{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pandas for Data Profiling & Analysis\n", "\n", "*Finally a better indexing interface in pandas 0.12*" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline\n", "import pandas as pd\n", "import numpy as np\n", "from IPython.core import display" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n", "For more information, type 'help(pylab)'.\n" ] } ], "prompt_number": 45 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Object Creation\n", "- Series: an iterable, and an index\n", "- DataFrame: values (matrix of diffent columns with different dtypes), index, columns (column index)\n", "- DataFrame: construction in 2 ways: (1) matrix way (iterable of rows) (2) column way (dictioniary of columns)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "s = pd.Series(data = [1, 3, 5, np.nan, 6, 8])\n", "s" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 2, "text": [ "0 1\n", "1 3\n", "2 5\n", "3 NaN\n", "4 6\n", "5 8\n", "dtype: float64" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "dates = pd.date_range(\"20130101\", periods=6) # periods in days\n", "df = pd.DataFrame(data = np.random.randn(6, 4), index = dates, columns=list(\"ABCD\"))\n", "df" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ABCD
2013-01-01-0.030813-0.865584-0.433447 0.507878
2013-01-02 0.170206-1.967499-0.362710-1.417809
2013-01-03-0.964603 0.761011-0.286228-0.162099
2013-01-04 1.204170 0.711299 0.451529 0.546794
2013-01-05-1.193963 0.739754-0.405187-0.366887
2013-01-06 1.015976-0.962043 0.178670 0.669697
\n", "
" ], "output_type": "pyout", "prompt_number": 73, "text": [ " A B C D\n", "2013-01-01 -0.030813 -0.865584 -0.433447 0.507878\n", "2013-01-02 0.170206 -1.967499 -0.362710 -1.417809\n", "2013-01-03 -0.964603 0.761011 -0.286228 -0.162099\n", "2013-01-04 1.204170 0.711299 0.451529 0.546794\n", "2013-01-05 -1.193963 0.739754 -0.405187 -0.366887\n", "2013-01-06 1.015976 -0.962043 0.178670 0.669697" ] } ], "prompt_number": 73 }, { "cell_type": "code", "collapsed": false, "input": [ "## ways of creating series/df with duplicate values\n", "df2 = pd.DataFrame({\n", " \"A\": 1.,\n", " \"B\": pd.Timestamp(\"20130102\"),\n", " \"C\": pd.Series(1, index = range(4), dtype=\"float32\"),\n", " \"D\": np.array([3] * 4, dtype = 'int32'),\n", " \"E\": 'foo'\n", "})\n", "print df2\n", "print df2.dtypes" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " A B C D E\n", "0 1 2013-01-02 00:00:00 1 3 foo\n", "1 1 2013-01-02 00:00:00 1 3 foo\n", "2 1 2013-01-02 00:00:00 1 3 foo\n", "3 1 2013-01-02 00:00:00 1 3 foo\n", "A float64\n", "B datetime64[ns]\n", "C float32\n", "D int32\n", "E object\n", "dtype: object\n" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Viewing Data\n", "- Antonomy of data: index, columns, values (data)\n", "- by rows: head(), tail()\n", "- summary: describe()\n", "- sorting: by axis or by value" ] }, { "cell_type": "code", "collapsed": false, "input": [ "## Basic elements of a dataframe\n", "print df2.values\n", "print\n", "print df2.values.dtype\n", "print \n", "print df2.index\n", "print \n", "print df2.columns\n", "print \n", "print df2.dtypes" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[1.0 datetime.datetime(2013, 1, 2, 0, 0) 1.0 3 'foo']\n", " [1.0 datetime.datetime(2013, 1, 2, 0, 0) 1.0 3 'foo']\n", " [1.0 datetime.datetime(2013, 1, 2, 0, 0) 1.0 3 'foo']\n", " [1.0 datetime.datetime(2013, 1, 2, 0, 0) 1.0 3 'foo']]\n", "\n", "object\n", "\n", "Int64Index([0, 1, 2, 3], dtype=int64)\n", "\n", "Index([u'A', u'B', u'C', u'D', u'E'], dtype=object)\n", "\n", "A float64\n", "B datetime64[ns]\n", "C float32\n", "D int32\n", "E object\n", "dtype: object\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "## rows, columns\n", "print df2.head(n=3)\n", "print \n", "print df2.tail(n=5)\n", "print \n", "print df2.describe() # B, E are missing\n", "print\n", "print df2.T" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " A B C D E\n", "0 1 2013-01-02 00:00:00 1 3 foo\n", "1 1 2013-01-02 00:00:00 1 3 foo\n", "2 1 2013-01-02 00:00:00 1 3 foo\n", "\n", " A B C D E\n", "0 1 2013-01-02 00:00:00 1 3 foo\n", "1 1 2013-01-02 00:00:00 1 3 foo\n", "2 1 2013-01-02 00:00:00 1 3 foo\n", "3 1 2013-01-02 00:00:00 1 3 foo\n", "\n", " A C D\n", "count 4 4 4\n", "mean 1 1 3\n", "std 0 0 0\n", "min 1 1 3\n", "25% 1 1 3\n", "50% 1 1 3\n", "75% 1 1 3\n", "max 1 1 3\n", "\n", " 0 1 2 3\n", "A 1 1 1 1\n", "B 2013-01-02 00:00:00 2013-01-02 00:00:00 2013-01-02 00:00:00 2013-01-02 00:00:00\n", "C 1 1 1 1\n", "D 3 3 3 3\n", "E foo foo foo foo\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "## MOST COMMON CASE - sort by columns, by specifying columns and axis must be 0\n", "## return new values\n", "sorted_df = df.sort(columns = [\"B\", \"C\"], ascending=[0, 1])\n", "print df\n", "print \n", "print sorted_df\n", "print \n", "## LESS COMMON CASE - sort by rows by specifying axis = 1\n", "## normal row order\n", "print df.sort(axis = 1, ascending=1)\n", "## reverse row order\n", "print \n", "print df.sort(axis = 1, ascending=0)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " A B C D\n", "2013-01-01 1.803674 1.435856 1.368229 1.351646\n", "2013-01-02 -2.017179 0.583955 1.025420 -1.153183\n", "2013-01-03 0.307859 1.068456 0.147881 -0.631105\n", "2013-01-04 -1.549112 -0.255675 0.135599 -0.935568\n", "2013-01-05 -0.046233 -0.512905 -1.658730 0.473678\n", "2013-01-06 -1.121827 -1.050659 2.499783 1.136633\n", "\n", " A B C D\n", "2013-01-01 1.803674 1.435856 1.368229 1.351646\n", "2013-01-03 0.307859 1.068456 0.147881 -0.631105\n", "2013-01-02 -2.017179 0.583955 1.025420 -1.153183\n", "2013-01-04 -1.549112 -0.255675 0.135599 -0.935568\n", "2013-01-05 -0.046233 -0.512905 -1.658730 0.473678\n", "2013-01-06 -1.121827 -1.050659 2.499783 1.136633\n", "\n", " A B C D\n", "2013-01-01 1.803674 1.435856 1.368229 1.351646\n", "2013-01-02 -2.017179 0.583955 1.025420 -1.153183\n", "2013-01-03 0.307859 1.068456 0.147881 -0.631105\n", "2013-01-04 -1.549112 -0.255675 0.135599 -0.935568\n", "2013-01-05 -0.046233 -0.512905 -1.658730 0.473678\n", "2013-01-06 -1.121827 -1.050659 2.499783 1.136633\n", "\n", " D C B A\n", "2013-01-01 1.351646 1.368229 1.435856 1.803674\n", "2013-01-02 -1.153183 1.025420 0.583955 -2.017179\n", "2013-01-03 -0.631105 0.147881 1.068456 0.307859\n", "2013-01-04 -0.935568 0.135599 -0.255675 -1.549112\n", "2013-01-05 0.473678 -1.658730 -0.512905 -0.046233\n", "2013-01-06 1.136633 2.499783 -1.050659 -1.121827\n" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Selection - ALL OF THEM USE an INDEXING SYNTAX\n", "- Select by label/index: df.at[,] or df.loc[,] - support slicing, scaler indexing, and explict index list\n", "- Select by position (more alike numpy): df.iat[], df.iloc[] - support slicing, scaler indexing and explict index list\n", "- Boolean (conditional) selection, always resulting in a matrix to be selected - (1) INDEX BOOLEAN is a MATRIX - zig-zag resulted matrix (2) INDEX BOOLEAN is a VECTOR - regular-shaped resulted matrix -- see examples below\n", "- **where** operation with setting\n", "- traditional ix[] or [] operations - ix[] is the most flexible indexing, it can **mix** index and position as the index. It accepts also the same type of parameters as to loc[] and iloc[] (slice, explict list or scaler). It will try to explain the parameter as index label first, and if that fails, it will fall back to explain it as positions.\n", "- suggest to use loc[] instead of at[] as the first one is more general in usage" ] }, { "cell_type": "code", "collapsed": false, "input": [ "## selection by index/row\n", "print df\n", "print '>> selecting one element by scalar index'\n", "print df.loc[pd.Timestamp(\"2013-01-01\", ), \"A\"].shape # key error if the index is NOT right\n", "print '>> selecting by slicing (inclusive at both ends)'\n", "print df.loc[pd.date_range(\"2013-01-01\",\"2013-01-05\")].shape # does not work with at\n", "print '>> selecting by explict listing'\n", "print df.loc[[pd.Timestamp(\"20130101\"), pd.Timestamp(\"20130104\")], [\"A\", \"D\"]].shape\n", "print \">> selecting columns\"\n", "print df.loc[:, \"A\"].shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " A B C D\n", "2013-01-01 1.803674 1.435856 1.368229 1.351646\n", "2013-01-02 -2.017179 0.583955 1.025420 -1.153183\n", "2013-01-03 0.307859 1.068456 0.147881 -0.631105\n", "2013-01-04 -1.549112 -0.255675 0.135599 -0.935568\n", "2013-01-05 -0.046233 -0.512905 -1.658730 0.473678\n", "2013-01-06 -1.121827 -1.050659 2.499783 1.136633\n", ">> selecting one element by scalar index\n", "()\n", ">> selecting by slicing (inclusive at both ends)\n", "(5, 4)\n", ">> selecting by explict listing\n", "(2, 2)\n", ">> selecting columns\n", "(6,)\n" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "## selection by position (from 0 ~ shape[]-1)\n", "print df\n", "print '>> selecting one element by scalar index'\n", "print df.iloc[1, 1]\n", "print '>> selecting by slicing (excluding due to slicing)'\n", "print df.iloc[1:3, :3]\n", "print '>> selecting by explicit listing'\n", "print df.iloc[[0, 3], [0, 3]]\n", "print '>> selecting a single column'\n", "print df.iloc[:, 1].shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " A B C D\n", "2013-01-01 1.803674 1.435856 1.368229 1.351646\n", "2013-01-02 -2.017179 0.583955 1.025420 -1.153183\n", "2013-01-03 0.307859 1.068456 0.147881 -0.631105\n", "2013-01-04 -1.549112 -0.255675 0.135599 -0.935568\n", "2013-01-05 -0.046233 -0.512905 -1.658730 0.473678\n", "2013-01-06 -1.121827 -1.050659 2.499783 1.136633\n", ">> selecting one element by scalar index\n", "0.583954623432\n", ">> selecting by slicing (excluding due to slicing)\n", " A B C\n", "2013-01-02 -2.017179 0.583955 1.025420\n", "2013-01-03 0.307859 1.068456 0.147881\n", ">> selecting by explicit listing\n", " A D\n", "2013-01-01 1.803674 1.351646\n", "2013-01-04 -1.549112 -0.935568\n", ">> selecting a single column\n", "(6,)\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "## Boolean / Conditional Selection - ON WHOLE MATRIX\n", "## MOST COMMON CASE - use boolean selection to do summarization or assignment\n", "print df\n", "print df > 0\n", "print df[df > 0] ## ZIG-ZAG matrix\n", "df[df > 0] = -100\n", "print df\n", "print '>> reset'\n", "df = pd.DataFrame(data = np.random.randn(6, 4), index = df.index, columns = df.columns)\n", "print df" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " A B C D\n", "2013-01-01 1.803674 1.435856 1.368229 1.351646\n", "2013-01-02 -2.017179 0.583955 1.025420 -1.153183\n", "2013-01-03 0.307859 1.068456 0.147881 -0.631105\n", "2013-01-04 -1.549112 -0.255675 0.135599 -0.935568\n", "2013-01-05 -0.046233 -0.512905 -1.658730 0.473678\n", "2013-01-06 -1.121827 -1.050659 2.499783 1.136633\n", " A B C D\n", "2013-01-01 True True True True\n", "2013-01-02 False True True False\n", "2013-01-03 True True True False\n", "2013-01-04 False False True False\n", "2013-01-05 False False False True\n", "2013-01-06 False False True True\n", " A B C D\n", "2013-01-01 1.803674 1.435856 1.368229 1.351646\n", "2013-01-02 NaN 0.583955 1.025420 NaN\n", "2013-01-03 0.307859 1.068456 0.147881 NaN\n", "2013-01-04 NaN NaN 0.135599 NaN\n", "2013-01-05 NaN NaN NaN 0.473678\n", "2013-01-06 NaN NaN 2.499783 1.136633\n", " A B C D\n", "2013-01-01 -100.000000 -100.000000 -100.00000 -100.000000\n", "2013-01-02 -2.017179 -100.000000 -100.00000 -1.153183\n", "2013-01-03 -100.000000 -100.000000 -100.00000 -0.631105\n", "2013-01-04 -1.549112 -0.255675 -100.00000 -0.935568\n", "2013-01-05 -0.046233 -0.512905 -1.65873 -100.000000\n", "2013-01-06 -1.121827 -1.050659 -100.00000 -100.000000\n", ">> reset\n", " A B C D\n", "2013-01-01 0.307393 -0.478925 0.888284 0.250375\n", "2013-01-02 0.689614 0.949111 -0.325068 -1.772610\n", "2013-01-03 0.449991 -0.698569 -0.660214 -1.574039\n", "2013-01-04 0.696428 -1.042774 0.482444 -2.030131\n", "2013-01-05 0.647665 -0.095332 0.178925 -1.374787\n", "2013-01-06 1.993792 2.377870 0.161765 -1.319822" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "## Boolean / Conditional Selection - ON CERTAIN COLUMN\n", "print df\n", "print df.A > 0\n", "print '\\nselecting df elements of df.A > 0'\n", "print df[df.A > 0] # selecting rows\n", "print '\\nselecting df elements of df.A > 0 and df.D < 0'\n", "print df[np.logical_and(df.A > 0, df.D < 0)]\n", "## SAME AS ABOVE - NOTE THE PARENTHSIS\n", "print df[(df.A > 0) & (df.D < 0)]\n", "df[df.A > 0] = 100 ## assiging whole bunch of rows\n", "print df\n", "## reset\n", "df = pd.DataFrame(data = np.random.randn(6, 4), index = df.index, columns = df.columns)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " A B C D\n", "2013-01-01 -0.030813 -0.865584 -0.433447 0.507878\n", "2013-01-02 0.170206 -1.967499 -0.362710 -1.417809\n", "2013-01-03 -0.964603 0.761011 -0.286228 -0.162099\n", "2013-01-04 1.204170 0.711299 0.451529 0.546794\n", "2013-01-05 -1.193963 0.739754 -0.405187 -0.366887\n", "2013-01-06 1.015976 -0.962043 0.178670 0.669697\n", "2013-01-01 False\n", "2013-01-02 True\n", "2013-01-03 False\n", "2013-01-04 True\n", "2013-01-05 False\n", "2013-01-06 True\n", "Freq: D, Name: A, dtype: bool\n", "\n", "selecting df elements of df.A > 0\n", " A B C D\n", "2013-01-02 0.170206 -1.967499 -0.362710 -1.417809\n", "2013-01-04 1.204170 0.711299 0.451529 0.546794\n", "2013-01-06 1.015976 -0.962043 0.178670 0.669697\n", "\n", "selecting df elements of df.A > 0 and df.D < 0\n", " A B C D\n", "2013-01-02 0.170206 -1.967499 -0.36271 -1.417809\n", " A B C D\n", "2013-01-02 0.170206 -1.967499 -0.36271 -1.417809\n", " A B C D\n", "2013-01-01 -0.030813 -0.865584 -0.433447 0.507878\n", "2013-01-02 100.000000 100.000000 100.000000 100.000000\n", "2013-01-03 -0.964603 0.761011 -0.286228 -0.162099\n", "2013-01-04 100.000000 100.000000 100.000000 100.000000\n", "2013-01-05 -1.193963 0.739754 -0.405187 -0.366887\n", "2013-01-06 100.000000 100.000000 100.000000 100.000000\n" ] } ], "prompt_number": 74 }, { "cell_type": "code", "collapsed": false, "input": [ "## WHERE operation with setting\n", "## COPY OF DATAFRAME - either DEEP (default) or SHALLOW \n", "dff = df.copy(deep = True)\n", "print dff\n", "print \">> reverse negative values\"\n", "dff[dff < 0] = - dff\n", "print dff\n", "dff[dff.A > 0] = - dff\n", "print dff" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " A B C D\n", "2013-01-01 -0.440529 0.320633 0.594432 -1.285903\n", "2013-01-02 0.353733 1.351794 -0.759201 -0.474171\n", "2013-01-03 -0.018955 -0.341444 -0.555280 -0.162339\n", "2013-01-04 -1.209282 0.977806 0.909265 0.710577\n", "2013-01-05 -0.005757 -1.061972 -0.782961 -1.449468\n", "2013-01-06 1.170326 0.011626 -1.814532 -0.245602\n", ">> reverse negative values\n", " A B C D\n", "2013-01-01 0.440529 0.320633 0.594432 1.285903\n", "2013-01-02 0.353733 1.351794 0.759201 0.474171\n", "2013-01-03 0.018955 0.341444 0.555280 0.162339\n", "2013-01-04 1.209282 0.977806 0.909265 0.710577\n", "2013-01-05 0.005757 1.061972 0.782961 1.449468\n", "2013-01-06 1.170326 0.011626 1.814532 0.245602\n", " A B C D\n", "2013-01-01 -0.440529 -0.320633 -0.594432 -1.285903\n", "2013-01-02 -0.353733 -1.351794 -0.759201 -0.474171\n", "2013-01-03 -0.018955 -0.341444 -0.555280 -0.162339\n", "2013-01-04 -1.209282 -0.977806 -0.909265 -0.710577\n", "2013-01-05 -0.005757 -1.061972 -0.782961 -1.449468\n", "2013-01-06 -1.170326 -0.011626 -1.814532 -0.245602\n" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Missing Data\n", "- Pandas primarily uses the value np.nan to respresent missing values.\n", "- Missing values are by default NOT included in computations.\n", "- THREE MAIN TYPES of operations on missing data - FINDING, DROPPING (ignoring) or FILLING (imputation)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "## CREATE missing values\n", "dff = df.copy(deep = True)\n", "dff['E'] = [1] * 3 + [np.nan] * 3\n", "dff.D.iloc[1] = np.nan\n", "dff['F'] = np.nan\n", "print dff" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " A B C D E F\n", "2013-01-01 -0.440529 0.320633 0.594432 -1.285903 1 NaN\n", "2013-01-02 0.353733 1.351794 -0.759201 NaN 1 NaN\n", "2013-01-03 -0.018955 -0.341444 -0.555280 -0.162339 1 NaN\n", "2013-01-04 -1.209282 0.977806 0.909265 0.710577 NaN NaN\n", "2013-01-05 -0.005757 -1.061972 -0.782961 -1.449468 NaN NaN\n", "2013-01-06 1.170326 0.011626 -1.814532 -0.245602 NaN NaN\n" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "## FINDING of missing values - BOOLEAN MATRIX\n", "dfff = dff.copy(deep = True)\n", "print pd.isnull(dfff)\n", "dfff[pd.isnull(dfff)] = 100\n", "print dfff" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " A B C D E F\n", "2013-01-01 False False False False False True\n", "2013-01-02 False False False True False True\n", "2013-01-03 False False False False False True\n", "2013-01-04 False False False False True True\n", "2013-01-05 False False False False True True\n", "2013-01-06 False False False False True True\n", " A B C D E F\n", "2013-01-01 -0.440529 0.320633 0.594432 -1.285903 1 100\n", "2013-01-02 0.353733 1.351794 -0.759201 100.000000 1 100\n", "2013-01-03 -0.018955 -0.341444 -0.555280 -0.162339 1 100\n", "2013-01-04 -1.209282 0.977806 0.909265 0.710577 100 100\n", "2013-01-05 -0.005757 -1.061972 -0.782961 -1.449468 100 100\n", "2013-01-06 1.170326 0.011626 -1.814532 -0.245602 100 100\n" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "## REMOVE missing data\n", "print dff\n", "print '\\n>> drop ANY na-rows axis = 0'\n", "print dff.dropna(axis = 0, how = 'any')\n", "print '\\n>> drop ALL na-rows axis = 0'\n", "print dff.dropna(axis = 0, how = 'all')\n", "print '\\n>> drop ANY na-cols axis = 1'\n", "print dff.dropna(axis = 1, how = 'any')\n", "print '\\n>> drop ALL na-cols axis = 1'\n", "print dff.dropna(axis = 1, how = 'all')\n", "print '\\n>> drop na-rows based on NON-NA threshold - how param is DEACTIVATED'\n", "print dff.dropna(axis = 0, thresh=5)\n", "print '\\n>> drop na-cols based on NON-NA threshold - how param is DEACTIVATED'\n", "print dff.dropna(axis = 1, thresh=4)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " A B C D E F\n", "2013-01-01 -0.440529 0.320633 0.594432 -1.285903 1 NaN\n", "2013-01-02 0.353733 1.351794 -0.759201 NaN 1 NaN\n", "2013-01-03 -0.018955 -0.341444 -0.555280 -0.162339 1 NaN\n", "2013-01-04 -1.209282 0.977806 0.909265 0.710577 NaN NaN\n", "2013-01-05 -0.005757 -1.061972 -0.782961 -1.449468 NaN NaN\n", "2013-01-06 1.170326 0.011626 -1.814532 -0.245602 NaN NaN\n", "\n", ">> drop ANY na-rows axis = 0\n", "Empty DataFrame\n", "Columns: [A, B, C, D, E, F]\n", "Index: []\n", "\n", ">> drop ALL na-rows axis = 0\n", " A B C D E F\n", "2013-01-01 -0.440529 0.320633 0.594432 -1.285903 1 NaN\n", "2013-01-02 0.353733 1.351794 -0.759201 NaN 1 NaN\n", "2013-01-03 -0.018955 -0.341444 -0.555280 -0.162339 1 NaN\n", "2013-01-04 -1.209282 0.977806 0.909265 0.710577 NaN NaN\n", "2013-01-05 -0.005757 -1.061972 -0.782961 -1.449468 NaN NaN\n", "2013-01-06 1.170326 0.011626 -1.814532 -0.245602 NaN NaN\n", "\n", ">> drop ANY na-cols axis = 1\n", " A B C\n", "2013-01-01 -0.440529 0.320633 0.594432\n", "2013-01-02 0.353733 1.351794 -0.759201\n", "2013-01-03 -0.018955 -0.341444 -0.555280\n", "2013-01-04 -1.209282 0.977806 0.909265\n", "2013-01-05 -0.005757 -1.061972 -0.782961\n", "2013-01-06 1.170326 0.011626 -1.814532\n", "\n", ">> drop ALL na-cols axis = 1\n", " A B C D E\n", "2013-01-01 -0.440529 0.320633 0.594432 -1.285903 1\n", "2013-01-02 0.353733 1.351794 -0.759201 NaN 1\n", "2013-01-03 -0.018955 -0.341444 -0.555280 -0.162339 1\n", "2013-01-04 -1.209282 0.977806 0.909265 0.710577 NaN\n", "2013-01-05 -0.005757 -1.061972 -0.782961 -1.449468 NaN\n", "2013-01-06 1.170326 0.011626 -1.814532 -0.245602 NaN\n", "\n", ">> drop na-rows based on NON-NA threshold - how param is DEACTIVATED\n", " A B C D E F\n", "2013-01-01 -0.440529 0.320633 0.594432 -1.285903 1 NaN\n", "2013-01-03 -0.018955 -0.341444 -0.555280 -0.162339 1 NaN" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\n", ">> drop na-cols based on NON-NA threshold - how param is DEACTIVATED\n", " A B C D\n", "2013-01-01 -0.440529 0.320633 0.594432 -1.285903\n", "2013-01-02 0.353733 1.351794 -0.759201 NaN\n", "2013-01-03 -0.018955 -0.341444 -0.555280 -0.162339\n", "2013-01-04 -1.209282 0.977806 0.909265 0.710577\n", "2013-01-05 -0.005757 -1.061972 -0.782961 -1.449468\n", "2013-01-06 1.170326 0.011626 -1.814532 -0.245602\n" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "## FILL UP missing values - IMPUTATION\n", "## Parameters\n", "## ----------\n", "## method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None\n", "## Method to use for filling holes in reindexed Series\n", "## pad / ffill: propagate last valid observation forward to next valid\n", "## backfill / bfill: use NEXT valid observation to fill gap\n", "## None: fill by value\n", "## value : scalar or dict\n", "## Value to use to fill holes (e.g. 0), alternately a dict of values\n", "## specifying which value to use for each column (columns not in the\n", "## dict will not be filled). This value cannot be a list.\n", "## axis : {0, 1}, default 0\n", "## 0: fill column-by-column\n", "## 1: fill row-by-row\n", "## inplace : boolean, default False\n", "## If True, fill the DataFrame in place. Note: this will modify any\n", "## other views on this DataFrame, like if you took a no-copy slice of\n", "## an existing DataFrame, for example a column in a DataFrame. Returns\n", "## a reference to the filled object, which is self if inplace=True\n", "## limit : int, default None\n", "## Maximum size gap to forward or backward fill\n", "## downcast : dict, default is None, a dict of item->dtype of what to\n", "## downcast if possible\n", "print dff\n", "print dff.fillna(method='backfill')\n", "print dff.fillna(method=\"ffill\")\n", "print dff.fillna(method = None, value = {\"D\": -100, \"E\": 0})" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " A B C D E F\n", "2013-01-01 -0.440529 0.320633 0.594432 -1.285903 1 NaN\n", "2013-01-02 0.353733 1.351794 -0.759201 NaN 1 NaN\n", "2013-01-03 -0.018955 -0.341444 -0.555280 -0.162339 1 NaN\n", "2013-01-04 -1.209282 0.977806 0.909265 0.710577 NaN NaN\n", "2013-01-05 -0.005757 -1.061972 -0.782961 -1.449468 NaN NaN\n", "2013-01-06 1.170326 0.011626 -1.814532 -0.245602 NaN NaN\n", " A B C D E F\n", "2013-01-01 -0.440529 0.320633 0.594432 -1.285903 1 NaN\n", "2013-01-02 0.353733 1.351794 -0.759201 -0.162339 1 NaN\n", "2013-01-03 -0.018955 -0.341444 -0.555280 -0.162339 1 NaN\n", "2013-01-04 -1.209282 0.977806 0.909265 0.710577 NaN NaN\n", "2013-01-05 -0.005757 -1.061972 -0.782961 -1.449468 NaN NaN\n", "2013-01-06 1.170326 0.011626 -1.814532 -0.245602 NaN NaN\n", " A B C D E F\n", "2013-01-01 -0.440529 0.320633 0.594432 -1.285903 1 NaN\n", "2013-01-02 0.353733 1.351794 -0.759201 -1.285903 1 NaN\n", "2013-01-03 -0.018955 -0.341444 -0.555280 -0.162339 1 NaN\n", "2013-01-04 -1.209282 0.977806 0.909265 0.710577 1 NaN\n", "2013-01-05 -0.005757 -1.061972 -0.782961 -1.449468 1 NaN\n", "2013-01-06 1.170326 0.011626 -1.814532 -0.245602 1 NaN\n", " A B C D E F\n", "2013-01-01 -0.440529 0.320633 0.594432 -1.285903 1 NaN\n", "2013-01-02 0.353733 1.351794 -0.759201 -100.000000 1 NaN\n", "2013-01-03 -0.018955 -0.341444 -0.555280 -0.162339 1 NaN\n", "2013-01-04 -1.209282 0.977806 0.909265 0.710577 0 NaN\n", "2013-01-05 -0.005757 -1.061972 -0.782961 -1.449468 0 NaN\n", "2013-01-06 1.170326 0.011626 -1.814532 -0.245602 0 NaN\n" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Operations\n", "- General Case - (1) pd.DataFrame.apply(cum_fn, axis) (2) pd.DataFrame.applymap(elewise_fn) for elementwise operation, Use parameter *raw =True* if the applied function is working on numpy.ndarray data instead of pd.Series\n", "- Histogramming and value_counts()\n", "- Discretization - USUALLY ALWAYS in a columnwise style (FEATURE BY FEATURE) - cut() and get factor.labels\n", "- REPLACE: pd.DataFrame.repalce" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print df\n", "print \">> apply mean on columns\"\n", "print df.apply(np.mean, raw = True, axis = 0)\n", "print \">> apply sum on rows\"\n", "print df.apply(np.sum, raw = True, axis = 1)\n", "print df.applymap(lambda x: x **2) # equlivanent to df ** 2" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " A B C D\n", "2013-01-01 -0.440529 0.320633 0.594432 -1.285903\n", "2013-01-02 0.353733 1.351794 -0.759201 -0.474171\n", "2013-01-03 -0.018955 -0.341444 -0.555280 -0.162339\n", "2013-01-04 -1.209282 0.977806 0.909265 0.710577\n", "2013-01-05 -0.005757 -1.061972 -0.782961 -1.449468\n", "2013-01-06 1.170326 0.011626 -1.814532 -0.245602\n", ">> apply mean on columns\n", "A -0.025077\n", "B 0.209740\n", "C -0.401380\n", "D -0.484485\n", "dtype: float64\n", ">> apply sum on rows\n", "2013-01-01 -0.811367\n", "2013-01-02 0.472155\n", "2013-01-03 -1.078019\n", "2013-01-04 1.388366\n", "2013-01-05 -3.300159\n", "2013-01-06 -0.878183\n", "Freq: D, dtype: float64\n", " A B C D\n", "2013-01-01 0.194066 0.102805 0.353350 1.653547\n", "2013-01-02 0.125127 1.827346 0.576386 0.224838\n", "2013-01-03 0.000359 0.116584 0.308336 0.026354\n", "2013-01-04 1.462363 0.956105 0.826763 0.504920\n", "2013-01-05 0.000033 1.127785 0.613029 2.100959\n", "2013-01-06 1.369662 0.000135 3.292527 0.060320\n" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "df['E'] = pd.Series(map(str, np.random.randint(0, 4, size = 6)), index = df.index)\n", "print df\n", "## HISTOGRAMMING by Value-Counting \n", "## - RETURN ANOTHER SEREIS (essentially convertable to dict)\n", "df.E.value_counts().to_dict()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " A B C D E\n", "2013-01-01 -0.440529 0.320633 0.594432 -1.285903 1\n", "2013-01-02 0.353733 1.351794 -0.759201 -0.474171 3\n", "2013-01-03 -0.018955 -0.341444 -0.555280 -0.162339 1\n", "2013-01-04 -1.209282 0.977806 0.909265 0.710577 0\n", "2013-01-05 -0.005757 -1.061972 -0.782961 -1.449468 1\n", "2013-01-06 1.170326 0.011626 -1.814532 -0.245602 0\n" ] }, { "output_type": "pyout", "prompt_number": 18, "text": [ "{'0': 2, '1': 3, '3': 1}" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "## Discretization and Quantiling -- for numeric data ONLY\n", "## very similiar to R factor now\n", "print df\n", "A_factors = pd.cut(df.A, 3) \n", "print type(A_factors)\n", "print A_factors\n", "print 'FACTOR LABELS >> ', A_factors.labels\n", "print 'FACTOR LEVELS >>', A_factors.levels\n", "print 'quatiling >>'\n", "A_quantiles = pd.qcut(df.A, [0, 0.25, 0.75, 1])\n", "print 'QFACTOR LABELS >> ', A_quantiles.labels\n", "print 'QFACTOR LEVELS >> ', A_quantiles.levels" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " A B C D E\n", "2013-01-01 -0.440529 0.320633 0.594432 -1.285903 1\n", "2013-01-02 0.353733 1.351794 -0.759201 -0.474171 3\n", "2013-01-03 -0.018955 -0.341444 -0.555280 -0.162339 1\n", "2013-01-04 -1.209282 0.977806 0.909265 0.710577 0\n", "2013-01-05 -0.005757 -1.061972 -0.782961 -1.449468 1\n", "2013-01-06 1.170326 0.011626 -1.814532 -0.245602 0\n", "\n", "Categorical: A\n", "[(-1.212, -0.416], (-0.416, 0.377], (-0.416, 0.377], (-1.212, -0.416], (-0.416, 0.377], (0.377, 1.17]]\n", "Levels (3): Index(['(-1.212, -0.416]', '(-0.416, 0.377]',\n", " '(0.377, 1.17]'], dtype=object)\n", "FACTOR LABELS >> [0 1 1 0 1 2]\n", "FACTOR LEVELS >> Index([u'(-1.212, -0.416]', u'(-0.416, 0.377]', u'(0.377, 1.17]'], dtype=object)\n", "quatiling >>\n", "QFACTOR LABELS >> [0 2 1 0 1 2]\n", "QFACTOR LEVELS >> Index([u'[-1.209, -0.335]', u'(-0.335, 0.264]', u'(0.264, 1.17]'], dtype=object)\n" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "## REPLACEMENT\n", "print df\n", "print df.replace({'E': {'3': '2'}}) ## COMBINE 3 and 2" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " A B C D E\n", "2013-01-01 -0.440529 0.320633 0.594432 -1.285903 1\n", "2013-01-02 0.353733 1.351794 -0.759201 -0.474171 3\n", "2013-01-03 -0.018955 -0.341444 -0.555280 -0.162339 1\n", "2013-01-04 -1.209282 0.977806 0.909265 0.710577 0\n", "2013-01-05 -0.005757 -1.061972 -0.782961 -1.449468 1\n", "2013-01-06 1.170326 0.011626 -1.814532 -0.245602 0\n", " A B C D E\n", "2013-01-01 -0.440529 0.320633 0.594432 -1.285903 1\n", "2013-01-02 0.353733 1.351794 -0.759201 -0.474171 2\n", "2013-01-03 -0.018955 -0.341444 -0.555280 -0.162339 1\n", "2013-01-04 -1.209282 0.977806 0.909265 0.710577 0\n", "2013-01-05 -0.005757 -1.061972 -0.782961 -1.449468 1\n", "2013-01-06 1.170326 0.011626 -1.814532 -0.245602 0\n" ] } ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. Merge\n", "- Concat (hstack, vstack like)\n", "- Join (database like join)\n", "- Append (column, row)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "## Horizontal concat\n", "print \"HORIZONTAL SPLITTING AND CONCATING\"\n", "print df\n", "hdf1 = df.iloc[:3]\n", "hdf2 = df.iloc[3:]\n", "print hdf1\n", "print hdf2\n", "print pd.concat([hdf2, hdf1], axis = 0)\n", "## Vertical concat\n", "print '\\nVERTICAL SPLITTING AND CONCATING'\n", "vdf1 = df.iloc[:, :3]\n", "vdf2 = df.iloc[:, 3:]\n", "print vdf1\n", "print vdf2\n", "print pd.concat([vdf2, vdf1], axis = 1)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "HORIZONTAL SPLITTING AND CONCATING\n", " A B C D E\n", "2013-01-01 -0.440529 0.320633 0.594432 -1.285903 1\n", "2013-01-02 0.353733 1.351794 -0.759201 -0.474171 3\n", "2013-01-03 -0.018955 -0.341444 -0.555280 -0.162339 1\n", "2013-01-04 -1.209282 0.977806 0.909265 0.710577 0\n", "2013-01-05 -0.005757 -1.061972 -0.782961 -1.449468 1\n", "2013-01-06 1.170326 0.011626 -1.814532 -0.245602 0\n", " A B C D E\n", "2013-01-01 -0.440529 0.320633 0.594432 -1.285903 1\n", "2013-01-02 0.353733 1.351794 -0.759201 -0.474171 3\n", "2013-01-03 -0.018955 -0.341444 -0.555280 -0.162339 1\n", " A B C D E\n", "2013-01-04 -1.209282 0.977806 0.909265 0.710577 0\n", "2013-01-05 -0.005757 -1.061972 -0.782961 -1.449468 1\n", "2013-01-06 1.170326 0.011626 -1.814532 -0.245602 0\n", " A B C D E\n", "2013-01-04 -1.209282 0.977806 0.909265 0.710577 0\n", "2013-01-05 -0.005757 -1.061972 -0.782961 -1.449468 1\n", "2013-01-06 1.170326 0.011626 -1.814532 -0.245602 0\n", "2013-01-01 -0.440529 0.320633 0.594432 -1.285903 1\n", "2013-01-02 0.353733 1.351794 -0.759201 -0.474171 3\n", "2013-01-03 -0.018955 -0.341444 -0.555280 -0.162339 1" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\n", "VERTICAL SPLITTING AND CONCATING\n", " A B C\n", "2013-01-01 -0.440529 0.320633 0.594432\n", "2013-01-02 0.353733 1.351794 -0.759201\n", "2013-01-03 -0.018955 -0.341444 -0.555280\n", "2013-01-04 -1.209282 0.977806 0.909265\n", "2013-01-05 -0.005757 -1.061972 -0.782961\n", "2013-01-06 1.170326 0.011626 -1.814532\n", " D E\n", "2013-01-01 -1.285903 1\n", "2013-01-02 -0.474171 3\n", "2013-01-03 -0.162339 1\n", "2013-01-04 0.710577 0\n", "2013-01-05 -1.449468 1\n", "2013-01-06 -0.245602 0\n", " D E A B C\n", "2013-01-01 -1.285903 1 -0.440529 0.320633 0.594432\n", "2013-01-02 -0.474171 3 0.353733 1.351794 -0.759201\n", "2013-01-03 -0.162339 1 -0.018955 -0.341444 -0.555280\n", "2013-01-04 0.710577 0 -1.209282 0.977806 0.909265\n", "2013-01-05 -1.449468 1 -0.005757 -1.061972 -0.782961\n", "2013-01-06 -0.245602 0 1.170326 0.011626 -1.814532\n" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "## JOINING / MERGING\n", "## pd.merge(left, right, how='inner', on=None, \n", "## left_on=None, right_on=None, \n", "## left_index=False, right_index=False, sort=False, \n", "## suffixes=('_x', '_y'), copy=True)\n", "left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})\n", "right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [6, 7]})\n", "print left\n", "print right\n", "print pd.merge(left, right, on = 'key')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " key lval\n", "0 foo 1\n", "1 foo 2\n", " key rval\n", "0 foo 6\n", "1 foo 7\n", " key lval rval\n", "0 foo 1 6\n", "1 foo 1 7\n", "2 foo 2 6\n", "3 foo 2 7" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "## APPENDING ROWS\n", "## NOT REALLY VERY USEFUL\n", "print df\n", "print type(df.iloc[3])\n", "print df.iloc[3].name\n", "df.append(pd.Series(np.random.randn(5), index = df.columns), ignore_index=True, )" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " A B C D E\n", "2013-01-01 -0.440529 0.320633 0.594432 -1.285903 1\n", "2013-01-02 0.353733 1.351794 -0.759201 -0.474171 3\n", "2013-01-03 -0.018955 -0.341444 -0.555280 -0.162339 1\n", "2013-01-04 -1.209282 0.977806 0.909265 0.710577 0\n", "2013-01-05 -0.005757 -1.061972 -0.782961 -1.449468 1\n", "2013-01-06 1.170326 0.011626 -1.814532 -0.245602 0\n", "\n", "2013-01-04 00:00:00\n" ] }, { "html": [ "
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" ], "output_type": "pyout", "prompt_number": 23, "text": [ " A B C D E\n", "0 -0.440529 0.320633 0.594432 -1.285903 1\n", "1 0.353733 1.351794 -0.759201 -0.474171 3\n", "2 -0.018955 -0.341444 -0.555280 -0.162339 1\n", "3 -1.209282 0.977806 0.909265 0.710577 0\n", "4 -0.005757 -1.061972 -0.782961 -1.449468 1\n", "5 1.170326 0.011626 -1.814532 -0.245602 0\n", "6 0.268328 1.146425 0.306675 2.480935 -2.537934" ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7. Grouping\n", "\n", "From Pandas:\n", "\n", "By \u201cgroup by\u201d we are referring to a process involving one or more of the following steps\n", "\n", "- **Splitting** the data into groups based on some criteria\n", "- **Applying** a function to each group independently\n", "- **Combining** the results into a data structure\n", "\n", "the most common case is to group matrix by columns (features)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df = pd.DataFrame({\n", " 'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo']\n", " , 'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three']\n", " , 'C': np.random.randn(8)\n", " , 'D': np.random.randn(8)\n", "})\n", "print df" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " A B C D\n", "0 foo one 0.684732 -1.999816\n", "1 bar one 0.502373 0.779502\n", "2 foo two -0.187921 2.815911\n", "3 bar three 1.588625 1.610015\n", "4 foo two -1.367470 0.239797\n", "5 bar two -0.287122 -0.418336\n", "6 foo one 0.122107 -1.787529\n", "7 foo three -0.676687 1.060654\n" ] } ], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "print df.groupby(by = 'A', axis = 0).sum()\n", "print \"\\nIterating Group Structure (WHAT GROUPBY IS REALLY ABOUT - A DICT) >>\"\n", "for grp_name, sub_df in df.groupby(by = ['B', 'A']):\n", " print grp_name\n", " print sub_df" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " C D\n", "A \n", "bar 1.803875 1.971181\n", "foo -1.425239 0.329017\n", "\n", "Iterating Group Structure (WHAT GROUPBY IS REALLY ABOUT - A DICT) >>\n", "('one', 'bar')\n", " A B C D\n", "1 bar one 0.502373 0.779502\n", "('one', 'foo')\n", " A B C D\n", "0 foo one 0.684732 -1.999816\n", "6 foo one 0.122107 -1.787529\n", "('three', 'bar')\n", " A B C D\n", "3 bar three 1.588625 1.610015\n", "('three', 'foo')\n", " A B C D\n", "7 foo three -0.676687 1.060654\n", "('two', 'bar')\n", " A B C D\n", "5 bar two -0.287122 -0.418336" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "('two', 'foo')\n", " A B C D\n", "2 foo two -0.187921 2.815911\n", "4 foo two -1.367470 0.239797\n" ] } ], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 8. Reshaping\n", "- pivot table" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3,\n", " 'B' : ['A', 'B', 'C'] * 4,\n", " 'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,\n", " 'D' : np.random.randn(12),\n", " 'E' : np.random.randn(12)})\n", "print df" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " A B C D E\n", "0 one A foo -0.257251 0.941467\n", "1 one B foo -2.041284 -1.070327\n", "2 two C foo -2.375388 0.539690\n", "3 three A bar 0.074301 1.018966\n", "4 one B bar -0.756021 0.669371\n", "5 one C bar 1.300727 0.330807\n", "6 two A foo 1.113186 0.263582\n", "7 three B foo 0.222825 -1.196940\n", "8 one C foo 0.749618 0.331946\n", "9 one A bar 0.876066 -2.755204\n", "10 two B bar 0.254959 -1.748141\n", "11 three C bar 2.029449 0.109468\n" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "## PIVOT TABLE\n", "## pd.pivot_table(data, values=None, rows=None, cols=None, aggfunc='mean', \n", "## fill_value=None, margins=False, dropna=True)\n", "## Create a spreadsheet-style pivot table as a DataFrame. The levels in the\n", "## pivot table will be stored in MultiIndex objects (hierarchical indexes) on\n", "## the index and columns of the result DataFrame\n", "## IMPORTANT Parameters \n", "## data : DataFrame\n", "## values : column to aggregate, optional\n", "## rows : list of column names or arrays to group on\n", "## Keys to group on the x-axis of the pivot table\n", "## cols : list of column names or arrays to group on\n", "## Keys to group on the y-axis of the pivot table\n", "## aggfunc : function, default numpy.mean, or list of functions\n", "## If list of functions passed, the resulting pivot table will have\n", "## hierarchical columns whose top level are the function names (inferred\n", "## from the function objects themselves)\n", "## rows, cols should be CATEGORICAL variables that can be grouped by\n", "## values are usually numeric variables to be calculated on (by applying aggfunc)\n", "## values are the remaining variables by default\n", "## IN THIS SENSE, PIVOT TABLE is more like a shortcut for groupby(), as group_rows, \n", "## group_cols, values() and aggregation statics no them are usually of the most interest\n", "## AGGFUNC IS ALWAYS THERE - DEFAULT IS NP.MEAN\n", "print df\n", "print \n", "print pd.pivot_table(df, values = ['D', 'E'], rows = ['A', 'B'], cols = 'C')\n", "print\n", "print pd.pivot_table(df, values = ['D'], rows = ['A', 'B'], cols = 'C')\n", "print\n", "print pd.pivot_table(df, rows = ['A'], cols = ['C'], values = ['D'], \n", " aggfunc=[np.mean, np.min, np.max])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " A B C D E\n", "0 one A foo -0.257251 0.941467\n", "1 one B foo -2.041284 -1.070327\n", "2 two C foo -2.375388 0.539690\n", "3 three A bar 0.074301 1.018966\n", "4 one B bar -0.756021 0.669371\n", "5 one C bar 1.300727 0.330807\n", "6 two A foo 1.113186 0.263582\n", "7 three B foo 0.222825 -1.196940\n", "8 one C foo 0.749618 0.331946\n", "9 one A bar 0.876066 -2.755204\n", "10 two B bar 0.254959 -1.748141\n", "11 three C bar 2.029449 0.109468\n", "\n", " D E \n", "C bar foo bar foo\n", "A B \n", "one A 0.876066 -0.257251 -2.755204 0.941467\n", " B -0.756021 -2.041284 0.669371 -1.070327\n", " C 1.300727 0.749618 0.330807 0.331946\n", "three A 0.074301 NaN 1.018966 NaN\n", " B NaN 0.222825 NaN -1.196940\n", " C 2.029449 NaN 0.109468 NaN\n", "two A NaN 1.113186 NaN 0.263582\n", " B 0.254959 NaN -1.748141 NaN\n", " C NaN -2.375388 NaN 0.539690\n", "\n", " D \n", "C bar foo\n", "A B \n", "one A 0.876066 -0.257251\n", " B -0.756021 -2.041284\n", " C 1.300727 0.749618\n", "three A 0.074301 NaN\n", " B NaN 0.222825\n", " C 2.029449 NaN\n", "two A NaN 1.113186\n", " B 0.254959 NaN\n", " C NaN -2.375388\n", "\n", " mean amin amax \n", " D D D \n", "C bar foo bar foo bar foo\n", "A \n", "one 0.473591 -0.516305 -0.756021 -2.041284 1.300727 0.749618\n", "three 1.051875 0.222825 0.074301 0.222825 2.029449 0.222825\n", "two 0.254959 -0.631101 0.254959 -2.375388 0.254959 1.113186" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 9. Time Series\n", "- Pandas has simple, powerful, and efficient functionality for performing resampling operations during frequency conversion (e.g., converting secondly data into 5-minutely data). But the sampling should be **regular**.\n", "- Representation in a certain time zone: (1) use tz_localize to localize to a certain time zone (2) After localizing the TimeSeries, you may use tz_convert() to get the Datetime values recomputed to a different tz.\n", "\n", "In summary\n", "\n", "- resample(): *squeeze or strech the time series*\n", "- tz_localize(), tz_convert(), to_period(), to_timestamp(): *change the index (different time offsets or time units) without changing the values in the series*" ] }, { "cell_type": "code", "collapsed": false, "input": [ "rng = pd.date_range('1/1/2012', periods=500, freq='S', ) ## periods and freq \n", "ts = pd.Series(randint(0, 500, len(rng)), index = rng)\n", "ts.plot()\n", "print '\\n>> time series with frequence = sencond >>'\n", "print ts\n", "tts = ts.resample('1Min', how='sum') ## default how = 'mean', which is downsampling\n", "figure()\n", "tts.plot()\n", "print '\\n>> time series with frequence resampled at 1 min >>'\n", "print tts" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", ">> time series with frequence = sencond >>\n", "2012-01-01 00:00:00 183\n", "2012-01-01 00:00:01 373\n", "2012-01-01 00:00:02 390\n", "2012-01-01 00:00:03 126\n", "2012-01-01 00:00:04 140\n", "2012-01-01 00:00:05 3\n", "2012-01-01 00:00:06 477\n", "2012-01-01 00:00:07 450\n", "2012-01-01 00:00:08 170\n", "2012-01-01 00:00:09 71\n", "2012-01-01 00:00:10 65\n", "2012-01-01 00:00:11 107\n", "2012-01-01 00:00:12 419\n", "2012-01-01 00:00:13 306\n", "2012-01-01 00:00:14 1\n", "...\n", "2012-01-01 00:08:05 111\n", "2012-01-01 00:08:06 148\n", "2012-01-01 00:08:07 45\n", "2012-01-01 00:08:08 132\n", "2012-01-01 00:08:09 197\n", "2012-01-01 00:08:10 348\n", "2012-01-01 00:08:11 102\n", "2012-01-01 00:08:12 124\n", "2012-01-01 00:08:13 363\n", "2012-01-01 00:08:14 141\n", "2012-01-01 00:08:15 34\n", "2012-01-01 00:08:16 20\n", "2012-01-01 00:08:17 25\n", "2012-01-01 00:08:18 305\n", "2012-01-01 00:08:19 165\n", "Freq: S, Length: 500, dtype: int64\n", "\n", ">> time series with frequence resampled at 1 min >>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2012-01-01 00:00:00 13107\n", "2012-01-01 00:01:00 14907\n", "2012-01-01 00:02:00 13006\n", "2012-01-01 00:03:00 13201\n", "2012-01-01 00:04:00 14889\n", "2012-01-01 00:05:00 14300\n", "2012-01-01 00:06:00 15604\n", "2012-01-01 00:07:00 13959\n", "2012-01-01 00:08:00 3772\n", "Freq: T, dtype: int64\n" ] }, { "output_type": "display_data", "png": 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BmL6+fEC32HJ7e6qhWx3VsqIaOoeTNHt6gDPPDDN0IO5JxGuucad5iuTyyivA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} ], "prompt_number": 62 }, { "cell_type": "code", "collapsed": false, "input": [ "## Time zone reprentation - tz_localize()\n", "rng = pd.date_range('3/6/2012 00:00', periods = 5, freq = 'D')\n", "ts = pd.Series(randn(len(rng)), rng)\n", "ts_utc = ts.tz_localize('UTC', )\n", "print '>> Time zone UTC >>'\n", "print ts_utc\n", "ts_utc.plot()\n", "## Convert to another time zone - tz_convert\n", "ts_us = ts_utc.tz_convert('US/Eastern')\n", "print '>> Time zone US >>'\n", "print ts_us\n", "figure()\n", "ts_us.plot()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ ">> Time zone UTC >>\n", "2012-03-06 00:00:00+00:00 0.146046\n", "2012-03-07 00:00:00+00:00 -1.695428\n", "2012-03-08 00:00:00+00:00 -0.956475\n", "2012-03-09 00:00:00+00:00 1.338961\n", "2012-03-10 00:00:00+00:00 -0.296398\n", "Freq: D, dtype: float64\n", ">> Time zone US >>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2012-03-05 19:00:00-05:00 0.146046\n", "2012-03-06 19:00:00-05:00 -1.695428\n", "2012-03-07 19:00:00-05:00 -0.956475\n", "2012-03-08 19:00:00-05:00 1.338961\n", "2012-03-09 19:00:00-05:00 -0.296398\n", "Freq: D, dtype: float64\n" ] }, { "output_type": "pyout", "prompt_number": 38, "text": [ "" ] }, { "output_type": "display_data", "png": 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O788qO09Bx0lJSYiMjNRNnpxjo9GIZcuWAYD587JAJFFAQAAdPHgw3+f27dtH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\nrFmzbJ5z4cKFcHR0xCOPPII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} ], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "## Converting between time span representations\n", "rng = pd.date_range('1/1/2012', periods=5, freq = 'M') # frequency = 1 month for a period of 5\n", "ts = pd.Series(randn(len(rng)), index = rng)\n", "\n", "print '>> Time series (Datetime Index) >>'\n", "print ts\n", "ps = ts.to_period()\n", "print '>> Period series (Period Index) >>'\n", "print ps\n", "print ' >> Time stamp series (Datetime Index again)>>'\n", "tm_s = ps.to_timestamp()\n", "print tm_s" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ ">> Time series (Datetime Index) >>\n", "2012-01-31 -0.369075\n", "2012-02-29 -0.244696\n", "2012-03-31 -1.621117\n", "2012-04-30 -1.972054\n", "2012-05-31 1.699680\n", "Freq: M, dtype: float64\n", ">> Period series (Period Index) >>\n", "2012-01 -0.369075\n", "2012-02 -0.244696\n", "2012-03 -1.621117\n", "2012-04 -1.972054\n", "2012-05 1.699680\n", "Freq: M, dtype: float64\n", " >> Time stamp series (Datetime Index again)>>\n", "2012-01-01 -0.369075\n", "2012-02-01 -0.244696\n", "2012-03-01 -1.621117\n", "2012-04-01 -1.972054\n", "2012-05-01 1.699680\n", "Freq: MS, dtype: float64\n" ] } ], "prompt_number": 55 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 10. Plotting" ] }, { "cell_type": "code", "collapsed": false, "input": [ "## PLOTTING TIME SERIES\n", "ts = pd.Series(randn(1000), index = pd.date_range('1/1/2000', periods=1000, freq='D'))\n", "ts.plot()\n", "cts = ts.cumsum()\n", "figure()\n", "cts.plot()\n", "\n", "print \">> time series >>\"\n", "print ts.head(5)\n", "print \">> cummulative sum of time series >>\"\n", "print cts.head(5)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ ">> time series >>\n", "2000-01-01 -0.070830\n", "2000-01-02 -1.180476\n", "2000-01-03 -0.036886\n", "2000-01-04 0.018825\n", "2000-01-05 0.280117\n", "Freq: D, dtype: float64\n", ">> cummulative sum of time series >>\n", "2000-01-01 -0.070830\n", "2000-01-02 -1.251305\n", "2000-01-03 -1.288192\n", "2000-01-04 -1.269367\n", "2000-01-05 -0.989250\n", "Freq: D, dtype: float64\n" ] }, { "output_type": "display_data", "png": 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7MLzJSymvFvdjj5H/qtmR7B6TNAxb8XQ3C1ar1g1OimZH8YUjayA2g5My6zMu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\nxl14hCbu66+/Hh9//DEWLFiAsWPH4tZbb40zXw4ODg4OEoQ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NUZg+Xd7X0kK/kYQEGkwfOpQE/Re/AD77zDdtNipG7udgQZO5bqdPn0YfRUB3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\nLmpiPbEoM5OWSgs9MlK24GNiyL99KcC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Qoqqgt7S0YPbs2bjjjjswc+ZMAHT3FoWmS0tL\n0bNnTwBkeZ88ebL9vadOnUJycjKSkpJw6tQpi/1JSUlqNpNRAW/7mvvUf1Czr++9916kpqbigQce\n8OEnCB5UE3RJkjB//nykp6fjwQcfbN9/4403tucKfvPNN9t/EDfeeCPee+89NDc3o6ioCIWFhRgz\nZgx69eqFTp06Yc+ePZAkCW+//Xb7exhjoFZfM8ZHzb5+4oknUF1djb/85S++/yDBglrO+F27dkkm\nk0nKyMiQMjMzpczMTGnLli1SRUWFNGnSJGnw4MFSTk6OdOHChfb3LFu2TBo4cKCUmpoq5eXlte//\n9ttvpeHDh0sDBw6U7r//frWayKiEmn29ZMkSKTk5WQoNDZWSk5OlZ555Ro+PxDhArb4+efKkZDKZ\npPT09PbzrF27Vq+PFbDwTFGGYZgAgWeKMgzDBAgs6AzDMAECCzrDMEyAwILOMAwTILCgMwzDBAgs\n6AzDMAHC/wdC3juJ1wdkdQAAAABJRU5ErkJggg==\n" } ], "prompt_number": 64 }, { "cell_type": "code", "collapsed": false, "input": [ "## PLOT OF DATAFRAME\n", "df = pd.DataFrame(randn(1000, 4), index = ts.index, columns=list(\"ABCD\"))\n", "df.plot()\n", "legend(loc='best')\n", "figure()\n", "cdf = df.cumsum(axis = 0)\n", "cdf.plot()\n", "legend(loc='best')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 70, "text": [ "" ] }, { "output_type": "display_data", "png": 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4XLF97avLXsUHaz9wXKMSzRAA5ZuAIUOAZ59l91Qbo3ktbmp/kERmfFYPqmCY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\nC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ttRuXCi6ZFcGqj6rqKjLUGcbWb052Trx+/esUVxQTsjaEpduWcsfgO5p8btP5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JhWcdn65dOnASAWiYntOx5hfHJHOj/ism2Xz507hxdffLHDjOdmWVYoFFi3bh0A6OWlOSyaXCZM\nmID8/PwG73/00Ue45557AAAffvghEhISsG3bNgDAggULMHz4cMzkWtw8/fTTuOuuuzB16lTjHYsm\nl1YnLQ348EPqO9qeKERnWadAnOe2weqwxf3791vc8Lp167B7924cEHQSDgwMRJagfmt2djYCxU7C\n7UKvXu0vzAGxxkdnQZzn9sdqG/qePXvw6aefYvv27XAUFCqZMmUKNm/eDI1Gg/T0dFy5cgVD2yNl\nUkRERKSTYbVAX7BgASorKzFhwgQMHDgQ8+bNAwBERUVh2rRpiIqKwqRJk/Dtt99CInYS7tQIbegi\nNy/iPLc/YmKRSKsj2lY7B+I8tw2WZKco0EVERERuIFolDl1EREREpGMhCnSRVke0rXYOxHluf0SB\nLiIiInKTINrQRURERG4gRBu6iIiISCdAFOgirY5oW+0ciPPc/ogCXUREROQmQbShi4iIiNxAiDZ0\nERERkU6AKNBFWh3Rtto5EOe5/REFuoiIiMhNgmhDFxEREbmBEG3oIiIiIp0AUaCLtDqibbVzIM5z\n+2O1QH/rrbcwYMAAxMTEYNy4cUZt55YuXYqwsDBERERg3759NhmoyI3LuXPn2nsIIm2AOM/tj9UC\n/dVXX8X58+dx7tw53HfffXj33XcBAElJSdiyZQuSkpKwZ88ezJs3DzqdzmYDFrnxKCsra+8hiLQB\n4jy3P1YLdDc3N/3/lZWV8PHxAQBs374dM2bMgEwmQ0hICHr37o34+PgWDbKpj3I3y3o3276bQ1O2\neTOdmxth3x19npuzbkdfr7nr1qdFNvQ33ngD3bt3x7p167BkyRIAQG5uLoKCgvTrBAUFIScnpyW7\n6fCTcDP9eFtjmxkZGTbd9810bm6EfXf0eW7Ouh19veauWx+LYYsTJkxAfn5+g/c/+ugj3HPPPfrl\nZcuWITk5GWvXrsWCBQswfPhwzJw5EwDw9NNP46677sLUqVONdyw2jhYRERGxCnNi297Sl/bv39+k\njT/yyCO46667AACBgYFGDtLs7GwEBgY2eUAiIiIiItZhtcnlypUr+v+3b9+OgQMHAgCmTJmCzZs3\nQ6PRID09HVeuXMHQoUNbPlIREREREYtY1NAtsWTJEiQnJ8POzg6hoaFYuXIlACAqKgrTpk1DVFQU\n7O3t8e2334rmFREREZE2oE1S/11dXVFZWdnau+mQNHbssbGxWL58OQYPHtyGo2o9Outci/PcOejo\n89wmmaKdWUNv7NglEslNdX5upmNpDuI8dw46+jy3Wep/VVUVxo8fj8GDB6N///7YsWMHAAp1ioyM\nxNy5cxEdHY2JEyeipqamrYbVJvzzzz9GUUHz58/H+vXr23FErUtnnWtxnsV5bm/aTKA7OTnh999/\nx5kzZ3Dw4EG8/PLL+s+uXr2K+fPn4+LFi/D09MS2bdvaaljtQnvfxVsbca4JcZ7FeW5rrHaKNhed\nToclS5bg8OHDkEqlyM3NRWFhIQCgZ8+e6N+/PwBg8ODBzUpQEOl4iHPdORDnuePRZgJ9w4YNuH79\nOhISEmBnZ4eePXvqH8Pkcrl+PTs7O6hUqrYaVptgb29vVM/mZju++nTWuRbnWZzn9qbNTC5KpRK+\nvr6ws7NDXFwcrl271la7bnd69OiBpKQkaDQalJWV4eDBg+09pFals861OM/iPLc3ra6ha7VayOVy\nzJw5E/fccw/69++PIUOGIDIyUr9OfftTR7FHtRT+2IOCgjBt2jRER0ejZ8+eGDRoUHsPrVXorHMt\nzrM4zx0G1sqcO3eODRs2rLV30yHpbMfe2Y6Xp7Mdd2c7Xp4b4bhbVaCvXLmSRUVFsf3797fmbjok\nne3YO9vx8nS24+5sx8tzoxx3uzWJFhERERGxLWJPUREREZGbBJsJ9KysLIwZMwZ9+/ZFdHQ0vvzy\nSwBASUkJJkyYgPDwcNxxxx1GbarM9R49c+YM+vXrh7CwMCxcuNBWQxSxEbaca75JirADlkjHwVZz\nrVKpMHnyZERGRiI6OlrfEEfExtjKdpOXl8fOnj3LGGOsoqKChYeHs6SkJLZo0SL28ccfM8YYW7Zs\nGXvttdcYY4wlJiayAQMGMI1Gw9LT01loaCjT6XSMMcZuueUWdvLkScYYY5MmTWJ//fWXrYYpYgNs\nOdcnT55keXl5zNXVtX0ORsQitprr6upqplAoGGOMaTQaNmrUKPF33Qq0mlP03nvvZfv372d9+vRh\n+fn5jDG6OPr06cMYY+yjjz5iy5Yt068/ceJEdvz4cZabm8siIiL072/atIk988wzrTVMERtg7VwL\nEQX6jYEt5poxxhYuXMhWr17dNoPuRLSKDT0jIwNnz57FsGHDUFBQAD8/PwCAn58fCgoKAJjvPVr/\n/cDAwBb3JBVpPVoy1yI3Fraa67KyMvz5558YN25c2w2+k2BzgV5ZWYkHHngAK1asaGAX7UhFbERa\nTkvmWrwObixsNddarRYzZszAwoULERIS0lrD7bTYVKDX1tbigQcewGOPPYb77rsPAN29+UbTeXl5\n8PX1BWC692hQUBACAwORnZ1t9L6pnqQi7UtL51qc0xsHW8713Llz0adPH7zwwgtteASdB5sJdMYY\nZs+ejaioKLz44ov696dMmaKvFbx+/Xr9BWGu96i/vz/c3d1x8uRJMMbw888/678j0jGw1VyLdHxs\nOddvvvkmysvL8cUXX7T9gXQWbGWMP3z4MJNIJGzAgAEsJiaGxcTEsL/++osVFxezcePGsbCwMDZh\nwgRWWlqq/86HH37IQkNDWZ8+fdiePXv0758+fZpFR0ez0NBQtmDBAlsNUcRG2HKuFy1axIKCgpid\nnR0LCgpi7777bnsckogZbDXXWVlZTCKRsKioKP12fvzxx/Y6rJsWMVNURERE5CZBzBQVERERuUkQ\nBbqIiIjITYIo0EVERERuEkSBLiIiInKTIAp0ERERkZsEUaCLiIiI3CT8P8IuJsnQ2rgIAAAAAElF\nTkSuQmCC\n" } ], "prompt_number": 70 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 11. Data IO\n", "- csv \n", "- hdf5\n", "- MS excel" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }