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{"nbformat": 3, "worksheets": [{"cells": [{"source": "# 13:00 Datenanalyse mit Pandas", "cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}}, {"source": "- data frame: load, select, simple func (mean, std), apply, drop, corr, reshape, pivot\n- pandas time series: simulation res to time series?, resample, delta\n\nPandas is provides the bread and butter of data analysis in python and allows data scientists to work on high-level objects, like DataFrames. A DataFrame (DF) is comparable to a single SQL database table or Excel worksheet.\n\nIn this lesson we will look on how data ends up in a DF and some operations, you can perform on a DF, like:\n\n- load from Excel, CSV, databases (or optimization results)\n- select\n- apply simple functions, like mean, std, etc.\n- drop lines or cols\n- resample data\n- pivot\n- export", "cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}}, {"source": "## Import DFs from Excel\n\nPandas can read tables from both xls and xlsx format. There are options for skipping rows or selecting a particular worksheet.", "cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}}, {"cell_type": "code", "language": "python", "outputs": [], "collapsed": false, "prompt_number": 5, "input": "census_df = pd.read_excel('Census_2000_2010.xlsx')", "trusted": true, "metadata": {"slideshow": {"slide_type": "fragment"}}}, {"cell_type": "code", "language": "python", "outputs": [{"output_type": "pyout", "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>FIPS</th>\n <th>Name</th>\n <th>Area</th>\n <th>Pop_2000</th>\n <th>Pop_2010</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td> 1001</td>\n <td> Autauga County</td>\n <td> 594.44</td>\n <td> 43,671.00</td>\n <td> 54571</td>\n </tr>\n <tr>\n <th>1</th>\n <td> 1003</td>\n <td> Baldwin County</td>\n <td>1,589.78</td>\n <td>140,415.00</td>\n <td> 182265</td>\n </tr>\n <tr>\n <th>2</th>\n <td> 1005</td>\n <td> Barbour County</td>\n <td> 884.88</td>\n <td> 29,038.00</td>\n <td> 27457</td>\n </tr>\n <tr>\n <th>3</th>\n <td> 1007</td>\n <td> Bibb County</td>\n <td> 622.58</td>\n <td> 20,826.00</td>\n <td> 22915</td>\n </tr>\n <tr>\n <th>4</th>\n <td> 1009</td>\n <td> Blount County</td>\n <td> 644.78</td>\n <td> 51,024.00</td>\n <td> 57322</td>\n </tr>\n </tbody>\n</table>\n</div>", "metadata": {}, "prompt_number": 7, "text": " FIPS Name Area Pop_2000 Pop_2010\n0 1001 Autauga County 594.44 43,671.00 54571\n1 1003 Baldwin County 1,589.78 140,415.00 182265\n2 1005 Barbour County 884.88 29,038.00 27457\n3 1007 Bibb County 622.58 20,826.00 22915\n4 1009 Blount County 644.78 51,024.00 57322"}], "collapsed": false, "prompt_number": 7, "input": "census_df.head()", "trusted": true, "metadata": {"slideshow": {"slide_type": "fragment"}}}, {"cell_type": "code", "language": "python", "outputs": [{"output_type": "pyout", "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>FIPS</th>\n <th>Name</th>\n <th>Pop_2010</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td> 1001</td>\n <td> Autauga County</td>\n <td> 54571</td>\n </tr>\n <tr>\n <th>1</th>\n <td> 1003</td>\n <td> Baldwin County</td>\n <td> 182265</td>\n </tr>\n <tr>\n <th>2</th>\n <td> 1005</td>\n <td> Barbour County</td>\n <td> 27457</td>\n </tr>\n <tr>\n <th>3</th>\n <td> 1007</td>\n <td> Bibb County</td>\n <td> 22915</td>\n </tr>\n <tr>\n <th>4</th>\n <td> 1009</td>\n <td> Blount County</td>\n <td> 57322</td>\n </tr>\n </tbody>\n</table>\n</div>", "metadata": {}, "prompt_number": 32, "text": " FIPS Name Pop_2010\n0 1001 Autauga County 54571\n1 1003 Baldwin County 182265\n2 1005 Barbour County 27457\n3 1007 Bibb County 22915\n4 1009 Blount County 57322"}], "collapsed": false, "prompt_number": 32, "input": "census_df[['FIPS', 'Name', 'Pop_2010']].head()", "trusted": true, "metadata": {"slideshow": {"slide_type": "slide"}}}, {"source": "### Multiple ways to select particular rows\n\nPython style (bool array)", "cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}}, {"cell_type": "code", "language": "python", "outputs": [{"output_type": "pyout", "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>FIPS</th>\n <th>Name</th>\n <th>Area</th>\n <th>Pop_2000</th>\n <th>Pop_2010</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>70</th>\n <td> 2050</td>\n <td> Bethel Census Area</td>\n <td>40,570.00</td>\n <td>16,006.00</td>\n <td> 17013</td>\n </tr>\n <tr>\n <th>72</th>\n <td> 2068</td>\n <td> Denali Borough</td>\n <td>12,751.41</td>\n <td> 1,893.00</td>\n <td> 1826</td>\n </tr>\n <tr>\n <th>73</th>\n <td> 2070</td>\n <td> Dillingham Census Area</td>\n <td>18,568.78</td>\n <td> 4,922.00</td>\n <td> 4847</td>\n </tr>\n <tr>\n <th>78</th>\n <td> 2122</td>\n <td> Kenai Peninsula Borough</td>\n <td>16,075.33</td>\n <td>49,691.00</td>\n <td> 55400</td>\n </tr>\n <tr>\n <th>81</th>\n <td> 2164</td>\n <td> Lake and Peninsula Borough</td>\n <td>23,652.01</td>\n <td> 1,823.00</td>\n <td> 1631</td>\n </tr>\n </tbody>\n</table>\n</div>", "metadata": {}, "prompt_number": 16, "text": " FIPS Name Area Pop_2000 Pop_2010\n70 2050 Bethel Census Area 40,570.00 16,006.00 17013\n72 2068 Denali Borough 12,751.41 1,893.00 1826\n73 2070 Dillingham Census Area 18,568.78 4,922.00 4847\n78 2122 Kenai Peninsula Borough 16,075.33 49,691.00 55400\n81 2164 Lake and Peninsula Borough 23,652.01 1,823.00 1631"}], "collapsed": false, "prompt_number": 16, "input": "census_df[census_df.Area > 10000].head()", "trusted": true, "metadata": {"slideshow": {"slide_type": "fragment"}}}, {"source": "Or with the rather new `query` function:", "cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}}, {"cell_type": "code", "language": "python", "outputs": [{"output_type": "pyout", "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>FIPS</th>\n <th>Name</th>\n <th>Area</th>\n <th>Pop_2000</th>\n <th>Pop_2010</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>70</th>\n <td> 2050</td>\n <td> Bethel Census Area</td>\n <td>40,570.00</td>\n <td>16,006.00</td>\n <td> 17013</td>\n </tr>\n <tr>\n <th>72</th>\n <td> 2068</td>\n <td> Denali Borough</td>\n <td>12,751.41</td>\n <td> 1,893.00</td>\n <td> 1826</td>\n </tr>\n <tr>\n <th>73</th>\n <td> 2070</td>\n <td> Dillingham Census Area</td>\n <td>18,568.78</td>\n <td> 4,922.00</td>\n <td> 4847</td>\n </tr>\n <tr>\n <th>78</th>\n <td> 2122</td>\n <td> Kenai Peninsula Borough</td>\n <td>16,075.33</td>\n <td>49,691.00</td>\n <td> 55400</td>\n </tr>\n <tr>\n <th>81</th>\n <td> 2164</td>\n <td> Lake and Peninsula Borough</td>\n <td>23,652.01</td>\n <td> 1,823.00</td>\n <td> 1631</td>\n </tr>\n </tbody>\n</table>\n</div>", "metadata": {}, "prompt_number": 18, "text": " FIPS Name Area Pop_2000 Pop_2010\n70 2050 Bethel Census Area 40,570.00 16,006.00 17013\n72 2068 Denali Borough 12,751.41 1,893.00 1826\n73 2070 Dillingham Census Area 18,568.78 4,922.00 4847\n78 2122 Kenai Peninsula Borough 16,075.33 49,691.00 55400\n81 2164 Lake and Peninsula Borough 23,652.01 1,823.00 1631"}], "collapsed": false, "prompt_number": 18, "input": "census_df.query('Area > 10000').head()", "trusted": true, "metadata": {"slideshow": {"slide_type": "fragment"}}}, {"cell_type": "code", "language": "python", "outputs": [{"output_type": "pyout", "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>FIPS</th>\n <th>Name</th>\n <th>Area</th>\n <th>Pop_2000</th>\n <th>Pop_2010</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>72 </th>\n <td> 2068</td>\n <td> Denali Borough</td>\n <td> 12,751.41</td>\n <td> 1,893.00</td>\n <td> 1826</td>\n </tr>\n <tr>\n <th>73 </th>\n <td> 2070</td>\n <td> Dillingham Census Area</td>\n <td> 18,568.78</td>\n <td> 4,922.00</td>\n <td> 4847</td>\n </tr>\n <tr>\n <th>81 </th>\n <td> 2164</td>\n <td> Lake and Peninsula Borough</td>\n <td> 23,652.01</td>\n <td> 1,823.00</td>\n <td> 1631</td>\n </tr>\n <tr>\n <th>91 </th>\n <td> 2261</td>\n <td> Valdez-Cordova Census Area</td>\n <td> 34,239.88</td>\n <td>10,195.00</td>\n <td> 9636</td>\n </tr>\n <tr>\n <th>95 </th>\n <td> 2290</td>\n <td> Yukon-Koyukuk Census Area</td>\n <td>145,504.79</td>\n <td> 6,551.00</td>\n <td> 5588</td>\n </tr>\n <tr>\n <th>2220</th>\n <td> 41025</td>\n <td> Harney County</td>\n <td> 10,133.17</td>\n <td> 7,609.00</td>\n <td> 7422</td>\n </tr>\n </tbody>\n</table>\n</div>", "metadata": {}, "prompt_number": 30, "text": " FIPS Name Area Pop_2000 Pop_2010\n72 2068 Denali Borough 12,751.41 1,893.00 1826\n73 2070 Dillingham Census Area 18,568.78 4,922.00 4847\n81 2164 Lake and Peninsula Borough 23,652.01 1,823.00 1631\n91 2261 Valdez-Cordova Census Area 34,239.88 10,195.00 9636\n95 2290 Yukon-Koyukuk Census Area 145,504.79 6,551.00 5588\n2220 41025 Harney County 10,133.17 7,609.00 7422"}], "collapsed": false, "prompt_number": 30, "input": "census_df.query('Area > 10000 & Pop_2000 > Pop_2010')", "trusted": true, "metadata": {"slideshow": {"slide_type": "slide"}}}, {"source": "### Use built-in functions\n\nPandas comes with a number of built-in functions.", "cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}}, {"cell_type": "code", "language": "python", "outputs": [{"output_type": "pyout", "prompt_number": 38, "metadata": {}, "text": "1123.7369583200764"}], "collapsed": false, "prompt_number": 38, "input": "census_df.Area.mean()", "trusted": true, "metadata": {"slideshow": {"slide_type": "fragment"}}}, {"cell_type": "code", "language": "python", "outputs": [{"output_type": "pyout", "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>FIPS</th>\n <th>Area</th>\n <th>Pop_2000</th>\n <th>Pop_2010</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td> 3,143.00</td>\n <td> 3,143.00</td>\n <td> 3,137.00</td>\n <td> 3,143.00</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>30,390.41</td>\n <td> 1,123.74</td>\n <td> 89,703.97</td>\n <td> 98,232.75</td>\n </tr>\n <tr>\n <th>std</th>\n <td>15,164.72</td>\n <td> 3,611.42</td>\n <td> 292,633.12</td>\n <td> 312,901.20</td>\n </tr>\n <tr>\n <th>min</th>\n <td> 1,001.00</td>\n <td> 2.00</td>\n <td> 67.00</td>\n <td> 82.00</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>18,178.00</td>\n <td> 430.73</td>\n <td> 11,253.00</td>\n <td> 11,104.50</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>29,177.00</td>\n <td> 615.63</td>\n <td> 24,653.00</td>\n <td> 25,857.00</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>45,082.00</td>\n <td> 923.95</td>\n <td> 61,792.00</td>\n <td> 66,699.00</td>\n </tr>\n <tr>\n <th>max</th>\n <td>56,045.00</td>\n <td>145,504.79</td>\n <td>9,519,338.00</td>\n <td>9,818,605.00</td>\n </tr>\n </tbody>\n</table>\n</div>", "metadata": {}, "prompt_number": 40, "text": " FIPS Area Pop_2000 Pop_2010\ncount 3,143.00 3,143.00 3,137.00 3,143.00\nmean 30,390.41 1,123.74 89,703.97 98,232.75\nstd 15,164.72 3,611.42 292,633.12 312,901.20\nmin 1,001.00 2.00 67.00 82.00\n25% 18,178.00 430.73 11,253.00 11,104.50\n50% 29,177.00 615.63 24,653.00 25,857.00\n75% 45,082.00 923.95 61,792.00 66,699.00\nmax 56,045.00 145,504.79 9,519,338.00 9,818,605.00"}], "collapsed": false, "prompt_number": 40, "input": "census_df.describe()", "trusted": true, "metadata": {"slideshow": {"slide_type": "fragment"}}}, {"cell_type": "code", "language": "python", "outputs": [{"output_type": "pyout", "prompt_number": 44, "metadata": {}, "text": "(308745538, 281401351.0)"}], "collapsed": false, "prompt_number": 44, "input": "census_df.Pop_2010.sum(), census_df.Pop_2000.sum()", "trusted": true, "metadata": {"slideshow": {"slide_type": "fragment"}}}, {"source": "### Slice DataFrame", "cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}}, {"cell_type": "code", "language": "python", "outputs": [], "collapsed": false, "prompt_number": 45, "input": "pop_area = census_df[['Area', 'Pop_2010']]", "trusted": true, "metadata": {"slideshow": {"slide_type": "fragment"}}}, {"cell_type": "code", "language": "python", "outputs": [{"output_type": "pyout", "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>Area</th>\n <th>Pop_2010</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>Area</th>\n <td>1.00</td>\n <td>0.03</td>\n </tr>\n <tr>\n <th>Pop_2010</th>\n <td>0.03</td>\n <td>1.00</td>\n </tr>\n </tbody>\n</table>\n</div>", "metadata": {}, "prompt_number": 47, "text": " Area Pop_2010\nArea 1.00 0.03\nPop_2010 0.03 1.00"}], "collapsed": false, "prompt_number": 47, "input": "pop_area.corr()", "trusted": true, "metadata": {"slideshow": {"slide_type": "fragment"}}}, {"source": "## Operations relevant for time series\n\nThe previous part showed simple operations on a general data set. Since most of the data in `pyquasar` are some kind of time series, the next part will deal with operations relevant to this data type.\n\nWe could import time series from CSV as well, but since most 'interesting data' is of large volume, it's usually not suited for CSV or Excel. By using custom connectors to our data warehouse, you can access a growing set of current data directly from your notebook. This includes:\n\n- weather data\n- EU electricity prices\n- EU gas prices\n- EU solar power production\n- EU wind power production\n\nWe are constantly working to add new data sources and keep existing ones updated. In case we don't have the required data sources available or you would like to use private sources, it's also possible to connect to most common databases and formats. Just let us know, if we can be of assistance.\n\nEvery Quantego-provided dataset is available as class for easy access:", "cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}}, {"cell_type": "code", "language": "python", "outputs": [], "collapsed": false, "prompt_number": 72, "input": "el = EEXTrade.df()", "trusted": true, "metadata": {"slideshow": {"slide_type": "fragment"}}}, {"source": "This command takes the `EEXTrade` class and calls the `df()`-function. This will query the underlying database and return a DataFrame.", "cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "fragment"}}}, {"source": "### Selecting and slicing time-series\n\nThose operations all work the same as for ordinary DFs. There are some convenient functions for working with date and time", "cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}}, {"cell_type": "code", "language": "python", "outputs": [{"output_type": "pyout", "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>price</th>\n <th>volume</th>\n </tr>\n <tr>\n <th>datetime</th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2014-09-30 23:00:00</th>\n <td>33.31</td>\n <td>24,717.70</td>\n </tr>\n <tr>\n <th>2014-09-30 22:00:00</th>\n <td>38.53</td>\n <td>25,058.80</td>\n </tr>\n <tr>\n <th>2014-09-30 21:00:00</th>\n <td>45.71</td>\n <td>25,262.10</td>\n </tr>\n <tr>\n <th>2014-09-30 20:00:00</th>\n <td>53.97</td>\n <td>25,776.70</td>\n </tr>\n <tr>\n <th>2014-09-30 19:00:00</th>\n <td>62.18</td>\n <td>25,273.70</td>\n </tr>\n </tbody>\n</table>\n</div>", "metadata": {}, "prompt_number": 85, "text": " price volume\ndatetime \n2014-09-30 23:00:00 33.31 24,717.70\n2014-09-30 22:00:00 38.53 25,058.80\n2014-09-30 21:00:00 45.71 25,262.10\n2014-09-30 20:00:00 53.97 25,776.70\n2014-09-30 19:00:00 62.18 25,273.70"}], "collapsed": false, "prompt_number": 85, "input": "el[el.index < '2014-10-01'].head()", "trusted": true, "metadata": {"slideshow": {"slide_type": "fragment"}}}, {"source": "### Resample time series\n\nA resolution of 1 hour is not very convenient for long-term analysis. The `.resample` command let's you easily change the resolution, based on a pre-defined or custom function.", "cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}}, {"cell_type": "code", "language": "python", "outputs": [{"output_type": "pyout", "prompt_number": 74, "metadata": {}, "text": "<matplotlib.axes._subplots.AxesSubplot at 0x7fbd516ecb10>"}, {"output_type": "display_data", "metadata": {}, "png": 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66qTkLwzPNY98CdoQL4+kTj4X+8kibPOt1xKp2+erUOFRfuP9AgGc7/4UunSD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\nMs88A5Yvwb3uO9h1a5oelzC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nCw/liR8It3b6OPbZV+XL9UMkbPdRrKnrutk/+Xm6ri5epDpfSOnW2AceiTV1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\n8G2PCHqhw7F6enNrLQhUroDXwLVaBHw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"text": "<matplotlib.figure.Figure at 0x7fbd51387250>"}], "collapsed": false, "prompt_number": 79, "input": "el.price.resample('d', how='mean').plot()", "trusted": true, "metadata": {"slideshow": {"slide_type": "fragment"}}}, {"source": "The `df()`-function also accepts optional arguments to only return a subset of the data. This is especially useful when dealing with very big datasets (weather), or if only a subset of the data is interesting. Since data is always loaded from a remote server, it can also be quicker to run a select on the database, rather the DF, after the data has already been transferred.", "cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}}, {"cell_type": "code", "language": "python", "outputs": [{"output_type": "pyout", "prompt_number": 97, "metadata": {}, "text": "<matplotlib.axes._subplots.AxesSubplot at 0x7fbd5137d250>"}, {"output_type": "display_data", "metadata": {}, "png": 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AENyeqeOy8ADnQ6gIkdmBXrWSHz30NyiexaadrujJjhgynWZXh0NRRzOf2TeT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\nq/Lugdy+1HHZ1NKwvwhcbx1+uQeBtuDSuA/vPp5vZvsDJwC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"text": "<matplotlib.figure.Figure at 0x7fbd51040750>"}], "collapsed": false, "prompt_number": 97, "input": "gas = EEXGasPrice.df(EEXGasPrice.q.date >= '2012-01-01')\ngas.plot()", "trusted": true, "metadata": {"slideshow": {"slide_type": "fragment"}}}, {"cell_type": "code", "language": "python", "outputs": [], "collapsed": false, "input": "", "trusted": true, "metadata": {}}], "metadata": {}}], "metadata": {"kernelspec": {"display_name": "IPython (Python 2)", "name": "python2", "language": "python", "codemirror_mode": {"version": 2, "name": "ipython"}}, "name": "", "celltoolbar": "Slideshow", "signature": "sha256:ac1fe2cafe2be85f7f9ff89116089b04eb01e8cdeac8c3f13e8686264e150eff"}, "nbformat_minor": 0}
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