Skip to content

Instantly share code, notes, and snippets.

@nicolasfauchereau

nicolasfauchereau/Pandas.ipynb

Created September 10, 2014 00:10
Show Gist options
  • Save nicolasfauchereau/7ff2df68a2861dede6fc to your computer and use it in GitHub Desktop.
Save nicolasfauchereau/7ff2df68a2861dede6fc to your computer and use it in GitHub Desktop.
Download ZIP
Display the source blob
Display the rendered blob
Raw
Pandas.ipynb
{
"worksheets": [
{
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "**NOTE**\n\nInclude the stuff from [https://github.com/datacarpentry/datacarpentry/blob/master/cheatsheets/R_pandas_compare.md](https://github.com/datacarpentry/datacarpentry/blob/master/cheatsheets/R_pandas_compare.md)"
},
{
"metadata": {},
"cell_type": "code",
"input": "%load_ext load_style\n%load_style talk.css",
"prompt_number": 2,
"outputs": [
{
"output_type": "display_data",
"html": "<style>\n\n.rendered_html {\n font-family: \"proxima-nova\", helvetica;\n font-size: 130%;\n line-height: 1.5;\n}\n\n.rendered_html h1 {\n margin: 0.25em 0em 0.5em;\n color: #015C9C;\n text-align: center;\n line-height: 1.2; \n page-break-before: always;\n}\n\n.rendered_html h2 {\n margin: 1.1em 0em 0.5em;\n color: #26465D;\n line-height: 1.2;\n}\n\n.rendered_html h3 {\n margin: 1.1em 0em 0.5em;\n color: #002845;\n line-height: 1.2;\n}\n\n.rendered_html li {\n line-height: 1.5; \n}\n\n/*.prompt {\n font-size: 120%; \n}*/\n\n.CodeMirror-lines {\n font-size: 110%; \n}\n\n/*.output_area {\n font-size: 120%; \n}*/\n\n/*#notebook {\n background-image: url('files/images/witewall_3.png');\n}*/\n\nh1.bigtitle {\n margin: 4cm 1cm 4cm 1cm;\n font-size: 300%;\n}\n\nh3.point {\n font-size: 200%;\n text-align: center;\n margin: 2em 0em 2em 0em;\n #26465D\n}\n\n.logo {\n margin: 20px 0 20px 0;\n}\n\na.anchor-link {\n display: none;\n}\n\nh1.title { \n font-size: 250%;\n}\n\n</style>",
"metadata": {},
"text": "<IPython.core.display.HTML at 0x104341cd0>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Pandas (<font color='blue'>Pan</font>el <font color='blue'>D</font>ata <font color='blue'>A</font>nalysi<font color='blue'>s</font>)"
},
{
"metadata": {},
"cell_type": "code",
"input": "from IPython.display import Image, HTML",
"prompt_number": 3,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "import numpy as np\nimport matplotlib.pyplot as plt",
"prompt_number": 4,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "%matplotlib inline",
"prompt_number": 5,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "HTML('<iframe src=http://pandas.pydata.org/index.html width=900 height=350></iframe>')",
"prompt_number": 6,
"outputs": [
{
"output_type": "pyout",
"html": "<iframe src=http://pandas.pydata.org/index.html width=900 height=350></iframe>",
"metadata": {},
"prompt_number": 6,
"text": "<IPython.core.display.HTML at 0x105ee2690>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "import pandas as pd",
"prompt_number": 7,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "pd.set_option(\"display.width\", 80)\n# toggle the line below that if one doesnt want DataFrames displayed as HTML tables\npd.set_option(\"notebook_repr_html\", False) \n#pd.set_option(\"notebook_repr_html\", True) ",
"prompt_number": 8,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "**pandas** is a Python package providing fast, flexible, and expressive data structures designed to work with *relational* or *labeled* data. It is a fundamental high-level building block for doing practical, real world data analysis in Python. \n\npandas is well suited for:\n\n- Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet\n- Ordered and unordered (not necessarily fixed-frequency) time series data.\n- Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels\n- Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure\n\n\nKey features: \n \n- Easy handling of **missing data**\n- **Size mutability**: columns can be inserted and deleted from DataFrame and higher dimensional objects\n- Automatic and explicit **data alignment**: objects can be explicitly aligned to a set of labels, or the data can be aligned automatically\n- Powerful, flexible **group by functionality** to perform split-apply-combine operations on data sets\n- Intelligent label-based **slicing, fancy indexing, and subsetting** of large data sets\n- Intuitive **merging and joining** data sets\n- Flexible **reshaping and pivoting** of data sets\n- **Hierarchical labeling** of axes\n- Robust **IO tools** for loading data from flat files, Excel files, databases, and HDF5\n- **Time series functionality**: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Pandas's data structures and functionalities will be familiar to R users, there's a section on Pandas's website where \nWes McKinney gives some translation of common idioms / operations between R and Pandas "
},
{
"metadata": {},
"cell_type": "code",
"input": "HTML('<iframe src=http://pandas.pydata.org/pandas-docs/stable/comparison_with_r.html#compare-with-r width=900 height=350></iframe>')",
"prompt_number": 9,
"outputs": [
{
"output_type": "pyout",
"html": "<iframe src=http://pandas.pydata.org/pandas-docs/stable/comparison_with_r.html#compare-with-r width=900 height=350></iframe>",
"metadata": {},
"prompt_number": 9,
"text": "<IPython.core.display.HTML at 0x1043317d0>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "heading",
"source": "Pandas data structures",
"level": 2
},
{
"metadata": {},
"cell_type": "heading",
"source": "Series",
"level": 3
},
{
"metadata": {},
"cell_type": "markdown",
"source": "\nA **Series** is a single vector of data values (think a NumPy array with shape N or (N,1)) with an **index** that labels each element in the vector."
},
{
"metadata": {},
"cell_type": "heading",
"source": "Series constructions",
"level": 4
},
{
"metadata": {},
"cell_type": "code",
"input": "a = pd.Series(np.random.normal(0,1,(10,)))",
"prompt_number": 10,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "a",
"prompt_number": 11,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 11,
"metadata": {},
"text": "0 -0.143033\n1 0.398921\n2 -1.120065\n3 1.286714\n4 -1.424762\n5 -1.731416\n6 1.406609\n7 1.611209\n8 0.375133\n9 -0.643369\ndtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "b = a",
"prompt_number": 12,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "b = b.mean()",
"prompt_number": 13,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "b",
"prompt_number": 14,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 14,
"metadata": {},
"text": "0.0015941475553171269"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "a",
"prompt_number": 15,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 15,
"metadata": {},
"text": "0 -0.143033\n1 0.398921\n2 -1.120065\n3 1.286714\n4 -1.424762\n5 -1.731416\n6 1.406609\n7 1.611209\n8 0.375133\n9 -0.643369\ndtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "a.index",
"prompt_number": 16,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 16,
"metadata": {},
"text": "Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "a.values",
"prompt_number": 17,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 17,
"metadata": {},
"text": "array([-0.14303316, 0.39892122, -1.12006537, 1.2867141 , -1.42476177,\n -1.73141587, 1.40660926, 1.61120906, 0.37513325, -0.64336924])"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### You can define your own **index**"
},
{
"metadata": {},
"cell_type": "code",
"input": "a = pd.Series(np.random.normal(0,1,(10,)), index=np.arange(1,11))",
"prompt_number": 18,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "a.index",
"prompt_number": 19,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 19,
"metadata": {},
"text": "Int64Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype='int64')"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "a = pd.Series(np.random.normal(0,1,5), index=['a','b','c','d','e'], name='my series')",
"prompt_number": 20,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "a",
"prompt_number": 21,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 21,
"metadata": {},
"text": "a 0.668915\nb 0.812820\nc -0.243394\nd 0.932724\ne 1.754268\nName: my series, dtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Pandas objects expose some powerful, **high level plotting functions** (built on top of Matplotlib)"
},
{
"metadata": {},
"cell_type": "code",
"input": "plot = a.plot(kind='bar', rot=0, color='.8', title=a.name, hatch='///', edgecolor='k')",
"prompt_number": 22,
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEKCAYAAADpfBXhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZI0E/8PapFplzmTXAYAAAAASUVORK5CYII=\n",
"text": "<matplotlib.figure.Figure at 0x107596550>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "f, ax = plt.subplots()\nbars = ax.bar(np.arange(len(a)), a.values, color='w', edgecolor='k', align='center', hatch='///')\nax.set_xticks(np.arange(len(a)))\nax.set_xlim(-0.5, len(a)-0.5)\nax.set_xticklabels(a.index, fontsize=16)\nax.set_title(a.name, fontsize=16)",
"prompt_number": 23,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 23,
"metadata": {},
"text": "<matplotlib.text.Text at 0x10778f610>"
},
{
"output_type": "display_data",
"metadata": {},
"png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAERCAYAAACAbee5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrGnhyWL06zAAAAAASUVORK5CYII=\n",
"text": "<matplotlib.figure.Figure at 0x107687410>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "heading",
"source": "Series indexing",
"level": 4
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Selecting from a Series is easy, using the corresponding index key (like a dict)"
},
{
"metadata": {},
"cell_type": "code",
"input": "a['c']",
"prompt_number": 24,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 24,
"metadata": {},
"text": "-0.24339441059125619"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "slices are permitted "
},
{
"metadata": {},
"cell_type": "code",
"input": "a['a':'c'] ### Note the difference with standard Python / Numpy positional, integer indexing",
"prompt_number": 25,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 25,
"metadata": {},
"text": "a 0.668915\nb 0.812820\nc -0.243394\nName: my series, dtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "a['c':]",
"prompt_number": 26,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 26,
"metadata": {},
"text": "c -0.243394\nd 0.932724\ne 1.754268\nName: my series, dtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "deleting an element "
},
{
"metadata": {},
"cell_type": "code",
"input": "a.drop('d')",
"prompt_number": 27,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 27,
"metadata": {},
"text": "a 0.668915\nb 0.812820\nc -0.243394\ne 1.754268\nName: my series, dtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Adding an element is (to my knowledge) not straightforward"
},
{
"metadata": {},
"cell_type": "code",
"input": "a.append(pd.Series({'f':5}))",
"prompt_number": 28,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 28,
"metadata": {},
"text": "a 0.668915\nb 0.812820\nc -0.243394\nd 0.932724\ne 1.754268\nf 5.000000\ndtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Mathematical operations involving two series will perform operations by *aligning indices*.\n\n1. The union of all indices is created\n2. The mathematical operation is performed on matching indices. \n\nIndices that do not match are given the value NaN (not a number), and values are computed for all unique pairs of repeated indices."
},
{
"metadata": {},
"cell_type": "code",
"input": "s1 = pd.Series(np.arange(1.0,4.0),index=['a','b','c'])\ns2 = pd.Series(np.arange(1.0,4.0),index=['b','c','d'])",
"prompt_number": 29,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "s3 = s1 + s2",
"prompt_number": 30,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "s3",
"prompt_number": 31,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 31,
"metadata": {},
"text": "a NaN\nb 3\nc 5\nd NaN\ndtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "NaNs are ignored in all operations "
},
{
"metadata": {},
"cell_type": "code",
"input": "s3.mean()",
"prompt_number": 32,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 32,
"metadata": {},
"text": "4.0"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "You can drop them from the Series"
},
{
"metadata": {},
"cell_type": "code",
"input": "s4 = s3.dropna()",
"prompt_number": 33,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "s4",
"prompt_number": 34,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 34,
"metadata": {},
"text": "b 3\nc 5\ndtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Or use the `fillna` method to replace them by a value"
},
{
"metadata": {},
"cell_type": "code",
"input": "s3.fillna(-999)",
"prompt_number": 35,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 35,
"metadata": {},
"text": "a -999\nb 3\nc 5\nd -999\ndtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "s3.fillna(s3.mean())",
"prompt_number": 36,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 36,
"metadata": {},
"text": "a 4\nb 3\nc 5\nd 4\ndtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "heading",
"source": "Series with a date / datetime index (timeseries)",
"level": 4
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Series can have indexes representing dates / times "
},
{
"metadata": {},
"cell_type": "code",
"input": "a",
"prompt_number": 37,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 37,
"metadata": {},
"text": "a 0.668915\nb 0.812820\nc -0.243394\nd 0.932724\ne 1.754268\nName: my series, dtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "a.index = pd.date_range(start='2014-1-1', periods=len(a)) # default 'period' is daily",
"prompt_number": 38,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "a.head()",
"prompt_number": 39,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 39,
"metadata": {},
"text": "2014-01-01 0.668915\n2014-01-02 0.812820\n2014-01-03 -0.243394\n2014-01-04 0.932724\n2014-01-05 1.754268\nFreq: D, Name: my series, dtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "a.index",
"prompt_number": 40,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 40,
"metadata": {},
"text": "<class 'pandas.tseries.index.DatetimeIndex'>\n[2014-01-01, ..., 2014-01-05]\nLength: 5, Freq: D, Timezone: None"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "### a datetime index in Pandas has its own type\na.index",
"prompt_number": 41,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 41,
"metadata": {},
"text": "<class 'pandas.tseries.index.DatetimeIndex'>\n[2014-01-01, ..., 2014-01-05]\nLength: 5, Freq: D, Timezone: None"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "### but you can convert it to an numpy array of python datetime objects if you want\npy_datetimes = a.index.to_pydatetime()",
"prompt_number": 43,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "And a number of useful methods for manipulation of time series is exposed"
},
{
"metadata": {},
"cell_type": "code",
"input": "### resample daily time-series to 5 minutes 'period', using forward filling method\na.resample('5min',fill_method='ffill')",
"prompt_number": 44,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 44,
"metadata": {},
"text": "2014-01-01 00:00:00 0.668915\n2014-01-01 00:05:00 0.668915\n2014-01-01 00:10:00 0.668915\n2014-01-01 00:15:00 0.668915\n2014-01-01 00:20:00 0.668915\n2014-01-01 00:25:00 0.668915\n2014-01-01 00:30:00 0.668915\n2014-01-01 00:35:00 0.668915\n2014-01-01 00:40:00 0.668915\n2014-01-01 00:45:00 0.668915\n2014-01-01 00:50:00 0.668915\n2014-01-01 00:55:00 0.668915\n2014-01-01 01:00:00 0.668915\n2014-01-01 01:05:00 0.668915\n2014-01-01 01:10:00 0.668915\n...\n2014-01-04 22:50:00 0.932724\n2014-01-04 22:55:00 0.932724\n2014-01-04 23:00:00 0.932724\n2014-01-04 23:05:00 0.932724\n2014-01-04 23:10:00 0.932724\n2014-01-04 23:15:00 0.932724\n2014-01-04 23:20:00 0.932724\n2014-01-04 23:25:00 0.932724\n2014-01-04 23:30:00 0.932724\n2014-01-04 23:35:00 0.932724\n2014-01-04 23:40:00 0.932724\n2014-01-04 23:45:00 0.932724\n2014-01-04 23:50:00 0.932724\n2014-01-04 23:55:00 0.932724\n2014-01-05 00:00:00 1.754268\nFreq: 5T, Name: my series, Length: 1153"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "a",
"prompt_number": 45,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 45,
"metadata": {},
"text": "2014-01-01 0.668915\n2014-01-02 0.812820\n2014-01-03 -0.243394\n2014-01-04 0.932724\n2014-01-05 1.754268\nFreq: D, Name: my series, dtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "### the ```shift``` method makes it easy e.g. to compare series with lead / lags \na.shift(periods=-1)",
"prompt_number": 46,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 46,
"metadata": {},
"text": "2014-01-01 0.812820\n2014-01-02 -0.243394\n2014-01-03 0.932724\n2014-01-04 1.754268\n2014-01-05 NaN\nFreq: D, Name: my series, dtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "### and the ```truncate`` method allows easy selection of time-slices\na.truncate(after='2014-1-2')",
"prompt_number": 47,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 47,
"metadata": {},
"text": "2014-01-01 0.668915\n2014-01-02 0.812820\nFreq: D, Name: my series, dtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "heading",
"source": "DataFrames",
"level": 3
},
{
"metadata": {},
"cell_type": "markdown",
"source": "**DataFrames** are IMHO one of the most powerful data structures in the Python / data analysis world. \n\nThey can be viewed as a *collection* of named Series. They feature two **indexes**, respectively for the rows and the columns, and can contain heteregoneous data types (although it must be consistent *within* each column). \nNote that a DataFrame index, either along the rows or the columns (or both !) can contain more than one level, they are called **hierarchical indexes** and allows the representation of complex data organisation. \n\nIf the index along the rows of a DataFrame is of **datetime** type, all the methods exposed for the Series (re-sampling, shifting, truncating, etc) are available for the DataFrame."
},
{
"metadata": {},
"cell_type": "heading",
"source": "DataFrame constructions",
"level": 4
},
{
"metadata": {},
"cell_type": "code",
"input": "import string # part of the standard library\nidx = list(string.lowercase[:10])\nprint(idx)",
"prompt_number": 48,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "df = pd.DataFrame(np.arange(100).reshape(10,10),columns=idx,index=np.arange(1,11))",
"prompt_number": 49,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "df",
"prompt_number": 50,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 50,
"metadata": {},
"text": " a b c d e f g h i j\n1 0 1 2 3 4 5 6 7 8 9\n2 10 11 12 13 14 15 16 17 18 19\n3 20 21 22 23 24 25 26 27 28 29\n4 30 31 32 33 34 35 36 37 38 39\n5 40 41 42 43 44 45 46 47 48 49\n6 50 51 52 53 54 55 56 57 58 59\n7 60 61 62 63 64 65 66 67 68 69\n8 70 71 72 73 74 75 76 77 78 79\n9 80 81 82 83 84 85 86 87 88 89\n10 90 91 92 93 94 95 96 97 98 99"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "### here I am creating a DataFrame from a dictionnary\n\ndf = pd.DataFrame({'A' : np.random.random(5),\\\n 'B' : np.random.random(5),\\\n 'C': np.random.random(5)})\nprint df",
"prompt_number": 51,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": " A B C\n0 0.619591 0.546340 0.246023\n1 0.843383 0.747847 0.421628\n2 0.560794 0.083948 0.392063\n3 0.118939 0.333326 0.742547\n4 0.771944 0.735098 0.057653\n"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "df",
"prompt_number": 52,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 52,
"metadata": {},
"text": " A B C\n0 0.619591 0.546340 0.246023\n1 0.843383 0.747847 0.421628\n2 0.560794 0.083948 0.392063\n3 0.118939 0.333326 0.742547\n4 0.771944 0.735098 0.057653"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "heading",
"source": "Indexing a DataFrame",
"level": 4
},
{
"metadata": {},
"cell_type": "code",
"input": "df['A']",
"prompt_number": 53,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 53,
"metadata": {},
"text": "0 0.619591\n1 0.843383\n2 0.560794\n3 0.118939\n4 0.771944\nName: A, dtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "To access a particular *row* instead of a column, you use the *ix* method"
},
{
"metadata": {},
"cell_type": "code",
"input": "df.ix[3]",
"prompt_number": 54,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 54,
"metadata": {},
"text": "A 0.118939\nB 0.333326\nC 0.742547\nName: 3, dtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "And you can combine of course row (with ix) and column indexing, using the same convention for slices as we saw for the Series "
},
{
"metadata": {},
"cell_type": "code",
"input": "df.ix[3]['A':'B']",
"prompt_number": 55,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 55,
"metadata": {},
"text": "A 0.118939\nB 0.333326\nName: 3, dtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "df.ix[3][['A','C']]",
"prompt_number": 56,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 56,
"metadata": {},
"text": "A 0.118939\nC 0.742547\nName: 3, dtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "heading",
"source": "Extending a DataFrame",
"level": 4
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Adding a column is easy "
},
{
"metadata": {},
"cell_type": "code",
"input": "df",
"prompt_number": 57,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 57,
"metadata": {},
"text": " A B C\n0 0.619591 0.546340 0.246023\n1 0.843383 0.747847 0.421628\n2 0.560794 0.083948 0.392063\n3 0.118939 0.333326 0.742547\n4 0.771944 0.735098 0.057653"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "df['D'] = np.random.random(5)",
"prompt_number": 58,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "df",
"prompt_number": 59,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 59,
"metadata": {},
"text": " A B C D\n0 0.619591 0.546340 0.246023 0.402826\n1 0.843383 0.747847 0.421628 0.789004\n2 0.560794 0.083948 0.392063 0.886438\n3 0.118939 0.333326 0.742547 0.303164\n4 0.771944 0.735098 0.057653 0.148836"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "The following works because Pandas understands that a single value must be repeated over the row length"
},
{
"metadata": {},
"cell_type": "code",
"input": "df['E'] = 2.5",
"prompt_number": 60,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "df",
"prompt_number": 61,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 61,
"metadata": {},
"text": " A B C D E\n0 0.619591 0.546340 0.246023 0.402826 2.5\n1 0.843383 0.747847 0.421628 0.789004 2.5\n2 0.560794 0.083948 0.392063 0.886438 2.5\n3 0.118939 0.333326 0.742547 0.303164 2.5\n4 0.771944 0.735098 0.057653 0.148836 2.5"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "The following doesn't work because there's no way to tell **where** to insert the missing value (1st or last index ?)"
},
{
"metadata": {},
"cell_type": "code",
"input": "df['F'] = np.random.random(4)",
"prompt_number": 62,
"outputs": [
{
"ename": "ValueError",
"evalue": "Length of values does not match length of index",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-62-3ee1a35781af>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'F'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/nicolasf/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 2005\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2006\u001b[0m \u001b[0;31m# set column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2007\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2008\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2009\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_setitem_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/nicolasf/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 2083\u001b[0m \u001b[0mis_existing\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2084\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_valid_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2085\u001b[0;31m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2086\u001b[0m \u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2087\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/nicolasf/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_sanitize_column\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 2139\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_is_sequence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2140\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2141\u001b[0;31m raise ValueError('Length of values does not match length of '\n\u001b[0m\u001b[1;32m 2142\u001b[0m 'index')\n\u001b[1;32m 2143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Length of values does not match length of index"
],
"output_type": "pyerr"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Unless we make a series out of it, with a index matching at least partly the DataFrame (row) index"
},
{
"metadata": {},
"cell_type": "code",
"input": "df['F'] = pd.Series(np.random.random(4)) #",
"prompt_number": 63,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "df",
"prompt_number": 64,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 64,
"metadata": {},
"text": " A B C D E F\n0 0.619591 0.546340 0.246023 0.402826 2.5 0.814928\n1 0.843383 0.747847 0.421628 0.789004 2.5 0.255344\n2 0.560794 0.083948 0.392063 0.886438 2.5 0.589248\n3 0.118939 0.333326 0.742547 0.303164 2.5 0.971431\n4 0.771944 0.735098 0.057653 0.148836 2.5 NaN"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "heading",
"source": "Useful DataFrames methods",
"level": 4
},
{
"metadata": {},
"cell_type": "code",
"input": "df.apply(np.sqrt) # or np.sqrt(df)",
"prompt_number": 65,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 65,
"metadata": {},
"text": " A B C D E F\n0 0.787141 0.739148 0.496007 0.634686 1.581139 0.902734\n1 0.918359 0.864781 0.649329 0.888259 1.581139 0.505316\n2 0.748862 0.289738 0.626150 0.941509 1.581139 0.767625\n3 0.344875 0.577344 0.861712 0.550604 1.581139 0.985612\n4 0.878603 0.857379 0.240111 0.385792 1.581139 NaN"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "df.describe()",
"prompt_number": 66,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 66,
"metadata": {},
"text": " A B C D E F\ncount 5.000000 5.000000 5.000000 5.000000 5.0 4.000000\nmean 0.582930 0.489312 0.371983 0.506054 2.5 0.657738\nstd 0.283145 0.282469 0.252416 0.317875 0.0 0.310764\nmin 0.118939 0.083948 0.057653 0.148836 2.5 0.255344\n25% 0.560794 0.333326 0.246023 0.303164 2.5 0.505772\n50% 0.619591 0.546340 0.392063 0.402826 2.5 0.702088\n75% 0.771944 0.735098 0.421628 0.789004 2.5 0.854054\nmax 0.843383 0.747847 0.742547 0.886438 2.5 0.971431"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "df.plot(ylim=[0,3]);",
"prompt_number": 67,
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
"text": "<matplotlib.figure.Figure at 0x107aa7ed0>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "df[(df['A'] >= 0.5) & (df['B'] <= 0.5)]",
"prompt_number": 68,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 68,
"metadata": {},
"text": " A B C D E F\n2 0.560794 0.083948 0.392063 0.886438 2.5 0.589248"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "df.plot(figsize=(10,8), subplots=True, sharex=True, kind='bar', rot=0); ",
"prompt_number": 69,
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"png": "iVBORw0KGgoAAAANSUhEUgAAAk8AAAHpCAYAAACbatgAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": "<matplotlib.figure.Figure at 0x107aa7c10>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "heading",
"source": "Input and Output in pandas",
"level": 3
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Pandas has very powerful IO methods, allowing to load csv, excel, tab-delimited files very easily. Pandas DataFrames can also be \nsaved also in csv, excel files. "
},
{
"metadata": {},
"cell_type": "code",
"input": "pd.read_",
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "SOI = pd.read_csv('./data/NIWA_SOI.csv')",
"prompt_number": 73,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "SOI.head()",
"prompt_number": 74,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 74,
"metadata": {},
"text": " Unnamed: 0 Jan Feb Mar Apr May Jun \\\n0 1876 NaN NaN NaN NaN NaN NaN \n1 1877 -1.044600 -0.834198 -0.759131 -1.103454 0.349381 -1.901213 \n2 1878 -0.940401 -2.339884 -1.981073 -1.009742 0.189664 -0.448601 \n3 1879 1.404088 1.302904 1.257072 1.426758 0.189664 1.602146 \n4 1880 1.195689 0.622917 1.379266 0.583354 1.227823 0.833116 \n\n Jul Aug Sep Oct Nov Dec \n0 NaN NaN NaN NaN NaN NaN \n1 -1.002934 -1.044881 -1.839545 -1.745311 -1.453885 -1.541774 \n2 1.568691 1.216173 1.734780 1.002227 1.460825 1.744423 \n3 2.147306 2.243925 1.858033 1.439335 0.905642 -0.774995 \n4 0.154297 1.353207 0.748760 0.377786 0.628051 -0.391605 "
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "SOI.tail()",
"prompt_number": 75,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 75,
"metadata": {},
"text": " Unnamed: 0 Jan Feb Mar Apr May Jun \\\n134 2010 -1.096700 -1.659897 -1.431199 1.707893 0.988248 0.064086 \n135 2011 2.185585 2.128602 2.173528 2.832432 0.189664 -0.106810 \n136 2012 1.039390 0.088641 0.096228 -0.822319 -0.289487 -1.217631 \n137 2013 -0.106804 -0.542775 1.012684 0.021085 0.828531 1.345802 \n138 2014 NaN NaN NaN NaN NaN NaN \n\n Jul Aug Sep Oct Nov Dec \n134 2.018725 1.832825 2.474296 1.751556 1.599621 2.730281 \n135 1.054366 0.051388 1.118517 0.627563 1.322029 2.292122 \n136 -0.167156 -0.702297 0.194123 0.128010 0.281061 -0.829765 \n137 0.797204 -0.222680 0.317375 -0.309098 0.836244 -0.117755 \n138 NaN NaN NaN NaN NaN NaN "
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "SOI = pd.read_csv('./data/NIWA_SOI.csv', index_col=0)",
"prompt_number": 76,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "SOI.head()",
"prompt_number": 77,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 77,
"metadata": {},
"text": " Jan Feb Mar Apr May Jun Jul \\\n1876 NaN NaN NaN NaN NaN NaN NaN \n1877 -1.044600 -0.834198 -0.759131 -1.103454 0.349381 -1.901213 -1.002934 \n1878 -0.940401 -2.339884 -1.981073 -1.009742 0.189664 -0.448601 1.568691 \n1879 1.404088 1.302904 1.257072 1.426758 0.189664 1.602146 2.147306 \n1880 1.195689 0.622917 1.379266 0.583354 1.227823 0.833116 0.154297 \n\n Aug Sep Oct Nov Dec \n1876 NaN NaN NaN NaN NaN \n1877 -1.044881 -1.839545 -1.745311 -1.453885 -1.541774 \n1878 1.216173 1.734780 1.002227 1.460825 1.744423 \n1879 2.243925 1.858033 1.439335 0.905642 -0.774995 \n1880 1.353207 0.748760 0.377786 0.628051 -0.391605 "
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "SOI.index",
"prompt_number": 78,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 78,
"metadata": {},
"text": "Float64Index([1876.0, 1877.0, 1878.0, 1879.0, 1880.0, 1881.0, 1882.0, 1883.0, 1884.0, 1885.0, 1886.0, 1887.0, 1888.0, 1889.0, 1890.0, 1891.0, 1892.0, 1893.0, 1894.0, 1895.0, 1896.0, 1897.0, 1898.0, 1899.0, 1900.0, 1901.0, 1902.0, 1903.0, 1904.0, 1905.0, 1906.0, 1907.0, 1908.0, 1909.0, 1910.0, 1911.0, 1912.0, 1913.0, 1914.0, 1915.0, 1916.0, 1917.0, 1918.0, 1919.0, 1920.0, 1921.0, 1922.0, 1923.0, 1924.0, 1925.0, 1926.0, 1927.0, 1928.0, 1929.0, 1930.0, 1931.0, 1932.0, 1933.0, 1934.0, 1935.0, 1936.0, 1937.0, 1938.0, 1939.0, 1940.0, 1941.0, 1942.0, 1943.0, 1944.0, 1945.0, 1946.0, 1947.0, 1948.0, 1949.0, 1950.0, 1951.0, 1952.0, 1953.0, 1954.0, 1955.0, 1956.0, 1957.0, 1958.0, 1959.0, 1960.0, 1961.0, 1962.0, 1963.0, 1964.0, 1965.0, 1966.0, 1967.0, 1968.0, 1969.0, 1970.0, 1971.0, 1972.0, 1973.0, 1974.0, 1975.0, ...], dtype='float64')"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "SOI = SOI.dropna()",
"prompt_number": 79,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "SOI.head()",
"prompt_number": 80,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 80,
"metadata": {},
"text": " Jan Feb Mar Apr May Jun Jul \\\n1877 -1.044600 -0.834198 -0.759131 -1.103454 0.349381 -1.901213 -1.002934 \n1878 -0.940401 -2.339884 -1.981073 -1.009742 0.189664 -0.448601 1.568691 \n1879 1.404088 1.302904 1.257072 1.426758 0.189664 1.602146 2.147306 \n1880 1.195689 0.622917 1.379266 0.583354 1.227823 0.833116 0.154297 \n1881 -0.784101 -0.737057 -0.025966 0.021085 -0.449204 -0.619496 -0.552899 \n\n Aug Sep Oct Nov Dec \n1877 -1.044881 -1.839545 -1.745311 -1.453885 -1.541774 \n1878 1.216173 1.734780 1.002227 1.460825 1.744423 \n1879 2.243925 1.858033 1.439335 0.905642 -0.774995 \n1880 1.353207 0.748760 0.377786 0.628051 -0.391605 \n1881 -1.387465 -1.469787 -2.557084 0.628051 0.868104 "
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "SOI.index",
"prompt_number": 81,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 81,
"metadata": {},
"text": "Float64Index([1877.0, 1878.0, 1879.0, 1880.0, 1881.0, 1882.0, 1883.0, 1884.0, 1885.0, 1886.0, 1887.0, 1888.0, 1889.0, 1890.0, 1891.0, 1892.0, 1893.0, 1894.0, 1895.0, 1896.0, 1897.0, 1898.0, 1899.0, 1900.0, 1901.0, 1902.0, 1903.0, 1904.0, 1905.0, 1906.0, 1907.0, 1908.0, 1909.0, 1910.0, 1911.0, 1912.0, 1913.0, 1914.0, 1915.0, 1916.0, 1917.0, 1918.0, 1919.0, 1920.0, 1921.0, 1922.0, 1923.0, 1924.0, 1925.0, 1926.0, 1927.0, 1928.0, 1929.0, 1930.0, 1931.0, 1932.0, 1933.0, 1934.0, 1935.0, 1936.0, 1937.0, 1938.0, 1939.0, 1940.0, 1941.0, 1942.0, 1943.0, 1944.0, 1945.0, 1946.0, 1947.0, 1948.0, 1949.0, 1950.0, 1951.0, 1952.0, 1953.0, 1954.0, 1955.0, 1956.0, 1957.0, 1958.0, 1959.0, 1960.0, 1961.0, 1962.0, 1963.0, 1964.0, 1965.0, 1966.0, 1967.0, 1968.0, 1969.0, 1970.0, 1971.0, 1972.0, 1973.0, 1974.0, 1975.0, 1976.0, ...], dtype='float64')"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "SOI.index = map(int, SOI.index)",
"prompt_number": 82,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "SOI.index",
"prompt_number": 83,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 83,
"metadata": {},
"text": "Int64Index([1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, ...], dtype='int64')"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### Stacking "
},
{
"metadata": {},
"cell_type": "code",
"input": "SOIs = SOI.stack()",
"prompt_number": 84,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "SOIs.head()",
"prompt_number": 85,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 85,
"metadata": {},
"text": "1877 Jan -1.044600\n Feb -0.834198\n Mar -0.759131\n Apr -1.103454\n May 0.349381\ndtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "SOIs.index",
"prompt_number": 86,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 86,
"metadata": {},
"text": "MultiIndex(levels=[[1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, ...], [u'Jan', u'Feb', u'Mar', u'Apr', u'May', u'Jun', u'Jul', u'Aug', u'Sep', u'Oct', u'Nov', u'Dec']],\n labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, ...], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2, 3, ...]])"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "from dateutil import parser",
"prompt_number": 87,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "dateindex = [parser.parse(\"-\".join(map(str,[x[0],x[1], 1]))) for x in SOIs.index]",
"prompt_number": 88,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "SOIs.index=dateindex",
"prompt_number": 89,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "SOIs.head()",
"prompt_number": 90,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 90,
"metadata": {},
"text": "1877-01-01 -1.044600\n1877-02-01 -0.834198\n1877-03-01 -0.759131\n1877-04-01 -1.103454\n1877-05-01 0.349381\ndtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "SOIs.plot(figsize=(14,8))",
"prompt_number": 91,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 91,
"metadata": {},
"text": "<matplotlib.axes.AxesSubplot at 0x10808d510>"
},
{
"output_type": "display_data",
"metadata": {},
"png": "iVBORw0KGgoAAAANSUhEUgAAAywAAAHfCAYAAACyF1wDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": "<matplotlib.figure.Figure at 0x1080f9890>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": ""
},
{
"metadata": {},
"cell_type": "markdown",
"source": "The **groupby** method is a very powerful method of pandas DataFrames, in a nutschell it allows you to\n\n1. **split** your data according to unique values of a variable (or unique *combinations* of *N* variables)\n\n2. **apply** some operation to the groups thus defined, either an *aggregation* or *transformation* method \n\n3. **combine** the results into a DataFrame\n\nThis process is illustrated below, where the operation is here calculating the mean of the groups's values\n\nA very nice explanation of the **groupby** method, with examples, is available from Pandas's documentation at: \n\n[http://pandas.pydata.org/pandas-docs/stable/groupby.html](http://pandas.pydata.org/pandas-docs/stable/groupby.html)\n\nand a short tutorial on Wes McKinney's blog [here](http://wesmckinney.com/blog/?p=125)"
},
{
"metadata": {},
"cell_type": "code",
"input": "Image(filename='images/split-apply-combine.png', width=800)",
"prompt_number": 92,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 92,
"metadata": {
"png": {
"width": 800
}
},
"png": "iVBORw0KGgoAAAANSUhEUgAAB1EAAAXDCAIAAAAOQFANAAABKWlDQ1BJQ0MgUHJvZmlsZQAAGJVj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\nTkSuQmCC\n",
"text": "<IPython.core.display.Image at 0x10852a7d0>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "url = \"ftp://ftp.cpc.ncep.noaa.gov/wd52dg/data/indices/ersst3b.nino.mth.81-10.ascii\"",
"prompt_number": 93,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "#!wget -P ./data ftp://ftp.cpc.ncep.noaa.gov/wd52dg/data/indices/ersst3b.nino.mth.81-10.ascii",
"prompt_number": 94,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "data = pd.read_table(url, sep='\\s+')",
"prompt_number": 95,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "data.tail()",
"prompt_number": 96,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 96,
"metadata": {},
"text": " YR MON NINO1+2 ANOM NINO3 ANOM.1 NINO4 ANOM.2 NINO3.4 ANOM.3\n770 2014 3 25.97 -0.54 27.04 -0.19 28.26 -0.03 26.84 -0.49\n771 2014 4 25.22 -0.39 27.55 0.00 28.80 0.25 27.66 -0.13\n772 2014 5 25.02 0.60 27.71 0.46 29.24 0.40 28.14 0.23\n773 2014 6 24.49 1.41 27.11 0.49 29.10 0.25 27.80 0.11\n774 2014 7 23.22 1.23 26.24 0.46 28.93 0.12 27.27 -0.01"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "I only keep the raw - monthly - values of NINO 3.4 "
},
{
"metadata": {},
"cell_type": "code",
"input": "nino = data[['YR','MON','NINO3.4']]",
"prompt_number": 97,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "nino.head()",
"prompt_number": 98,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 98,
"metadata": {},
"text": " YR MON NINO3.4\n0 1950 1 24.83\n1 1950 2 25.20\n2 1950 3 26.03\n3 1950 4 26.36\n4 1950 5 26.19"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Now I want to calculate a climatology (over the whole period available)\n\nI first group by UNIQUE values of the variable months, I should get 12 groups"
},
{
"metadata": {},
"cell_type": "code",
"input": "groups = nino.groupby('MON')",
"prompt_number": 99,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "for month, group in groups:\n print month\n print group.head()",
"prompt_number": 100,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "1\n YR MON NINO3.4\n0 1950 1 24.83\n12 1951 1 25.46\n24 1952 1 26.85\n36 1953 1 26.85\n48 1954 1 27.03\n2\n YR MON NINO3.4\n1 1950 2 25.20\n13 1951 2 25.78\n25 1952 2 26.79\n37 1953 2 27.19\n49 1954 2 27.22\n3\n YR MON NINO3.4\n2 1950 3 26.03\n14 1951 3 26.72\n26 1952 3 27.32\n38 1953 3 27.68\n50 1954 3 27.21\n4\n YR MON NINO3.4\n3 1950 4 26.36\n15 1951 4 27.24\n27 1952 4 27.88\n39 1953 4 28.19\n51 1954 4 26.87\n5\n YR MON NINO3.4\n4 1950 5 26.19\n16 1951 5 27.68\n28 1952 5 27.99\n40 1953 5 28.29\n52 1954 5 27.07\n6\n YR MON NINO3.4\n5 1950 6 26.52\n17 1951 6 27.46\n29 1952 6 27.33\n41 1953 6 28.02\n53 1954 6 26.93\n7\n YR MON NINO3.4\n6 1950 7 26.42\n18 1951 7 27.72\n30 1952 7 26.72\n42 1953 7 27.52\n54 1954 7 26.37\n8\n YR MON NINO3.4\n7 1950 8 25.98\n19 1951 8 27.36\n31 1952 8 26.46\n43 1953 8 27.16\n55 1954 8 25.73\n9\n YR MON NINO3.4\n8 1950 9 25.78\n20 1951 9 27.51\n32 1952 9 26.54\n44 1953 9 27.13\n56 1954 9 25.38\n10\n YR MON NINO3.4\n9 1950 10 25.96\n21 1951 10 27.43\n33 1952 10 26.54\n45 1953 10 27.02\n57 1954 10 25.51\n11\n YR MON NINO3.4\n10 1950 11 25.64\n22 1951 11 27.48\n34 1952 11 26.36\n46 1953 11 26.96\n58 1954 11 25.67\n12\n YR MON NINO3.4\n11 1950 12 25.50\n23 1951 12 27.12\n35 1952 12 26.53\n47 1953 12 26.99\n59 1954 12 25.37\n"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "climatology = groups.mean()",
"prompt_number": 101,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Same as \n\n climatology = groups.aggregate(np.mean)\n \n "
},
{
"metadata": {},
"cell_type": "code",
"input": "climatology['NINO3.4'].head(12)",
"prompt_number": 102,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 102,
"metadata": {},
"text": "MON\n1 26.484615\n2 26.676923\n3 27.184154\n4 27.623538\n5 27.739538\n6 27.544462\n7 27.151692\n8 26.764531\n9 26.667344\n10 26.632656\n11 26.593750\n12 26.529062\nName: NINO3.4, dtype: float64"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "climatology['NINO3.4'].plot(kind='bar',ylim=[26,28], rot=0)",
"prompt_number": 103,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 103,
"metadata": {},
"text": "<matplotlib.axes.AxesSubplot at 0x1085a9190>"
},
{
"output_type": "display_data",
"metadata": {},
"png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAEPCAYAAABWc+9sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAFYNJREFUeJzt3X2wXPVdx/H3hZtAIblcYh9iAvVSptjSBy6PRVPKhUEM\nRQvqWMyIbWAG7YwUxLFF8A9gpg6gdESn9g9baForDzWtDIVGHsZsJePwaC4QklASiAUCiAVK0Gqh\nXP/4nctu7j2b3b13z9nz+533a2bn7u7ds5+zsPu9Zz979gQkSZIkSZIkSZIkSZIkSZKidjCwHngM\n2ARckF1/HHA/sBF4ADi2zfIrga3AE8DFha6pJKkrS4Hx7Pwi4HHg/UAD+NXs+tMIw3+mvYFtwBiw\nAJjMlpUkFWivDr9/njCQAV4DtgDLgeeAA7LrR4Fnc5Y9jjDYdwCvAzcBZ8xvdSVJnQz3cNsx4Ejg\nXkK1sgG4hvDH4Zdybr8ceLrl8jPAR+a0lpKkrnXaYp+2CFgLXEjYcr+O0Le/G7gIuD5nmal+rKAk\nqTfdbLEvAL4NfBO4JbvuOOCU7Pxa4Ks5yz1L+PB12sGErfbdHHrooVPbt2/vdn0lScHDND8D3U2n\nLfYhwtb5ZuDaluu3ASdm508GfpCz7IPAewkVzkLgLODWmTfavn07U1NTPZ8uu+yyOS0315N55lUx\ny7z65gFHtBvcnbbYVwBnA48Qdm0EuBT4feBvgX2An2SXAZYBXwFOB94AzgfuIOwhcx3hw9e+2LFj\nR7/uyjzzos0yz7w8nQb7Btpv1ed9ELqTMNSnrctOkqSS7D3oFQAuv/zyy3teaHR0lLGxsb6vjHnm\nxZRlXn3zrrjiCoAr8n43NL9V6ouprC+SJHVpaGgI2szwbnd3rJxGo2GeeZXMS/mxmRdHXrSDXZKU\nzypGkiKUZBUjScoX7WBPoQczL828lB+beXHkRTvYJUn57NglKUJ27JJUI9EO9hR6MPPSzEv5sZkX\nR160g12SlM+OXZIiZMcuSTUS7WBPoQczL828lB+beXHkRTvYJUn57NglKUJ27JJUI9EO9hR6MPPS\nzEv5sZkXR160g12SlM+OXZIiZMcuSTUS7WBPoQczL828lB+beXHkRTvYJUn57NglKUJ27BqIkZEl\nDA0N9XwaGVky6FWXohbtYE+hB0s9b9eul4GpNqf1bX8XlusvO3bz6pQX7WCXJOWzY1dhQgc4l/+3\nQ/ickPbMjl2SaiTawZ5CD1bnPCg3z47dvDrlRTvYJUn57NhVGDt2qTh27JJUI9EO9hR6sDrn2bGb\nZ15xedEOdklSPjt2FcaOXSqOHbsk1Ui0gz2FHqzsvGodlKtRwH3uIc2O3bwa5UU72NW7Kh2US1Jx\n7NhrpOzO245dKo4duyTVSLSDPYUerEp5ZXfeduzmmVdcXrSDXZKUz469RuzYpXTYsUtSjUQ72FPo\nwaqUl0LHXpX99FN/rphX/bzhDr8/GPgG8E7Ce+q/A/4GuBk4LLvNKPAKcGTO8juAV4GfAa8Dx817\njaU2mvvp52kAE22Wq0IjKfVPp2f00uw0CSwCHgLOBLa03OYawmD/Qs7yTwFHAy/tIcOOvSSpd+x2\n+qqTPXXsnbbYn89OAK8RBvoymoN9CPgkcNKe8rtdUUnS/PXSsY8R6pb7Wq47AXgB2N5mmSngbuBB\n4Lw5rF9bKfRgVcpLoWOvSl7qzxXzqp/XaYt92iJgLXAhYct92irghj0stwJ4DngHcBewFbhn5o1W\nr17N2NgYAKOjo4yPjzMxMQE0H/TMy9Pa/b7fl1PJaw64mZfp6vfmdb48OTlZ+PPDvPrlNRoN1qxZ\nA/DWvGynm5pkAXAbsA64tuX6YeAZ4ChgZxf3cxnhj8IXZ1xvx16S1DtvO3bVyXz2Yx8CrgM2s/tQ\nBziF0LW3G+r7AYuz8/sDpwKPdl5dSdJ8dBrsK4CzCR+ObsxOK7PfnQXcOOP2y4Dbs/NLCbXLJKGX\nvw24c/6rHMysLIqWel7KnXfZeak/V8yrfl6njn0D7Yf/OTnX7QROz84/CYzPcb0kSXNUhV0R7dhL\nknrnbceuOvFYMZJUI9EO9hR6sCrlpdx5l52X+nPFvOrnRTvYJUn57NhrJPXO245ddWLHLkk1Eu1g\nT6EHq1Jeyp132XmpP1fMq35etINdkpTPjr1GUu+87dhVJ3bsklQj0Q72FHqwKuWl3HmXnZf6c8W8\n6udFO9glSfns2Gsk9c7bjl11YscuSTUS7WBPoQerUl7KnXfZeak/V8yrfl60g12SlM+OvUZS77zt\n2FUnduySVCPRDvYUerAq5aXceZedl/pzxbzq50U72CVJ+ezYayT1ztuOXXVixy5JNRLtYE+hB6tS\nXsqdd9l5qT9XzKt+XrSDXZKUz469RlLvvO3YVSd27JJUI9EO9hR6sCrlpdx5l52X+nPFvOrnRTvY\nJUn57NhrJPXO245ddWLHLkk1Eu1gT6EHq1Jeyp132XmpP1fMq37ecN/vUV0bGVnCrl0v97zc4sUH\n8uqrLxWwRpJSYMc+QKl30KnnSYNkxy5JNRLtYE+hB+uQaF6F80ZGljA0NNTzaWRkSV/XA9J/LZjX\nu2gHuzRI4bORqTan9W1/N5fPVKRe2bEPUOoddMp59vkaNDt2SaqRaAd7Cj1Yh0Tzos0rMyv914J5\nvYt2sEuS8tmxD1DKHXTqeXbsGjQ7dkmqkWgHewo9WIdE86LNKzMr/deCeb2LdrBLkvLZsQ9Qyh10\n6nl27Bo0O3ZJqpFoB3sKPViHRPOizSszK/3Xgnm96zTYDyYc+OIxYBNwQXb9zcDG7PRU9jPPSmAr\n8ARw8XxXVpLUWaeOfWl2mgQWAQ8BZwJbWm5zDfAK8IUZy+4NPA6cAjwLPACsmrEs2LHPZcnKd9Cp\n59mxa9Dm07E/TxjqAK8RhvKy1vsGPgncmLPsccA2YAfwOnATcEaX6yxJmqNeOvYx4EjgvpbrTgBe\nALbn3H458HTL5Wey6/oihR6sQ6J50eaVmZX+a8G83nX7b54uAtYCFxK23KetAm5os0zX7zdXr17N\n2NgYAKOjo4yPjzMxMQE0H/TMy9Pa/b7fl4vKaw6BmZfp6vfmDSZvz/c3mXP/05fDffTz+Tk5OVn4\n89+8wec1Gg3WrFkD8Na8bKeb/dgXALcB64BrW64fJmyFHwXszFnueOBywgeoAJcAbwJXz7idHXvv\nS1a+g049z45dgzafjn0IuA7YzO5DHcKHolvIH+oADwLvJVQ4C4GzgFu7WWFJ0tx1GuwrgLOBk2ju\n3ji9BX4Wsz80XQbcnp1/AzgfuIPwh+FmZu8RM2dF9FJVyku7g049r8ys9F8L5vWuU8e+gfbD/5yc\n63YCp7dcXpedJEkl8VgxA5RyB516nh27Bs1jxUhSjUQ72FPowTokmhdtXplZ6b8WzOtdtINdkpTP\njn2AUu6gU8+zY9eg2bFLUo1EO9hT6ME6JJoXbV6ZWem/FszrXbSDXZKUz459gFLuoFPPs2PXoNmx\nS1KNRDvYU+jBOiSaF21emVnpvxbM6120g12SlM+OfYBS7qBTz7Nj16DZsUtSjUQ72FPowTokmhdt\nXplZ6b8WzOtdtINdkpTPjn2AUu6gU8+zY9eg2bFLUo1EO9hT6ME6JJoXbV6ZWem/FszrXbSDXZKU\nz459gFLuoFPPs2PXoNmxS5EbGVnC0NBQz6eRkSWDXnUNQLSDPYUerEOiedHm9T9r166XCe8Q8k7r\n2/4uLNdfqb/2UsiLdrBLkvLZsQ9Qyh106nkpPzbFwY5dkmok2sGeQg/WIdG8aPPKzCo/L/XXXgp5\n0Q52SVI+O/YBSr2nTTkv5cemONixS1KNRDvYU+jBOiSaF21emVnl56X+2kshb7jv9xixkZElc/pC\nx+LFB/Lqqy8VsEaS1Ds79hap96bm9S8v5ccGbuTEYE8du1vskmZpHsKg1+WqsK0oO/buE80zr4JZ\n6eel0HmXnRftYJck5avC+yY7dvOiy0v5sQ0iz06/d3bskirNTr+/oq1i7NjNq25emVnm9T3Njl2S\nVDVVeB9jx25edHkpP7Y65KXAY8VIUovU/w3ZaAe7Hbt51c0rM8u8uUj935CNdrBLkvLZsbdIvVc0\nr395KT828/qfVwQ7dkmqkWgHux27edXNKzPLvNjzBtGxH0z4JOExYBNwQcvvPgtsya6/us3yO4BH\ngI3A/fNZUUlSdzp17Euz0ySwCHgIODO77lLg48DrwDuAF3OWfwo4GtjTwRzs2M2LLi/lx2Ze//OK\nOBbOfI4V83x2AniNsIW+HDgPuJIw1CF/qL+V3yFDkpJW9rFweunYx4AjgfuAw4CPAfcSCqlj2iwz\nBdwNPEj4Y9A3duzmVTevzCzzzJut26M7LgLWAhcCu7LlDgSOB44FvgW8J2e5FcBzhKrmLmArcM/M\nG61evZqxsTEARkdHGR8fZ2JiAmgO8JmXp7X7/VwvN/8jz7xMV783rx55e76/yZz7n74c7qPX56d5\n5jUaDdasWQPw1rxsp5vt/AXAbcA64NrsunXAVcD3s8vbgI8AP9rD/VxGqHO+OON6O3bzostL+bGZ\nF0fefPZjHwKuAzbTHOoAtwAnZ+cPAxYye6jvByzOzu8PnAo82iFvN6kfz0GSitBpsK8AzgZOIuyy\nuBFYCVxPqF4eBW4EPpXdfhlwe3Z+KaF2mST08rcBd/ayclU6nkMKvZt5KWaZZ95snTr2DbQf/r+X\nc91O4PTs/JPA+BzXS5I0R1XYFbFtx55CD2ZemnkpPzbz4sjzWDGSVCMRD/aGeeZVNK/MLPPMmy3i\nwS5JymPHbp55duzmRZhnxy5JNRLxYG+YZ15F88rMMs+82SIe7JKkPHbs5plnx25ehHl27JJUIxEP\n9oZ55lU0r8ws88ybLeLBLknKY8dunnl27OZFmGfHLkk1EvFgb5hnXkXzyswyz7zZIh7skqQ8duzm\nmWfHbl6EeXbsklQjEQ/2hnnmVTSvzCzzzJst4sEuScpjx26eeXbs5kWYZ8cuSTUS8WBvmGdeRfPK\nzDLPvNkiHuySpDx27OaZZ8duXoR5duySVCMRD/aGeeZVNK/MLPPMmy3iwS5JymPHbp55duzmRZhn\nxy5JNRLxYG+YZ15F88rMMs+82SIe7JKkPHbs5plnx25ehHl27JJUIxEP9oZ55lU0r8ws88ybLeLB\nLknKY8dunnl27OZFmGfHLkk1EvFgb5hnXkXzyswyz7zZIh7skqQ8duzmmWfHbl6EeXbsklQjEQ/2\nhnnmVTSvzCzzzJst4sEuScpjx26eeXbs5kWYZ8cuSTXSabAfDKwHHgM2ARe0/O6zwJbs+qvbLL8S\n2Ao8AVw8rzWdpdHfuzPPvCizzDNvtk6D/XXgIuADwPHAHwLvB04CPgF8GPggcE3OsnsDXyIM98OB\nVdmyfTLZv7syz7xos8wzb7ZOg/35ltTXCFvoy4HPAFcSBj/AiznLHgdsA3Zkt7sJOGN+q9vqlf7d\nlXnmRZtlnnmz9dKxjwFHAvcBhwEfA+4lvI84Juf2y4GnWy4/k10nSSrQcJe3WwSsBS4EdmXLHUio\nZ44FvgW8Z8Yyc/kIuAc7ir1788yLIss88+ZmAXAH8Ect160DTmy5vA34uRnLHQ/8c8vlS8j/AHWS\n8EfAkydPnjx1f5pzOT8EfAP4qxnX/wFwRXb+MOCHOcsOA9sJFc7CbCX6+OGpJGkuPgq8SRjKG7PT\nSsJW/N8DjwIPARPZ7ZcBt7csfxrwOGGL/pJS1liSJEnSYF0PvEB4t1CGPX1Jq9/2Jex1NAlsJuxS\nWoa9Ce/GvltC1g7gkSzv/hLyRgkf/G8h/Dc9vsCsX6T5znYj8GOKfb5AeCf8GOH1cAOwT8F5F2ZZ\nm7Lz/Zb3+l4C3AX8ALiT8P+0yLzfJvw3/RlwVB+z2uX9JeH5+TDwHeCAPmdG4QTCbpdlDfalwHh2\nfhGhWirys4L9sp/DhN1JP1pg1rQ/Bv4BuLWErKcIL9SyfB04Nzs/THkvmr2A5wgbBkUZA56kOcxv\nBj5dYN4HCa+7fQkbA3cBh/Y5I+/1/RfA57PzFwNXFZz3PsJnh+vp/2DPy/sVmrueX0UfHl+Mx4q5\nB3i5xLy8L2ktKzDvf7KfCwkvnpcKzAI4CPg48FXKOyhcWTkHEF5I12eX3yBsRZfhFMLOA093uuE8\nvEr48t9+hD9a+wHPFpj3PsI7yv8lbM1+H/jNPmfkvb4/QfgDTfbzzILzthLeHRQhL+8uwmeZEP77\nHjTfkBgH+yCN0fySVlH2IvwheYGwxbC5wCwIezx9juYTq2hTwN3Ag8B5BWcdQvhW9NeAfwe+QvMd\nUdF+h1CNFOkl4IuEvdJ2Er7CeHeBeZsIfyiXEP47nk4fhlAX3kV4PZD9fFcJmYNyLvC9+d6Jg717\nrV/Seq3AnDcJ1c9BhG/3ThSY9WvAfxL64LK2olcQ/jieRjj20AkFZg0T3kp/Ofv538CfFpg3bSHw\n68A/FpxzKOH7JWOEd5GLgN8tMG8r4YB/dxK+y7KR8jYIpk3vw52iPwN+Sh82CBzs3VkAfBv4JnBL\nSZk/Juw6mne4hn75ZcLb3KeAG4GTCd9bKNJz2c8XgX8iHFOoKM9kpweyy2vpf2ea5zTCbsB5x1Dq\np2OAfwN+RKiZvkP4f1qk67PcEwnvEB4vOA/CVvrS7PzPEzZGUrOaUIn25Q+zg72zIeA6QiVybcFZ\nb6f5if/bCB+qbCww71LCh3uHEKqDfwE+VWDefsDi7Pz+wKkU+yH484SO+7Ds8imEvR2Ktorwh7Jo\nWwl7+byN8Dw9heKru3dmP98N/AbF100QPtSf/lD405S3cQXlvJNdSahDzyB8flFLNxL6xP8jvGjP\nKTiv3Ze0ivAhQhc8Sdgl8HMF5eQ5keL3ijmE8NgmCX1tGV9aO4KwxV7WrmT7A/9F8w9Y0T5Pc3fH\nrxPeXRbpX7O8ScLhu/tt+vX9U5qv7yWEzw6K2N1xZt65hA9nnwZ+Qtg4WFdw3hPAf9CcL1/uY54k\nSZIkSZIkSZIkSZIkSZKkaniT8A/FTBsmfEu09bDFZxL2fd9M+E7BGS2/W0P4RuvC7PLbCd/elSQN\nyC7CF8H2zS6fRvhiyPQXtI4gfGnkF7LLY9nlD2WX1xCOK/+Z7LKDXZXiIQVUV98jHJ0QmocAmP76\n+J8Af074NiCEIX4lzW8CTwF/DVyEryFVkE9K1dXNhOPj7EPYEm89FPPhhIN4tXoI+EDL5R8CGwjH\n1kn1aIOKlINddfUooWJZxe7/AHu3pmhuxfs6UqX4hFSd3Qpcw+41DIQPTGceLvlowoHLWm0jHAzr\nrKJWUJLUnV3Zz+XA+dn5CZp7xRxBOJLgzA9PP5xd/hrwW9n5wwkdvB+eqjKGB70C0gBMd+LPAl9q\nuW76+ocJ/2jydwmHwX2dULk8knMfmwn9+5EFrq8kSZIkSZIkSZIkSZIkSZIkSZIkSeX4f1aGTM2n\nUh4FAAAAAElFTkSuQmCC\n",
"text": "<matplotlib.figure.Figure at 0x1085a97d0>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Now suppose we want to apply a function that doesnt **aggregate** the values in the groups (such as sum, or mean) but rather want to apply a function to those values ... \n\nAn example would be calculating the standardized anomalies per month (to each value subtract the mean of the corresponding month, then divide by the standard-deviation)"
},
{
"metadata": {},
"cell_type": "code",
"input": "def zscore(x): \n z = (x - x.mean()) / x.std()\n return z",
"prompt_number": 104,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "transformed = groups.transform(zscore)",
"prompt_number": 105,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "transformed['NINO3.4'].plot()",
"prompt_number": 106,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 106,
"metadata": {},
"text": "<matplotlib.axes.AxesSubplot at 0x1080b0410>"
},
{
"output_type": "display_data",
"metadata": {},
"png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAEACAYAAAB4ayemAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": "<matplotlib.figure.Figure at 0x107b94ed0>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "data['ANOM.3'].plot()",
"prompt_number": 107,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 107,
"metadata": {},
"text": "<matplotlib.axes.AxesSubplot at 0x107b351d0>"
},
{
"output_type": "display_data",
"metadata": {},
"png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAEACAYAAAB4ayemAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKCgoKCgoKCgoKCgoKCgo/H/qpJmr1iozfgAAAABJRU5ErkJggg==\n",
"text": "<matplotlib.figure.Figure at 0x107bdc050>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## 4. Some more data manipulation with Pandas\n\nThis is some real data provided by Dan Smale (NIWA Lauder). It is a summary file of daily QA/QC (data quality) diagnostics of raw GPS data collected at NIWA Lauder. The GPS data can be used to infer the amount of total column water vapour in the atmosphere (above lauder).\n\nSummary of data\n/data/gruan/gnss_processing/data/netr9_laud/rinex_211/LAUD0010.13S:SUM 13 1 1 00:00 13 1 1 23:59 24.00 30 25298 23597 93 0.57 0.43 11799\n\n+ 13 1 1 00:00 13 1 1 23:59: time period the QC/QA variables are calculated for.\n+ 24 = number of hourly entries\n+ 30 = dt\n+ 25298 = #expt = number of data points expected\n+ 23597 = #have = number of data points collected\n+ 93 =% #have/#expt*100\n+ 0.57 = mp1 = mean path 1\n+ 0.43 = mp2 = mean path 2\n+ 11799 =o/slps = number of time slips\n"
},
{
"metadata": {},
"cell_type": "code",
"input": "data = pd.read_table('./data/teqc_SUM.dat', sep='\\s+', header=None) ## the '\\s+' is a regular expression",
"prompt_number": 108,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "data.head()",
"prompt_number": 109,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 109,
"metadata": {},
"text": " 0 1 2 3 4 5 \\\n0 /data/gruan/gnss_processing/data/netr9_laud/ri... 13 1 1 00:00 13 \n1 /data/gruan/gnss_processing/data/netr9_laud/ri... 13 1 2 00:00 13 \n2 /data/gruan/gnss_processing/data/netr9_laud/ri... 13 1 3 00:00 13 \n3 /data/gruan/gnss_processing/data/netr9_laud/ri... 13 1 4 00:00 13 \n4 /data/gruan/gnss_processing/data/netr9_laud/ri... 13 1 5 00:00 13 \n\n 6 7 8 9 10 11 12 13 14 15 16 \n0 1 1 23:59 24 30 25298 23597 93 0.57 0.43 11799 \n1 1 2 23:59 24 30 24530 22652 92 0.58 0.42 22652 \n2 1 3 23:59 24 30 25296 23235 92 0.61 0.43 5809 \n3 1 4 23:59 24 30 25300 23939 95 0.59 0.45 7980 \n4 1 5 23:59 24 30 25301 24581 97 0.64 0.43 12291 "
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "data = pd.read_table('./data/teqc_SUM.dat', header=None, sep='\\s+', \\\n names=['path','year1','m1','d1','t1','year2','m2','d2','t2','nh','nd','c1','c2','c3','c4','c5','c6'])\n ",
"prompt_number": 110,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "data.head()",
"prompt_number": 111,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 111,
"metadata": {},
"text": " path year1 m1 d1 t1 \\\n0 /data/gruan/gnss_processing/data/netr9_laud/ri... 13 1 1 00:00 \n1 /data/gruan/gnss_processing/data/netr9_laud/ri... 13 1 2 00:00 \n2 /data/gruan/gnss_processing/data/netr9_laud/ri... 13 1 3 00:00 \n3 /data/gruan/gnss_processing/data/netr9_laud/ri... 13 1 4 00:00 \n4 /data/gruan/gnss_processing/data/netr9_laud/ri... 13 1 5 00:00 \n\n year2 m2 d2 t2 nh nd c1 c2 c3 c4 c5 c6 \n0 13 1 1 23:59 24 30 25298 23597 93 0.57 0.43 11799 \n1 13 1 2 23:59 24 30 24530 22652 92 0.58 0.42 22652 \n2 13 1 3 23:59 24 30 25296 23235 92 0.61 0.43 5809 \n3 13 1 4 23:59 24 30 25300 23939 95 0.59 0.45 7980 \n4 13 1 5 23:59 24 30 25301 24581 97 0.64 0.43 12291 "
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We want the index (row wise) to properly represent datetime info "
},
{
"metadata": {},
"cell_type": "code",
"input": "from datetime import datetime",
"prompt_number": 112,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "data.index = [datetime(y,m,d) for y,m,d in zip(data.year1+2000,data.m1,data.d1)] # note that we are using list comprehension",
"prompt_number": 113,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "data.head()",
"prompt_number": 114,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 114,
"metadata": {},
"text": " path year1 m1 d1 \\\n2013-01-01 /data/gruan/gnss_processing/data/netr9_laud/ri... 13 1 1 \n2013-01-02 /data/gruan/gnss_processing/data/netr9_laud/ri... 13 1 2 \n2013-01-03 /data/gruan/gnss_processing/data/netr9_laud/ri... 13 1 3 \n2013-01-04 /data/gruan/gnss_processing/data/netr9_laud/ri... 13 1 4 \n2013-01-05 /data/gruan/gnss_processing/data/netr9_laud/ri... 13 1 5 \n\n t1 year2 m2 d2 t2 nh nd c1 c2 c3 c4 c5 \\\n2013-01-01 00:00 13 1 1 23:59 24 30 25298 23597 93 0.57 0.43 \n2013-01-02 00:00 13 1 2 23:59 24 30 24530 22652 92 0.58 0.42 \n2013-01-03 00:00 13 1 3 23:59 24 30 25296 23235 92 0.61 0.43 \n2013-01-04 00:00 13 1 4 23:59 24 30 25300 23939 95 0.59 0.45 \n2013-01-05 00:00 13 1 5 23:59 24 30 25301 24581 97 0.64 0.43 \n\n c6 \n2013-01-01 11799 \n2013-01-02 22652 \n2013-01-03 5809 \n2013-01-04 7980 \n2013-01-05 12291 "
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "data.pop('path') # we pop the 'path' variable, ve careful it operates in place",
"prompt_number": 115,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 115,
"metadata": {},
"text": "2013-01-01 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2013-01-02 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2013-01-03 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2013-01-04 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2013-01-05 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2013-01-06 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2013-01-07 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2013-01-08 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2013-01-09 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2013-01-10 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2013-01-11 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2013-01-12 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2013-01-13 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2013-01-14 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2013-01-15 /data/gruan/gnss_processing/data/netr9_laud/ri...\n...\n2012-12-17 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2012-12-18 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2012-12-19 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2012-12-20 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2012-12-21 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2012-12-22 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2012-12-23 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2012-12-24 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2012-12-25 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2012-12-26 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2012-12-27 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2012-12-28 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2012-12-29 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2012-12-30 /data/gruan/gnss_processing/data/netr9_laud/ri...\n2012-12-31 /data/gruan/gnss_processing/data/netr9_laud/ri...\nName: path, Length: 551"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "data.head()",
"prompt_number": 116,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 116,
"metadata": {},
"text": " year1 m1 d1 t1 year2 m2 d2 t2 nh nd c1 c2 \\\n2013-01-01 13 1 1 00:00 13 1 1 23:59 24 30 25298 23597 \n2013-01-02 13 1 2 00:00 13 1 2 23:59 24 30 24530 22652 \n2013-01-03 13 1 3 00:00 13 1 3 23:59 24 30 25296 23235 \n2013-01-04 13 1 4 00:00 13 1 4 23:59 24 30 25300 23939 \n2013-01-05 13 1 5 00:00 13 1 5 23:59 24 30 25301 24581 \n\n c3 c4 c5 c6 \n2013-01-01 93 0.57 0.43 11799 \n2013-01-02 92 0.58 0.42 22652 \n2013-01-03 92 0.61 0.43 5809 \n2013-01-04 95 0.59 0.45 7980 \n2013-01-05 97 0.64 0.43 12291 "
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Sort the dataframe according to the index values (rowwise) \n\nNote that for plotting purposes, you don't even have to do that, pandas + matplotlib understand that the dates need to be in chronological order "
},
{
"metadata": {},
"cell_type": "code",
"input": "data2 = data.sort()",
