td> 5.000000</td>\n <td> 0.000000</td>\n <td> 3.621989</td>\n <td> 3.333333</td>\n <td> 4.189655</td>\n <td> 1.386294</td>\n <td> 1.418971</td>\n </tr>\n <tr>\n <th>68</th>\n <td> 5.00</td>\n <td> 0.000000</td>\n <td> 8.200000</td>\n <td> 0.000000</td>\n <td> 5.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 4.521789</td>\n <td> 4.127134</td>\n <td> 2.113313</td>\n </tr>\n <tr>\n <th>69</th>\n <td> 2.90</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 2.500000</td>\n <td> 3.333333</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 4.653960</td>\n <td> 3.555348</td>\n <td> 1.881917</td>\n </tr>\n <tr>\n <th>70</th>\n <td> 5.40</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 3.333333</td>\n <td> 3.750000</td>\n <td> 6.666666</td>\n <td> 6.666666</td>\n <td> 1.485166</td>\n <td> 4.477337</td>\n <td> 3.091042</td>\n <td> 1.987909</td>\n </tr>\n <tr>\n <th>71</th>\n <td> 7.50</td>\n <td> 8.800000</td>\n <td> 9.999998</td>\n <td> 6.066666</td>\n <td> 7.500000</td>\n <td> 6.666666</td>\n <td> 9.999998</td>\n <td> 6.666666</td>\n <td> 5.337538</td>\n <td> 5.631212</td>\n <td> 3.491004</td>\n </tr>\n <tr>\n <th>72</th>\n <td> 7.50</td>\n <td> 7.000000</td>\n <td> 9.999998</td>\n <td> 6.852998</td>\n <td> 7.500000</td>\n <td> 6.348340</td>\n <td> 6.666666</td>\n <td> 7.508044</td>\n <td> 6.129050</td>\n <td> 6.403574</td>\n <td> 5.001796</td>\n </tr>\n <tr>\n <th>73</th>\n <td> 10.00</td>\n <td> 6.666666</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 10.000000</td>\n <td> 6.666666</td>\n <td> 9.999998</td>\n <td> 10.000000</td>\n <td> 5.003946</td>\n <td> 4.962845</td>\n <td> 3.976994</td>\n </tr>\n <tr>\n <th>74</th>\n <td> 3.75</td>\n <td> 3.333333</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 1.250000</td>\n <td> 3.333333</td>\n <td> 0.000000</td>\n <td> 0.000000</td>\n <td> 4.488636</td>\n <td> 4.897840</td>\n <td> 2.867566</td>\n </tr>\n </tbody>\n</table>", | |
| "output_type": "pyout", | |
| "metadata": {}, | |
| "prompt_number": 17 | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "`2. Change the pandas size threshold for displaying dataframe content by default\n" | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "pd.set_option('display.max_rows', 80) # Forces IPython notebook to show large tables\npd.set_option('display.max_columns', 15)\n\n# (also handy to set the floating point precision\ndef fff(x): return '%1.3f' %x # float formatting function\npd.set_option('display.float_format', fff)\nPD2_df", | |
| "prompt_number": 18, | |
| "outputs": [ | |
| { | |
| "text": " y1 y2 y3 y4 y5 y6 y7 y8 x1 x2 x3\n0 2.500 0.000 3.333 0.000 1.250 0.000 3.726 3.333 4.443 3.638 2.558\n1 1.250 0.000 3.333 0.000 6.250 1.100 6.667 0.737 5.384 5.063 3.568\n2 7.500 8.800 10.000 9.200 8.750 8.094 10.000 8.212 5.961 6.256 5.224\n3 8.900 8.800 10.000 9.200 8.908 8.128 10.000 4.615 6.286 7.568 6.267\n4 10.000 3.333 10.000 6.667 7.500 3.333 10.000 6.667 5.864 6.819 4.574\n5 7.500 3.333 6.667 6.667 6.250 1.100 6.667 0.368 5.533 5.136 3.892\n6 7.500 3.333 6.667 6.667 5.000 2.233 8.271 1.485 5.308 5.075 3.316\n7 7.500 2.233 10.000 1.496 6.250 3.333 10.000 6.667 5.347 4.852 4.263\n8 2.500 3.333 3.333 3.333 6.250 3.333 3.333 3.333 5.521 5.242 4.115\n9 10.000 6.667 10.000 8.900 8.750 6.667 10.000 10.000 5.829 5.371 4.446\n10 7.500 3.333 10.000 6.667 8.750 3.333 10.000 6.667 5.916 6.423 3.792\n11 7.500 3.333 6.667 6.667 8.750 3.333 6.667 6.667 5.398 6.246 4.536\n12 7.500 3.333 10.000 6.667 7.500 3.333 6.667 10.000 6.623 7.872 4.906\n13 7.500 7.767 10.000 6.667 7.500 0.000 10.000 0.000 5.204 5.226 4.561\n14 7.500 10.000 3.333 10.000 7.500 6.667 10.000 10.000 5.509 6.203 4.586\n15 7.500 10.000 10.000 7.767 7.500 1.100 6.667 6.667 5.263 5.820 3.949\n16 2.500 3.333 6.667 6.667 5.000 1.100 6.667 0.368 4.700 5.024 4.394\n17 1.250 0.000 3.333 3.333 1.250 3.333 3.333 3.333 5.209 4.466 4.510\n18 10.000 10.000 10.000 10.000 8.750 10.000 10.000 10.000 5.916 6.732 5.829\n19 7.500 3.333 3.333 6.667 7.500 2.233 6.667 2.948 6.524 6.992 6.425\n20 10.000 10.000 10.000 10.000 10.000 10.000 10.000 10.000 6.238 6.746 5.742\n21 1.250 0.000 0.000 0.000 2.500 0.000 0.000 0.000 5.976 6.713 5.948\n22 2.500 0.000 3.333 3.333 2.500 0.000 3.333 3.333 5.631 5.938 5.687\n23 7.500 6.667 10.000 10.000 7.500 6.667 10.000 7.767 6.033 6.094 4.611\n24 8.500 10.000 6.667 6.667 8.750 10.000 7.351 6.667 6.196 6.704 5.475\n25 6.100 0.000 5.400 3.333 0.000 0.000 4.696 3.333 4.248 2.708 1.741\n26 3.300 0.000 6.667 3.333 6.250 0.000 6.667 3.333 5.142 4.564 2.255\n27 2.900 3.333 6.667 3.333 2.386 0.000 3.178 1.117 4.174 3.689 3.047\n28 9.200 0.000 9.900 3.333 7.610 0.000 8.119 3.333 4.382 2.890 1.711\n29 6.900 0.000 6.667 3.333 4.226 0.000 0.000 0.000 4.290 1.609 1.002\n30 2.900 0.000 3.333 3.333 5.000 0.000 3.333 3.333 4.934 4.234 1.419\n31 2.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 3.850 1.946 2.345\n32 5.000 0.000 3.333 3.333 5.000 0.000 3.333 3.333 5.182 4.394 3.167\n33 5.000 0.000 10.000 3.333 0.000 0.000 3.333 0.744 5.063 4.595 3.835\n34 4.100 10.000 4.700 6.667 3.750 0.000 7.828 6.667 4.691 4.143 2.255\n35 6.300 10.000 10.000 6.667 6.250 2.233 6.667 2.956 4.248 3.367 3.218\n36 5.200 5.000 6.600 3.333 3.633 1.100 3.314 3.333 5.565 5.236 2.678\n37 5.000 3.333 6.400 6.667 2.845 0.000 4.430 1.485 4.727 3.611 1.419\n38 3.100 5.000 4.200 5.000 3.750 0.000 6.164 3.333 4.143 2.303 1.419\n39 4.100 10.000 6.667 3.333 5.000 0.000 4.938 2.233 4.317 4.956 4.250\n40 5.000 10.000 6.667 1.667 5.000 0.000 6.667 0.368 5.142 4.431 3.047\n41 5.000 7.700 6.667 8.400 6.250 4.358 10.000 4.141 4.489 3.466 2.014\n42 5.000 6.200 10.000 6.061 5.000 2.783 6.667 4.975 4.615 4.942 2.255\n43 5.600 4.900 0.000 0.000 6.556 4.055 6.667 3.822 3.850 2.398 1.741\n44 5.700 4.800 0.000 0.000 6.556 4.055 0.000 0.000 3.970 2.398 1.051\n45 7.500 10.000 7.900 6.667 3.750 10.000 7.632 6.667 3.784 3.091 2.113\n46 2.500 0.000 6.667 3.333 2.500 0.000 0.000 0.000 3.807 2.079 2.138\n47 8.900 10.000 9.700 6.667 5.000 10.000 9.556 6.667 4.533 3.611 1.588\n48 7.600 0.000 10.000 0.000 5.000 1.100 6.667 1.100 5.118 4.934 3.835\n49 7.800 10.000 6.667 6.667 5.000 3.333 6.667 6.667 5.050 5.112 4.381\n50 2.500 0.000 6.667 3.333 5.000 0.000 6.667 3.333 5.394 5.638 4.169\n51 3.800 0.000 5.100 0.000 3.750 0.000 6.667 1.485 4.477 3.932 2.475\n52 5.000 3.333 3.333 2.233 5.000 3.333 6.667 5.567 5.257 5.841 5.002\n53 6.250 3.333 10.000 2.956 6.250 5.567 10.000 6.667 5.380 5.505 3.300\n54 1.250 0.000 3.333 0.000 2.500 0.000 0.000 0.000 5.298 6.275 4.381\n55 1.250 0.000 4.700 0.737 2.500 0.000 3.333 3.333 4.860 5.670 3.537\n56 1.250 0.000 6.667 0.000 2.500 0.000 5.228 0.000 4.970 5.565 4.510\n57 7.500 7.767 10.000 6.667 7.500 3.333 10.000 6.667 6.011 6.254 5.002\n58 2.500 0.000 6.667 4.433 5.000 0.000 6.667 1.485 5.075 5.252 5.351\n59 7.500 10.000 10.000 10.000 8.750 10.000 10.000 10.000 6.737 7.125 6.331\n60 1.250 0.000 0.000 0.000 1.250 0.000 0.000 0.000 5.226 5.451 3.167\n61 1.250 0.000 0.000 0.000 0.000 0.000 0.000 0.000 4.025 1.792 2.658\n62 2.500 0.000 0.000 0.000 0.000 0.000 6.667 2.948 4.234 2.708 2.475\n63 6.250 2.233 6.667 2.970 3.750 3.333 6.667 3.333 4.644 5.565 3.047\n64 7.500 10.000 10.000 10.000 7.500 10.000 10.000 10.000 4.419 4.942 3.381\n65 5.000 0.000 6.100 0.000 5.000 3.333 10.000 3.333 4.263 4.220 4.368\n66 7.500 10.000 10.000 10.000 3.750 10.000 10.000 10.000 4.875 4.700 3.835\n67 4.900 2.233 10.000 0.000 5.000 0.000 3.622 3.333 4.190 1.386 1.419\n68 5.000 0.000 8.200 0.000 5.000 0.000 0.000 0.000 4.522 4.127 2.113\n69 2.900 3.333 6.667 3.333 2.500 3.333 6.667 3.333 4.654 3.555 1.882\n70 5.400 10.000 6.667 3.333 3.750 6.667 6.667 1.485 4.477 3.091 1.988\n71 7.500 8.800 10.000 6.067 7.500 6.667 10.000 6.667 5.338 5.631 3.491\n72 7.500 7.000 10.000 6.853 7.500 6.348 6.667 7.508 6.129 6.404 5.002\n73 10.000 6.667 10.000 10.000 10.000 6.667 10.000 10.000 5.004 4.963 3.977\n74 3.750 3.333 0.000 0.000 1.250 3.333 0.000 0.000 4.489 4.898 2.868\n\n[75 rows x 11 columns]", | |
| "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>y1</th>\n <th>y2</th>\n <th>y3</th>\n <th>y4</th>\n <th>y5</th>\n <th>y6</th>\n <th>y7</th>\n <th>y8</th>\n <th>x1</th>\n <th>x2</th>\n <th>x3</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0 </th>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 0.000</td>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 3.726</td>\n <td> 3.333</td>\n <td>4.443</td>\n <td>3.638</td>\n <td>2.558</td>\n </tr>\n <tr>\n <th>1 </th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 0.000</td>\n <td> 6.250</td>\n <td> 1.100</td>\n <td> 6.667</td>\n <td> 0.737</td>\n <td>5.384</td>\n <td>5.063</td>\n <td>3.568</td>\n </tr>\n <tr>\n <th>2 </th>\n <td> 7.500</td>\n <td> 8.800</td>\n <td>10.000</td>\n <td> 9.200</td>\n <td> 8.750</td>\n <td> 8.094</td>\n <td>10.000</td>\n <td> 8.212</td>\n <td>5.961</td>\n <td>6.256</td>\n <td>5.224</td>\n </tr>\n <tr>\n <th>3 </th>\n <td> 8.900</td>\n <td> 8.800</td>\n <td>10.000</td>\n <td> 9.200</td>\n <td> 8.908</td>\n <td> 8.128</td>\n <td>10.000</td>\n <td> 4.615</td>\n <td>6.286</td>\n <td>7.568</td>\n <td>6.267</td>\n </tr>\n <tr>\n <th>4 </th>\n <td>10.000</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 7.500</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td>5.864</td>\n <td>6.819</td>\n <td>4.574</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> 7.500</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td> 6.250</td>\n <td> 1.100</td>\n <td> 6.667</td>\n <td> 0.368</td>\n <td>5.533</td>\n <td>5.136</td>\n <td>3.892</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> 7.500</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td> 5.000</td>\n <td> 2.233</td>\n <td> 8.271</td>\n <td> 1.485</td>\n <td>5.308</td>\n <td>5.075</td>\n <td>3.316</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> 7.500</td>\n <td> 2.233</td>\n <td>10.000</td>\n <td> 1.496</td>\n <td> 6.250</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td>5.347</td>\n <td>4.852</td>\n <td>4.263</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> 2.500</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 6.250</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td>5.521</td>\n <td>5.242</td>\n <td>4.115</td>\n </tr>\n <tr>\n <th>9 </th>\n <td>10.000</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td> 8.900</td>\n <td> 8.750</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>5.829</td>\n <td>5.371</td>\n <td>4.446</td>\n </tr>\n <tr>\n <th>10</th>\n <td> 7.500</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 8.750</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td>5.916</td>\n <td>6.423</td>\n <td>3.792</td>\n </tr>\n <tr>\n <th>11</th>\n <td> 7.500</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td> 8.750</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td>5.398</td>\n <td>6.246</td>\n <td>4.536</td>\n </tr>\n <tr>\n <th>12</th>\n <td> 7.500</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 7.500</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td>6.623</td>\n <td>7.872</td>\n <td>4.906</td>\n </tr>\n <tr>\n <th>13</th>\n <td> 7.500</td>\n <td> 7.767</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 7.500</td>\n <td> 0.000</td>\n <td>10.000</td>\n <td> 0.000</td>\n <td>5.204</td>\n <td>5.226</td>\n <td>4.561</td>\n </tr>\n <tr>\n <th>14</th>\n <td> 7.500</td>\n <td>10.000</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 7.500</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>5.509</td>\n <td>6.203</td>\n <td>4.586</td>\n </tr>\n <tr>\n <th>15</th>\n <td> 7.500</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 7.767</td>\n <td> 7.500</td>\n <td> 1.100</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td>5.263</td>\n <td>5.820</td>\n <td>3.949</td>\n </tr>\n <tr>\n <th>16</th>\n <td> 2.500</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td> 5.000</td>\n <td> 1.100</td>\n <td> 6.667</td>\n <td> 0.368</td>\n <td>4.700</td>\n <td>5.024</td>\n <td>4.394</td>\n </tr>\n <tr>\n <th>17</th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 1.250</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td>5.209</td>\n <td>4.466</td>\n <td>4.510</td>\n </tr>\n <tr>\n <th>18</th>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 8.750</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>5.916</td>\n <td>6.732</td>\n <td>5.829</td>\n </tr>\n <tr>\n <th>19</th>\n <td> 7.500</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 7.500</td>\n <td> 2.233</td>\n <td> 6.667</td>\n <td> 2.948</td>\n <td>6.524</td>\n <td>6.992</td>\n <td>6.425</td>\n </tr>\n <tr>\n <th>20</th>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>6.238</td>\n <td>6.746</td>\n <td>5.742</td>\n </tr>\n <tr>\n <th>21</th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>5.976</td>\n <td>6.713</td>\n <td>5.948</td>\n </tr>\n <tr>\n <th>22</th>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td>5.631</td>\n <td>5.938</td>\n <td>5.687</td>\n </tr>\n <tr>\n <th>23</th>\n <td> 7.500</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 7.500</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td> 7.767</td>\n <td>6.033</td>\n <td>6.094</td>\n <td>4.611</td>\n </tr>\n <tr>\n <th>24</th>\n <td> 8.500</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td> 8.750</td>\n <td>10.000</td>\n <td> 7.351</td>\n <td> 6.667</td>\n <td>6.196</td>\n <td>6.704</td>\n <td>5.475</td>\n </tr>\n <tr>\n <th>25</th>\n <td> 6.100</td>\n <td> 0.000</td>\n <td> 5.400</td>\n <td> 3.333</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 4.696</td>\n <td> 3.333</td>\n <td>4.248</td>\n <td>2.708</td>\n <td>1.741</td>\n </tr>\n <tr>\n <th>26</th>\n <td> 3.300</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 6.250</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td>5.142</td>\n <td>4.564</td>\n <td>2.255</td>\n </tr>\n <tr>\n <th>27</th>\n <td> 2.900</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 2.386</td>\n <td> 0.000</td>\n <td> 3.178</td>\n <td> 1.117</td>\n <td>4.174</td>\n <td>3.689</td>\n <td>3.047</td>\n </tr>\n <tr>\n <th>28</th>\n <td> 9.200</td>\n <td> 0.000</td>\n <td> 9.900</td>\n <td> 3.333</td>\n <td> 7.610</td>\n <td> 0.000</td>\n <td> 8.119</td>\n <td> 3.333</td>\n <td>4.382</td>\n <td>2.890</td>\n <td>1.711</td>\n </tr>\n <tr>\n <th>29</th>\n <td> 6.900</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 4.226</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>4.290</td>\n <td>1.609</td>\n <td>1.002</td>\n </tr>\n <tr>\n <th>30</th>\n <td> 2.900</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td>4.934</td>\n <td>4.234</td>\n <td>1.419</td>\n </tr>\n <tr>\n <th>31</th>\n <td> 2.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>3.850</td>\n <td>1.946</td>\n <td>2.345</td>\n </tr>\n <tr>\n <th>32</th>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td>5.182</td>\n <td>4.394</td>\n <td>3.167</td>\n </tr>\n <tr>\n <th>33</th>\n <td> 5.000</td>\n <td> 0.000</td>\n <td>10.000</td>\n <td> 3.333</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 0.744</td>\n <td>5.063</td>\n <td>4.595</td>\n <td>3.835</td>\n </tr>\n <tr>\n <th>34</th>\n <td> 4.100</td>\n <td>10.000</td>\n <td> 4.700</td>\n <td> 6.667</td>\n <td> 3.750</td>\n <td> 0.000</td>\n <td> 7.828</td>\n <td> 6.667</td>\n <td>4.691</td>\n <td>4.143</td>\n <td>2.255</td>\n </tr>\n <tr>\n <th>35</th>\n <td> 6.300</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 6.250</td>\n <td> 2.233</td>\n <td> 6.667</td>\n <td> 2.956</td>\n <td>4.248</td>\n <td>3.367</td>\n <td>3.218</td>\n </tr>\n <tr>\n <th>36</th>\n <td> 5.200</td>\n <td> 5.000</td>\n <td> 6.600</td>\n <td> 3.333</td>\n <td> 3.633</td>\n <td> 1.100</td>\n <td> 3.314</td>\n <td> 3.333</td>\n <td>5.565</td>\n <td>5.236</td>\n <td>2.678</td>\n </tr>\n <tr>\n <th>37</th>\n <td> 5.000</td>\n <td> 3.333</td>\n <td> 6.400</td>\n <td> 6.667</td>\n <td> 2.845</td>\n <td> 0.000</td>\n <td> 4.430</td>\n <td> 1.485</td>\n <td>4.727</td>\n <td>3.611</td>\n <td>1.419</td>\n </tr>\n <tr>\n <th>38</th>\n <td> 3.100</td>\n <td> 5.000</td>\n <td> 4.200</td>\n <td> 5.000</td>\n <td> 3.750</td>\n <td> 0.000</td>\n <td> 6.164</td>\n <td> 3.333</td>\n <td>4.143</td>\n <td>2.303</td>\n <td>1.419</td>\n </tr>\n <tr>\n <th>39</th>\n <td> 4.100</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 4.938</td>\n <td> 2.233</td>\n <td>4.317</td>\n <td>4.956</td>\n <td>4.250</td>\n </tr>\n <tr>\n <th>40</th>\n <td> 5.000</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 1.667</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 0.368</td>\n <td>5.142</td>\n <td>4.431</td>\n <td>3.047</td>\n </tr>\n <tr>\n <th>41</th>\n <td> 5.000</td>\n <td> 7.700</td>\n <td> 6.667</td>\n <td> 8.400</td>\n <td> 6.250</td>\n <td> 4.358</td>\n <td>10.000</td>\n <td> 4.141</td>\n <td>4.489</td>\n <td>3.466</td>\n <td>2.014</td>\n </tr>\n <tr>\n <th>42</th>\n <td> 5.000</td>\n <td> 6.200</td>\n <td>10.000</td>\n <td> 6.061</td>\n <td> 5.000</td>\n <td> 2.783</td>\n <td> 6.667</td>\n <td> 4.975</td>\n <td>4.615</td>\n <td>4.942</td>\n <td>2.255</td>\n </tr>\n <tr>\n <th>43</th>\n <td> 5.600</td>\n <td> 4.900</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 6.556</td>\n <td> 4.055</td>\n <td> 6.667</td>\n <td> 3.822</td>\n <td>3.850</td>\n <td>2.398</td>\n <td>1.741</td>\n </tr>\n <tr>\n <th>44</th>\n <td> 5.700</td>\n <td> 4.800</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 6.556</td>\n <td> 4.055</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>3.970</td>\n <td>2.398</td>\n <td>1.051</td>\n </tr>\n <tr>\n <th>45</th>\n <td> 7.500</td>\n <td>10.000</td>\n <td> 7.900</td>\n <td> 6.667</td>\n <td> 3.750</td>\n <td>10.000</td>\n <td> 7.632</td>\n <td> 6.667</td>\n <td>3.784</td>\n <td>3.091</td>\n <td>2.113</td>\n </tr>\n <tr>\n <th>46</th>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>3.807</td>\n <td>2.079</td>\n <td>2.138</td>\n </tr>\n <tr>\n <th>47</th>\n <td> 8.900</td>\n <td>10.000</td>\n <td> 9.700</td>\n <td> 6.667</td>\n <td> 5.000</td>\n <td>10.000</td>\n <td> 9.556</td>\n <td> 6.667</td>\n <td>4.533</td>\n <td>3.611</td>\n <td>1.588</td>\n </tr>\n <tr>\n <th>48</th>\n <td> 7.600</td>\n <td> 0.000</td>\n <td>10.000</td>\n <td> 0.000</td>\n <td> 5.000</td>\n <td> 1.100</td>\n <td> 6.667</td>\n <td> 1.100</td>\n <td>5.118</td>\n <td>4.934</td>\n <td>3.835</td>\n </tr>\n <tr>\n <th>49</th>\n <td> 7.800</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td> 5.000</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td>5.050</td>\n <td>5.112</td>\n <td>4.381</td>\n </tr>\n <tr>\n <th>50</th>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td>5.394</td>\n <td>5.638</td>\n <td>4.169</td>\n </tr>\n <tr>\n <th>51</th>\n <td> 3.800</td>\n <td> 0.000</td>\n <td> 5.100</td>\n <td> 0.000</td>\n <td> 3.750</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 1.485</td>\n <td>4.477</td>\n <td>3.932</td>\n <td>2.475</td>\n </tr>\n <tr>\n <th>52</th>\n <td> 5.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 2.233</td>\n <td> 5.000</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 5.567</td>\n <td>5.257</td>\n <td>5.841</td>\n <td>5.002</td>\n </tr>\n <tr>\n <th>53</th>\n <td> 6.250</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 2.956</td>\n <td> 6.250</td>\n <td> 5.567</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td>5.380</td>\n <td>5.505</td>\n <td>3.300</td>\n </tr>\n <tr>\n <th>54</th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 0.000</td>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>5.298</td>\n <td>6.275</td>\n <td>4.381</td>\n </tr>\n <tr>\n <th>55</th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 4.700</td>\n <td> 0.737</td>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td>4.860</td>\n <td>5.670</td>\n <td>3.537</td>\n </tr>\n <tr>\n <th>56</th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 0.000</td>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 5.228</td>\n <td> 0.000</td>\n <td>4.970</td>\n <td>5.565</td>\n <td>4.510</td>\n </tr>\n <tr>\n <th>57</th>\n <td> 7.500</td>\n <td> 7.767</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 7.500</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td>6.011</td>\n <td>6.254</td>\n <td>5.002</td>\n </tr>\n <tr>\n <th>58</th>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 4.433</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 1.485</td>\n <td>5.075</td>\n <td>5.252</td>\n <td>5.351</td>\n </tr>\n <tr>\n <th>59</th>\n <td> 7.500</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 8.750</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>6.737</td>\n <td>7.125</td>\n <td>6.331</td>\n </tr>\n <tr>\n <th>60</th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>5.226</td>\n <td>5.451</td>\n <td>3.167</td>\n </tr>\n <tr>\n <th>61</th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>4.025</td>\n <td>1.792</td>\n <td>2.658</td>\n </tr>\n <tr>\n <th>62</th>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 2.948</td>\n <td>4.234</td>\n <td>2.708</td>\n <td>2.475</td>\n </tr>\n <tr>\n <th>63</th>\n <td> 6.250</td>\n <td> 2.233</td>\n <td> 6.667</td>\n <td> 2.970</td>\n <td> 3.750</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td>4.644</td>\n <td>5.565</td>\n <td>3.047</td>\n </tr>\n <tr>\n <th>64</th>\n <td> 7.500</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 7.500</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>4.419</td>\n <td>4.942</td>\n <td>3.381</td>\n </tr>\n <tr>\n <th>65</th>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 6.100</td>\n <td> 0.000</td>\n <td> 5.000</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 3.333</td>\n <td>4.263</td>\n <td>4.220</td>\n <td>4.368</td>\n </tr>\n <tr>\n <th>66</th>\n <td> 7.500</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 3.750</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>4.875</td>\n <td>4.700</td>\n <td>3.835</td>\n </tr>\n <tr>\n <th>67</th>\n <td> 4.900</td>\n <td> 2.233</td>\n <td>10.000</td>\n <td> 0.000</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 3.622</td>\n <td> 3.333</td>\n <td>4.190</td>\n <td>1.386</td>\n <td>1.419</td>\n </tr>\n <tr>\n <th>68</th>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 8.200</td>\n <td> 0.000</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>4.522</td>\n <td>4.127</td>\n <td>2.113</td>\n </tr>\n <tr>\n <th>69</th>\n <td> 2.900</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 2.500</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td>4.654</td>\n <td>3.555</td>\n <td>1.882</td>\n </tr>\n <tr>\n <th>70</th>\n <td> 5.400</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 3.750</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td> 1.485</td>\n <td>4.477</td>\n <td>3.091</td>\n <td>1.988</td>\n </tr>\n <tr>\n <th>71</th>\n <td> 7.500</td>\n <td> 8.800</td>\n <td>10.000</td>\n <td> 6.067</td>\n <td> 7.500</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td>5.338</td>\n <td>5.631</td>\n <td>3.491</td>\n </tr>\n <tr>\n <th>72</th>\n <td> 7.500</td>\n <td> 7.000</td>\n <td>10.000</td>\n <td> 6.853</td>\n <td> 7.500</td>\n <td> 6.348</td>\n <td> 6.667</td>\n <td> 7.508</td>\n <td>6.129</td>\n <td>6.404</td>\n <td>5.002</td>\n </tr>\n <tr>\n <th>73</th>\n <td>10.000</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>5.004</td>\n <td>4.963</td>\n <td>3.977</td>\n </tr>\n <tr>\n <th>74</th>\n <td> 3.750</td>\n <td> 3.333</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 1.250</td>\n <td> 3.333</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>4.489</td>\n <td>4.898</td>\n <td>2.868</td>\n </tr>\n </tbody>\n</table>\n<p>75 rows × 11 columns</p>\n</div>", | |
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| "slide_type": "subslide" | |
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| "cell_type": "markdown", | |
| "source": "( It's also possible and very useful to decrease the font size and make your dataframes more compact, albeit a bit fiddly. See here [here](http://stackoverflow.com/questions/19536817/manipulate-html-module-font-size-in-ipython-notebook) )\n" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "So PD1_df is formed form a conventional numpy array, and PD2_df is formed from a named numpy array that comes when we add the '-d' flag in %Rpull. Note a few things:" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "PD2_df is transposed relative to PD1_df; " | |
| }, | |
| { | |
| "metadata": { | |
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| "breakpoint": false | |
| }, | |
| "slideshow": { | |
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| "cell_type": "code", | |
| "input": "PD1_df.shape ", | |
| "prompt_number": 19, | |
| "outputs": [ | |
| { | |
| "text": "(11, 75)", | |
| "output_type": "pyout", | |
| "metadata": {}, | |
| "prompt_number": 19 | |
| } | |
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| "cell_type": "code", | |
| "input": "PD2_df.shape", | |
| "prompt_number": 20, | |
| "outputs": [ | |
| { | |
| "text": "(75, 11)", | |
| "output_type": "pyout", | |
| "metadata": {}, | |
| "prompt_number": 20 | |
| } | |
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| "language": "python", | |
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| "slide_type": "subslide" | |
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| }, | |
| "cell_type": "markdown", | |
| "source": "...so transposing PD1_df you can see that the only difference is that the columns in PD2_df now have the correct variable names" | |
| }, | |
| { | |
| "metadata": { | |
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| "cell_type": "code", | |
| "input": "PD1_df.T.columns", | |
| "prompt_number": 21, | |
| "outputs": [ | |
| { | |
| "text": "Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype='int64')", | |
| "output_type": "pyout", | |
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| "prompt_number": 21 | |
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| "cell_type": "code", | |
| "input": "PD2_df.columns", | |
| "prompt_number": 22, | |
| "outputs": [ | |
| { | |
| "text": "Index([u'y1', u'y2', u'y3', u'y4', u'y5', u'y6', u'y7', u'y8', u'x1', u'x2', u'x3'], dtype='object')", | |
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| "text": " 0 1 2 3 4 5 6 7 8 9 10\n0 2.500 0.000 3.333 0.000 1.250 0.000 3.726 3.333 4.443 3.638 2.558\n1 1.250 0.000 3.333 0.000 6.250 1.100 6.667 0.737 5.384 5.063 3.568\n2 7.500 8.800 10.000 9.200 8.750 8.094 10.000 8.212 5.961 6.256 5.224\n3 8.900 8.800 10.000 9.200 8.908 8.128 10.000 4.615 6.286 7.568 6.267\n4 10.000 3.333 10.000 6.667 7.500 3.333 10.000 6.667 5.864 6.819 4.574\n5 7.500 3.333 6.667 6.667 6.250 1.100 6.667 0.368 5.533 5.136 3.892\n6 7.500 3.333 6.667 6.667 5.000 2.233 8.271 1.485 5.308 5.075 3.316\n7 7.500 2.233 10.000 1.496 6.250 3.333 10.000 6.667 5.347 4.852 4.263\n8 2.500 3.333 3.333 3.333 6.250 3.333 3.333 3.333 5.521 5.242 4.115\n9 10.000 6.667 10.000 8.900 8.750 6.667 10.000 10.000 5.829 5.371 4.446\n10 7.500 3.333 10.000 6.667 8.750 3.333 10.000 6.667 5.916 6.423 3.792\n11 7.500 3.333 6.667 6.667 8.750 3.333 6.667 6.667 5.398 6.246 4.536\n12 7.500 3.333 10.000 6.667 7.500 3.333 6.667 10.000 6.623 7.872 4.906\n13 7.500 7.767 10.000 6.667 7.500 0.000 10.000 0.000 5.204 5.226 4.561\n14 7.500 10.000 3.333 10.000 7.500 6.667 10.000 10.000 5.509 6.203 4.586\n15 7.500 10.000 10.000 7.767 7.500 1.100 6.667 6.667 5.263 5.820 3.949\n16 2.500 3.333 6.667 6.667 5.000 1.100 6.667 0.368 4.700 5.024 4.394\n17 1.250 0.000 3.333 3.333 1.250 3.333 3.333 3.333 5.209 4.466 4.510\n18 10.000 10.000 10.000 10.000 8.750 10.000 10.000 10.000 5.916 6.732 5.829\n19 7.500 3.333 3.333 6.667 7.500 2.233 6.667 2.948 6.524 6.992 6.425\n20 10.000 10.000 10.000 10.000 10.000 10.000 10.000 10.000 6.238 6.746 5.742\n21 1.250 0.000 0.000 0.000 2.500 0.000 0.000 0.000 5.976 6.713 5.948\n22 2.500 0.000 3.333 3.333 2.500 0.000 3.333 3.333 5.631 5.938 5.687\n23 7.500 6.667 10.000 10.000 7.500 6.667 10.000 7.767 6.033 6.094 4.611\n24 8.500 10.000 6.667 6.667 8.750 10.000 7.351 6.667 6.196 6.704 5.475\n25 6.100 0.000 5.400 3.333 0.000 0.000 4.696 3.333 4.248 2.708 1.741\n26 3.300 0.000 6.667 3.333 6.250 0.000 6.667 3.333 5.142 4.564 2.255\n27 2.900 3.333 6.667 3.333 2.386 0.000 3.178 1.117 4.174 3.689 3.047\n28 9.200 0.000 9.900 3.333 7.610 0.000 8.119 3.333 4.382 2.890 1.711\n29 6.900 0.000 6.667 3.333 4.226 0.000 0.000 0.000 4.290 1.609 1.002\n30 2.900 0.000 3.333 3.333 5.000 0.000 3.333 3.333 4.934 4.234 1.419\n31 2.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 3.850 1.946 2.345\n32 5.000 0.000 3.333 3.333 5.000 0.000 3.333 3.333 5.182 4.394 3.167\n33 5.000 0.000 10.000 3.333 0.000 0.000 3.333 0.744 5.063 4.595 3.835\n34 4.100 10.000 4.700 6.667 3.750 0.000 7.828 6.667 4.691 4.143 2.255\n35 6.300 10.000 10.000 6.667 6.250 2.233 6.667 2.956 4.248 3.367 3.218\n36 5.200 5.000 6.600 3.333 3.633 1.100 3.314 3.333 5.565 5.236 2.678\n37 5.000 3.333 6.400 6.667 2.845 0.000 4.430 1.485 4.727 3.611 1.419\n38 3.100 5.000 4.200 5.000 3.750 0.000 6.164 3.333 4.143 2.303 1.419\n39 4.100 10.000 6.667 3.333 5.000 0.000 4.938 2.233 4.317 4.956 4.250\n40 5.000 10.000 6.667 1.667 5.000 0.000 6.667 0.368 5.142 4.431 3.047\n41 5.000 7.700 6.667 8.400 6.250 4.358 10.000 4.141 4.489 3.466 2.014\n42 5.000 6.200 10.000 6.061 5.000 2.783 6.667 4.975 4.615 4.942 2.255\n43 5.600 4.900 0.000 0.000 6.556 4.055 6.667 3.822 3.850 2.398 1.741\n44 5.700 4.800 0.000 0.000 6.556 4.055 0.000 0.000 3.970 2.398 1.051\n45 7.500 10.000 7.900 6.667 3.750 10.000 7.632 6.667 3.784 3.091 2.113\n46 2.500 0.000 6.667 3.333 2.500 0.000 0.000 0.000 3.807 2.079 2.138\n47 8.900 10.000 9.700 6.667 5.000 10.000 9.556 6.667 4.533 3.611 1.588\n48 7.600 0.000 10.000 0.000 5.000 1.100 6.667 1.100 5.118 4.934 3.835\n49 7.800 10.000 6.667 6.667 5.000 3.333 6.667 6.667 5.050 5.112 4.381\n50 2.500 0.000 6.667 3.333 5.000 0.000 6.667 3.333 5.394 5.638 4.169\n51 3.800 0.000 5.100 0.000 3.750 0.000 6.667 1.485 4.477 3.932 2.475\n52 5.000 3.333 3.333 2.233 5.000 3.333 6.667 5.567 5.257 5.841 5.002\n53 6.250 3.333 10.000 2.956 6.250 5.567 10.000 6.667 5.380 5.505 3.300\n54 1.250 0.000 3.333 0.000 2.500 0.000 0.000 0.000 5.298 6.275 4.381\n55 1.250 0.000 4.700 0.737 2.500 0.000 3.333 3.333 4.860 5.670 3.537\n56 1.250 0.000 6.667 0.000 2.500 0.000 5.228 0.000 4.970 5.565 4.510\n57 7.500 7.767 10.000 6.667 7.500 3.333 10.000 6.667 6.011 6.254 5.002\n58 2.500 0.000 6.667 4.433 5.000 0.000 6.667 1.485 5.075 5.252 5.351\n59 7.500 10.000 10.000 10.000 8.750 10.000 10.000 10.000 6.737 7.125 6.331\n60 1.250 0.000 0.000 0.000 1.250 0.000 0.000 0.000 5.226 5.451 3.167\n61 1.250 0.000 0.000 0.000 0.000 0.000 0.000 0.000 4.025 1.792 2.658\n62 2.500 0.000 0.000 0.000 0.000 0.000 6.667 2.948 4.234 2.708 2.475\n63 6.250 2.233 6.667 2.970 3.750 3.333 6.667 3.333 4.644 5.565 3.047\n64 7.500 10.000 10.000 10.000 7.500 10.000 10.000 10.000 4.419 4.942 3.381\n65 5.000 0.000 6.100 0.000 5.000 3.333 10.000 3.333 4.263 4.220 4.368\n66 7.500 10.000 10.000 10.000 3.750 10.000 10.000 10.000 4.875 4.700 3.835\n67 4.900 2.233 10.000 0.000 5.000 0.000 3.622 3.333 4.190 1.386 1.419\n68 5.000 0.000 8.200 0.000 5.000 0.000 0.000 0.000 4.522 4.127 2.113\n69 2.900 3.333 6.667 3.333 2.500 3.333 6.667 3.333 4.654 3.555 1.882\n70 5.400 10.000 6.667 3.333 3.750 6.667 6.667 1.485 4.477 3.091 1.988\n71 7.500 8.800 10.000 6.067 7.500 6.667 10.000 6.667 5.338 5.631 3.491\n72 7.500 7.000 10.000 6.853 7.500 6.348 6.667 7.508 6.129 6.404 5.002\n73 10.000 6.667 10.000 10.000 10.000 6.667 10.000 10.000 5.004 4.963 3.977\n74 3.750 3.333 0.000 0.000 1.250 3.333 0.000 0.000 4.489 4.898 2.868\n\n[75 rows x 11 columns]", | |
| "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>0</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n <th>6</th>\n <th>7</th>\n <th>8</th>\n <th>9</th>\n <th>10</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0 </th>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 0.000</td>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 3.726</td>\n <td> 3.333</td>\n <td>4.443</td>\n <td>3.638</td>\n <td>2.558</td>\n </tr>\n <tr>\n <th>1 </th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 0.000</td>\n <td> 6.250</td>\n <td> 1.100</td>\n <td> 6.667</td>\n <td> 0.737</td>\n <td>5.384</td>\n <td>5.063</td>\n <td>3.568</td>\n </tr>\n <tr>\n <th>2 </th>\n <td> 7.500</td>\n <td> 8.800</td>\n <td>10.000</td>\n <td> 9.200</td>\n <td> 8.750</td>\n <td> 8.094</td>\n <td>10.000</td>\n <td> 8.212</td>\n <td>5.961</td>\n <td>6.256</td>\n <td>5.224</td>\n </tr>\n <tr>\n <th>3 </th>\n <td> 8.900</td>\n <td> 8.800</td>\n <td>10.000</td>\n <td> 9.200</td>\n <td> 8.908</td>\n <td> 8.128</td>\n <td>10.000</td>\n <td> 4.615</td>\n <td>6.286</td>\n <td>7.568</td>\n <td>6.267</td>\n </tr>\n <tr>\n <th>4 </th>\n <td>10.000</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 7.500</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td>5.864</td>\n <td>6.819</td>\n <td>4.574</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> 7.500</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td> 6.250</td>\n <td> 1.100</td>\n <td> 6.667</td>\n <td> 0.368</td>\n <td>5.533</td>\n <td>5.136</td>\n <td>3.892</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> 7.500</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td> 5.000</td>\n <td> 2.233</td>\n <td> 8.271</td>\n <td> 1.485</td>\n <td>5.308</td>\n <td>5.075</td>\n <td>3.316</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> 7.500</td>\n <td> 2.233</td>\n <td>10.000</td>\n <td> 1.496</td>\n <td> 6.250</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td>5.347</td>\n <td>4.852</td>\n <td>4.263</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> 2.500</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 6.250</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td>5.521</td>\n <td>5.242</td>\n <td>4.115</td>\n </tr>\n <tr>\n <th>9 </th>\n <td>10.000</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td> 8.900</td>\n <td> 8.750</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>5.829</td>\n <td>5.371</td>\n <td>4.446</td>\n </tr>\n <tr>\n <th>10</th>\n <td> 7.500</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 8.750</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td>5.916</td>\n <td>6.423</td>\n <td>3.792</td>\n </tr>\n <tr>\n <th>11</th>\n <td> 7.500</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td> 8.750</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td>5.398</td>\n <td>6.246</td>\n <td>4.536</td>\n </tr>\n <tr>\n <th>12</th>\n <td> 7.500</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 7.500</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td>6.623</td>\n <td>7.872</td>\n <td>4.906</td>\n </tr>\n <tr>\n <th>13</th>\n <td> 7.500</td>\n <td> 7.767</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 7.500</td>\n <td> 0.000</td>\n <td>10.000</td>\n <td> 0.000</td>\n <td>5.204</td>\n <td>5.226</td>\n <td>4.561</td>\n </tr>\n <tr>\n <th>14</th>\n <td> 7.500</td>\n <td>10.000</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 7.500</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>5.509</td>\n <td>6.203</td>\n <td>4.586</td>\n </tr>\n <tr>\n <th>15</th>\n <td> 7.500</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 7.767</td>\n <td> 7.500</td>\n <td> 1.100</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td>5.263</td>\n <td>5.820</td>\n <td>3.949</td>\n </tr>\n <tr>\n <th>16</th>\n <td> 2.500</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td> 5.000</td>\n <td> 1.100</td>\n <td> 6.667</td>\n <td> 0.368</td>\n <td>4.700</td>\n <td>5.024</td>\n <td>4.394</td>\n </tr>\n <tr>\n <th>17</th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 1.250</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td>5.209</td>\n <td>4.466</td>\n <td>4.510</td>\n </tr>\n <tr>\n <th>18</th>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 8.750</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>5.916</td>\n <td>6.732</td>\n <td>5.829</td>\n </tr>\n <tr>\n <th>19</th>\n <td> 7.500</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 7.500</td>\n <td> 2.233</td>\n <td> 6.667</td>\n <td> 2.948</td>\n <td>6.524</td>\n <td>6.992</td>\n <td>6.425</td>\n </tr>\n <tr>\n <th>20</th>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>6.238</td>\n <td>6.746</td>\n <td>5.742</td>\n </tr>\n <tr>\n <th>21</th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>5.976</td>\n <td>6.713</td>\n <td>5.948</td>\n </tr>\n <tr>\n <th>22</th>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td>5.631</td>\n <td>5.938</td>\n <td>5.687</td>\n </tr>\n <tr>\n <th>23</th>\n <td> 7.500</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 7.500</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td> 7.767</td>\n <td>6.033</td>\n <td>6.094</td>\n <td>4.611</td>\n </tr>\n <tr>\n <th>24</th>\n <td> 8.500</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td> 8.750</td>\n <td>10.000</td>\n <td> 7.351</td>\n <td> 6.667</td>\n <td>6.196</td>\n <td>6.704</td>\n <td>5.475</td>\n </tr>\n <tr>\n <th>25</th>\n <td> 6.100</td>\n <td> 0.000</td>\n <td> 5.400</td>\n <td> 3.333</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 4.696</td>\n <td> 3.333</td>\n <td>4.248</td>\n <td>2.708</td>\n <td>1.741</td>\n </tr>\n <tr>\n <th>26</th>\n <td> 3.300</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 6.250</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td>5.142</td>\n <td>4.564</td>\n <td>2.255</td>\n </tr>\n <tr>\n <th>27</th>\n <td> 2.900</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 2.386</td>\n <td> 0.000</td>\n <td> 3.178</td>\n <td> 1.117</td>\n <td>4.174</td>\n <td>3.689</td>\n <td>3.047</td>\n </tr>\n <tr>\n <th>28</th>\n <td> 9.200</td>\n <td> 0.000</td>\n <td> 9.900</td>\n <td> 3.333</td>\n <td> 7.610</td>\n <td> 0.000</td>\n <td> 8.119</td>\n <td> 3.333</td>\n <td>4.382</td>\n <td>2.890</td>\n <td>1.711</td>\n </tr>\n <tr>\n <th>29</th>\n <td> 6.900</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 4.226</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>4.290</td>\n <td>1.609</td>\n <td>1.002</td>\n </tr>\n <tr>\n <th>30</th>\n <td> 2.900</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td>4.934</td>\n <td>4.234</td>\n <td>1.419</td>\n </tr>\n <tr>\n <th>31</th>\n <td> 2.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>3.850</td>\n <td>1.946</td>\n <td>2.345</td>\n </tr>\n <tr>\n <th>32</th>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td>5.182</td>\n <td>4.394</td>\n <td>3.167</td>\n </tr>\n <tr>\n <th>33</th>\n <td> 5.000</td>\n <td> 0.000</td>\n <td>10.000</td>\n <td> 3.333</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 0.744</td>\n <td>5.063</td>\n <td>4.595</td>\n <td>3.835</td>\n </tr>\n <tr>\n <th>34</th>\n <td> 4.100</td>\n <td>10.000</td>\n <td> 4.700</td>\n <td> 6.667</td>\n <td> 3.750</td>\n <td> 0.000</td>\n <td> 7.828</td>\n <td> 6.667</td>\n <td>4.691</td>\n <td>4.143</td>\n <td>2.255</td>\n </tr>\n <tr>\n <th>35</th>\n <td> 6.300</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 6.250</td>\n <td> 2.233</td>\n <td> 6.667</td>\n <td> 2.956</td>\n <td>4.248</td>\n <td>3.367</td>\n <td>3.218</td>\n </tr>\n <tr>\n <th>36</th>\n <td> 5.200</td>\n <td> 5.000</td>\n <td> 6.600</td>\n <td> 3.333</td>\n <td> 3.633</td>\n <td> 1.100</td>\n <td> 3.314</td>\n <td> 3.333</td>\n <td>5.565</td>\n <td>5.236</td>\n <td>2.678</td>\n </tr>\n <tr>\n <th>37</th>\n <td> 5.000</td>\n <td> 3.333</td>\n <td> 6.400</td>\n <td> 6.667</td>\n <td> 2.845</td>\n <td> 0.000</td>\n <td> 4.430</td>\n <td> 1.485</td>\n <td>4.727</td>\n <td>3.611</td>\n <td>1.419</td>\n </tr>\n <tr>\n <th>38</th>\n <td> 3.100</td>\n <td> 5.000</td>\n <td> 4.200</td>\n <td> 5.000</td>\n <td> 3.750</td>\n <td> 0.000</td>\n <td> 6.164</td>\n <td> 3.333</td>\n <td>4.143</td>\n <td>2.303</td>\n <td>1.419</td>\n </tr>\n <tr>\n <th>39</th>\n <td> 4.100</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 4.938</td>\n <td> 2.233</td>\n <td>4.317</td>\n <td>4.956</td>\n <td>4.250</td>\n </tr>\n <tr>\n <th>40</th>\n <td> 5.000</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 1.667</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 0.368</td>\n <td>5.142</td>\n <td>4.431</td>\n <td>3.047</td>\n </tr>\n <tr>\n <th>41</th>\n <td> 5.000</td>\n <td> 7.700</td>\n <td> 6.667</td>\n <td> 8.400</td>\n <td> 6.250</td>\n <td> 4.358</td>\n <td>10.000</td>\n <td> 4.141</td>\n <td>4.489</td>\n <td>3.466</td>\n <td>2.014</td>\n </tr>\n <tr>\n <th>42</th>\n <td> 5.000</td>\n <td> 6.200</td>\n <td>10.000</td>\n <td> 6.061</td>\n <td> 5.000</td>\n <td> 2.783</td>\n <td> 6.667</td>\n <td> 4.975</td>\n <td>4.615</td>\n <td>4.942</td>\n <td>2.255</td>\n </tr>\n <tr>\n <th>43</th>\n <td> 5.600</td>\n <td> 4.900</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 6.556</td>\n <td> 4.055</td>\n <td> 6.667</td>\n <td> 3.822</td>\n <td>3.850</td>\n <td>2.398</td>\n <td>1.741</td>\n </tr>\n <tr>\n <th>44</th>\n <td> 5.700</td>\n <td> 4.800</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 6.556</td>\n <td> 4.055</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>3.970</td>\n <td>2.398</td>\n <td>1.051</td>\n </tr>\n <tr>\n <th>45</th>\n <td> 7.500</td>\n <td>10.000</td>\n <td> 7.900</td>\n <td> 6.667</td>\n <td> 3.750</td>\n <td>10.000</td>\n <td> 7.632</td>\n <td> 6.667</td>\n <td>3.784</td>\n <td>3.091</td>\n <td>2.113</td>\n </tr>\n <tr>\n <th>46</th>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>3.807</td>\n <td>2.079</td>\n <td>2.138</td>\n </tr>\n <tr>\n <th>47</th>\n <td> 8.900</td>\n <td>10.000</td>\n <td> 9.700</td>\n <td> 6.667</td>\n <td> 5.000</td>\n <td>10.000</td>\n <td> 9.556</td>\n <td> 6.667</td>\n <td>4.533</td>\n <td>3.611</td>\n <td>1.588</td>\n </tr>\n <tr>\n <th>48</th>\n <td> 7.600</td>\n <td> 0.000</td>\n <td>10.000</td>\n <td> 0.000</td>\n <td> 5.000</td>\n <td> 1.100</td>\n <td> 6.667</td>\n <td> 1.100</td>\n <td>5.118</td>\n <td>4.934</td>\n <td>3.835</td>\n </tr>\n <tr>\n <th>49</th>\n <td> 7.800</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td> 5.000</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td>5.050</td>\n <td>5.112</td>\n <td>4.381</td>\n </tr>\n <tr>\n <th>50</th>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td>5.394</td>\n <td>5.638</td>\n <td>4.169</td>\n </tr>\n <tr>\n <th>51</th>\n <td> 3.800</td>\n <td> 0.000</td>\n <td> 5.100</td>\n <td> 0.000</td>\n <td> 3.750</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 1.485</td>\n <td>4.477</td>\n <td>3.932</td>\n <td>2.475</td>\n </tr>\n <tr>\n <th>52</th>\n <td> 5.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 2.233</td>\n <td> 5.000</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 5.567</td>\n <td>5.257</td>\n <td>5.841</td>\n <td>5.002</td>\n </tr>\n <tr>\n <th>53</th>\n <td> 6.250</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 2.956</td>\n <td> 6.250</td>\n <td> 5.567</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td>5.380</td>\n <td>5.505</td>\n <td>3.300</td>\n </tr>\n <tr>\n <th>54</th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 0.000</td>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>5.298</td>\n <td>6.275</td>\n <td>4.381</td>\n </tr>\n <tr>\n <th>55</th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 4.700</td>\n <td> 0.737</td>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td>4.860</td>\n <td>5.670</td>\n <td>3.537</td>\n </tr>\n <tr>\n <th>56</th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 0.000</td>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 5.228</td>\n <td> 0.000</td>\n <td>4.970</td>\n <td>5.565</td>\n <td>4.510</td>\n </tr>\n <tr>\n <th>57</th>\n <td> 7.500</td>\n <td> 7.767</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 7.500</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td>6.011</td>\n <td>6.254</td>\n <td>5.002</td>\n </tr>\n <tr>\n <th>58</th>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 4.433</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 1.485</td>\n <td>5.075</td>\n <td>5.252</td>\n <td>5.351</td>\n </tr>\n <tr>\n <th>59</th>\n <td> 7.500</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 8.750</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>6.737</td>\n <td>7.125</td>\n <td>6.331</td>\n </tr>\n <tr>\n <th>60</th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>5.226</td>\n <td>5.451</td>\n <td>3.167</td>\n </tr>\n <tr>\n <th>61</th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>4.025</td>\n <td>1.792</td>\n <td>2.658</td>\n </tr>\n <tr>\n <th>62</th>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 2.948</td>\n <td>4.234</td>\n <td>2.708</td>\n <td>2.475</td>\n </tr>\n <tr>\n <th>63</th>\n <td> 6.250</td>\n <td> 2.233</td>\n <td> 6.667</td>\n <td> 2.970</td>\n <td> 3.750</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td>4.644</td>\n <td>5.565</td>\n <td>3.047</td>\n </tr>\n <tr>\n <th>64</th>\n <td> 7.500</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 7.500</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>4.419</td>\n <td>4.942</td>\n <td>3.381</td>\n </tr>\n <tr>\n <th>65</th>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 6.100</td>\n <td> 0.000</td>\n <td> 5.000</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 3.333</td>\n <td>4.263</td>\n <td>4.220</td>\n <td>4.368</td>\n </tr>\n <tr>\n <th>66</th>\n <td> 7.500</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 3.750</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>4.875</td>\n <td>4.700</td>\n <td>3.835</td>\n </tr>\n <tr>\n <th>67</th>\n <td> 4.900</td>\n <td> 2.233</td>\n <td>10.000</td>\n <td> 0.000</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 3.622</td>\n <td> 3.333</td>\n <td>4.190</td>\n <td>1.386</td>\n <td>1.419</td>\n </tr>\n <tr>\n <th>68</th>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 8.200</td>\n <td> 0.000</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>4.522</td>\n <td>4.127</td>\n <td>2.113</td>\n </tr>\n <tr>\n <th>69</th>\n <td> 2.900</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 2.500</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td>4.654</td>\n <td>3.555</td>\n <td>1.882</td>\n </tr>\n <tr>\n <th>70</th>\n <td> 5.400</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 3.750</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td> 1.485</td>\n <td>4.477</td>\n <td>3.091</td>\n <td>1.988</td>\n </tr>\n <tr>\n <th>71</th>\n <td> 7.500</td>\n <td> 8.800</td>\n <td>10.000</td>\n <td> 6.067</td>\n <td> 7.500</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td>5.338</td>\n <td>5.631</td>\n <td>3.491</td>\n </tr>\n <tr>\n <th>72</th>\n <td> 7.500</td>\n <td> 7.000</td>\n <td>10.000</td>\n <td> 6.853</td>\n <td> 7.500</td>\n <td> 6.348</td>\n <td> 6.667</td>\n <td> 7.508</td>\n <td>6.129</td>\n <td>6.404</td>\n <td>5.002</td>\n </tr>\n <tr>\n <th>73</th>\n <td>10.000</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>5.004</td>\n <td>4.963</td>\n <td>3.977</td>\n </tr>\n <tr>\n <th>74</th>\n <td> 3.750</td>\n <td> 3.333</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 1.250</td>\n <td> 3.333</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>4.489</td>\n <td>4.898</td>\n <td>2.868</td>\n </tr>\n </tbody>\n</table>\n<p>75 rows × 11 columns</p>\n</div>", | |
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| "text": " y1 y2 y3 y4 y5 y6 y7 y8 x1 x2 x3\n0 2.500 0.000 3.333 0.000 1.250 0.000 3.726 3.333 4.443 3.638 2.558\n1 1.250 0.000 3.333 0.000 6.250 1.100 6.667 0.737 5.384 5.063 3.568\n2 7.500 8.800 10.000 9.200 8.750 8.094 10.000 8.212 5.961 6.256 5.224\n3 8.900 8.800 10.000 9.200 8.908 8.128 10.000 4.615 6.286 7.568 6.267\n4 10.000 3.333 10.000 6.667 7.500 3.333 10.000 6.667 5.864 6.819 4.574\n5 7.500 3.333 6.667 6.667 6.250 1.100 6.667 0.368 5.533 5.136 3.892\n6 7.500 3.333 6.667 6.667 5.000 2.233 8.271 1.485 5.308 5.075 3.316\n7 7.500 2.233 10.000 1.496 6.250 3.333 10.000 6.667 5.347 4.852 4.263\n8 2.500 3.333 3.333 3.333 6.250 3.333 3.333 3.333 5.521 5.242 4.115\n9 10.000 6.667 10.000 8.900 8.750 6.667 10.000 10.000 5.829 5.371 4.446\n10 7.500 3.333 10.000 6.667 8.750 3.333 10.000 6.667 5.916 6.423 3.792\n11 7.500 3.333 6.667 6.667 8.750 3.333 6.667 6.667 5.398 6.246 4.536\n12 7.500 3.333 10.000 6.667 7.500 3.333 6.667 10.000 6.623 7.872 4.906\n13 7.500 7.767 10.000 6.667 7.500 0.000 10.000 0.000 5.204 5.226 4.561\n14 7.500 10.000 3.333 10.000 7.500 6.667 10.000 10.000 5.509 6.203 4.586\n15 7.500 10.000 10.000 7.767 7.500 1.100 6.667 6.667 5.263 5.820 3.949\n16 2.500 3.333 6.667 6.667 5.000 1.100 6.667 0.368 4.700 5.024 4.394\n17 1.250 0.000 3.333 3.333 1.250 3.333 3.333 3.333 5.209 4.466 4.510\n18 10.000 10.000 10.000 10.000 8.750 10.000 10.000 10.000 5.916 6.732 5.829\n19 7.500 3.333 3.333 6.667 7.500 2.233 6.667 2.948 6.524 6.992 6.425\n20 10.000 10.000 10.000 10.000 10.000 10.000 10.000 10.000 6.238 6.746 5.742\n21 1.250 0.000 0.000 0.000 2.500 0.000 0.000 0.000 5.976 6.713 5.948\n22 2.500 0.000 3.333 3.333 2.500 0.000 3.333 3.333 5.631 5.938 5.687\n23 7.500 6.667 10.000 10.000 7.500 6.667 10.000 7.767 6.033 6.094 4.611\n24 8.500 10.000 6.667 6.667 8.750 10.000 7.351 6.667 6.196 6.704 5.475\n25 6.100 0.000 5.400 3.333 0.000 0.000 4.696 3.333 4.248 2.708 1.741\n26 3.300 0.000 6.667 3.333 6.250 0.000 6.667 3.333 5.142 4.564 2.255\n27 2.900 3.333 6.667 3.333 2.386 0.000 3.178 1.117 4.174 3.689 3.047\n28 9.200 0.000 9.900 3.333 7.610 0.000 8.119 3.333 4.382 2.890 1.711\n29 6.900 0.000 6.667 3.333 4.226 0.000 0.000 0.000 4.290 1.609 1.002\n30 2.900 0.000 3.333 3.333 5.000 0.000 3.333 3.333 4.934 4.234 1.419\n31 2.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 3.850 1.946 2.345\n32 5.000 0.000 3.333 3.333 5.000 0.000 3.333 3.333 5.182 4.394 3.167\n33 5.000 0.000 10.000 3.333 0.000 0.000 3.333 0.744 5.063 4.595 3.835\n34 4.100 10.000 4.700 6.667 3.750 0.000 7.828 6.667 4.691 4.143 2.255\n35 6.300 10.000 10.000 6.667 6.250 2.233 6.667 2.956 4.248 3.367 3.218\n36 5.200 5.000 6.600 3.333 3.633 1.100 3.314 3.333 5.565 5.236 2.678\n37 5.000 3.333 6.400 6.667 2.845 0.000 4.430 1.485 4.727 3.611 1.419\n38 3.100 5.000 4.200 5.000 3.750 0.000 6.164 3.333 4.143 2.303 1.419\n39 4.100 10.000 6.667 3.333 5.000 0.000 4.938 2.233 4.317 4.956 4.250\n40 5.000 10.000 6.667 1.667 5.000 0.000 6.667 0.368 5.142 4.431 3.047\n41 5.000 7.700 6.667 8.400 6.250 4.358 10.000 4.141 4.489 3.466 2.014\n42 5.000 6.200 10.000 6.061 5.000 2.783 6.667 4.975 4.615 4.942 2.255\n43 5.600 4.900 0.000 0.000 6.556 4.055 6.667 3.822 3.850 2.398 1.741\n44 5.700 4.800 0.000 0.000 6.556 4.055 0.000 0.000 3.970 2.398 1.051\n45 7.500 10.000 7.900 6.667 3.750 10.000 7.632 6.667 3.784 3.091 2.113\n46 2.500 0.000 6.667 3.333 2.500 0.000 0.000 0.000 3.807 2.079 2.138\n47 8.900 10.000 9.700 6.667 5.000 10.000 9.556 6.667 4.533 3.611 1.588\n48 7.600 0.000 10.000 0.000 5.000 1.100 6.667 1.100 5.118 4.934 3.835\n49 7.800 10.000 6.667 6.667 5.000 3.333 6.667 6.667 5.050 5.112 4.381\n50 2.500 0.000 6.667 3.333 5.000 0.000 6.667 3.333 5.394 5.638 4.169\n51 3.800 0.000 5.100 0.000 3.750 0.000 6.667 1.485 4.477 3.932 2.475\n52 5.000 3.333 3.333 2.233 5.000 3.333 6.667 5.567 5.257 5.841 5.002\n53 6.250 3.333 10.000 2.956 6.250 5.567 10.000 6.667 5.380 5.505 3.300\n54 1.250 0.000 3.333 0.000 2.500 0.000 0.000 0.000 5.298 6.275 4.381\n55 1.250 0.000 4.700 0.737 2.500 0.000 3.333 3.333 4.860 5.670 3.537\n56 1.250 0.000 6.667 0.000 2.500 0.000 5.228 0.000 4.970 5.565 4.510\n57 7.500 7.767 10.000 6.667 7.500 3.333 10.000 6.667 6.011 6.254 5.002\n58 2.500 0.000 6.667 4.433 5.000 0.000 6.667 1.485 5.075 5.252 5.351\n59 7.500 10.000 10.000 10.000 8.750 10.000 10.000 10.000 6.737 7.125 6.331\n60 1.250 0.000 0.000 0.000 1.250 0.000 0.000 0.000 5.226 5.451 3.167\n61 1.250 0.000 0.000 0.000 0.000 0.000 0.000 0.000 4.025 1.792 2.658\n62 2.500 0.000 0.000 0.000 0.000 0.000 6.667 2.948 4.234 2.708 2.475\n63 6.250 2.233 6.667 2.970 3.750 3.333 6.667 3.333 4.644 5.565 3.047\n64 7.500 10.000 10.000 10.000 7.500 10.000 10.000 10.000 4.419 4.942 3.381\n65 5.000 0.000 6.100 0.000 5.000 3.333 10.000 3.333 4.263 4.220 4.368\n66 7.500 10.000 10.000 10.000 3.750 10.000 10.000 10.000 4.875 4.700 3.835\n67 4.900 2.233 10.000 0.000 5.000 0.000 3.622 3.333 4.190 1.386 1.419\n68 5.000 0.000 8.200 0.000 5.000 0.000 0.000 0.000 4.522 4.127 2.113\n69 2.900 3.333 6.667 3.333 2.500 3.333 6.667 3.333 4.654 3.555 1.882\n70 5.400 10.000 6.667 3.333 3.750 6.667 6.667 1.485 4.477 3.091 1.988\n71 7.500 8.800 10.000 6.067 7.500 6.667 10.000 6.667 5.338 5.631 3.491\n72 7.500 7.000 10.000 6.853 7.500 6.348 6.667 7.508 6.129 6.404 5.002\n73 10.000 6.667 10.000 10.000 10.000 6.667 10.000 10.000 5.004 4.963 3.977\n74 3.750 3.333 0.000 0.000 1.250 3.333 0.000 0.000 4.489 4.898 2.868\n\n[75 rows x 11 columns]", | |
| "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>y1</th>\n <th>y2</th>\n <th>y3</th>\n <th>y4</th>\n <th>y5</th>\n <th>y6</th>\n <th>y7</th>\n <th>y8</th>\n <th>x1</th>\n <th>x2</th>\n <th>x3</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0 </th>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 0.000</td>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 3.726</td>\n <td> 3.333</td>\n <td>4.443</td>\n <td>3.638</td>\n <td>2.558</td>\n </tr>\n <tr>\n <th>1 </th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 0.000</td>\n <td> 6.250</td>\n <td> 1.100</td>\n <td> 6.667</td>\n <td> 0.737</td>\n <td>5.384</td>\n <td>5.063</td>\n <td>3.568</td>\n </tr>\n <tr>\n <th>2 </th>\n <td> 7.500</td>\n <td> 8.800</td>\n <td>10.000</td>\n <td> 9.200</td>\n <td> 8.750</td>\n <td> 8.094</td>\n <td>10.000</td>\n <td> 8.212</td>\n <td>5.961</td>\n <td>6.256</td>\n <td>5.224</td>\n </tr>\n <tr>\n <th>3 </th>\n <td> 8.900</td>\n <td> 8.800</td>\n <td>10.000</td>\n <td> 9.200</td>\n <td> 8.908</td>\n <td> 8.128</td>\n <td>10.000</td>\n <td> 4.615</td>\n <td>6.286</td>\n <td>7.568</td>\n <td>6.267</td>\n </tr>\n <tr>\n <th>4 </th>\n <td>10.000</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 7.500</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td>5.864</td>\n <td>6.819</td>\n <td>4.574</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> 7.500</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td> 6.250</td>\n <td> 1.100</td>\n <td> 6.667</td>\n <td> 0.368</td>\n <td>5.533</td>\n <td>5.136</td>\n <td>3.892</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> 7.500</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td> 5.000</td>\n <td> 2.233</td>\n <td> 8.271</td>\n <td> 1.485</td>\n <td>5.308</td>\n <td>5.075</td>\n <td>3.316</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> 7.500</td>\n <td> 2.233</td>\n <td>10.000</td>\n <td> 1.496</td>\n <td> 6.250</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td>5.347</td>\n <td>4.852</td>\n <td>4.263</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> 2.500</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 6.250</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td>5.521</td>\n <td>5.242</td>\n <td>4.115</td>\n </tr>\n <tr>\n <th>9 </th>\n <td>10.000</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td> 8.900</td>\n <td> 8.750</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>5.829</td>\n <td>5.371</td>\n <td>4.446</td>\n </tr>\n <tr>\n <th>10</th>\n <td> 7.500</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 8.750</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td>5.916</td>\n <td>6.423</td>\n <td>3.792</td>\n </tr>\n <tr>\n <th>11</th>\n <td> 7.500</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td> 8.750</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td>5.398</td>\n <td>6.246</td>\n <td>4.536</td>\n </tr>\n <tr>\n <th>12</th>\n <td> 7.500</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 7.500</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td>6.623</td>\n <td>7.872</td>\n <td>4.906</td>\n </tr>\n <tr>\n <th>13</th>\n <td> 7.500</td>\n <td> 7.767</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 7.500</td>\n <td> 0.000</td>\n <td>10.000</td>\n <td> 0.000</td>\n <td>5.204</td>\n <td>5.226</td>\n <td>4.561</td>\n </tr>\n <tr>\n <th>14</th>\n <td> 7.500</td>\n <td>10.000</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 7.500</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>5.509</td>\n <td>6.203</td>\n <td>4.586</td>\n </tr>\n <tr>\n <th>15</th>\n <td> 7.500</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 7.767</td>\n <td> 7.500</td>\n <td> 1.100</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td>5.263</td>\n <td>5.820</td>\n <td>3.949</td>\n </tr>\n <tr>\n <th>16</th>\n <td> 2.500</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td> 5.000</td>\n <td> 1.100</td>\n <td> 6.667</td>\n <td> 0.368</td>\n <td>4.700</td>\n <td>5.024</td>\n <td>4.394</td>\n </tr>\n <tr>\n <th>17</th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 1.250</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td>5.209</td>\n <td>4.466</td>\n <td>4.510</td>\n </tr>\n <tr>\n <th>18</th>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 8.750</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>5.916</td>\n <td>6.732</td>\n <td>5.829</td>\n </tr>\n <tr>\n <th>19</th>\n <td> 7.500</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 7.500</td>\n <td> 2.233</td>\n <td> 6.667</td>\n <td> 2.948</td>\n <td>6.524</td>\n <td>6.992</td>\n <td>6.425</td>\n </tr>\n <tr>\n <th>20</th>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>6.238</td>\n <td>6.746</td>\n <td>5.742</td>\n </tr>\n <tr>\n <th>21</th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>5.976</td>\n <td>6.713</td>\n <td>5.948</td>\n </tr>\n <tr>\n <th>22</th>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td>5.631</td>\n <td>5.938</td>\n <td>5.687</td>\n </tr>\n <tr>\n <th>23</th>\n <td> 7.500</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 7.500</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td> 7.767</td>\n <td>6.033</td>\n <td>6.094</td>\n <td>4.611</td>\n </tr>\n <tr>\n <th>24</th>\n <td> 8.500</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td> 8.750</td>\n <td>10.000</td>\n <td> 7.351</td>\n <td> 6.667</td>\n <td>6.196</td>\n <td>6.704</td>\n <td>5.475</td>\n </tr>\n <tr>\n <th>25</th>\n <td> 6.100</td>\n <td> 0.000</td>\n <td> 5.400</td>\n <td> 3.333</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 4.696</td>\n <td> 3.333</td>\n <td>4.248</td>\n <td>2.708</td>\n <td>1.741</td>\n </tr>\n <tr>\n <th>26</th>\n <td> 3.300</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 6.250</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td>5.142</td>\n <td>4.564</td>\n <td>2.255</td>\n </tr>\n <tr>\n <th>27</th>\n <td> 2.900</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 2.386</td>\n <td> 0.000</td>\n <td> 3.178</td>\n <td> 1.117</td>\n <td>4.174</td>\n <td>3.689</td>\n <td>3.047</td>\n </tr>\n <tr>\n <th>28</th>\n <td> 9.200</td>\n <td> 0.000</td>\n <td> 9.900</td>\n <td> 3.333</td>\n <td> 7.610</td>\n <td> 0.000</td>\n <td> 8.119</td>\n <td> 3.333</td>\n <td>4.382</td>\n <td>2.890</td>\n <td>1.711</td>\n </tr>\n <tr>\n <th>29</th>\n <td> 6.900</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 4.226</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>4.290</td>\n <td>1.609</td>\n <td>1.002</td>\n </tr>\n <tr>\n <th>30</th>\n <td> 2.900</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td>4.934</td>\n <td>4.234</td>\n <td>1.419</td>\n </tr>\n <tr>\n <th>31</th>\n <td> 2.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>3.850</td>\n <td>1.946</td>\n <td>2.345</td>\n </tr>\n <tr>\n <th>32</th>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td>5.182</td>\n <td>4.394</td>\n <td>3.167</td>\n </tr>\n <tr>\n <th>33</th>\n <td> 5.000</td>\n <td> 0.000</td>\n <td>10.000</td>\n <td> 3.333</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 0.744</td>\n <td>5.063</td>\n <td>4.595</td>\n <td>3.835</td>\n </tr>\n <tr>\n <th>34</th>\n <td> 4.100</td>\n <td>10.000</td>\n <td> 4.700</td>\n <td> 6.667</td>\n <td> 3.750</td>\n <td> 0.000</td>\n <td> 7.828</td>\n <td> 6.667</td>\n <td>4.691</td>\n <td>4.143</td>\n <td>2.255</td>\n </tr>\n <tr>\n <th>35</th>\n <td> 6.300</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 6.250</td>\n <td> 2.233</td>\n <td> 6.667</td>\n <td> 2.956</td>\n <td>4.248</td>\n <td>3.367</td>\n <td>3.218</td>\n </tr>\n <tr>\n <th>36</th>\n <td> 5.200</td>\n <td> 5.000</td>\n <td> 6.600</td>\n <td> 3.333</td>\n <td> 3.633</td>\n <td> 1.100</td>\n <td> 3.314</td>\n <td> 3.333</td>\n <td>5.565</td>\n <td>5.236</td>\n <td>2.678</td>\n </tr>\n <tr>\n <th>37</th>\n <td> 5.000</td>\n <td> 3.333</td>\n <td> 6.400</td>\n <td> 6.667</td>\n <td> 2.845</td>\n <td> 0.000</td>\n <td> 4.430</td>\n <td> 1.485</td>\n <td>4.727</td>\n <td>3.611</td>\n <td>1.419</td>\n </tr>\n <tr>\n <th>38</th>\n <td> 3.100</td>\n <td> 5.000</td>\n <td> 4.200</td>\n <td> 5.000</td>\n <td> 3.750</td>\n <td> 0.000</td>\n <td> 6.164</td>\n <td> 3.333</td>\n <td>4.143</td>\n <td>2.303</td>\n <td>1.419</td>\n </tr>\n <tr>\n <th>39</th>\n <td> 4.100</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 4.938</td>\n <td> 2.233</td>\n <td>4.317</td>\n <td>4.956</td>\n <td>4.250</td>\n </tr>\n <tr>\n <th>40</th>\n <td> 5.000</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 1.667</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 0.368</td>\n <td>5.142</td>\n <td>4.431</td>\n <td>3.047</td>\n </tr>\n <tr>\n <th>41</th>\n <td> 5.000</td>\n <td> 7.700</td>\n <td> 6.667</td>\n <td> 8.400</td>\n <td> 6.250</td>\n <td> 4.358</td>\n <td>10.000</td>\n <td> 4.141</td>\n <td>4.489</td>\n <td>3.466</td>\n <td>2.014</td>\n </tr>\n <tr>\n <th>42</th>\n <td> 5.000</td>\n <td> 6.200</td>\n <td>10.000</td>\n <td> 6.061</td>\n <td> 5.000</td>\n <td> 2.783</td>\n <td> 6.667</td>\n <td> 4.975</td>\n <td>4.615</td>\n <td>4.942</td>\n <td>2.255</td>\n </tr>\n <tr>\n <th>43</th>\n <td> 5.600</td>\n <td> 4.900</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 6.556</td>\n <td> 4.055</td>\n <td> 6.667</td>\n <td> 3.822</td>\n <td>3.850</td>\n <td>2.398</td>\n <td>1.741</td>\n </tr>\n <tr>\n <th>44</th>\n <td> 5.700</td>\n <td> 4.800</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 6.556</td>\n <td> 4.055</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>3.970</td>\n <td>2.398</td>\n <td>1.051</td>\n </tr>\n <tr>\n <th>45</th>\n <td> 7.500</td>\n <td>10.000</td>\n <td> 7.900</td>\n <td> 6.667</td>\n <td> 3.750</td>\n <td>10.000</td>\n <td> 7.632</td>\n <td> 6.667</td>\n <td>3.784</td>\n <td>3.091</td>\n <td>2.113</td>\n </tr>\n <tr>\n <th>46</th>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>3.807</td>\n <td>2.079</td>\n <td>2.138</td>\n </tr>\n <tr>\n <th>47</th>\n <td> 8.900</td>\n <td>10.000</td>\n <td> 9.700</td>\n <td> 6.667</td>\n <td> 5.000</td>\n <td>10.000</td>\n <td> 9.556</td>\n <td> 6.667</td>\n <td>4.533</td>\n <td>3.611</td>\n <td>1.588</td>\n </tr>\n <tr>\n <th>48</th>\n <td> 7.600</td>\n <td> 0.000</td>\n <td>10.000</td>\n <td> 0.000</td>\n <td> 5.000</td>\n <td> 1.100</td>\n <td> 6.667</td>\n <td> 1.100</td>\n <td>5.118</td>\n <td>4.934</td>\n <td>3.835</td>\n </tr>\n <tr>\n <th>49</th>\n <td> 7.800</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td> 5.000</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td>5.050</td>\n <td>5.112</td>\n <td>4.381</td>\n </tr>\n <tr>\n <th>50</th>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td>5.394</td>\n <td>5.638</td>\n <td>4.169</td>\n </tr>\n <tr>\n <th>51</th>\n <td> 3.800</td>\n <td> 0.000</td>\n <td> 5.100</td>\n <td> 0.000</td>\n <td> 3.750</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 1.485</td>\n <td>4.477</td>\n <td>3.932</td>\n <td>2.475</td>\n </tr>\n <tr>\n <th>52</th>\n <td> 5.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td> 2.233</td>\n <td> 5.000</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 5.567</td>\n <td>5.257</td>\n <td>5.841</td>\n <td>5.002</td>\n </tr>\n <tr>\n <th>53</th>\n <td> 6.250</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 2.956</td>\n <td> 6.250</td>\n <td> 5.567</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td>5.380</td>\n <td>5.505</td>\n <td>3.300</td>\n </tr>\n <tr>\n <th>54</th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 0.000</td>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>5.298</td>\n <td>6.275</td>\n <td>4.381</td>\n </tr>\n <tr>\n <th>55</th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 4.700</td>\n <td> 0.737</td>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 3.333</td>\n <td> 3.333</td>\n <td>4.860</td>\n <td>5.670</td>\n <td>3.537</td>\n </tr>\n <tr>\n <th>56</th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 0.000</td>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 5.228</td>\n <td> 0.000</td>\n <td>4.970</td>\n <td>5.565</td>\n <td>4.510</td>\n </tr>\n <tr>\n <th>57</th>\n <td> 7.500</td>\n <td> 7.767</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 7.500</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td>6.011</td>\n <td>6.254</td>\n <td>5.002</td>\n </tr>\n <tr>\n <th>58</th>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 4.433</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 1.485</td>\n <td>5.075</td>\n <td>5.252</td>\n <td>5.351</td>\n </tr>\n <tr>\n <th>59</th>\n <td> 7.500</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 8.750</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>6.737</td>\n <td>7.125</td>\n <td>6.331</td>\n </tr>\n <tr>\n <th>60</th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>5.226</td>\n <td>5.451</td>\n <td>3.167</td>\n </tr>\n <tr>\n <th>61</th>\n <td> 1.250</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>4.025</td>\n <td>1.792</td>\n <td>2.658</td>\n </tr>\n <tr>\n <th>62</th>\n <td> 2.500</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 6.667</td>\n <td> 2.948</td>\n <td>4.234</td>\n <td>2.708</td>\n <td>2.475</td>\n </tr>\n <tr>\n <th>63</th>\n <td> 6.250</td>\n <td> 2.233</td>\n <td> 6.667</td>\n <td> 2.970</td>\n <td> 3.750</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td>4.644</td>\n <td>5.565</td>\n <td>3.047</td>\n </tr>\n <tr>\n <th>64</th>\n <td> 7.500</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 7.500</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>4.419</td>\n <td>4.942</td>\n <td>3.381</td>\n </tr>\n <tr>\n <th>65</th>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 6.100</td>\n <td> 0.000</td>\n <td> 5.000</td>\n <td> 3.333</td>\n <td>10.000</td>\n <td> 3.333</td>\n <td>4.263</td>\n <td>4.220</td>\n <td>4.368</td>\n </tr>\n <tr>\n <th>66</th>\n <td> 7.500</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 3.750</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>4.875</td>\n <td>4.700</td>\n <td>3.835</td>\n </tr>\n <tr>\n <th>67</th>\n <td> 4.900</td>\n <td> 2.233</td>\n <td>10.000</td>\n <td> 0.000</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 3.622</td>\n <td> 3.333</td>\n <td>4.190</td>\n <td>1.386</td>\n <td>1.419</td>\n </tr>\n <tr>\n <th>68</th>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 8.200</td>\n <td> 0.000</td>\n <td> 5.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>4.522</td>\n <td>4.127</td>\n <td>2.113</td>\n </tr>\n <tr>\n <th>69</th>\n <td> 2.900</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 2.500</td>\n <td> 3.333</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td>4.654</td>\n <td>3.555</td>\n <td>1.882</td>\n </tr>\n <tr>\n <th>70</th>\n <td> 5.400</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td> 3.333</td>\n <td> 3.750</td>\n <td> 6.667</td>\n <td> 6.667</td>\n <td> 1.485</td>\n <td>4.477</td>\n <td>3.091</td>\n <td>1.988</td>\n </tr>\n <tr>\n <th>71</th>\n <td> 7.500</td>\n <td> 8.800</td>\n <td>10.000</td>\n <td> 6.067</td>\n <td> 7.500</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td>5.338</td>\n <td>5.631</td>\n <td>3.491</td>\n </tr>\n <tr>\n <th>72</th>\n <td> 7.500</td>\n <td> 7.000</td>\n <td>10.000</td>\n <td> 6.853</td>\n <td> 7.500</td>\n <td> 6.348</td>\n <td> 6.667</td>\n <td> 7.508</td>\n <td>6.129</td>\n <td>6.404</td>\n <td>5.002</td>\n </tr>\n <tr>\n <th>73</th>\n <td>10.000</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>10.000</td>\n <td> 6.667</td>\n <td>10.000</td>\n <td>10.000</td>\n <td>5.004</td>\n <td>4.963</td>\n <td>3.977</td>\n </tr>\n <tr>\n <th>74</th>\n <td> 3.750</td>\n <td> 3.333</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 1.250</td>\n <td> 3.333</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>4.489</td>\n <td>4.898</td>\n <td>2.868</td>\n </tr>\n </tbody>\n</table>\n<p>75 rows × 11 columns</p>\n</div>", | |
| "output_type": "pyout", | |
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| "prompt_number": 24 | |
| } | |
| ], | |
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| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "...the indices (row names) are identical" | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "PD1_df.T.index", | |
| "prompt_number": 25, | |
| "outputs": [ | |
| { | |
| "text": "Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74], dtype='int64')", | |
| "output_type": "pyout", | |
| "metadata": {}, | |
| "prompt_number": 25 | |
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| ], | |
| "language": "python", | |
| "trusted": true, | |
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| { | |
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| }, | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "PD2_df.index", | |
| "prompt_number": 26, | |
| "outputs": [ | |
| { | |
| "text": "Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74], dtype='int64')", | |
| "output_type": "pyout", | |
| "metadata": {}, | |
| "prompt_number": 26 | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "PD1_df.T.index == PD2_df.index", | |
| "prompt_number": 27, | |
| "outputs": [ | |
| { | |
| "text": "array([ True, True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True, True,\n True, True, True], dtype=bool)", | |
| "output_type": "pyout", | |
| "metadata": {}, | |
| "prompt_number": 27 | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "Where did those column names comes from you ask? It's a bit sneaky..." | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "PD2.dtype.names", | |
| "prompt_number": 28, | |
| "outputs": [ | |
| { | |
| "text": "('y1', 'y2', 'y3', 'y4', 'y5', 'y6', 'y7', 'y8', 'x1', 'x2', 'x3')", | |
| "output_type": "pyout", | |
| "metadata": {}, | |
| "prompt_number": 28 | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "Note: on some occasions I have found that '%Rpull -d' returns an array with apparently no numbers in (but still the named array info). In these situations you need to do something like" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "```python\n%Rpull rfoo\npyfoo1 = rfoo\n%Rpull -d rfoo\npyfoo2 = rfoo\npyfoo_df = pd.DataFrame(pyfoo1, index=pyfoo2.dtype.names)\n```" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "...and in fact we'll see an example of that later when looking at the Lavaan SEM results" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "<a name=\"TutorialExample\">\n\n[back to top](#Contents)" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "cell_type": "heading", | |
| "source": "Tutorial Example", | |
| "level": 2 | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "So, proceeding with the tutorial: " | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "A neat trick is specifying the model syntax in python, then pushing to R. This is very convenient for looping, string manipulations, substitutions, etc., which is more fiddly to do in R." | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "model = \\\n\"\"\"\n# measurement model\nind60 =~ x1 + x2 + x3\ndem60 =~ y1 + y2 + y3 + y4\ndem65 =~ y5 + y6 + y7 + y8\n# regressions\ndem60 ~ ind60\ndem65 ~ ind60 + dem60\n# residual correlations\ny1 ~~ y5\ny2 ~~ y4 + y6\ny3 ~~ y7\ny4 ~~ y8\ny6 ~~ y8\n\"\"\"", | |
| "prompt_number": 29, | |
| "outputs": [], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "Let's grab the data again just to keep this section self-contained" | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "%Rpull -d PoliticalDemocracy\nPD_df = pd.DataFrame(PoliticalDemocracy)", | |
| "prompt_number": 30, | |
| "outputs": [], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "Now we're ready to roll: " | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "%%R -i model,PD_df\nfit <- sem(model, data = PD_df)\nsummary(fit, standardized = TRUE)", | |
| "prompt_number": 31, | |
| "outputs": [ | |
| { | |
| "text": "lavaan (0.5-15) converged normally after 68 iterations\n\n Number of observations 75\n\n Estimator ML\n Minimum Function Test Statistic 38.125\n Degrees of freedom 35\n P-value (Chi-square) 0.329\n\nParameter estimates:\n\n Information Expected\n Standard Errors Standard\n\n Estimate Std.err Z-value P(>|z|) Std.lv Std.all\nLatent variables:\n ind60 =~\n x1 1.000 0.670 0.920\n x2 2.180 0.139 15.742 0.000 1.460 0.973\n x3 1.819 0.152 11.967 0.000 1.218 0.872\n dem60 =~\n y1 1.000 2.223 0.850\n y2 1.257 0.182 6.889 0.000 2.794 0.717\n y3 1.058 0.151 6.987 0.000 2.351 0.722\n y4 1.265 0.145 8.722 0.000 2.812 0.846\n dem65 =~\n y5 1.000 2.103 0.808\n y6 1.186 0.169 7.024 0.000 2.493 0.746\n y7 1.280 0.160 8.002 0.000 2.691 0.824\n y8 1.266 0.158 8.007 0.000 2.662 0.828\n\nRegressions:\n dem60 ~\n ind60 1.483 0.399 3.715 0.000 0.447 0.447\n dem65 ~\n ind60 0.572 0.221 2.586 0.010 0.182 0.182\n dem60 0.837 0.098 8.514 0.000 0.885 0.885\n\nCovariances:\n y1 ~~\n y5 0.624 0.358 1.741 0.082 0.624 0.296\n y2 ~~\n y4 1.313 0.702 1.871 0.061 1.313 0.273\n y6 2.153 0.734 2.934 0.003 2.153 0.356\n y3 ~~\n y7 0.795 0.608 1.308 0.191 0.795 0.191\n y4 ~~\n y8 0.348 0.442 0.787 0.431 0.348 0.109\n y6 ~~\n y8 1.356 0.568 2.386 0.017 1.356 0.338\n\nVariances:\n x1 0.082 0.019 0.082 0.154\n x2 0.120 0.070 0.120 0.053\n x3 0.467 0.090 0.467 0.239\n y1 1.891 0.444 1.891 0.277\n y2 7.373 1.374 7.373 0.486\n y3 5.067 0.952 5.067 0.478\n y4 3.148 0.739 3.148 0.285\n y5 2.351 0.480 2.351 0.347\n y6 4.954 0.914 4.954 0.443\n y7 3.431 0.713 3.431 0.322\n y8 3.254 0.695 3.254 0.315\n ind60 0.448 0.087 1.000 1.000\n dem60 3.956 0.921 0.800 0.800\n dem65 0.172 0.215 0.039 0.039\n\n", | |
| "output_type": "display_data", | |
| "metadata": {} | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "<a name=\"Beyond\">\n\n[back to top](#Contents)" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "cell_type": "heading", | |
| "source": "Beyond the tutorial", | |
| "level": 2 | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "Now let's go beyond the tutorial a bit and extract some information about the fitted model" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "*Figures*" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "SemPlot is useful both for creating final figures and for getting a quick visual representation of the fitted model and the results" | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "%R semPaths(fit, \"std\", curvePivot = TRUE);", | |
| "prompt_number": 32, | |
| "outputs": [ | |
| { | |
| "png": 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ThFKCGmdVARk0dRG10iMsRzwuf+z7r29BfYGyZwRUHiTOqgXWoCnNRs/mwtgLxzOPn3xz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| |
| "output_type": "display_data", | |
| "metadata": {} | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "For final figures it's more useful to save figures to file from R, and then render in the notebook. \n\n\n(Note: seems that you need to put all the commands on one line for this to work properly)\n" | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "figname = '/tmp/tmp_semfig.svg'\n%R -i figname svg(filename=figname, width=8, height=8, antialias = \"subpixel\", bg=\"transparent\"); semPaths(fit,title = FALSE, \"std\",curvePivot = TRUE,edge.label.cex =1); dev.off();", | |
| "prompt_number": 33, | |
| "outputs": [], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "SVG(figname)", | |
| "prompt_number": 34, | |
| "outputs": [ | |
| { | |
| "text": "<IPython.core.display.SVG at 0x73a94d0>", | |
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| "output_type": "pyout", | |
| "metadata": {}, | |
| "prompt_number": 34 | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "PNG alternative with various different semplot options" | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "figname = '/tmp/tmp_semfig.png'\n%R -i figname Graph <- semPaths(fit,title=FALSE, whatLabels='omit', layoutr=\"spring\", nCharNodes=10, residuals=FALSE); print('Graph written')\n%R png(height=1000, width=2000, res=3000, filename=figname, antialias = \"subpixel\", bg=\"transparent\"); plot(Graph); dev.off();", | |
| "prompt_number": 35, | |
| "outputs": [ | |
| { | |
| "text": "[1] \"Graph written\"\n", | |
| "output_type": "display_data", | |
| "metadata": {} | |
| }, | |
| { | |
| "png": 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| |
| "output_type": "display_data", | |
| "metadata": {} | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "Image(figname , width=\"50%\")", | |
| "prompt_number": 36, | |
| "outputs": [ | |
| { | |
| "png": 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+Ll397JkzZ2ZHzD8OLmmbWnLJlLaS63b0k0vivV8AYkbQAbcdLSsXT5QEPbs4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| |
| "text": "<IPython.core.display.Image at 0x7391c50>", | |
| "output_type": "pyout", | |
| "metadata": { | |
| "png": { | |
| "width": "50%" | |
| } | |
| }, | |
| "prompt_number": 36 | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "*Extracting more information*\n" | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "%R -o PEs PEs = parameterEstimates(fit);", | |
| "prompt_number": 37, | |
| "outputs": [], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "%R print(PEs)", | |
| "prompt_number": 38, | |
| "outputs": [ | |
| { | |
| "text": " lhs op rhs est se z pvalue ci.lower ci.upper\n1 ind60 =~ x1 1.000 0.000 NA NA 1.000 1.000\n2 ind60 =~ x2 2.180 0.139 15.742 0.000 1.909 2.452\n3 ind60 =~ x3 1.819 0.152 11.967 0.000 1.521 2.116\n4 dem60 =~ y1 1.000 0.000 NA NA 1.000 1.000\n5 dem60 =~ y2 1.257 0.182 6.889 0.000 0.899 1.614\n6 dem60 =~ y3 1.058 0.151 6.987 0.000 0.761 1.354\n7 dem60 =~ y4 1.265 0.145 8.722 0.000 0.981 1.549\n8 dem65 =~ y5 1.000 0.000 NA NA 1.000 1.000\n9 dem65 =~ y6 1.186 0.169 7.024 0.000 0.855 1.517\n10 dem65 =~ y7 1.280 0.160 8.002 0.000 0.966 1.593\n11 dem65 =~ y8 1.266 0.158 8.007 0.000 0.956 1.576\n12 dem60 ~ ind60 1.483 0.399 3.715 0.000 0.701 2.265\n13 dem65 ~ ind60 0.572 0.221 2.586 0.010 0.139 1.006\n14 dem65 ~ dem60 0.837 0.098 8.514 0.000 0.645 1.030\n15 y1 ~~ y5 0.624 0.358 1.741 0.082 -0.079 1.326\n16 y2 ~~ y4 1.313 0.702 1.871 0.061 -0.063 2.689\n17 y2 ~~ y6 2.153 0.734 2.934 0.003 0.715 3.591\n18 y3 ~~ y7 0.795 0.608 1.308 0.191 -0.396 1.986\n19 y4 ~~ y8 0.348 0.442 0.787 0.431 -0.519 1.215\n20 y6 ~~ y8 1.356 0.568 2.386 0.017 0.242 2.470\n21 x1 ~~ x1 0.082 0.019 4.184 0.000 0.043 0.120\n22 x2 ~~ x2 0.120 0.070 1.718 0.086 -0.017 0.256\n23 x3 ~~ x3 0.467 0.090 5.177 0.000 0.290 0.643\n24 y1 ~~ y1 1.891 0.444 4.256 0.000 1.020 2.762\n25 y2 ~~ y2 7.373 1.374 5.366 0.000 4.680 10.066\n26 y3 ~~ y3 5.067 0.952 5.324 0.000 3.202 6.933\n27 y4 ~~ y4 3.148 0.739 4.261 0.000 1.700 4.596\n28 y5 ~~ y5 2.351 0.480 4.895 0.000 1.410 3.292\n29 y6 ~~ y6 4.954 0.914 5.419 0.000 3.162 6.746\n30 y7 ~~ y7 3.431 0.713 4.814 0.000 2.034 4.829\n31 y8 ~~ y8 3.254 0.695 4.685 0.000 1.893 4.615\n32 ind60 ~~ ind60 0.448 0.087 5.173 0.000 0.279 0.618\n33 dem60 ~~ dem60 3.956 0.921 4.295 0.000 2.151 5.762\n34 dem65 ~~ dem65 0.172 0.215 0.803 0.422 -0.249 0.593\n", | |
| "output_type": "display_data", | |
| "metadata": {} | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "%Rpull PEs\nPEs1 = PEs\n%Rpull -d PEs\nPEs2 = PEs", | |
| "prompt_number": 39, | |
| "outputs": [], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "PEs_df = pd.DataFrame(PEs, columns=PEs2.dtype.names)", | |
| "prompt_number": 40, | |
| "outputs": [], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "PEs2.dtype.names", | |
| "prompt_number": 41, | |
| "outputs": [ | |
| { | |
| "text": "('lhs', 'op', 'rhs', 'est', 'se', 'z', 'pvalue', 'ci.lower', 'ci.upper')", | |
| "output_type": "pyout", | |
| "metadata": {}, | |
| "prompt_number": 41 | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "PEs_df", | |
| "prompt_number": 42, | |
| "outputs": [ | |
| { | |
| "text": " lhs op rhs est se z pvalue ci.lower ci.upper\n0 ind60 =~ x1 1.000 0.000 nan nan 1.000 1.000\n1 ind60 =~ x2 2.180 0.139 15.742 0.000 1.909 2.452\n2 ind60 =~ x3 1.819 0.152 11.967 0.000 1.521 2.116\n3 dem60 =~ y1 1.000 0.000 nan nan 1.000 1.000\n4 dem60 =~ y2 1.257 0.182 6.889 0.000 0.899 1.614\n5 dem60 =~ y3 1.058 0.151 6.987 0.000 0.761 1.354\n6 dem60 =~ y4 1.265 0.145 8.722 0.000 0.981 1.549\n7 dem65 =~ y5 1.000 0.000 nan nan 1.000 1.000\n8 dem65 =~ y6 1.186 0.169 7.024 0.000 0.855 1.517\n9 dem65 =~ y7 1.280 0.160 8.002 0.000 0.966 1.593\n10 dem65 =~ y8 1.266 0.158 8.007 0.000 0.956 1.576\n11 dem60 ~ ind60 1.483 0.399 3.715 0.000 0.701 2.265\n12 dem65 ~ ind60 0.572 0.221 2.586 0.010 0.139 1.006\n13 dem65 ~ dem60 0.837 0.098 8.514 0.000 0.645 1.030\n14 y1 ~~ y5 0.624 0.358 1.741 0.082 -0.079 1.326\n15 y2 ~~ y4 1.313 0.702 1.871 0.061 -0.063 2.689\n16 y2 ~~ y6 2.153 0.734 2.934 0.003 0.715 3.591\n17 y3 ~~ y7 0.795 0.608 1.308 0.191 -0.396 1.986\n18 y4 ~~ y8 0.348 0.442 0.787 0.431 -0.519 1.215\n19 y6 ~~ y8 1.356 0.568 2.386 0.017 0.242 2.470\n20 x1 ~~ x1 0.082 0.019 4.184 0.000 0.043 0.120\n21 x2 ~~ x2 0.120 0.070 1.718 0.086 -0.017 0.256\n22 x3 ~~ x3 0.467 0.090 5.177 0.000 0.290 0.643\n23 y1 ~~ y1 1.891 0.444 4.256 0.000 1.020 2.762\n24 y2 ~~ y2 7.373 1.374 5.366 0.000 4.680 10.066\n25 y3 ~~ y3 5.067 0.952 5.324 0.000 3.202 6.933\n26 y4 ~~ y4 3.148 0.739 4.261 0.000 1.700 4.596\n27 y5 ~~ y5 2.351 0.480 4.895 0.000 1.410 3.292\n28 y6 ~~ y6 4.954 0.914 5.419 0.000 3.162 6.746\n29 y7 ~~ y7 3.431 0.713 4.814 0.000 2.034 4.829\n30 y8 ~~ y8 3.254 0.695 4.685 0.000 1.893 4.615\n31 ind60 ~~ ind60 0.448 0.087 5.173 0.000 0.279 0.618\n32 dem60 ~~ dem60 3.956 0.921 4.295 0.000 2.151 5.762\n33 dem65 ~~ dem65 0.172 0.215 0.803 0.422 -0.249 0.593\n\n[34 rows x 9 columns]", | |
| "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>lhs</th>\n <th>op</th>\n <th>rhs</th>\n <th>est</th>\n <th>se</th>\n <th>z</th>\n <th>pvalue</th>\n <th>ci.lower</th>\n <th>ci.upper</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0 </th>\n <td> ind60</td>\n <td> =~</td>\n <td> x1</td>\n <td>1.000</td>\n <td>0.000</td>\n <td> nan</td>\n <td> nan</td>\n <td> 1.000</td>\n <td> 1.000</td>\n </tr>\n <tr>\n <th>1 </th>\n <td> ind60</td>\n <td> =~</td>\n <td> x2</td>\n <td>2.180</td>\n <td>0.139</td>\n <td>15.742</td>\n <td>0.000</td>\n <td> 1.909</td>\n <td> 2.452</td>\n </tr>\n <tr>\n <th>2 </th>\n <td> ind60</td>\n <td> =~</td>\n <td> x3</td>\n <td>1.819</td>\n <td>0.152</td>\n <td>11.967</td>\n <td>0.000</td>\n <td> 1.521</td>\n <td> 2.116</td>\n </tr>\n <tr>\n <th>3 </th>\n <td> dem60</td>\n <td> =~</td>\n <td> y1</td>\n <td>1.000</td>\n <td>0.000</td>\n <td> nan</td>\n <td> nan</td>\n <td> 1.000</td>\n <td> 1.000</td>\n </tr>\n <tr>\n <th>4 </th>\n <td> dem60</td>\n <td> =~</td>\n <td> y2</td>\n <td>1.257</td>\n <td>0.182</td>\n <td> 6.889</td>\n <td>0.000</td>\n <td> 0.899</td>\n <td> 1.614</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> dem60</td>\n <td> =~</td>\n <td> y3</td>\n <td>1.058</td>\n <td>0.151</td>\n <td> 6.987</td>\n <td>0.000</td>\n <td> 0.761</td>\n <td> 1.354</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> dem60</td>\n <td> =~</td>\n <td> y4</td>\n <td>1.265</td>\n <td>0.145</td>\n <td> 8.722</td>\n <td>0.000</td>\n <td> 0.981</td>\n <td> 1.549</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> dem65</td>\n <td> =~</td>\n <td> y5</td>\n <td>1.000</td>\n <td>0.000</td>\n <td> nan</td>\n <td> nan</td>\n <td> 1.000</td>\n <td> 1.000</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> dem65</td>\n <td> =~</td>\n <td> y6</td>\n <td>1.186</td>\n <td>0.169</td>\n <td> 7.024</td>\n <td>0.000</td>\n <td> 0.855</td>\n <td> 1.517</td>\n </tr>\n <tr>\n <th>9 </th>\n <td> dem65</td>\n <td> =~</td>\n <td> y7</td>\n <td>1.280</td>\n <td>0.160</td>\n <td> 8.002</td>\n <td>0.000</td>\n <td> 0.966</td>\n <td> 1.593</td>\n </tr>\n <tr>\n <th>10</th>\n <td> dem65</td>\n <td> =~</td>\n <td> y8</td>\n <td>1.266</td>\n <td>0.158</td>\n <td> 8.007</td>\n <td>0.000</td>\n <td> 0.956</td>\n <td> 1.576</td>\n </tr>\n <tr>\n <th>11</th>\n <td> dem60</td>\n <td> ~</td>\n <td> ind60</td>\n <td>1.483</td>\n <td>0.399</td>\n <td> 3.715</td>\n <td>0.000</td>\n <td> 0.701</td>\n <td> 2.265</td>\n </tr>\n <tr>\n <th>12</th>\n <td> dem65</td>\n <td> ~</td>\n <td> ind60</td>\n <td>0.572</td>\n <td>0.221</td>\n <td> 2.586</td>\n <td>0.010</td>\n <td> 0.139</td>\n <td> 1.006</td>\n </tr>\n <tr>\n <th>13</th>\n <td> dem65</td>\n <td> ~</td>\n <td> dem60</td>\n <td>0.837</td>\n <td>0.098</td>\n <td> 8.514</td>\n <td>0.000</td>\n <td> 0.645</td>\n <td> 1.030</td>\n </tr>\n <tr>\n <th>14</th>\n <td> y1</td>\n <td> ~~</td>\n <td> y5</td>\n <td>0.624</td>\n <td>0.358</td>\n <td> 1.741</td>\n <td>0.082</td>\n <td>-0.079</td>\n <td> 1.326</td>\n </tr>\n <tr>\n <th>15</th>\n <td> y2</td>\n <td> ~~</td>\n <td> y4</td>\n <td>1.313</td>\n <td>0.702</td>\n <td> 1.871</td>\n <td>0.061</td>\n <td>-0.063</td>\n <td> 2.689</td>\n </tr>\n <tr>\n <th>16</th>\n <td> y2</td>\n <td> ~~</td>\n <td> y6</td>\n <td>2.153</td>\n <td>0.734</td>\n <td> 2.934</td>\n <td>0.003</td>\n <td> 0.715</td>\n <td> 3.591</td>\n </tr>\n <tr>\n <th>17</th>\n <td> y3</td>\n <td> ~~</td>\n <td> y7</td>\n <td>0.795</td>\n <td>0.608</td>\n <td> 1.308</td>\n <td>0.191</td>\n <td>-0.396</td>\n <td> 1.986</td>\n </tr>\n <tr>\n <th>18</th>\n <td> y4</td>\n <td> ~~</td>\n <td> y8</td>\n <td>0.348</td>\n <td>0.442</td>\n <td> 0.787</td>\n <td>0.431</td>\n <td>-0.519</td>\n <td> 1.215</td>\n </tr>\n <tr>\n <th>19</th>\n <td> y6</td>\n <td> ~~</td>\n <td> y8</td>\n <td>1.356</td>\n <td>0.568</td>\n <td> 2.386</td>\n <td>0.017</td>\n <td> 0.242</td>\n <td> 2.470</td>\n </tr>\n <tr>\n <th>20</th>\n <td> x1</td>\n <td> ~~</td>\n <td> x1</td>\n <td>0.082</td>\n <td>0.019</td>\n <td> 4.184</td>\n <td>0.000</td>\n <td> 0.043</td>\n <td> 0.120</td>\n </tr>\n <tr>\n <th>21</th>\n <td> x2</td>\n <td> ~~</td>\n <td> x2</td>\n <td>0.120</td>\n <td>0.070</td>\n <td> 1.718</td>\n <td>0.086</td>\n <td>-0.017</td>\n <td> 0.256</td>\n </tr>\n <tr>\n <th>22</th>\n <td> x3</td>\n <td> ~~</td>\n <td> x3</td>\n <td>0.467</td>\n <td>0.090</td>\n <td> 5.177</td>\n <td>0.000</td>\n <td> 0.290</td>\n <td> 0.643</td>\n </tr>\n <tr>\n <th>23</th>\n <td> y1</td>\n <td> ~~</td>\n <td> y1</td>\n <td>1.891</td>\n <td>0.444</td>\n <td> 4.256</td>\n <td>0.000</td>\n <td> 1.020</td>\n <td> 2.762</td>\n </tr>\n <tr>\n <th>24</th>\n <td> y2</td>\n <td> ~~</td>\n <td> y2</td>\n <td>7.373</td>\n <td>1.374</td>\n <td> 5.366</td>\n <td>0.000</td>\n <td> 4.680</td>\n <td>10.066</td>\n </tr>\n <tr>\n <th>25</th>\n <td> y3</td>\n <td> ~~</td>\n <td> y3</td>\n <td>5.067</td>\n <td>0.952</td>\n <td> 5.324</td>\n <td>0.000</td>\n <td> 3.202</td>\n <td> 6.933</td>\n </tr>\n <tr>\n <th>26</th>\n <td> y4</td>\n <td> ~~</td>\n <td> y4</td>\n <td>3.148</td>\n <td>0.739</td>\n <td> 4.261</td>\n <td>0.000</td>\n <td> 1.700</td>\n <td> 4.596</td>\n </tr>\n <tr>\n <th>27</th>\n <td> y5</td>\n <td> ~~</td>\n <td> y5</td>\n <td>2.351</td>\n <td>0.480</td>\n <td> 4.895</td>\n <td>0.000</td>\n <td> 1.410</td>\n <td> 3.292</td>\n </tr>\n <tr>\n <th>28</th>\n <td> y6</td>\n <td> ~~</td>\n <td> y6</td>\n <td>4.954</td>\n <td>0.914</td>\n <td> 5.419</td>\n <td>0.000</td>\n <td> 3.162</td>\n <td> 6.746</td>\n </tr>\n <tr>\n <th>29</th>\n <td> y7</td>\n <td> ~~</td>\n <td> y7</td>\n <td>3.431</td>\n <td>0.713</td>\n <td> 4.814</td>\n <td>0.000</td>\n <td> 2.034</td>\n <td> 4.829</td>\n </tr>\n <tr>\n <th>30</th>\n <td> y8</td>\n <td> ~~</td>\n <td> y8</td>\n <td>3.254</td>\n <td>0.695</td>\n <td> 4.685</td>\n <td>0.000</td>\n <td> 1.893</td>\n <td> 4.615</td>\n </tr>\n <tr>\n <th>31</th>\n <td> ind60</td>\n <td> ~~</td>\n <td> ind60</td>\n <td>0.448</td>\n <td>0.087</td>\n <td> 5.173</td>\n <td>0.000</td>\n <td> 0.279</td>\n <td> 0.618</td>\n </tr>\n <tr>\n <th>32</th>\n <td> dem60</td>\n <td> ~~</td>\n <td> dem60</td>\n <td>3.956</td>\n <td>0.921</td>\n <td> 4.295</td>\n <td>0.000</td>\n <td> 2.151</td>\n <td> 5.762</td>\n </tr>\n <tr>\n <th>33</th>\n <td> dem65</td>\n <td> ~~</td>\n <td> dem65</td>\n <td>0.172</td>\n <td>0.215</td>\n <td> 0.803</td>\n <td>0.422</td>\n <td>-0.249</td>\n <td> 0.593</td>\n </tr>\n </tbody>\n</table>\n<p>34 rows × 9 columns</p>\n</div>", | |
| "output_type": "pyout", | |
| "metadata": {}, | |
| "prompt_number": 42 | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "%R FMs = fitmeasures(fit)\n%Rpull FMs\nFMs1 = FMs\n%Rpull -d FMs\nFMs2 = FMs", | |
| "prompt_number": 43, | |
| "outputs": [], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "FMs2.dtype.names", | |
| "prompt_number": 44, | |
| "outputs": [ | |
| { | |
| "text": "('fmin',\n 'chisq',\n 'df',\n 'pvalue',\n 'baseline.chisq',\n 'baseline.df',\n 'baseline.pvalue',\n 'cfi',\n 'tli',\n 'nnfi',\n 'rfi',\n 'nfi',\n 'pnfi',\n 'ifi',\n 'rni',\n 'logl',\n 'unrestricted.logl',\n 'npar',\n 'aic',\n 'bic',\n 'ntotal',\n 'bic2',\n 'rmsea',\n 'rmsea.ci.lower',\n 'rmsea.ci.upper',\n 'rmsea.pvalue',\n 'rmr',\n 'rmr_nomean',\n 'srmr',\n 'srmr_nomean',\n 'cn_05',\n 'cn_01',\n 'gfi',\n 'agfi',\n 'pgfi',\n 'mfi',\n 'ecvi')", | |
| "output_type": "pyout", | |
| "metadata": {}, | |
| "prompt_number": 44 | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "FMs_df = pd.DataFrame(FMs1, index=FMs2.dtype.names)\nFMs_df", | |
| "prompt_number": 45, | |
| "outputs": [ | |
| { | |
| "text": " 0\nfmin 0.254\nchisq 38.125\ndf 35.000\npvalue 0.329\nbaseline.chisq 730.654\nbaseline.df 55.000\nbaseline.pvalue 0.000\ncfi 0.995\ntli 0.993\nnnfi 0.993\nrfi 0.918\nnfi 0.948\npnfi 0.603\nifi 0.996\nrni 0.995\nlogl -1547.791\nunrestricted.logl -1528.728\nnpar 31.000\naic 3157.582\nbic 3229.424\nntotal 75.000\nbic2 3131.720\nrmsea 0.035\nrmsea.ci.lower 0.000\nrmsea.ci.upper 0.092\nrmsea.pvalue 0.611\nrmr 0.276\nrmr_nomean 0.276\nsrmr 0.044\nsrmr_nomean 0.044\ncn_05 98.970\ncn_01 113.803\ngfi 0.923\nagfi 0.854\npgfi 0.489\nmfi 0.979\necvi 1.335\n\n[37 rows x 1 columns]", | |
| "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>0</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>fmin</th>\n <td> 0.254</td>\n </tr>\n <tr>\n <th>chisq</th>\n <td> 38.125</td>\n </tr>\n <tr>\n <th>df</th>\n <td> 35.000</td>\n </tr>\n <tr>\n <th>pvalue</th>\n <td> 0.329</td>\n </tr>\n <tr>\n <th>baseline.chisq</th>\n <td> 730.654</td>\n </tr>\n <tr>\n <th>baseline.df</th>\n <td> 55.000</td>\n </tr>\n <tr>\n <th>baseline.pvalue</th>\n <td> 0.000</td>\n </tr>\n <tr>\n <th>cfi</th>\n <td> 0.995</td>\n </tr>\n <tr>\n <th>tli</th>\n <td> 0.993</td>\n </tr>\n <tr>\n <th>nnfi</th>\n <td> 0.993</td>\n </tr>\n <tr>\n <th>rfi</th>\n <td> 0.918</td>\n </tr>\n <tr>\n <th>nfi</th>\n <td> 0.948</td>\n </tr>\n <tr>\n <th>pnfi</th>\n <td> 0.603</td>\n </tr>\n <tr>\n <th>ifi</th>\n <td> 0.996</td>\n </tr>\n <tr>\n <th>rni</th>\n <td> 0.995</td>\n </tr>\n <tr>\n <th>logl</th>\n <td>-1547.791</td>\n </tr>\n <tr>\n <th>unrestricted.logl</th>\n <td>-1528.728</td>\n </tr>\n <tr>\n <th>npar</th>\n <td> 31.000</td>\n </tr>\n <tr>\n <th>aic</th>\n <td> 3157.582</td>\n </tr>\n <tr>\n <th>bic</th>\n <td> 3229.424</td>\n </tr>\n <tr>\n <th>ntotal</th>\n <td> 75.000</td>\n </tr>\n <tr>\n <th>bic2</th>\n <td> 3131.720</td>\n </tr>\n <tr>\n <th>rmsea</th>\n <td> 0.035</td>\n </tr>\n <tr>\n <th>rmsea.ci.lower</th>\n <td> 0.000</td>\n </tr>\n <tr>\n <th>rmsea.ci.upper</th>\n <td> 0.092</td>\n </tr>\n <tr>\n <th>rmsea.pvalue</th>\n <td> 0.611</td>\n </tr>\n <tr>\n <th>rmr</th>\n <td> 0.276</td>\n </tr>\n <tr>\n <th>rmr_nomean</th>\n <td> 0.276</td>\n </tr>\n <tr>\n <th>srmr</th>\n <td> 0.044</td>\n </tr>\n <tr>\n <th>srmr_nomean</th>\n <td> 0.044</td>\n </tr>\n <tr>\n <th>cn_05</th>\n <td> 98.970</td>\n </tr>\n <tr>\n <th>cn_01</th>\n <td> 113.803</td>\n </tr>\n <tr>\n <th>gfi</th>\n <td> 0.923</td>\n </tr>\n <tr>\n <th>agfi</th>\n <td> 0.854</td>\n </tr>\n <tr>\n <th>pgfi</th>\n <td> 0.489</td>\n </tr>\n <tr>\n <th>mfi</th>\n <td> 0.979</td>\n </tr>\n <tr>\n <th>ecvi</th>\n <td> 1.335</td>\n </tr>\n </tbody>\n</table>\n<p>37 rows × 1 columns</p>\n</div>", | |
| "output_type": "pyout", | |
| "metadata": {}, | |
| "prompt_number": 45 | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "%R VC <- vcov(fit); \n%Rpull VC\nVC1 = VC\n%Rpull -d VC\nVC2 = VC", | |
| "prompt_number": 46, | |
| "outputs": [], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "pd.DataFrame(VC, index=VC2.dtype.names)", | |
| "prompt_number": 47, | |
| "outputs": [ | |
| { | |
| "text": " ind60=~x2 ind60=~x3 dem60=~y2 dem60=~y3 dem60=~y4 \\\nind60=~x2 0.019 0.010 -0.000 0.000 0.000 \nind60=~x3 0.010 0.023 -0.000 0.000 0.000 \ndem60=~y2 -0.000 -0.000 0.033 0.008 0.015 \ndem60=~y3 -0.000 -0.000 0.008 0.023 0.009 \ndem60=~y4 -0.000 -0.000 0.015 0.009 0.021 \ndem65=~y6 -0.000 0.000 0.010 0.003 0.004 \ndem65=~y7 -0.000 -0.000 0.004 0.006 0.004 \ndem65=~y8 -0.000 0.000 0.004 0.003 0.005 \ndem60~ind60 0.009 0.007 -0.012 -0.010 -0.012 \ndem65~ind60 0.003 0.003 -0.001 -0.002 -0.001 \ndem65~dem60 0.000 0.000 0.004 0.004 0.004 \ny1~~y5 -0.000 -0.000 0.009 0.007 0.010 \ny2~~y4 0.000 0.000 -0.025 -0.001 -0.017 \ny2~~y6 0.000 0.000 -0.009 0.000 0.002 \ny3~~y7 0.000 0.000 -0.000 -0.014 0.000 \ny4~~y8 -0.000 -0.000 -0.001 -0.001 -0.010 \ny6~~y8 -0.000 -0.000 0.000 0.000 -0.000 \nx1~~x1 0.001 0.000 -0.000 0.000 -0.000 \nx2~~x2 -0.004 -0.000 0.000 -0.000 -0.000 \nx3~~x3 0.001 -0.001 -0.000 0.000 -0.000 \ny1~~y1 -0.000 -0.000 0.015 0.013 0.017 \ny2~~y2 0.000 0.000 -0.041 -0.001 -0.010 \ny3~~y3 0.000 0.000 -0.001 -0.023 0.000 \ny4~~y4 0.000 -0.000 -0.015 -0.002 -0.028 \ny5~~y5 -0.000 -0.000 0.005 0.005 0.006 \ny6~~y6 -0.000 -0.000 -0.004 0.000 0.001 \ny7~~y7 0.000 0.000 -0.001 -0.007 -0.000 \ny8~~y8 -0.000 -0.000 -0.001 -0.000 -0.006 \nind60~~ind60 -0.006 -0.004 0.000 -0.000 0.000 \ndem60~~dem60 0.001 0.000 -0.063 -0.053 -0.064 \ndem65~~dem65 0.000 0.000 0.000 -0.001 -0.000 \n\n dem65=~y6 dem65=~y7 dem65=~y8 dem60~ind60 dem65~ind60 \\\nind60=~x2 0.000 0.000 0.000 0.009 0.003 \nind60=~x3 0.000 0.000 0.000 0.007 0.003 \ndem60=~y2 0.010 0.004 0.004 -0.012 -0.001 \ndem60=~y3 0.003 0.006 0.003 -0.010 -0.002 \ndem60=~y4 0.004 0.004 0.005 -0.012 -0.001 \ndem65=~y6 0.028 0.013 0.017 -0.004 -0.005 \ndem65=~y7 0.013 0.026 0.013 -0.005 -0.006 \ndem65=~y8 0.017 0.013 0.025 -0.005 -0.005 \ndem60~ind60 -0.004 -0.005 -0.005 0.159 -0.011 \ndem65~ind60 -0.005 -0.006 -0.005 -0.011 0.049 \ndem65~dem60 -0.006 -0.007 -0.007 -0.003 -0.006 \ny1~~y5 0.009 0.010 0.010 -0.007 -0.000 \ny2~~y4 -0.002 -0.002 -0.002 0.010 -0.013 \ny2~~y6 -0.007 0.000 0.003 -0.000 0.000 \ny3~~y7 -0.000 -0.011 0.000 0.003 0.006 \ny4~~y8 -0.001 -0.001 -0.010 0.005 0.003 \ny6~~y8 -0.013 -0.001 -0.011 0.000 0.002 \nx1~~x1 0.000 0.000 0.000 0.000 0.000 \nx2~~x2 -0.000 -0.000 -0.000 -0.000 -0.000 \nx3~~x3 0.000 -0.000 0.000 0.000 0.000 \ny1~~y1 0.006 0.007 0.007 -0.012 -0.008 \ny2~~y2 -0.008 -0.002 0.001 0.009 -0.013 \ny3~~y3 -0.001 -0.007 -0.000 0.006 -0.002 \ny4~~y4 -0.003 -0.002 -0.008 0.014 -0.011 \ny5~~y5 0.012 0.013 0.014 -0.005 -0.002 \ny6~~y6 -0.020 -0.001 -0.006 -0.000 0.002 \ny7~~y7 -0.001 -0.019 -0.000 0.002 0.005 \ny8~~y8 -0.009 -0.001 -0.021 0.003 0.004 \nind60~~ind60 0.000 0.000 -0.000 -0.003 -0.001 \ndem60~~dem60 -0.021 -0.023 -0.023 0.039 0.032 \ndem65~~dem65 -0.002 -0.003 -0.002 0.001 0.008 \n\n dem65~dem60 y1~~y5 y2~~y4 y2~~y6 y3~~y7 \nind60=~x2 0.000 0.000 0.000 0.000 0.000 ... \nind60=~x3 0.000 0.000 0.000 0.000 0.000 ... \ndem60=~y2 0.004 0.009 -0.025 -0.009 -0.000 ... \ndem60=~y3 0.004 0.007 -0.001 0.000 -0.014 ... \ndem60=~y4 0.004 0.010 -0.017 0.002 0.000 ... \ndem65=~y6 -0.006 0.009 -0.002 -0.007 -0.000 ... \ndem65=~y7 -0.007 0.010 -0.002 0.000 -0.011 ... \ndem65=~y8 -0.007 0.010 -0.002 0.003 0.000 ... \ndem60~ind60 -0.003 -0.007 0.010 -0.000 0.003 ... \ndem65~ind60 -0.006 -0.000 -0.013 0.000 0.006 ... \ndem65~dem60 0.010 -0.003 0.005 -0.000 -0.003 ... \ny1~~y5 -0.003 0.128 -0.029 0.000 -0.012 ... \ny2~~y4 0.005 -0.029 0.493 0.020 -0.028 ... \ny2~~y6 -0.000 0.000 0.020 0.538 0.000 ... \ny3~~y7 -0.003 -0.012 -0.028 0.000 0.369 ... \ny4~~y8 -0.002 -0.017 -0.010 -0.064 -0.016 ... \ny6~~y8 0.003 -0.018 -0.034 0.009 -0.020 ... \nx1~~x1 0.000 0.000 0.000 0.000 0.000 ... \nx2~~x2 -0.000 -0.000 -0.000 -0.000 -0.000 ... \nx3~~x3 0.000 0.000 0.000 0.000 0.000 ... \ny1~~y1 0.007 0.086 -0.025 0.000 -0.023 ... \ny2~~y2 0.005 -0.027 0.486 0.504 -0.025 ... \ny3~~y3 0.001 -0.018 -0.019 0.000 0.230 ... \ny4~~y4 0.003 -0.044 0.273 -0.037 -0.041 ... \ny5~~y5 -0.005 0.092 -0.009 0.000 -0.018 ... \ny6~~y6 0.003 -0.015 -0.028 0.321 -0.017 ... \ny7~~y7 0.002 -0.024 -0.001 0.000 0.166 ... \ny8~~y8 0.002 -0.030 -0.015 -0.035 -0.031 ... \nind60~~ind60 -0.000 0.000 -0.000 -0.000 -0.000 ... \ndem60~~dem60 -0.030 -0.042 0.013 -0.000 0.017 ... \ndem65~~dem65 -0.004 0.012 -0.028 -0.000 0.017 ... \n\n[31 rows x 31 columns]", | |
| "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>ind60=~x2</th>\n <th>ind60=~x3</th>\n <th>dem60=~y2</th>\n <th>dem60=~y3</th>\n <th>dem60=~y4</th>\n <th>dem65=~y6</th>\n <th>dem65=~y7</th>\n <th>dem65=~y8</th>\n <th>dem60~ind60</th>\n <th>dem65~ind60</th>\n <th>dem65~dem60</th>\n <th>y1~~y5</th>\n <th>y2~~y4</th>\n <th>y2~~y6</th>\n <th>y3~~y7</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>ind60=~x2</th>\n <td> 0.019</td>\n <td> 0.010</td>\n <td>-0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.009</td>\n <td> 0.003</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>...</td>\n </tr>\n <tr>\n <th>ind60=~x3</th>\n <td> 0.010</td>\n <td> 0.023</td>\n <td>-0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.007</td>\n <td> 0.003</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>...</td>\n </tr>\n <tr>\n <th>dem60=~y2</th>\n <td>-0.000</td>\n <td>-0.000</td>\n <td> 0.033</td>\n <td> 0.008</td>\n <td> 0.015</td>\n <td> 0.010</td>\n <td> 0.004</td>\n <td> 0.004</td>\n <td>-0.012</td>\n <td>-0.001</td>\n <td> 0.004</td>\n <td> 0.009</td>\n <td>-0.025</td>\n <td>-0.009</td>\n <td>-0.000</td>\n <td>...</td>\n </tr>\n <tr>\n <th>dem60=~y3</th>\n <td>-0.000</td>\n <td>-0.000</td>\n <td> 0.008</td>\n <td> 0.023</td>\n <td> 0.009</td>\n <td> 0.003</td>\n <td> 0.006</td>\n <td> 0.003</td>\n <td>-0.010</td>\n <td>-0.002</td>\n <td> 0.004</td>\n <td> 0.007</td>\n <td>-0.001</td>\n <td> 0.000</td>\n <td>-0.014</td>\n <td>...</td>\n </tr>\n <tr>\n <th>dem60=~y4</th>\n <td>-0.000</td>\n <td>-0.000</td>\n <td> 0.015</td>\n <td> 0.009</td>\n <td> 0.021</td>\n <td> 0.004</td>\n <td> 0.004</td>\n <td> 0.005</td>\n <td>-0.012</td>\n <td>-0.001</td>\n <td> 0.004</td>\n <td> 0.010</td>\n <td>-0.017</td>\n <td> 0.002</td>\n <td> 0.000</td>\n <td>...</td>\n </tr>\n <tr>\n <th>dem65=~y6</th>\n <td>-0.000</td>\n <td> 0.000</td>\n <td> 0.010</td>\n <td> 0.003</td>\n <td> 0.004</td>\n <td> 0.028</td>\n <td> 0.013</td>\n <td> 0.017</td>\n <td>-0.004</td>\n <td>-0.005</td>\n <td>-0.006</td>\n <td> 0.009</td>\n <td>-0.002</td>\n <td>-0.007</td>\n <td>-0.000</td>\n <td>...</td>\n </tr>\n <tr>\n <th>dem65=~y7</th>\n <td>-0.000</td>\n <td>-0.000</td>\n <td> 0.004</td>\n <td> 0.006</td>\n <td> 0.004</td>\n <td> 0.013</td>\n <td> 0.026</td>\n <td> 0.013</td>\n <td>-0.005</td>\n <td>-0.006</td>\n <td>-0.007</td>\n <td> 0.010</td>\n <td>-0.002</td>\n <td> 0.000</td>\n <td>-0.011</td>\n <td>...</td>\n </tr>\n <tr>\n <th>dem65=~y8</th>\n <td>-0.000</td>\n <td> 0.000</td>\n <td> 0.004</td>\n <td> 0.003</td>\n <td> 0.005</td>\n <td> 0.017</td>\n <td> 0.013</td>\n <td> 0.025</td>\n <td>-0.005</td>\n <td>-0.005</td>\n <td>-0.007</td>\n <td> 0.010</td>\n <td>-0.002</td>\n <td> 0.003</td>\n <td> 0.000</td>\n <td>...</td>\n </tr>\n <tr>\n <th>dem60~ind60</th>\n <td> 0.009</td>\n <td> 0.007</td>\n <td>-0.012</td>\n <td>-0.010</td>\n <td>-0.012</td>\n <td>-0.004</td>\n <td>-0.005</td>\n <td>-0.005</td>\n <td> 0.159</td>\n <td>-0.011</td>\n <td>-0.003</td>\n <td>-0.007</td>\n <td> 0.010</td>\n <td>-0.000</td>\n <td> 0.003</td>\n <td>...</td>\n </tr>\n <tr>\n <th>dem65~ind60</th>\n <td> 0.003</td>\n <td> 0.003</td>\n <td>-0.001</td>\n <td>-0.002</td>\n <td>-0.001</td>\n <td>-0.005</td>\n <td>-0.006</td>\n <td>-0.005</td>\n <td>-0.011</td>\n <td> 0.049</td>\n <td>-0.006</td>\n <td>-0.000</td>\n <td>-0.013</td>\n <td> 0.000</td>\n <td> 0.006</td>\n <td>...</td>\n </tr>\n <tr>\n <th>dem65~dem60</th>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.004</td>\n <td> 0.004</td>\n <td> 0.004</td>\n <td>-0.006</td>\n <td>-0.007</td>\n <td>-0.007</td>\n <td>-0.003</td>\n <td>-0.006</td>\n <td> 0.010</td>\n <td>-0.003</td>\n <td> 0.005</td>\n <td>-0.000</td>\n <td>-0.003</td>\n <td>...</td>\n </tr>\n <tr>\n <th>y1~~y5</th>\n <td>-0.000</td>\n <td>-0.000</td>\n <td> 0.009</td>\n <td> 0.007</td>\n <td> 0.010</td>\n <td> 0.009</td>\n <td> 0.010</td>\n <td> 0.010</td>\n <td>-0.007</td>\n <td>-0.000</td>\n <td>-0.003</td>\n <td> 0.128</td>\n <td>-0.029</td>\n <td> 0.000</td>\n <td>-0.012</td>\n <td>...</td>\n </tr>\n <tr>\n <th>y2~~y4</th>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>-0.025</td>\n <td>-0.001</td>\n <td>-0.017</td>\n <td>-0.002</td>\n <td>-0.002</td>\n <td>-0.002</td>\n <td> 0.010</td>\n <td>-0.013</td>\n <td> 0.005</td>\n <td>-0.029</td>\n <td> 0.493</td>\n <td> 0.020</td>\n <td>-0.028</td>\n <td>...</td>\n </tr>\n <tr>\n <th>y2~~y6</th>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>-0.009</td>\n <td> 0.000</td>\n <td> 0.002</td>\n <td>-0.007</td>\n <td> 0.000</td>\n <td> 0.003</td>\n <td>-0.000</td>\n <td> 0.000</td>\n <td>-0.000</td>\n <td> 0.000</td>\n <td> 0.020</td>\n <td> 0.538</td>\n <td> 0.000</td>\n <td>...</td>\n </tr>\n <tr>\n <th>y3~~y7</th>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>-0.000</td>\n <td>-0.014</td>\n <td> 0.000</td>\n <td>-0.000</td>\n <td>-0.011</td>\n <td> 0.000</td>\n <td> 0.003</td>\n <td> 0.006</td>\n <td>-0.003</td>\n <td>-0.012</td>\n <td>-0.028</td>\n <td> 0.000</td>\n <td> 0.369</td>\n <td>...</td>\n </tr>\n <tr>\n <th>y4~~y8</th>\n <td>-0.000</td>\n <td>-0.000</td>\n <td>-0.001</td>\n <td>-0.001</td>\n <td>-0.010</td>\n <td>-0.001</td>\n <td>-0.001</td>\n <td>-0.010</td>\n <td> 0.005</td>\n <td> 0.003</td>\n <td>-0.002</td>\n <td>-0.017</td>\n <td>-0.010</td>\n <td>-0.064</td>\n <td>-0.016</td>\n <td>...</td>\n </tr>\n <tr>\n <th>y6~~y8</th>\n <td>-0.000</td>\n <td>-0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>-0.000</td>\n <td>-0.013</td>\n <td>-0.001</td>\n <td>-0.011</td>\n <td> 0.000</td>\n <td> 0.002</td>\n <td> 0.003</td>\n <td>-0.018</td>\n <td>-0.034</td>\n <td> 0.009</td>\n <td>-0.020</td>\n <td>...</td>\n </tr>\n <tr>\n <th>x1~~x1</th>\n <td> 0.001</td>\n <td> 0.000</td>\n <td>-0.000</td>\n <td> 0.000</td>\n <td>-0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>...</td>\n </tr>\n <tr>\n <th>x2~~x2</th>\n <td>-0.004</td>\n <td>-0.000</td>\n <td> 0.000</td>\n <td>-0.000</td>\n <td>-0.000</td>\n <td>-0.000</td>\n <td>-0.000</td>\n <td>-0.000</td>\n <td>-0.000</td>\n <td>-0.000</td>\n <td>-0.000</td>\n <td>-0.000</td>\n <td>-0.000</td>\n <td>-0.000</td>\n <td>-0.000</td>\n <td>...</td>\n </tr>\n <tr>\n <th>x3~~x3</th>\n <td> 0.001</td>\n <td>-0.001</td>\n <td>-0.000</td>\n <td> 0.000</td>\n <td>-0.000</td>\n <td> 0.000</td>\n <td>-0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>...</td>\n </tr>\n <tr>\n <th>y1~~y1</th>\n <td>-0.000</td>\n <td>-0.000</td>\n <td> 0.015</td>\n <td> 0.013</td>\n <td> 0.017</td>\n <td> 0.006</td>\n <td> 0.007</td>\n <td> 0.007</td>\n <td>-0.012</td>\n <td>-0.008</td>\n <td> 0.007</td>\n <td> 0.086</td>\n <td>-0.025</td>\n <td> 0.000</td>\n <td>-0.023</td>\n <td>...</td>\n </tr>\n <tr>\n <th>y2~~y2</th>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>-0.041</td>\n <td>-0.001</td>\n <td>-0.010</td>\n <td>-0.008</td>\n <td>-0.002</td>\n <td> 0.001</td>\n <td> 0.009</td>\n <td>-0.013</td>\n <td> 0.005</td>\n <td>-0.027</td>\n <td> 0.486</td>\n <td> 0.504</td>\n <td>-0.025</td>\n <td>...</td>\n </tr>\n <tr>\n <th>y3~~y3</th>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>-0.001</td>\n <td>-0.023</td>\n <td> 0.000</td>\n <td>-0.001</td>\n <td>-0.007</td>\n <td>-0.000</td>\n <td> 0.006</td>\n <td>-0.002</td>\n <td> 0.001</td>\n <td>-0.018</td>\n <td>-0.019</td>\n <td> 0.000</td>\n <td> 0.230</td>\n <td>...</td>\n </tr>\n <tr>\n <th>y4~~y4</th>\n <td> 0.000</td>\n <td>-0.000</td>\n <td>-0.015</td>\n <td>-0.002</td>\n <td>-0.028</td>\n <td>-0.003</td>\n <td>-0.002</td>\n <td>-0.008</td>\n <td> 0.014</td>\n <td>-0.011</td>\n <td> 0.003</td>\n <td>-0.044</td>\n <td> 0.273</td>\n <td>-0.037</td>\n <td>-0.041</td>\n <td>...</td>\n </tr>\n <tr>\n <th>y5~~y5</th>\n <td>-0.000</td>\n <td>-0.000</td>\n <td> 0.005</td>\n <td> 0.005</td>\n <td> 0.006</td>\n <td> 0.012</td>\n <td> 0.013</td>\n <td> 0.014</td>\n <td>-0.005</td>\n <td>-0.002</td>\n <td>-0.005</td>\n <td> 0.092</td>\n <td>-0.009</td>\n <td> 0.000</td>\n <td>-0.018</td>\n <td>...</td>\n </tr>\n <tr>\n <th>y6~~y6</th>\n <td>-0.000</td>\n <td>-0.000</td>\n <td>-0.004</td>\n <td> 0.000</td>\n <td> 0.001</td>\n <td>-0.020</td>\n <td>-0.001</td>\n <td>-0.006</td>\n <td>-0.000</td>\n <td> 0.002</td>\n <td> 0.003</td>\n <td>-0.015</td>\n <td>-0.028</td>\n <td> 0.321</td>\n <td>-0.017</td>\n <td>...</td>\n </tr>\n <tr>\n <th>y7~~y7</th>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>-0.001</td>\n <td>-0.007</td>\n <td>-0.000</td>\n <td>-0.001</td>\n <td>-0.019</td>\n <td>-0.000</td>\n <td> 0.002</td>\n <td> 0.005</td>\n <td> 0.002</td>\n <td>-0.024</td>\n <td>-0.001</td>\n <td> 0.000</td>\n <td> 0.166</td>\n <td>...</td>\n </tr>\n <tr>\n <th>y8~~y8</th>\n <td>-0.000</td>\n <td>-0.000</td>\n <td>-0.001</td>\n <td>-0.000</td>\n <td>-0.006</td>\n <td>-0.009</td>\n <td>-0.001</td>\n <td>-0.021</td>\n <td> 0.003</td>\n <td> 0.004</td>\n <td> 0.002</td>\n <td>-0.030</td>\n <td>-0.015</td>\n <td>-0.035</td>\n <td>-0.031</td>\n <td>...</td>\n </tr>\n <tr>\n <th>ind60~~ind60</th>\n <td>-0.006</td>\n <td>-0.004</td>\n <td> 0.000</td>\n <td>-0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>-0.000</td>\n <td>-0.003</td>\n <td>-0.001</td>\n <td>-0.000</td>\n <td> 0.000</td>\n <td>-0.000</td>\n <td>-0.000</td>\n <td>-0.000</td>\n <td>...</td>\n </tr>\n <tr>\n <th>dem60~~dem60</th>\n <td> 0.001</td>\n <td> 0.000</td>\n <td>-0.063</td>\n <td>-0.053</td>\n <td>-0.064</td>\n <td>-0.021</td>\n <td>-0.023</td>\n <td>-0.023</td>\n <td> 0.039</td>\n <td> 0.032</td>\n <td>-0.030</td>\n <td>-0.042</td>\n <td> 0.013</td>\n <td>-0.000</td>\n <td> 0.017</td>\n <td>...</td>\n </tr>\n <tr>\n <th>dem65~~dem65</th>\n <td> 0.000</td>\n <td> 0.000</td>\n <td> 0.000</td>\n <td>-0.001</td>\n <td>-0.000</td>\n <td>-0.002</td>\n <td>-0.003</td>\n <td>-0.002</td>\n <td> 0.001</td>\n <td> 0.008</td>\n <td>-0.004</td>\n <td> 0.012</td>\n <td>-0.028</td>\n <td>-0.000</td>\n <td> 0.017</td>\n <td>...</td>\n </tr>\n </tbody>\n</table>\n<p>31 rows × 31 columns</p>\n</div>", | |
| "output_type": "pyout", | |
| "metadata": {}, | |
| "prompt_number": 47 | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "Fitted values" | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "%R fitted <- fitted(fit);\n# (or: %R fitted <- fitted.values(fit);\n%R print(fitted)", | |
| "prompt_number": 48, | |
| "outputs": [ | |
| { | |
| "text": "$cov\n x1 x2 x3 y1 y2 y3 y4 y5 y6 y7 y8 \nx1 0.530 \nx2 0.978 2.252 \nx3 0.815 1.778 1.950 \ny1 0.665 1.450 1.209 6.834 \ny2 0.836 1.822 1.520 6.211 15.179 \ny3 0.703 1.534 1.279 5.228 6.570 10.597 \ny4 0.841 1.834 1.530 6.251 9.169 6.612 11.054 \ny5 0.814 1.774 1.479 5.143 5.679 4.780 5.716 6.773 \ny6 0.965 2.103 1.754 5.358 8.887 5.667 6.777 5.243 11.171 \ny7 1.041 2.270 1.893 5.782 7.267 6.911 7.313 5.658 6.709 10.671 \ny8 1.030 2.245 1.873 5.721 7.190 6.051 7.584 5.598 7.994 7.163 10.341\n\n$mean\nx1 x2 x3 y1 y2 y3 y4 y5 y6 y7 y8 \n 0 0 0 0 0 0 0 0 0 0 0 \n\n", | |
| "output_type": "display_data", | |
| "metadata": {} | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "Residuals" | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "# Works:\n%R residuals <- resid(fit, type = \"standardized\"); \n%R print(residuals)", | |
| "prompt_number": 49, | |
| "outputs": [ | |
| { | |
| "text": "$cov\n x1 x2 x3 y1 y2 y3 y4 y5 y6 y7 y8 \nx1 0.000 \nx2 NA 0.000 \nx3 NA 0.273 0.000 \ny1 0.567 -1.363 -1.563 NA \ny2 -1.146 -0.940 -0.902 -0.146 0.439 \ny3 0.421 -0.008 -0.760 1.452 -1.629 0.199 \ny4 1.859 1.429 0.923 NA 0.639 -0.040 0.154 \ny5 1.951 1.227 0.364 NA -0.374 0.265 -0.446 NA \ny6 -0.854 -1.260 -0.663 0.862 0.811 -2.581 1.095 -1.512 0.232 \ny7 -1.038 -1.741 -1.178 -0.280 0.302 0.016 0.242 0.355 -0.143 NA \ny8 0.449 -0.198 -0.817 -0.841 0.767 -1.591 1.014 NA 0.567 0.911 0.304\n\n$mean\nx1 x2 x3 y1 y2 y3 y4 y5 y6 y7 y8 \n 0 0 0 0 0 0 0 0 0 0 0 \n\n", | |
| "output_type": "display_data", | |
| "metadata": {} | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "AIC and BIC\n\n*These are just scalar values so can use the single line %R magic syntax*" | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "AIC = %R AIC(fit)\nBIC = %R BIC(fit)\nprint 'AIC = %s' %AIC\nprint 'BIC = %s' %BIC", | |
| "prompt_number": 50, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": "AIC = [ 3157.58188681]\nBIC = [ 3229.42401833]\n", | |
| "stream": "stdout" | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "Inspect function" | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "%R inspect_list <- inspect(fit, what=\"list\");\n%Rpull -d inspect_list\nil = pd.DataFrame(inspect_list)\nil", | |
| "prompt_number": 51, | |
| "outputs": [ | |
| { | |
| "text": " id lhs op rhs user group free ustart exo label eq.id unco\n0 1 ind60 =~ x1 1 1 0 1.000 0 0 0\n1 2 ind60 =~ x2 1 1 1 nan 0 0 1\n2 3 ind60 =~ x3 1 1 2 nan 0 0 2\n3 4 dem60 =~ y1 1 1 0 1.000 0 0 0\n4 5 dem60 =~ y2 1 1 3 nan 0 0 3\n5 6 dem60 =~ y3 1 1 4 nan 0 0 4\n6 7 dem60 =~ y4 1 1 5 nan 0 0 5\n7 8 dem65 =~ y5 1 1 0 1.000 0 0 0\n8 9 dem65 =~ y6 1 1 6 nan 0 0 6\n9 10 dem65 =~ y7 1 1 7 nan 0 0 7\n10 11 dem65 =~ y8 1 1 8 nan 0 0 8\n11 12 dem60 ~ ind60 1 1 9 nan 0 0 9\n12 13 dem65 ~ ind60 1 1 10 nan 0 0 10\n13 14 dem65 ~ dem60 1 1 11 nan 0 0 11\n14 15 y1 ~~ y5 1 1 12 nan 0 0 12\n15 16 y2 ~~ y4 1 1 13 nan 0 0 13\n16 17 y2 ~~ y6 1 1 14 nan 0 0 14\n17 18 y3 ~~ y7 1 1 15 nan 0 0 15\n18 19 y4 ~~ y8 1 1 16 nan 0 0 16\n19 20 y6 ~~ y8 1 1 17 nan 0 0 17\n20 21 x1 ~~ x1 0 1 18 nan 0 0 18\n21 22 x2 ~~ x2 0 1 19 nan 0 0 19\n22 23 x3 ~~ x3 0 1 20 nan 0 0 20\n23 24 y1 ~~ y1 0 1 21 nan 0 0 21\n24 25 y2 ~~ y2 0 1 22 nan 0 0 22\n25 26 y3 ~~ y3 0 1 23 nan 0 0 23\n26 27 y4 ~~ y4 0 1 24 nan 0 0 24\n27 28 y5 ~~ y5 0 1 25 nan 0 0 25\n28 29 y6 ~~ y6 0 1 26 nan 0 0 26\n29 30 y7 ~~ y7 0 1 27 nan 0 0 27\n30 31 y8 ~~ y8 0 1 28 nan 0 0 28\n31 32 ind60 ~~ ind60 0 1 29 nan 0 0 29\n32 33 dem60 ~~ dem60 0 1 30 nan 0 0 30\n33 34 dem65 ~~ dem65 0 1 31 nan 0 0 31\n\n[34 rows x 12 columns]", | |
| "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>id</th>\n <th>lhs</th>\n <th>op</th>\n <th>rhs</th>\n <th>user</th>\n <th>group</th>\n <th>free</th>\n <th>ustart</th>\n <th>exo</th>\n <th>label</th>\n <th>eq.id</th>\n <th>unco</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0 </th>\n <td> 1</td>\n <td> ind60</td>\n <td> =~</td>\n <td> x1</td>\n <td> 1</td>\n <td> 1</td>\n <td> 0</td>\n <td>1.000</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 0</td>\n </tr>\n <tr>\n <th>1 </th>\n <td> 2</td>\n <td> ind60</td>\n <td> =~</td>\n <td> x2</td>\n <td> 1</td>\n <td> 1</td>\n <td> 1</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 1</td>\n </tr>\n <tr>\n <th>2 </th>\n <td> 3</td>\n <td> ind60</td>\n <td> =~</td>\n <td> x3</td>\n <td> 1</td>\n <td> 1</td>\n <td> 2</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 2</td>\n </tr>\n <tr>\n <th>3 </th>\n <td> 4</td>\n <td> dem60</td>\n <td> =~</td>\n <td> y1</td>\n <td> 1</td>\n <td> 1</td>\n <td> 0</td>\n <td>1.000</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 0</td>\n </tr>\n <tr>\n <th>4 </th>\n <td> 5</td>\n <td> dem60</td>\n <td> =~</td>\n <td> y2</td>\n <td> 1</td>\n <td> 1</td>\n <td> 3</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 3</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> 6</td>\n <td> dem60</td>\n <td> =~</td>\n <td> y3</td>\n <td> 1</td>\n <td> 1</td>\n <td> 4</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 4</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> 7</td>\n <td> dem60</td>\n <td> =~</td>\n <td> y4</td>\n <td> 1</td>\n <td> 1</td>\n <td> 5</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 5</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> 8</td>\n <td> dem65</td>\n <td> =~</td>\n <td> y5</td>\n <td> 1</td>\n <td> 1</td>\n <td> 0</td>\n <td>1.000</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 0</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> 9</td>\n <td> dem65</td>\n <td> =~</td>\n <td> y6</td>\n <td> 1</td>\n <td> 1</td>\n <td> 6</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 6</td>\n </tr>\n <tr>\n <th>9 </th>\n <td> 10</td>\n <td> dem65</td>\n <td> =~</td>\n <td> y7</td>\n <td> 1</td>\n <td> 1</td>\n <td> 7</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 7</td>\n </tr>\n <tr>\n <th>10</th>\n <td> 11</td>\n <td> dem65</td>\n <td> =~</td>\n <td> y8</td>\n <td> 1</td>\n <td> 1</td>\n <td> 8</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 8</td>\n </tr>\n <tr>\n <th>11</th>\n <td> 12</td>\n <td> dem60</td>\n <td> ~</td>\n <td> ind60</td>\n <td> 1</td>\n <td> 1</td>\n <td> 9</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 9</td>\n </tr>\n <tr>\n <th>12</th>\n <td> 13</td>\n <td> dem65</td>\n <td> ~</td>\n <td> ind60</td>\n <td> 1</td>\n <td> 1</td>\n <td> 10</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 10</td>\n </tr>\n <tr>\n <th>13</th>\n <td> 14</td>\n <td> dem65</td>\n <td> ~</td>\n <td> dem60</td>\n <td> 1</td>\n <td> 1</td>\n <td> 11</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 11</td>\n </tr>\n <tr>\n <th>14</th>\n <td> 15</td>\n <td> y1</td>\n <td> ~~</td>\n <td> y5</td>\n <td> 1</td>\n <td> 1</td>\n <td> 12</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 12</td>\n </tr>\n <tr>\n <th>15</th>\n <td> 16</td>\n <td> y2</td>\n <td> ~~</td>\n <td> y4</td>\n <td> 1</td>\n <td> 1</td>\n <td> 13</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 13</td>\n </tr>\n <tr>\n <th>16</th>\n <td> 17</td>\n <td> y2</td>\n <td> ~~</td>\n <td> y6</td>\n <td> 1</td>\n <td> 1</td>\n <td> 14</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 14</td>\n </tr>\n <tr>\n <th>17</th>\n <td> 18</td>\n <td> y3</td>\n <td> ~~</td>\n <td> y7</td>\n <td> 1</td>\n <td> 1</td>\n <td> 15</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 15</td>\n </tr>\n <tr>\n <th>18</th>\n <td> 19</td>\n <td> y4</td>\n <td> ~~</td>\n <td> y8</td>\n <td> 1</td>\n <td> 1</td>\n <td> 16</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 16</td>\n </tr>\n <tr>\n <th>19</th>\n <td> 20</td>\n <td> y6</td>\n <td> ~~</td>\n <td> y8</td>\n <td> 1</td>\n <td> 1</td>\n <td> 17</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 17</td>\n </tr>\n <tr>\n <th>20</th>\n <td> 21</td>\n <td> x1</td>\n <td> ~~</td>\n <td> x1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 18</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 18</td>\n </tr>\n <tr>\n <th>21</th>\n <td> 22</td>\n <td> x2</td>\n <td> ~~</td>\n <td> x2</td>\n <td> 0</td>\n <td> 1</td>\n <td> 19</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 19</td>\n </tr>\n <tr>\n <th>22</th>\n <td> 23</td>\n <td> x3</td>\n <td> ~~</td>\n <td> x3</td>\n <td> 0</td>\n <td> 1</td>\n <td> 20</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 20</td>\n </tr>\n <tr>\n <th>23</th>\n <td> 24</td>\n <td> y1</td>\n <td> ~~</td>\n <td> y1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 21</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 21</td>\n </tr>\n <tr>\n <th>24</th>\n <td> 25</td>\n <td> y2</td>\n <td> ~~</td>\n <td> y2</td>\n <td> 0</td>\n <td> 1</td>\n <td> 22</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 22</td>\n </tr>\n <tr>\n <th>25</th>\n <td> 26</td>\n <td> y3</td>\n <td> ~~</td>\n <td> y3</td>\n <td> 0</td>\n <td> 1</td>\n <td> 23</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 23</td>\n </tr>\n <tr>\n <th>26</th>\n <td> 27</td>\n <td> y4</td>\n <td> ~~</td>\n <td> y4</td>\n <td> 0</td>\n <td> 1</td>\n <td> 24</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 24</td>\n </tr>\n <tr>\n <th>27</th>\n <td> 28</td>\n <td> y5</td>\n <td> ~~</td>\n <td> y5</td>\n <td> 0</td>\n <td> 1</td>\n <td> 25</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 25</td>\n </tr>\n <tr>\n <th>28</th>\n <td> 29</td>\n <td> y6</td>\n <td> ~~</td>\n <td> y6</td>\n <td> 0</td>\n <td> 1</td>\n <td> 26</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 26</td>\n </tr>\n <tr>\n <th>29</th>\n <td> 30</td>\n <td> y7</td>\n <td> ~~</td>\n <td> y7</td>\n <td> 0</td>\n <td> 1</td>\n <td> 27</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 27</td>\n </tr>\n <tr>\n <th>30</th>\n <td> 31</td>\n <td> y8</td>\n <td> ~~</td>\n <td> y8</td>\n <td> 0</td>\n <td> 1</td>\n <td> 28</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 28</td>\n </tr>\n <tr>\n <th>31</th>\n <td> 32</td>\n <td> ind60</td>\n <td> ~~</td>\n <td> ind60</td>\n <td> 0</td>\n <td> 1</td>\n <td> 29</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 29</td>\n </tr>\n <tr>\n <th>32</th>\n <td> 33</td>\n <td> dem60</td>\n <td> ~~</td>\n <td> dem60</td>\n <td> 0</td>\n <td> 1</td>\n <td> 30</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 30</td>\n </tr>\n <tr>\n <th>33</th>\n <td> 34</td>\n <td> dem65</td>\n <td> ~~</td>\n <td> dem65</td>\n <td> 0</td>\n <td> 1</td>\n <td> 31</td>\n <td> nan</td>\n <td> 0</td>\n <td> </td>\n <td> 0</td>\n <td> 31</td>\n </tr>\n </tbody>\n</table>\n<p>34 rows × 12 columns</p>\n</div>", | |
| "output_type": "pyout", | |
| "metadata": {}, | |
| "prompt_number": 51 | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
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| "cell_type": "code", | |
| "input": "# Compare to native R output:\n%R print(inspect_list)", | |
| "prompt_number": 52, | |
| "outputs": [ | |
| { | |
| "text": " id lhs op rhs user group free ustart exo label eq.id unco\n1 1 ind60 =~ x1 1 1 0 1 0 0 0\n2 2 ind60 =~ x2 1 1 1 NA 0 0 1\n3 3 ind60 =~ x3 1 1 2 NA 0 0 2\n4 4 dem60 =~ y1 1 1 0 1 0 0 0\n5 5 dem60 =~ y2 1 1 3 NA 0 0 3\n6 6 dem60 =~ y3 1 1 4 NA 0 0 4\n7 7 dem60 =~ y4 1 1 5 NA 0 0 5\n8 8 dem65 =~ y5 1 1 0 1 0 0 0\n9 9 dem65 =~ y6 1 1 6 NA 0 0 6\n10 10 dem65 =~ y7 1 1 7 NA 0 0 7\n11 11 dem65 =~ y8 1 1 8 NA 0 0 8\n12 12 dem60 ~ ind60 1 1 9 NA 0 0 9\n13 13 dem65 ~ ind60 1 1 10 NA 0 0 10\n14 14 dem65 ~ dem60 1 1 11 NA 0 0 11\n15 15 y1 ~~ y5 1 1 12 NA 0 0 12\n16 16 y2 ~~ y4 1 1 13 NA 0 0 13\n17 17 y2 ~~ y6 1 1 14 NA 0 0 14\n18 18 y3 ~~ y7 1 1 15 NA 0 0 15\n19 19 y4 ~~ y8 1 1 16 NA 0 0 16\n20 20 y6 ~~ y8 1 1 17 NA 0 0 17\n21 21 x1 ~~ x1 0 1 18 NA 0 0 18\n22 22 x2 ~~ x2 0 1 19 NA 0 0 19\n23 23 x3 ~~ x3 0 1 20 NA 0 0 20\n24 24 y1 ~~ y1 0 1 21 NA 0 0 21\n25 25 y2 ~~ y2 0 1 22 NA 0 0 22\n26 26 y3 ~~ y3 0 1 23 NA 0 0 23\n27 27 y4 ~~ y4 0 1 24 NA 0 0 24\n28 28 y5 ~~ y5 0 1 25 NA 0 0 25\n29 29 y6 ~~ y6 0 1 26 NA 0 0 26\n30 30 y7 ~~ y7 0 1 27 NA 0 0 27\n31 31 y8 ~~ y8 0 1 28 NA 0 0 28\n32 32 ind60 ~~ ind60 0 1 29 NA 0 0 29\n33 33 dem60 ~~ dem60 0 1 30 NA 0 0 30\n34 34 dem65 ~~ dem65 0 1 31 NA 0 0 31\n", | |
| "output_type": "display_data", | |
| "metadata": {} | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
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| { | |
| "metadata": { | |
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| "slide_type": "slide" | |
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| }, | |
| "cell_type": "markdown", | |
| "source": "**That's a wrap.**" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "That should be enough of the basics to get started doing lavaan SEM via the ipython notebook, as well as some useful tips for using R in the notebook more generally. A very powerful combination. \n\n\nAlways interested to hear about any useful modifications and/or extensions to the procedures outlined here. \n\nThe appendices give some pointers to other useful functionality\n\n\nAdios, \n\n\nJohn" | |
| }, | |
| { | |
| "metadata": { | |
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| "slide_type": "skip" | |
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| }, | |
| "cell_type": "markdown", | |
| "source": "<a name=\"Appendix1\">\n\n[back to top](#Contents)\n" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "cell_type": "heading", | |
| "source": "Appendix 1: Lavaan syntax for mediation and multiple mediation", | |
| "level": 2 | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "Syntax for mediation model in lavaan" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "```python\nmediation_covJ_model_str = \\\n\"\"\"# direct effect\n<Y> ~ c*<X> \n# mediator\n<M> ~ a*<X> + <J>\n<Y> ~ b*<M>\n# indirect effect (a*b)\nab := a*b\n# total effect\ntotal := c + (a*b)\n\"\"\"\n```" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "Syntax for multiple mediation model in lavaan" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "```python\nmultiple_mediation_model_str = \\\n\"\"\"# direct effect\n<Y>~c*<X>\n# mediators\n<M1>~a1*<X>\n<Y>~b1*<M1>\n<M2>~a2*<X>\n<Y>~b2*<M2>\n# indirect and total effects\nab1:=a1*b1\nab2:=a2*b2\ntotal:=c+(a1*b1)+(a2*b2)\n\"\"\"\n```" | |
| }, | |
| { | |
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| "cell_type": "markdown", | |
| "source": "<a name=\"Appendix2\">\n\n[back to top](#Contents)" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "heading", | |
| "source": "Appendix 2: Using R magics within python loops", | |
| "level": 2 | |
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| { | |
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| "source": "Here's an example of how you could put the above into a loop. \n\nBUT BE WARNED! This is very dangerous practice. You won't see warnings issued from R, which can include very important things like 'this model didn't fit properly' , 'covariance matrix not positive definite', etc. The code below does try to pick up on these, but it's still a concern. You also want to be very diligant about cleaning up after each loop iteration, since the above error, and other scenarios, could lead to you picking up outputs from a previous iteration. But once you're sure you've ironed these things our, this can make managing large analyses a lot easier. " | |
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| "cell_type": "markdown", | |
| "source": "```python\nG_fl_vars = ['Catt_SS1_z', 'Catt_SS2_z', 'Catt_SS3_z', 'Catt_SS4_z']\nFMs_all = {}; Ps_all = {}; warnings_all = {}; \n\nfor sfx in ['', '__contrSexEd', '__contrAgeSexEd']:\n\n thesevars = [g.split('_z')[0] + sfx + '_z' for g in G_fl_vars]\n thisdf = df_all[thesevars + ['Age', 'AgeGroup']]\n\n modelstr = 'G_fl =~ ' + ' + '.join(thesevars)\n fitobjname = 'fit_G_fl' + sfx\n fitfuncall = fitobjname + ' ' + ' <- cfa(modelstr, data=thisdf)'\n \n # Want to be diligant about noting all warnings from R\n warnings_all[fitobjname] = {}\n %R assign(\"last.warning\", NULL, envir = baseenv()) \n \n # Fit cfa model\n %R -i thisdf,modelstr $fitfuncall\n %R -o warns warns <- warnings(); assign(\"last.warning\", NULL, envir = baseenv()); \n if warns.shape is not (): warnings_all[fitobjname]['fit'] = warns; del warns\n \n print 'fit for %s' %fitobjname\n %R summary( $fitobjname , fit.measures=TRUE)\n \n # Fit measures\n %R -o FMs FMs <- fitMeasures( $fitobjname )\n FMs_df = pd.DataFrame(FMs, columns=[fitobjname]); del FMs\n %Rpull -d FMs\n FMs_df.index = FMs.dtype.names\n FMs_all[fitobjname] = FMs_df\n %R rm('FMs')\n %R -o warns warns <- warnings(); assign(\"last.warning\", NULL, envir = baseenv()); \n if warns.shape is not (): warnings_all[fitobjname]['fitMeasures'] = warns; del warns \n \n # Latent variable values\n %R -o Ps Ps <- predict( $fitobjname )\n Ps_df = pd.DataFrame(Ps, index=thisdf.index, columns=['G_fl_cfa' + sfx]); del Ps\n Ps_all[fitobjname] = Ps_df\n df_all = pd.concat([df_all, Ps_df], axis=1); del Ps_df\n %R rm('Ps')\n %R -o warns warns <- warnings(); assign(\"last.warning\", NULL, envir = baseenv()); \n if warns.shape is not (): warnings_all[fitobjname]['predict'] = warns; del warns\n \n # Variance explained and loadings on G, and (later) push from R to Python\n #%R rsq <- inspect(fit, \"rsquare\")\n #%Rpull -d rsq\n #rsq_d = rsq.copy()\n #%Rpull rsq\n #rsq_df = pd.DataFrame(rsq,index = rsq_d.dtype.names) \n \n # Parameter estimates\n #%Rpull -d PEs\n # ...get G loadings and add to main dataframe\n #PEs_df = pd.DataFrame(PEs)\n #loadings = PEs_df.ix[0:3].est\n #for n, l in zip(['1', '2', '3', '4'], loadings): df_all['G_sp_cfa_loadings_SS' + n] = l\n #df_all['G_cfa'] = df_all.Catt_SS1 * G_loadings[0] + df_all.Catt_SS2 * G_loadings[1] + df_all.Catt_SS3 * G_loadings[2] + df_all.Catt_SS4 * G_loadings[3]\n \n # Graphical representation of the fitted model\n #%R semPaths(fit,title = FALSE, \"std\",curvePivot = TRUE,edge.label.cex =1);\n```" | |
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| }, | |
| "cell_type": "markdown", | |
| "source": "Multiple mediation loop" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "fragment" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "```python\nmyPD_tracts = ['Fmaj', 'Fmin', 'SlfL', 'SlfR', 'UfL', 'UfR', 'CcgL', 'CcgR']\ntractfa_vars = [p + '_FA_thr_10_mean__contrSexEd_z' for p in myPD_tracts]\ntractfa_vars_cc = [p + '_FA_thr_10_mean__contrSexEd_z' for p in ['Fmaj', 'Fmin'] ]\ntractfa_vars_lh = [p + '_FA_thr_10_mean__contrSexEd_z' for p in ['SlfL', 'UfL', 'CcgL'] ]\ntractfa_vars_rh = [p + '_FA_thr_10_mean__contrSexEd_z' for p in ['SlfR', 'UfR', 'CcgR'] ]\n\nXi = ['Age']; \nMi = tractfa_vars #[eval(t) for t in tractfa_vars] #[Gwm, SEMcc_all, SEMlh_all, SEMrh_all, SEMcc_grp, SEMlh_grp, SEMrh_grp] + tractfa_vars #['G_wm_pca_PD_tracts_%s_thr_10_mean' %s for s in ['FA', 'MD', 'AD', 'RD'] ]\n#Mi = ['G_wm_pca_PD_tracts_%s_thr_10_mean' %s for s in ['FA', 'MD', 'AD', 'RD'] ]\nYi = ['G_fl_pca__contrSexEd', 'G_sp_pca__contrSexEd'] \n%R mediation_fits <- list()\ncnt=0; fignames = []; ParameterEstimates = []\nfor x,m2,y in itertools.product(Xi,Mi,Yi):\n if m2 in tractfa_vars_cc: m1 = SEMcc_all \n elif m2 in tractfa_vars_lh: m1 = SEMlh_all\n elif m2 in tractfa_vars_rh: m1 = SEMrh_all \n mms = multiple_mediation_model_str.replace('<X>',x).replace('<M1>', m1).replace('<M2>',m2).replace('<Y>', y)\n figname = 'Mediation_X_%s__M1_%s_M2_%s__Y_%s.svg' %(x,m1,m2,y) ; os.system('rm ' + figname)\n print '\\n\\n\\n%s\\n' %figname.split('.svg')[0] \n dat = df_all[[x,m1,m2,y]].dropna(); \n %Rpush mms figname dat cnt\n %R -i mms,dat fit <- sem(mms,data=dat)\n %R PEs = parameterEstimates(fit)\n %R -i figname svg(filename=figname, width=5, height=5, antialias = \"subpixel\", bg=\"transparent\"); semPaths(fit,title = FALSE, \"std\",curvePivot = TRUE,edge.label.cex =5); dev.off(); \n %R -i cnt mediation_fits[cnt+1] = fit\n %Rpull -d PEs\n ParameterEstimates.append(pd.DataFrame(PEs))\n cnt+=1; fignames.append(figname)\n %R semPaths(fit, layout='spring', 'std', residuals=FALSE, nCharNodes=10, sizeMan=15)\n %R summary(fit) \n %R rm('fit','PEs', 'mms', 'dat', 'figname', 'cnt')\n \npvals = pd.concat([pd.DataFrame(p.pvalue, columns=[fignames[p_it].split('.svg')[0]]) for p_it, p in enumerate(ParameterEstimates)],\n axis=1).set_index(p.label).T[['a', 'b', 'c', 'ab', 'total']]\npvals.sort(columns=['ab']) \n```" | |
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| "source": "<a name=\"Appendix3\">\n\n\n[back to top](#Contents)" | |
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| "cell_type": "heading", | |
| "source": "Appendix 3: Some handy statsmodels plotting options", | |
| "level": 2 | |
| }, | |
| { | |
| "metadata": { | |
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| "cell_type": "markdown", | |
| "source": "Statsmodels is my library of choice for most statistical analyses these days. So I thought I'd just randomly throw in a few useful bits and bobs to whet the appetite." | |
| }, | |
| { | |
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| "cell_type": "code", | |
| "input": "matplotlib.rcParams['figure.figsize'] = (12,9)\nfig = scatter_ellipse(PD_df)", | |
| "prompt_number": 53, | |
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GDhwaU6ah51VVVURHR1NRUUFISIheMC2EEEIIIVo/o7Z8zJo1i27durFhwwamTJlCZmYm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| |
| "text": "<matplotlib.figure.Figure at 0x73a9450>", | |
| "output_type": "display_data", | |
| "metadata": {} | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "fix, ax = plt.subplots(nrows=1,ncols=3,figsize=(10,3))\n \nfig = plot_partregress('x1', 'y1', [ 'x2', 'x3'], data=PD_df.dropna(), obs_labels=False, ax=ax[0])\nfig = plot_partregress('x2', 'y1', [ 'x1', 'x3'], data=PD_df.dropna(), obs_labels=False, ax=ax[1])\nfig = plot_partregress('x3', 'y1', [ 'x1', 'x2'], data=PD_df.dropna(), obs_labels=False, ax=ax[2])\n\nax[0].set_title(\"x1 ~ y1 | x2, x3\")\nax[1].set_title(\"x2 ~ y1 | x1, x3\")\nax[2].set_title(\"x3 ~ y1 | x1, x2\")\n\nplt.tight_layout()", | |
| "prompt_number": 54, | |
| "outputs": [ | |
| { | |
| "png": 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LdRpG+f3HO7bhz+obgLsvZJGj0BYSqWZoWzKGku7ORukMdASvDsU0uO5qO3Lk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\niIhAbm6u2XwbMWIETp06haFDh3b5OULkrNS2I4rLD0vylTrJhIiUWA+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| |
| "text": "<matplotlib.figure.Figure at 0x9c48fd0>", | |
| "output_type": "display_data", | |
| "metadata": {} | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "run_control": { | |
| "breakpoint": false | |
| }, | |
| "slideshow": { | |
| "slide_type": "subslide" | |
| } | |
| }, | |
| "cell_type": "code", | |
| "input": "fig = PD_df.hist(figsize=(10,5))", | |
| "prompt_number": 55, | |
| "outputs": [ | |
| { | |
| "png": 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iO+h4U76wG4QyaHoRpbb/B/lEKYdlXJ+TJ0+iV69eSE9PR4sWLbhdg3yi1G/f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| |
| "text": "<matplotlib.figure.Figure at 0x7ff720b3eb90>", | |
| "output_type": "display_data", | |
| "metadata": {} | |
| } | |
| ], | |
| "language": "python", | |
| "trusted": true, | |
| "collapsed": false | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "cell_type": "markdown", | |
| "source": "[back to top](#Contents)" | |
| } | |
| ], | |
| "metadata": {} | |
| } | |
| ], | |
| "metadata": { | |
| "gist_id": "8478146", | |
| "name": "", | |
| "celltoolbar": "Slideshow" | |
| }, | |
| "nbformat": 3 | |
| } |
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