"prompt_number": 117,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "data2.head()",
"prompt_number": 118,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 118,
"metadata": {},
"text": " year1 m1 d1 t1 year2 m2 d2 t2 nh nd c1 c2 \\\n2012-04-28 12 4 28 00:00 12 4 28 23:59 24 30 25462 25461 \n2012-05-02 12 5 2 01:00 12 5 2 23:59 23 30 24480 24480 \n2012-05-03 12 5 3 00:00 12 5 3 23:59 24 30 25455 25455 \n2012-05-04 12 5 4 00:00 12 5 4 23:59 24 30 25459 25458 \n2012-05-05 12 5 5 00:00 12 5 5 23:59 24 30 25451 25450 \n\n c3 c4 c5 c6 \n2012-04-28 100 0.55 0.42 25461 \n2012-05-02 100 0.56 0.42 24480 \n2012-05-03 100 0.55 0.43 12728 \n2012-05-04 100 0.55 0.42 25458 \n2012-05-05 100 0.54 0.42 12725 "
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "data['c3'].plot(rot=90)",
"prompt_number": 119,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 119,
"metadata": {},
"text": "<matplotlib.axes.AxesSubplot at 0x107ddc4d0>"
},
{
"output_type": "display_data",
"metadata": {},
"png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAEQCAYAAACtGP9YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQxQOqwAAAABJRU5ErkJggg==\n",
"text": "<matplotlib.figure.Figure at 0x107dbac10>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "plt.plot(data2.c3.values)",
"prompt_number": 120,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 120,
"metadata": {},
"text": "[<matplotlib.lines.Line2D at 0x1021a6610>]"
},
{
"output_type": "display_data",
"metadata": {},
"png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": "<matplotlib.figure.Figure at 0x107f97c10>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "data.columns",
"prompt_number": 121,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 121,
"metadata": {},
"text": "Index([u'year1', u'm1', u'd1', u't1', u'year2', u'm2', u'd2', u't2', u'nh', u'nd', u'c1', u'c2', u'c3', u'c4', u'c5', u'c6'], dtype='object')"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "data2 = data[data.columns[-6:]] \n# same as data2 = data[['c1','c2','c3','c4','c5','c6']] or data2 = data.ix[:,10::]",
"prompt_number": 122,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "data2.head()",
"prompt_number": 123,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 123,
"metadata": {},
"text": " c1 c2 c3 c4 c5 c6\n2013-01-01 25298 23597 93 0.57 0.43 11799\n2013-01-02 24530 22652 92 0.58 0.42 22652\n2013-01-03 25296 23235 92 0.61 0.43 5809\n2013-01-04 25300 23939 95 0.59 0.45 7980\n2013-01-05 25301 24581 97 0.64 0.43 12291"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "data2.plot(subplots=True, sharex=True, color='b', title='teqc_SUM', figsize=(14,13), rot=90)\nplt.savefig('teqc_SUM.pdf')",
"prompt_number": 124,
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"png": "iVBORw0KGgoAAAANSUhEUgAAAz0AAAMbCAYAAABnnfteAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
"text": "<matplotlib.figure.Figure at 0x107be22d0>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "f, ax = plt.subplots(1,1, figsize=(7,7))\ndata.groupby(data['m1']).mean()['c1'].plot(kind='bar', ylim=[24000,26000], color='steelblue',alpha=.7, ax=ax)\nax.set_xticklabels(list('JFMAMJJASOND'), rotation=None, fontsize=15);",
"prompt_number": 125,
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"png": "iVBORw0KGgoAAAANSUhEUgAAAbcAAAG8CAYAAABdUOsoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZIkSZIkSZIkSZIkSZIkSVIP/x9rETJJ6hUcrgAAAABJRU5ErkJggg==\n",
"text": "<matplotlib.figure.Figure at 0x10a8e70d0>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "f, ax = plt.subplots(1,1, figsize=(7,7))\ndata.groupby(data.index.month).mean()['c1'].plot(kind='bar',\\\n ylim=[24000,26000], color='steelblue',alpha=.7, ax=ax)\nax.set_xticklabels(list('JFMAMJJASOND'), rotation=None, fontsize=13);\n[f.set_fontsize(13) for f in ax.yaxis.get_ticklabels()] # list comprehension again",
"prompt_number": 126,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 126,
"metadata": {},
"text": "[None, None, None, None, None]"
},
{
"output_type": "display_data",
"metadata": {},
"png": "iVBORw0KGgoAAAANSUhEUgAAAcAAAAGsCAYAAABKE3dSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X2QpWdd5vHvJBF0zbxkCFQpRCek9A/FnVGRkK1aMhoG\nKSVIJNHVoCZmCaTcRdwlAQcFX9ZsAlYZWd1AVoQ1y6plZLRiQWTQNO6y2YAxCULWF0LGaGBlJsP0\nDGGDhJn94z6dueeke8689NPXL/18P1VdmT7P6XNdfXLO/Prc93N6QJIkSZIkSZIkSZIkSZIkSZIk\nSZIkPVldD3wcmAceAm4Czpi6zjnADmDf5OMO4LTu+HOBjwCPAJ8ELp36+mcA7wX2A58FrgPWdMdP\nBd46ObYfuAV42sl9W5IkHd0vAZtpQ+hM4H3AH3bHnw58GngTsJY2uL6NwwNsPbAbuBr4CuCFwAHg\n+d1t7KQNtbXA2cBfA9d0x984uWwTsG5y3fctz7cnSdKxeTHt1eCC/wj8r6Nc/3LgganLfgv4zcmf\nzwYOTv674MeBT3Wf/93kdhY8e/I1Zx1za0mSppxynNe/ALin+/w7gX8A/gh4GLgX+OHu+Gbg7qnb\nuHty+cLxeY4cknfTXu2dDmygDbq7uuOfoi2FbkaSpBN02uyrPO7lwKuAF3SXnUnb4/sB4KXAdwG3\n0l61fZi2rLl/6nb20ZYymRyfX+Q4k+ucOvnzYtdZhyRJJ+hYB+AlwNuBCznyFeB+2hLoeyeffxC4\njTYMP0zb7/v6qdvawOGheIC2Tzh9fOHYwgBc7DrTg5Vzzjnn0P333z/7u5EkjcW9wJbFDhzLEujl\ntOH3EuBDU8fueeLVWUPbo1s4Ph38bd3X3UsbbmdPHX+ANgD3AQ8C394dP4f26u9j08H3338/hw4d\nOuGPN7/5zSf19Sf7kc63Q438Ch3S+RU6pPMrdEjnL0cHjrJdNmsAvob2FoQX0d7eMO0dtDM6v29y\nW98JbAP+YHJ8B/DVwOuAp9LOAn0Z7e0UTAbdB4G30JZDn007A/QdXcZNwOtp+4LrJ9e9jTYYl9Wu\nXbuW+yafVPl2qJFfoUM6v0KHdH6FDun8oTvMGoA30AbTHO0V2QGOXHq8k3bSy/WTy38V+NHJ5dD2\n7r6HtoT6OdoryVd1x6G9L/AU2vsM76QNzbd0x6+j7St+FPh74BDwimP+DiVJGoFDJ+P2228/qa8/\nWel8O9TIr9AhnV+hQzq/Qod0/nJ0oL1oWtSapQ48SU2+X0mSYM2aNbDErDve9wGuanNzc6POt0ON\n/Aod0vkVOqTzK3RI5w/dwQEoSRoll0AlSauWS6CSJE1xAHbS693pfDvUyK/QIZ1foUM6v0KHdP7Q\nHRyAkqRRcg9QkrRquQcoSdIUB2Anvd6dzrdDjfwKHdL5FTqk8yt0SOcP3cEBKEkaJfcAJUmrlnuA\nkiRNcQB20uvd6Xw71Miv0CGdX6FDOr9Ch3T+0B0cgJKkUXIPUJK0arkHKEnSFAdgJ73enc63Q438\nCh3S+RU6pPMrdEjnD93BAShJGiX3ACVJq5Z7gJIkTXEAdtLr3el8O9TIr9AhnV+hQzq/Qod0/tAd\nHICSpFFyD1CStGq5ByhJ0hQHYCe93p3Ot0ON/Aod0vkVOqTzK3RI5w/dwQEoSRol9wAlSauWe4CS\nJE1xAHbS693pfDvUyK/QIZ1foUM6v0KHdP7QHRyAkqRRcg9QkrRquQcoSdIUB2Anvd6dzrdDjfwK\nHdL5FTqk8yt0SOcP3cEBKEkaJfcAJUmrlnuAkiRNcQB20uvd6Xw71Miv0CGdX6FDOr9Ch3T+0B0c\ngJKkUXIPUJK0arkHKEnSFAdgJ73enc63Q438Ch3S+RU6pPMrdEjnD93BAShJGiX3ACVJq5Z7gJIk\nTXEAdtLr3el8O9TIr9AhnV+hQzq/Qod0/tAdHICSpFFyD1CStGq5ByhJ0hQHYCe93p3Ot0ON/Aod\n0vkVOqTzK3RI5w/dwQEoSRol9wAlSauWe4CSJE1xAHbS693pfDvUyK/QIZ1foUM6v0KHdP7QHRyA\nkqRRcg9QkrRquQcoSdIUB2Anvd6dzrdDjfwKHdL5FTqk8yt0SOcP3cEBKEkaJfcAJUmrlnuAkiRN\ncQB20uvd6Xw71Miv0CGdX6FDOr9Ch3T+0B0cgJKkUXIPUJK0arkHKEnSFAdgJ73enc63Q438Ch3S\n+RU6pPMrdEjnD93BAShJGiX3ACVJq5Z7gJIkTXEAdtLr3el8O9TIr9AhnV+hQzq/Qod0/tAdHICS\npFFyD1CStGq5ByhJ0hQHYCe93p3Ot0ON/Aod0vkVOqTzK3RI5w/dwQEoSRol9wAlSauWe4CSJE1x\nAHbS693pfDvUyK/QIZ1foUM6v0KHdP7QHRyAkqRRcg9QkrRquQcoSdIUB2Anvd6dzrdDjfwKHdL5\nFTqk8yt0SOcP3cEBKEkaJfcAJUmrlnuAkiRNcQB20uvd6Xw71Miv0CGdX6FDOr9Ch3T+0B0cgJKk\nUXIPUJK0ap3MHuD1wMeBeeAh4CbgjO74ZcBB4ED38Z7u+KbJ8c93xx+cyngG8F5gP/BZ4LqpsqcC\nb50c2w/cAjxtRm9Jko5q1gB8DLgU2AhsBp4FvHvqOp8E1nYfly5yO9/QHf+6qWPvoQ3JZwLnAhcB\nV3fH3wC8FHjeJB/g5hm9T0h6vTudb4ca+RU6pPMrdEjnV+iQzh+6w2kzjr+x+/Me4G3A705d51iW\nUZcatGcDFwDncPgV4vXAzwBvmVznSuDngF2Tz6+hDd2zgL8/hmxJkp7gePcA30p7JXb+5PMfA94B\nfA74EvBh4Kc5PKw2AZ8CPg08BfgEbZh9aHL8ZcC7OHJZ9VuBu4B1tAG9F9gCfKy7zj7gFcAfTfVz\nD1CS9Ljleh/gy4FXAT/ZXfZnwHOArwG+A3gU2An8s8nx3cDzaYPw64HfB94PfMvk+Fra/mJv3+S/\n6ybHWeI6646juyRJR5i1BLrgEuDtwIXAPd3lD3R//kfglbThdC5wO/AI8JHJ8ceAX5vcxiXAX9KW\nPNdPZW2Y/PcA7QQYlrjO/sWKXnbZZWzatKldacMGtmzZwtatW4HDa8lLfX7DDTcc1/WX+/N0/tzc\nHPfccw+vfe1rY/kLtm7dOtr8Pnus+ZB/PqTzKzwf0/kLjuf5uPDnXbt2sRwuBx4GzjuG655GO+Pz\nu45ynT8GfmHy57NpJ8Cc3R2/Ari/+3zXpMOCcyZfM30yDbQl0BN2++23n9TXn6x0vh1q5FfokM6v\n0CGdX6FDOn85OgBL7ovN2gN8DfAm4Ltp+3LTvhe4l/YWiTNob2HYBnwz8AXaK8FHgL+iDccfBX4V\n+BfA3ZPb+ABtifPHgafTlkjfyeGTYLZPvu7FtL3G3wS+CvieJQbgjG9JkjQWJ7MHeANtH26Ow2dp\n9kuP5wN3Ti7/OG1pchtt+EF7ZbeDtiz6D7S3SLyEw8OPyWWn0IbonZPrv6U7fh1wK/BR2lmfh2gn\nwEiSdMJmDcBTgKdy5Pv8+pNPrqG9f+904GuBH6C9RWHB79DeA3g6cCZtYP7JVMZu2gk262ivAN8w\ndfwg7X2BT59c52LamaHLrl9DTkjn26FGfoUO6fwKHdL5FTqk84fu4O8ClSSNkr8LVJK0avnvAUqS\nNMUB2Emvd6fz7VAjv0KHdH6FDun8Ch3S+UN3cABKkkbJPUBJ0qrlHqAkSVMcgJ30enc63w418it0\nSOdX6JDOr9AhnT90BwegJGmU3AOUJK1a7gFKkjTFAdhJr3en8+1QI79Ch3R+hQ7p/Aod0vlDd3AA\nSpJGyT1ASdKq5R6gJElTHICd9Hp3Ot8ONfIrdEjnV+iQzq/QIZ0/dAcHoCRplNwDlCStWu4BSpI0\nxQHYSa93p/PtUCO/Qod0foUO6fwKHdL5Q3dwAEqSRsk9QEnSquUeoCRJUxyAnfR6dzrfDjXyK3RI\n51fokM6v0CGdP3QHB6AkaZTcA5QkrVruAUqSNMUB2Emvd6fz7VAjv0KHdH6FDun8Ch3S+UN3cABK\nkkbJPUBJ0qrlHqAkSVMcgJ30enc63w418it0SOdX6JDOr9AhnT90BwegJGmU3AOUJK1a7gFKkjTF\nAdhJr3en8+1QI79Ch3R+hQ7p/Aod0vlDd3AASpJGaVR7gBdedDF79s6f8I2fuXE9t+645YS/XpK0\nso62B3jaylbJ2rN3nm1XXXvCX7/zxu3L2EaSlOQSaGfXfXdF81f7evuTpUM6v0KHdH6FDun8Ch3S\n+UN3GNUrQEl1zNqSmN+3l/UbNi553C0JnaxR7QGed/62k14CveNDO0/46yUd5vNRK8E9QEkqyBPz\nshyAnV333cWmb/r2WP7c3Bxbt26N5duhRn6FDul8GMfzcdaJebPug6FPzKvwOBiygyfBSJJGyQHY\nSf60CcR/0rJDjfwKHdL54PMRvA+G7uAAlCSNkgOw4/sA7VAhv0KHdD74fATvg6E7OAAlSaPkAOy4\n3m6HCvkVOqTzwecjeB8M3cEBKEkaJQdgx/V2O1TIr9AhnQ8+H8H7YOgODkBJ0ig5ADuut9uhQn6F\nDul88PkI3gdDd3AASpJGyQHYcb3dDhXyK3RI54PPR/A+GLqDA1CSNEoOwI7r7XaokF+hQzoffD6C\n98HQHRyAkqRRcgB2XG+3Q4X8Ch3S+eDzEbwPhu7gAJQkjZIDsON6ux0q5FfokM4Hn4/gfTB0Bweg\nJGmUHIAd19vtUCG/Qod0Pvh8BO+DoTs4ACVJo+QA7LjebocK+RU6pPPB5yN4HwzdwQEoSRolB2DH\n9XY7VMiv0CGdDz4fwftg6A4OQEnSKJ2WLlCJ6+12qJBfoUM6H3w+wjjugwsvupg9e+dnXOuXljxy\n5sb13LrjlhPKdgBKkmL27J1n21XXnvDX77xx+wl/rUugHdfb7VAhv0KHdD74fATvAxj2PnAASpJG\nyQHYGcN6ux3q51fokM4Hn4/gfQDD3gcOQEnSKDkAO66326FCfoUO6Xzw+QjeB+AeoCRJy84B2HG9\n3Q4V8it0SOeDz0fwPgD3ACVJWnYOwI7r7XaokF+hQzoffD6C9wG4ByhJ0rJzAHZcb7dDhfwKHdL5\n4PMRvA/APUBJkpadA7DjersdKuRX6JDOB5+P4H0A7gFKkrTsHIAd19vtUCG/Qod0Pvh8BO8DcA9Q\nkqRl5wDsuN5uhwr5FTqk88HnI3gfQHYP8Hrg48A88BBwE3BGd/wy4CBwoPt4z9RtPBf4CPAI8Eng\n0qnjzwDeC+wHPgtcB6zpjp8KvHVybD9wC/C0Wd+YJElHM2sAPkYbWBuBzcCzgHdPXeeTwNruox9w\n64H3A78HbABeDbwdeH53nffQhugzgXOBi4Cru+NvAF4KPG+SD3DzrG/sRLjebocK+RU6pPPB5yN4\nH0B2D/CNwL3Al4E9wNuArVPXWcPSvh/4PO0V3JeADwI7gCsnx88GLqANvAPAA7RXna/ubuNK2qvC\nXbRXgNcALwbOmtFdkqQlHe8e4AXAPd3nh2iD6DPAg8BvA5u645uBu6du4+7J5QvH52mDrz++CTid\n9qrxLKBfBP4UbRBuZpm53m6HCvkVOqTzwecjeB9AnfcBvhx4FfCT3WV/BjwH+BrgO4BHgZ3AV02O\nn04bVr19wLrJn9fSBuD0cSbXWTv582LXWYckSSfotGO83iW0vbsLOfIVYP/K7R+BV9KG0/OB22nL\nmpumbmsDh4fiAdo+4fTxhWOnTv682HWmBysAl112GZs2tcgNGzawZcuWx9ex5/ftZdd9dz2+przw\nk0W/xny04/P79jI3N/f47S38dLRcny9cNtTtH+vnfZdEvp9vZevWras+3+fjHPP79h7xvfbf3/Qr\nn8WO91//ZP374Gjf37F8Pt1v4c+7du1ilqPt3y24HPhl4CXAHTOuexptAL4U+FPaWaJvpu31LbgZ\n+Cfgisnl9wPncHiYXgFsn1wGbe/v54F3TT4/B/hb2mB9cCr/0KFDh5Ysd97529h21bUzvoWl7bxx\nO3d8aOcJf72kw3w+eh/A8PfBmjVrYIlZN2sJ9DW0E1hexOLD73tpZ2auoZ0p+p+B3cD/nhzfAXw1\n8DrgqcALgZfR3k4Bbeh9EHgLbbnz2bSTXN7RZdwEvJ428NZPrnsbTxx+J831djtUyK/QIZ0PPh/B\n+wCGvQ9mLYHeQDt7c6677BCH99/Opw2o9bQlyf8JbAO+MDk+D3wP8OvALwCfpu0j3tnd3qW05dWH\ngC8C76QNuQXX0d57+FHaEP0A8Ipj+/YkaXEXXnQxe/ZOn15w2Py+vazfsHHJ42duXM+tO24ZoppW\nyKwBOOsV4jWTj6P5c9r7+5aym3aCzVIO0t4mcfVRrrMsfM+NHSrkV+iQzofhn4979s6f9NLb0Pw7\nyd8FKknSsnMAdlxvt0OF/Aod0vmQfz6m8yt0WO2Pg2N9G4QkaZUZ+z6oA7DjersdKuRX6JDOh/zz\nMZ2/Eh3Gvg/qEqgkaZQcgB3X2+1QIb9Ch3Q+5J+P6fwKHdL5Q3dwAEqSRskB2Emv+VfYd7FDPr9C\nh3Q+5J+P6fwKHdL5Q3dwAEqSRskB2Emvd1fYd7FDPr9Ch3Q+5J+P6fwKHdL5Q3dwAEqSRskB2Emv\nd1fYd7FDPr9Ch3Q+5J+P6fwKHdL5Q3dwAEqSRskB2Emvd1fYd7FDPr9Ch3Q+5J+P6fwKHdL5Q3dw\nAEqSRskB2Emvd1fYd7FDPr9Ch3Q+5J+P6fwKHdL5Q3dwAEqSRskB2Emvd1fYd7FDPr9Ch3Q+5J+P\n6fwKHdL5Q3dwAEqSRskB2Emvd1fYd7FDPr9Ch3Q+5J+P6fwKHdL5Q3dwAEqSRskB2Emvd1fYd7FD\nPr9Ch3Q+5J+P6fwKHdL5Q3dwAEqSRskB2Emvd1fYd7FDPr9Ch3Q+5J+P6fwKHdL5Q3dwAEqSRskB\n2Emvd1fYd7FDPr9Ch3Q+5J+P6fwKHdL5Q3dwAEqSRskB2Emvd1fYd7FDPr9Ch3Q+5J+P6fwKHdL5\nQ3dwAEqSRskB2Emvd1fYd7FDPr9Ch3Q+5J+P6fwKHdL5Q3dwAEqSRskB2Emvd1fYd7FDPr9Ch3Q+\n5J+P6fwKHdL5Q3dwAEqSRskB2Emvd1fYd7FDPr9Ch3Q+5J+P6fwKHdL5Q3dwAEqSRskB2Emvd1fY\nd7FDPr9Ch3Q+5J+P6fwKHdL5Q3dwAEqSRskB2Emvd1fYd7FDPr9Ch3Q+5J+P6fwKHdL5Q3dwAEqS\nRskB2Emvd1fYd7FDPr9Ch3Q+5J+P6fwKHdL5Q3c4bbBblrSoCy+6mD1750/qNs7cuJ5bd9yyTI2k\ncXIAdnbdd1f0J565ubn4T952GD5/z955tl117VGvM+uxuPPG7ctd6wjp/weQfz6m8yt0SOcP3cEl\nUEnSKDkAO+mfdNI/cduhRj74WIT8fZDOr9AhnT90BwegJGmUHICd9HteKrz3yg75fPCxCPn7IJ1f\noUM6f+gOngSzgmad/Te/by/rN2w86m149p8kLQ8HYGfo9e5jOftvlqHP/quw95PukM6H/N6L90E+\nv0KHdP7QHVwClSSNkgOwk17vTudDjb2fdId0PuQfC94H+fwKHdL5Q3dwAEqSRskB2Emvd6fzocbe\nT7pDOh/yjwXvg3x+hQ7p/KE7OAAlSaPkWaCd9O+9S+dDjd8Bme6QzofhHwsn+5aclXg7Tvr5kM6v\n0CGdP3QHB6A0QrPekpP+ZdzSSnAJtJP+SSedDzX2ftId0vmQfyyk8yt0SOdX6JDOH7qDA1CSNEoO\nwE76PS/pfKjx/q90h3Q+5B8L6fwKHdL5FTqk84fu4ACUJI2SA7CTXu9O50ON/a90h3Q+5B8L6fwK\nHdL5FTqk84fu4ACUJI2SA7CTXu9O50ON/a90h3Q+5B8L6fwKHdL5FTqk84fu4ACUJI2SA7CTXu9O\n50ON/a90h3Q+5B8L6fwKHdL5FTqk84fu4ACUJI2SA7CTXu9O50ON/a90h3Q+5B8L6fwKHdL5FTqk\n84fu4ACUJI2SA7CTXu9O50ON/a90h3Q+5B8L6fwKHdL5FTqk84fu4ACUJI2SA7CTXu9O50ON/a90\nh3Q+5B8L6fwKHdL5FTqk84fu4ACUJI2SA7CTXu9O50ON/a90h3Q+5B8L6fwKHdL5FTqk84fu4ACU\nJI2SA7CTXu9O50ON/a90h3Q+5B8L6fwKHdL5FTqk84fu4ACUJI2SA7CTXu9O50ON/a90h3Q+5B8L\n6fwKHdL5FTqk84fu4ACUJI2SA7CTXu9O50ON/a90h3Q+5B8L6fwKHdL5FTqk84fu4ACUJI2SA7CT\nXu9O50ON/a90h3Q+5B8L6fwKHdL5FTqk84fu4ACUJI2SA7CTXu9O50ON/a90h3Q+5B8L6fwKHdL5\nFTqk84fu4ACUJI2SA7CTXu9O50ON/a90h3Q+5B8L6fwKHdL5FTqk84fu4ACUJI2SA7CTXu9O50ON\n/a90h3Q+5B8L6fwKHdL5FTqk84fu4ACUJI3SrAF4PfBxYB54CLgJOOMo1z0IXNpdtmly2eeBA5OP\nB6e+7hnAe4H9wGeB64A13fFTgbdOju0HbgGeNqP3CUmvd6fzocb+V7pDOh/yj4V0foUO6fwKHdL5\nQ3eYNQAfow20jcBm4FnAuxe53vOAFwOfBg4tcvwbgLWTj6+bOvYe2pB8JnAucBFwdXf8DcBLJxnP\nmlx284zekiQd1awB+EbgXuDLwB7gbcDWqes8FfgN4ErgS8eZczZwAW3gHQAeoL2SfHV3nStprwp3\n0V4BXkMbtmfN6H7c0uvd6Xyosf+V7pDOh/xjIZ1foUM6v0KHdP7QHY53D/AC4J6py34O+BPgzqN8\n3Z20JczbgfO7yzfTllcf6C67m7Z0ejqwgTbo+nvgU7RBuPk4u0uS9LjTjuO6LwdeBbygu+y5wMUs\nPYx2A88H/gL4CuAK4P20pc6/pC2Jzk99zb7Jf9fR9v9Y4jrrjqP7MUmvd6fzocb+V7pDOh/yj4V0\nfoUO6fwKHdL5Q3c41gF4CfB24EIOvwJ8CvAu4CeAL3TX7U9geQT4yOTPjwG/NrmNS2gD8ACwfipr\nw+S/Bzg8ABe7zv7Fil522WVs2rSpXWnDBrZs2fL4X2jz+/ay6767Hr9DF15aH+vn8/v2Mjc39/jt\nLSyVHevnJ5u/6767mN+39/Hv9Xjz/bzG5wtO5P9///nJ9knnP9mfjyebPzc3d8TzOfH3QTq/d6KP\nx+n8hT/v2rWLWdbMvAZcDvwy8BLgju7yTcD9wN7usjNow/APgR9Z4vb+mLYk+ibaHuD9wDkcXga9\nAtg+uQza3t/P04Ytk8v/dpI/fUbpoUOHFjsHpznv/G1su+raJY/3T4bF7LxxO3d8aOeSx2c52fzl\n6DBL/4ROSXcYOn/W4wDqPxaX43GY7pDOr9Ahnb8SHdasWQNLzLpZe4Cvob0F4UUcOfygDZ+zaMuf\nm4EttLNAf3ryddCWOp9De6X5lbQTWl4A7JgcfwD4IPAW2nLos2knubyjy7kJeD1t4K2fXPc2njj8\nJEk6ZrOWQG+gndk51112iLb/dpA28HpfBj43+YD2Cu8Xga8BHgU+QXsleXf3NZfSllcfAr4IvJM2\n5BZcR3tl+VHaGacfAF4x6xs7Een17nQ+1Nj/SndI50P+sZDOr9AhnV+hQzp/6A6zBuDxniV69tTn\nvzP5OJrdtBNslnKQ9jaJq49yHUmSjou/Cq2Tfs9LOh9qvAcu3SGdD/nHQjq/Qod0foUO6fyhOzgA\nJUmj5ADspNe70/lQY/8r3SGdD/nHQjq/Qod0foUO6fyhOzgAJUmj5ADspNe70/lQY/8r3SGdD/nH\nQjq/Qod0foUO6fyhOzgAJUmj5ADspNe70/lQY/8r3SGdD/nHQjq/Qod0foUO6fyhOzgAJUmj5ADs\npNe70/lQY/8r3SGdD/nHQjq/Qod0foUO6fyhOzgAJUmj5ADspNe70/lQY/8r3SGdD/nHQjq/Qod0\nfoUO6fyhOzgAJUmj5ADspNe70/lQY/8r3SGdD/nHQjq/Qod0foUO6fyhOzgAJUmjNOufQxqV9Hp3\nOh+G3/+68KKL2bN3/hiu+UtLHjlz43pu3XHL8pWa4h5gPr9Ch3R+hQ7p/KE7OAC1ovbsnWfbVdee\n1G3svHH7MrWRNGYugXbS693pfHD/C7wPKuRX6JDOr9AhnT90BwegJGmUHICd9Hp3Oh/c/wLvgwr5\nFTqk8yt0SOcP3cEBKEkaJQdgJ73enc4H97/A+6BCfoUO6fwKHdL5Q3dwAEqSRskB2Emvd6fzwf0v\n8D6okF+hQzq/Qod0/tAdHICSpFFyAHbS693pfHD/C7wPKuRX6JDOr9AhnT90BwegJGmUHICd9Hp3\nOh/c/wLvgwr5FTqk8yt0SOcP3cEBKEkaJQdgJ73enc4H97/A+6BCfoUO6fwKHdL5Q3dwAEqSRsl/\nDqmTXu9O58M49r+O7d8kzP17hJB/LKTzK3RI51fokM4fuoMDUKNzsv8mof8eobQ6uATaSa93p/PB\n/a8K+RU6pPMrdEjnV+iQzh+6gwNQkjRKDsBOer07nQ/j2AOsnl+hQzq/Qod0foUO6fyhOzgAJUmj\n5ADspNe70/ngHmCF/Aod0vkVOqTzK3RI5w/dwQEoSRolB2Anvd6dzgf3ACvkV+iQzq/QIZ1foUM6\nf+gODkBJ0ig5ADvp9e50PrgHWCG/Qod0foUO6fwKHdL5Q3dwAEqSRskB2Emvd6fzwT3ACvkVOqTz\nK3RI51fokM4fuoMDUJI0Sg7ATnq9O50P7gFWyK/QIZ1foUM6v0KHdP7QHRyAkqRRcgB20uvd6Xxw\nD7BCfoUO6fwKHdL5FTqk84fu4ACUJI2SA7CTXu9O54N7gBXyK3RI51fokM6v0CGdP3QHB6AkaZQc\ngJ30enc6H9wDrJBfoUM6v0KHdH6FDun8oTs4ACVJo+QA7KTXu9P54B5ghfwKHdL5FTqk8yt0SOcP\n3cEBKEkaJQdgJ73enc4H9wAr5FfokM6v0CGdX6FDOn/oDg5ASdIoOQA76fXudD64B1ghv0KHdH6F\nDun8Ch1hHM9kAAAIwElEQVTS+UN3cABKkkbJAdhJr3en88E9wAr5FTqk8yt0SOdX6JDOH7qDA1CS\nNEoOwE56vTudD+4BVsiv0CGdX6FDOr9Ch3T+0B0cgJKkUXIAdtLr3el8cA+wQn6FDun8Ch3S+RU6\npPOH7uAAlCSNkgOwk17vTueDe4AV8it0SOdX6JDOr9AhnT90BwegJGmUHICd9Hp3Oh/cA6yQX6FD\nOr9Ch3R+hQ7p/KE7OAAlSaPkAOyk17vT+eAeYIX8Ch3S+RU6pPMrdEjnD93BAShJGiUHYCe93p3O\nB/cAK+RX6JDOr9AhnV+hQzp/6A4OQEnSKDkAO+n17nQ+uAdYIb9Ch3R+hQ7p/Aod0vlDd3AASpJG\nyQHYSa93p/PBPcAK+RU6pPMrdEjnV+iQzh+6gwNQkjRKDsBOer07nQ/uAVbIr9AhnV+hQzq/Qod0\n/tAdThvsllXShRddzJ6980sen9+3l/UbNi55/MyN67l1xy1DVJOkFeUA7KTXu1cif8/eebZdde0J\nf/3OG7cvY5vFjeH/Q/UO6fwKHdL5FTqk84fu4BKoJGmUHICd9Hp3Ot8ONfIrdEjnV+iQzq/QIZ0/\ndAcHoCRplByAnfR6dzrfDjXyK3RI51fokM6v0CGdP3QHB6AkaZQcgJ30enc63w418it0SOdX6JDO\nr9AhnT90BwegJGmUHICd9Hp3Ot8ONfIrdEjnV+iQzq/QIZ0/dAcHoCRplGYNwOuBjwPzwEPATcAZ\nR7nuQeDSqcufC3wEeAT45CLHnwG8F9gPfBa4DljTHT8VeOvk2H7gFuBpM3qfkPR6dzrfDjXyK3RI\n51fokM6v0CGdP3SHWQPwMdrA2ghsBp4FvHuR6z0PeDHwaeBQd/l64P3A7wEbgFcDbwee313nPbTB\n+UzgXOAi4Oru+BuAl04ynjW57OYZvU/I/931N0Pc7JMm3w418it0SOdX6JDOr9AhnT90h1kD8I3A\nvcCXgT3A24CtU9d5KvAbwJXAl6aOfT/wedoruC8BHwR2TK4LcDZwAW3gHQAeoL2SfHV3G1fSXhXu\nor0CvIY2bM+a+d0dpy9+4cBy3+STKt8ONfIrdEjnV+iQzq/QIZ0/dIfj3QO8ALhn6rKfA/4EuHOR\n628G7p667O7J5QvH52mDrz++CTid9qrxLKB/Dfwp2iDcjCRJJ+h4/jWIlwOvAl7QXfZc4GKWHkan\n04ZVbx+wbvLntbQBOH2cyXVOnfx5seusY5nt2/2Z5b7JJ1W+HWrkV+iQzq/QIZ1foUM6v0qHS4CH\ngfO7y54C/CXwou6yBzjyJJdfoS159n6Kw6/oXgZ8bur4t9L2BNfSXgEeBP751HX2AS9ZpOc9tD1I\nP/zwww8//DjEE1ctj8vltOF33tTlm2h7g7u7j8dor/gWTlK5jCOXN5kce+fkz2fTBtzZ3fErgPu7\nz3dNOiw4Z/I1X3ec34ckScfsNbSTXxZ7J+IpwNd2H88EHgR+gsNvlVhPe/vC62gny7yQdrLLud3t\nfIB2luha4NnAX9NOdFmwHfgr2sBdD/w+8L6T/cYkSTqag8AXaUNr4WN6T6/3APDDU5c9l3aCzBdo\n7wOcPv502lDbT3sVed3U8VNoZ5Hu5vD7ADce5/chSZIkSdKR5mjvfUzkPsqRr7RvWsHsg7QTnXrn\nTi6f3sMd0hsnmT+6gplQ6z5YMEfmsVihQ+pxsODZtG2Zz9Ceiw/SflvVV6xQ/nnAbbST/T4P/Dkr\ne1/M0e7/fzl1+SeBH1uh/EdpK377aOeE3MziW3Enxd8FeqSFs4YSub9A2wdd+LjyqF+xvNn/B3jl\n1OWvnFy+UvfHKZPMe1i5731Blfugl3ospjskHwcL3kf71Y/fSHsuLgykNUf7omXyIuBPgQ/TTg48\nk/bLQW6gved6JRyinfj4y4tcvhKPh4W/D9fR3gnwnbSTIe+gvXNg2TgABe2n22/l8Nm4a2m/xedd\nrMyTHuC7ab/j9RW0v3C+eYVyF1S4D5R/HDyNNvjeTnv1B4d/D/I/rUD+rwP/HfhF2lvEHqW9Gv0p\n2ivjr1+BDgD/hfarJ39ohfKO5kHgZ4HfAv7Tct6wA7CO5F+yj9J+J+sVk89/iLYMsZLvQL2SNoTu\nA/4H7ZcurKQK94Hyj4OHgU/Q3qr1I8A3sXLPzW+kvc3rvy1y7LcnPV64Ql0eAd4EXMvKLf3O8ru0\ndxt843LdoAOwhjW0n+4+1308bwXzD9F+4ruc9tt3rpx8vlJP/K8FvpfD7x+9mfYK4CtXKB/y94Fq\nPA6g/b7jOeC1tF/N+BlWZi/06ZP/PrTIsX+ivSXtGSvQA9rz4V20PcjXrlDmLP8w+e+y/WtADsAa\nDgH/gfb+yYWPj6xwh08Af0f7qe9M2p7HSrmC9jaXP518fgvtL70fXMEOkL0PVOdx8DBt4H077b3H\nrwfezJG/kGMIuyf/feYix55Ce0zuXuTYUA7S/qGC7dR469nCvwb08HLdoANQvZuAnwF+k5U9+eUK\n2k91D9F+2v4r2u+pXenlL8jcB6r3OFjwKPBfgY8x/C/g/xvaL/uf/jdTAf4V7fG4c+AO024DPkr7\nASDtB2mvApft30c6nl+GrWGlltrWdNm/Tdtw/osVzH8x7Se77+DIpZ8ttCffc2j/KPOQ0veBajwO\noJ11+HraPtzf0IbO903y/+MK5P8b4A9ob725Efh/tGXhX6H9kpC/W4EOcOTfR6+jrUg9ukLZ0/ln\nAf+a9haMH1jOEF8BPlHqp/5k7kL2F2nLT/sWOTaUK2m/MP1u2q/NW/j4AO2055U4FT59HyylwivQ\nlepQ4XEAba/t6bQTcR6edNgO/Fvab6wa2m20f3buBbQhuBv4aeDf086EXCn9//eP0c5MXbuC+T9L\nex/gPPAh2nszz+OJ/7iCltFf8MT3gkkJFR6LFTpIWgFbaD/9f0u6iEavwmOxQgdJK+B3gL8H/l26\niEavwmOxQgdJkiRJkiRJkiRJkiRJkiRJkiRJzf8HfUZ+ugWGDQIAAAAASUVORK5CYII=\n",
"text": "<matplotlib.figure.Figure at 0x10a6de590>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
}
],
"metadata": {}
}
],
"metadata": {
"name": "",
"signature": "sha256:2d1a451f49240922eb0485c7327a5b6c3abe3ef5bc4d88b8d8c8a72a4d5a26d1"
},
"nbformat": 3
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment