diff --git a/pandaswork.ipynb b/pandaswork.ipynb new file mode 100644 index 0000000..56edb46 --- /dev/null +++ b/pandaswork.ipynb @@ -0,0 +1,1140 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:8c607b0d7360d9117445c84af5cd94010d6261087d7e7f16ed3ed96e72f3c043" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import seaborn as sb\n", + "import numpy as np" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 294 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%matplotlib inline" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 173 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def read_data():\n", + " myfile = \"./complaints_dec_2014.csv\"\n", + " df = pd.read_csv(myfile, parse_dates=['Date received'],index_col='Date received')\n", + " return df" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 174 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "df = read_data()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 175 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Examining the list of Products\n" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "df.Product.unique()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 176, + "text": [ + "array(['Debt collection', 'Credit card', 'Bank account or service',\n", + " 'Credit reporting', 'Mortgage', 'Money transfers', 'Consumer loan',\n", + " 'Student loan', 'Payday loan', 'Prepaid card',\n", + " 'Other financial service'], dtype=object)" + ] + } + ], + "prompt_number": 176 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Getting the complaints by product" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "pr_counts = df['Product'].value_counts()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 177 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "pr_counts.plot(kind='bar')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 178, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAFxCAYAAABweRMUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n488MAAAAAElFTkSuQmCC\n", + "text": [ + "" + ] + } + ], + "prompt_number": 178 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Getting the top 10 companies for complaints" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "company_counts = df['Company'].value_counts().head(10)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 179 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "company_counts.plot(kind='bar')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 180, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAFfCAYAAABa/eebAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nM7OGcWI3M2sYJ3Yzs4b5/1H0MV1Er3QUAAAAAElFTkSuQmCC\n", + "text": [ + "" + ] + } + ], + "prompt_number": 180 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "df.columns.values" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 181, + "text": [ + "array(['Complaint ID', 'Product', 'Sub-product', 'Issue', 'Sub-issue',\n", + " 'State', 'ZIP code', 'Submitted via', 'Date sent to company',\n", + " 'Company', 'Company response', 'Timely response?',\n", + " 'Consumer disputed?'], dtype=object)" + ] + } + ], + "prompt_number": 181 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at average complaints by weekday\n" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "df.index.day\n", + "df['day'] = df.index.day\n", + "df.index.weekday\n", + "df['weekday']= df.index.weekday\n", + "df.head(10)\n", + "df_avg= df.groupby('weekday').mean()\n", + "df_avg.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n", + "df_to_plot = df_avg['day']\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 198 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "df_to_plot.plot(kind='bar')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 195, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAEyCAYAAAA1LFE9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGUVJREFUeJzt3Xu8XXV55/FPSIgQE0KQgIoIloHHS71iGRVbGYdBRylC\nVdTqFMWxM9OqDDrMqK0tiMxoGaxoZ1SuBYYyKFSL14LWCxTFAt4q8ogOYJRCUnOIhKgBOfPHWpuc\nhOScvXfOyVpP8nm/Xud19l57n5XH5eK71/6t32Xe5OQkkqRaduq6AEnS6AxvSSrI8JakggxvSSrI\n8JakggxvSSpowXQvRsS+wIXAXsAkcFZmfiAi9gAuBfYDbgOOzcy757hWSVJrpivv+4ATM/NJwLOA\nP4yIJwBvA67KzIOAL7TPJUnbyLThnZl3ZuY328drge8B+wBHARe0b7sAOHoui5QkbWzoNu+I2B94\nOnAdsHdm3tW+dBew9+yXJknakmnbvAciYjFwOXBCZt4TEQ++lpmTETHtGPv77//V5IIF87eqUEna\nAc3b0gszhndE7EwT3Bdl5ifazXdFxCMz886IeBSwcrp9TEysG6XYkS1fvoRVq+6Z039jLll/t6y/\nW5Xrn+valy9fssXXpm02iYh5wLnATZn5/ikvXQEc1z4+DvjEpn8rSZo7M115Hwq8Bvh2RHyj3fZ2\n4D3ARyPi9bRdBeesQknSQ0wb3pl5DVu+Oj989suRJA3DEZaSVJDhLUkFGd6SVJDhLUkFGd6SVJDh\nLUkFGd6SVJDhLUkFGd6SVJDhLUkFGd6SVJDhLUkFGd6SVJDhLUkFDbUMmiRt79avX8+KFbeP9DcT\nE4tZvXrt0O/fd9/9WLhw4ailbZbhLUnAihW3c8LpV7Bo6V5zsv91a1Zy5klHccABB87K/gxvSWot\nWroXi5ft03UZQ7HNW5IKMrwlqSDDW5IKMrwlqSDDW5IKMrwlqSDDW5IKMrwlqSDDW5IKMrwlqSDD\nW5IKMrwlqSDDW5IKMrwlqSDDW5IKMrwlqSDDW5IKMrwlqSDDW5IKMrwlqSAXIJ5l69evZ8WK20f6\nm4mJxaxevXbo9++7734sXLhw1NIkbUcM71m2YsXtnHD6FSxautec7H/dmpWcedJRHHDAgXOyf0k1\nGN5zYNHSvVi8bJ+uy5C0HbPNW5IKMrwlqSCbTSTNmrm+Ye/N+g0Mb0mzZi5v2HuzfmOGt6RZ5Q37\nbcPw1kbspy7V0LvwNjy6ZT91qYYZwzsizgNeDKzMzCe3204G/j2wqn3b2zPzc7NRkOHRPb/2Sv03\nzJX3+cAHgQunbJsE3peZ75uLogwPSZrejP28M/NqYGIzL82b/XIkScPYmjbvN0XE7wHXA2/NzLtn\nqSZJ0gzGDe8PAe9qH58KnAG8fktvXrZsEQsWzB9qxxMTi8csaXh77LGY5cuXzMm+rX9mc1n/uPpW\nz6j6Uv9cnz+e+xuMFd6ZuXLwOCLOAT453fsnJtYNve9Reo2Ma/Xqtaxadc+c7XuuWf/sWr58Sa/q\nGVWf6p/r82dHO/enC/qx5jaJiEdNeXoM8J1x9iNJGs8wXQUvAZ4H7BkRK4A/BQ6LiKfR9Dq5FfgP\nc1qlJGkjM4Z3Zr5qM5vPm4NaJElDckpYSSrI8JakggxvSSrI8JakggxvSSrI8Jakgno3n7e0I3M+\new3L8JZ6xPnsNSzDW+oZ57PXMGzzlqSCDG9JKsjwlqSCDG9JKsjwlqSCDG9JKsjwlqSCDG9JKsjw\nlqSCDG9JKsjwlqSCDG9JKsjwlqSCDG9JKsjwlqSCDG9JKsjwlqSCDG9JKsjwlqSCDG9JKsjwlqSC\nDG9JKsjwlqSCDG9JKsjwlqSCDG9JKsjwlqSCFnRdgDSb1q9fz4oVt4/0NxMTi1m9eu3Q79933/1Y\nuHDhqKVJs8rw1nZlxYrbOeH0K1i0dK852f+6NSs586SjOOCAA+dk/9KwDG9tdxYt3YvFy/bpugxp\nTtnmLUkFGd6SVJDhLUkFGd6SVJDhLUkFGd6SVJDhLUkFGd6SVNCMg3Qi4jzgxcDKzHxyu20P4FJg\nP+A24NjMvHsO65QkTTHMlff5wAs32fY24KrMPAj4QvtckrSNzBjemXk1MLHJ5qOAC9rHFwBHz3Jd\nkqRpjNvmvXdm3tU+vgvYe5bqkSQNYasnpsrMyYiYnO49y5YtYsGC+UPtb2Ji8daWNKM99ljM8uVL\n5mTf1j8z698y659e5dphdusfN7zviohHZuadEfEoYOV0b56YWDf0jkeZV3lcq1evZdWqe+Zs33PN\n+qff91yz/un3PZcq1z74N0apf7qgH7fZ5ArguPbxccAnxtyPJGkMw3QVvAR4HrBnRKwA/gR4D/DR\niHg9bVfBuSxSkrSxGcM7M1+1hZcOn+VaJElDcoSlJBVkeEtSQYa3JBVkeEtSQYa3JBVkeEtSQYa3\nJBVkeEtSQYa3JBVkeEtSQYa3JBVkeEtSQYa3JBVkeEtSQYa3JBVkeEtSQYa3JBVkeEtSQYa3JBVk\neEtSQYa3JBVkeEtSQYa3JBVkeEtSQYa3JBVkeEtSQYa3JBVkeEtSQYa3JBVkeEtSQYa3JBVkeEtS\nQYa3JBVkeEtSQYa3JBVkeEtSQYa3JBVkeEtSQYa3JBVkeEtSQYa3JBVkeEtSQYa3JBVkeEtSQYa3\nJBVkeEtSQYa3JBW0YGv+OCJuA34G/Aq4LzMPmYWaJEkz2KrwBiaBwzJz9WwUI0kazmw0m8ybhX1I\nkkawteE9CXw+Iq6PiDfMRkGSpJltbbPJoZn5TxGxHLgqIm7OzKs3fdOyZYtYsGD+UDucmFi8lSXN\nbI89FrN8+ZI52bf1z8z6t8z6p1e5dpjd+rcqvDPzn9rfqyLi48AhwEPCe2Ji3dD7XL167daUNPS/\nsWrVPXO277lm/dPve65Z//T7nkuVax/8G6PUP13Qj91sEhGLImJJ+/jhwBHAd8bdnyRpeFtz5b03\n8PGIGOzn4sy8claqkiRNa+zwzsxbgafNYi2SpCE5wlKSCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jakg\nw1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jakgw1uS\nCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8\nJakgw1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jakg\nw1uSCjK8JamgBeP+YUS8EHg/MB84JzPfO2tVSZKmNdaVd0TMB/4CeCHwROBVEfGE2SxMkrRl4zab\nHAL8IDNvy8z7gP8LvGT2ypIkTWfc8N4HWDHl+Y/bbZKkbWDcNu/JUd588MG/vtntN9zwj5vdfu2l\n72DeTvMfsv3ZLz91s+//6sfeudntm3v/ujUrOeaYI9l5552Hrsf6t1wPWP9M9Vj/luuB4etft2bl\nWPWM8v51a1aOdDxh7uvfknmTkyPlMAAR8Szg5Mx8Yfv87cAD3rSUpG1j3Cvv64EDI2J/4A7gFcCr\nZqsoSdL0xmrzzsz7gTcCfwvcBFyamd+bzcIkSVs2VrOJJKlbjrCUpIIMb0kqyPCWpIIMb0kqqGR4\nt3OrlBURT+66hh1ZRDyi6xq2RvXzv7I+HfuS4Q3cEhGnR8QTuy5kTB+KiH+IiD+IiKVdFzOqiHhz\nRCzruo6t8LWI+FhEvCgi5nVdzBhKn/8R8b6IeFLXdYypN8e+ZFfBiNgNeCXwWpopac8DLsnMn3VZ\n1ygi4iDgeODlwNeB8zPzym6rGk5EnEYzMOtGmmP/t5lZ5kSKiJ2Aw2mO/28AH6U5/t/vtLAhVT//\nI+INNLXvzIba13Ra1JD6dOxLhvdUEXEYcDGwDPgYcGpm/qDTooYUEQuAo4EPAGtovgm9IzMv77Sw\nIbQBeATNSfxMmgA8NzN/2GVdo4qI5wP/B3g48E3g7Zl5bbdVDa/4+f94mvPnd4FrgLMz84udFjWC\nro/92IsxdKkNvRcDrwP2B84A/gp4LvAZ4KDOihtCRDyV5qQ9ErgKODIzb4yIRwNfA3of3pn5QETc\nCdwF/IrmBL4sIj6fmSd1W930ImJP4NXA79HU/0bgk8BTgctozqneqn7+w4Ntx48HngCsAr4FvCUi\n/mNmvqLT4qbRp2NfMryB7wNfAv5sk6ukyyLied2UNJIPAOcCf5SZ6wYbM/OOiPjj7soaTkScQBN8\nPwXOAf5LZt7XXo3fAvQ6vIFraa62X5KZP56y/fqI+HBHNY2i9PkfEX8O/Dbwd8Bpmfn19qX3RkR2\nV9lQenPsSzabRMSSzLyn6zp2VBFxCnBeZt6+mdeemJk3dVDW0CJip8x8oOs6xlX9/I+I42nmQ7p3\nM6/tnpl3d1DWUPp07KuG967A62mWYNu13TyZmcd3V9Xw2puV/x14ErBLu3kyM3+tu6pGFxF7saF+\nMvNHHZYztLbu/8pDj//zu6tqeNXPf4C2t9KBbHz+fKW7iobTp2NftavgRcDeNGtofgl4DLC2y4JG\ndD7wYeA+4DDgApobHyVExFERcQtwK/Bl4Dbgs50WNZqLgZuBxwEn09R/fYf1jKr0+d/2NvkKcCVw\nCs3spCd3WdMIenPsq4b3v8jMdwJrM/MC4EXAv+y4plHsmpmfB+Zl5u2ZeTLNTZAq3g08G/h+Zj4O\n+NfAdd2WNJJHZOY5wPrM/HJmvg4ocdXdqn7+n0CzDu5tmfmvgKfT9LaqoDfHvmp4r29/r2lHK+4O\nLO+wnlH9or3b/oOIeGNE/A5NV7Uq7svMfwZ2ioj5bfeuZ3Zd1AgG58+dEXFkRDyDprdMFeXP/8z8\nOUBE7JKZNwPRcU3D6s2xr9rb5OyI2AP4Y+AKYDGw+YXk+uk/A4uANwOnArsBx3Va0WgmImIJcDVw\ncUSspNDXduC0iNgdeCvwQZrjf2K3JY2k+vm/om3z/gRwVURM0DRdVdCbY1/yhqW6FRGLgZ/TfHN7\nNU34XZyZP+20MJXTDnTZDfhcZq6f4e2aolR4R8RbpzydBOYxZSX7zHzfNi9qBBHxySlPB/UPHpOZ\nR23zonYgEfHBKU83d/zfvM2LGsF2cP7vMd3rmbl6W9Uyqj4e+2rNJktoDljQzElxBc1BPJJmfpC+\nO6P9fQzwSJqBIvNoFm++q6uihhURa5lywm5iMjN325b1jOGG9vdzaLp6XUpz/F8OfLerokZQ/fy/\nkQ3B91hgot2+DLidpvdPX/Xu2Je68h6IiKuBFw06y7ftr5/JzN/strLhRMQNmXnwTNv6KiLeDdxB\n8+EDTdPJo9u78L0XEdcBz83M+9rnOwPXZGaJHhvbwfl/NvDxzPxM+/zfAsdk5u93W9nM+nTsq/Y2\n2Yumj/TAfe22KhZFxAGDJxHxazQ3MKs4KjP/d2b+rP35EPCSrosawe407awDS9ptVVQ//589CG6A\nzPwszbehCnpz7Ks1mwxcCHw9Iv6a5qvL0TQDXao4EfhiRNzaPt8f6P1VxxT3RsRrgEva56+kVm+T\n9wA3RsQXac6f51FnkAjUP/8Hc/gMmg1/F/hJtyUNrTfHvmSzCUBEHAz8Jk071Fcy8xsdlzSSiNiF\nZla1SeDmzPxlxyUNLSIeB5zJhqulvwdOyMzbOitqRBHxKJrBFZPAdZl5Z8cljaTy+d/euDyZpn5o\nRlue0ucbllP15dhXDu/5NDf9FrCht0CVuTWOpeka9bOIeCfNCLN3Z+aNHZe2Q4iIQ4FvZebaiPh3\nNMf/zM1NtNUnEbFbe84Mem1s2lum9+HXTql6QWa+uutaxtWX7CnZbBIRbwL+FFhJM5f0QJW1Id+Z\nmR+NiOfSDC3/nzRznRzSbVnDiYjTaQYX/Rz4HM082Cdm5kWdFja8DwNPbedVfwvNtLYX0jSf9Nkl\nNNMoDHptTDUJ9H5is8y8PyL2i4iHVfq2OdCn7CkZ3jQjFKPwoJDB/+lH0qwe8qmIOLXLgkZ0RGae\nFBHH0IyM+x2a0ZZVwvv+djGJo4H/lZnnRMTruy5qJpn54mjW3PytKt8yt+BW4JqIuAIYzGc/2fd+\n6q3eZE/V3iY/Akqs17cFP4mIs2jWgfx02/5d6f+LwYf+kcBl7fqDldrf7omIdwCvAT7Vfg3eueOa\nRvGZmd/Saz8EPk1zzi+m6e2zpNOKhteb7Kl65X0rTW+NT7Nhopgqn9wAxwIvAE7PzLvbm2d9X31m\nqk9GxM3AL4D/1M6P/YuOaxrFsTR904/PzDsj4rHA6R3XNJTMnIyIGyLikCkr0JTSzqJZVW+yp2p4\n/6j9Wdj+bDRUte8y896IWEWz7t0twP1AiUVjATLzbRHxZ8CazPxVRNxLkX7e7Q2zS9qpSIEHbzZd\n2F1VI3sW8JqIuB0YrEYzmZlP6bCmobVdNDdVZTGM3mRPyfAefHK3o5voy7JEw4qIk4GDaYbank9z\nElwEHNphWUOLiIcDf0gzxPkNwKNp/rd8qsu6htHeMHug78ttbU5EPLb9oHkBG8/NUs3Ub5m7AC+l\nuYDpvT59aygZ3u08uhcCj2ifrwKOy8x/7LSw4R1D0z3tBoDM/Mngg6iI82lqH/TzvoNm1fXeh3fr\nXuA7EXElG98w6/XEVMDfAE/PzNsi4vLMfGnXBY0jMzddteiaiPiHTooZUZ++NZQMb+As4C3tIgCD\naSXPos4Q21+2vR2AB69kKzkgM4+NiFfCg81AXdc0ir9uf6Yq0+zW6n23wC3ZZHbBnWgW8uj7pGYD\nvfnWUDW8Fw2CGyAzv1QsAD8WER8Bdo+I3weOp+lrXMUv24VYAWjnaSnTZzcz/7LrGnZwU/up30/T\n3bT3XTWhX98aqob3re3IxIto2v1eDfy/bksaXmaeHhFHAPcAB9EM2rmq47JGcTLN4JzHRMRf0bTV\nv7bLgkYxZU6ZqSYzs+9Xs0+JiMH9nV2nPIYaU/IOPD4zN+qd1HaX7b0+fWuoGt7H06w6Pfjqe3W7\nrYzMvJJm9exyMvPKiLiRptcDwJvbNS2r+I0pj3cBXkZ7/6TPMnN+1zXMkmuBZwyxrY96862hZHi3\nczi8qes6xrXJogYLaQaIrC105QTwMJrJ9BcAT4wIMvMrHdc0lM180Ly//TAqMR95Ve14hkfTTIn8\nDDZ0s9uNnk+JHBGHACsyc//2+Wtp2rtvA27qoqZS4d0uI7alLlKTVZYRy8zFg8cRsRNwFBuuYnsv\nIt5LMzr0Jjae36FEeLezwg0+PAdffbeXq9o+O4KmeW0fNqwqBU3z4Tu6KGgEH6GZh4iI+C3gfwBv\npOk1dhbNt7dtqlR40wTcj2km6Lmu3bbRzGp9FhE7D1ZvGcjMB4BPtH2/39ZJYaM7hmZ+hzI3KTdx\nBg/96ntsZ9XsIDLzAuCCiHhZZl7WdT0j2mnKrI2vAD6SmZcDl0fEt7ooqFp4Pwr4NzRrPr6KZn6E\nSzKzwvqD0HzgPCMipvbP3YlmwM7PuylpLD+kae4pGd6ZeVjXNezIMvOyiDiSZh3RXaZsf1d3Vc1o\n/pSLr8PZePGUTnK0VHhn5v3AZ4HPRsTDaAL8yxFxcmb+RbfVDWXwLeHIKdsGV34lhpe3fg58MyK+\nwIYArzDIBXiwZ8NLaVYwmk/b9trz8NhutN1kdwWeD5xNswD0ddP+Ufcuocmaf6YZ2HU1QEQcCHQy\nUrdUeMOD/+G9mGbprf1pVnT5eJc1jWB5RLwF2NxI0NcAVSbWuqL9mar3zVZT/A3Nf3A3UGtCre3F\nczLzyRHx7cw8JSLOoOl62luZeVpE/B3NIgxXts2d0Hzwd9J5olR4R8RFwJNopsR8V2Z+p+OSRjWf\nOlNfbtF2MMhln8x8QddF7MAGTYTrImIf4Kc0odhrmfnVzWz7fhe1QLHwphmMcy9wAnDCJkOyKwxS\nuDMzT+m6iHFFxHQflmVmtQOujYinZOa3uy5kB/XJiFhGMw3vDTTf2iqNMO6FUuGdmZUWLNge/Xb7\n+w/a31NHuPZeRHwXeIDmG9Dr2pGWU9vsq3z4lDSlr/Sp7fPFwHeAm4H3d1lbRaXCeztweNcFbI3B\n6vARcURmPm3KS9+OiG8A/62Twob3aOBp1J1KtbpN+0q/hw19pT9CB32lKzO8t6E+rHs3S+ZFxHMz\n8xp4cDX2CoF4W99XiN/O9a6vdGWGt8ZxPHB+RCxtn98NvK7DeoY16O2zpRG6VXr7VNW7vtKVecA0\nssy8gWaGu6XAvEIr0mwXvX0K611f6coMb40sIh4JnEbT5e6FEfFE4NmZeW7Hpc2kdG+f6vrYV7oy\nw1vj+EuapdD+qH1+C/BRoO/hrY71ra90ZXa90zj2zMxLaWcUbNswKywgW7q3jzSV4a1xrI2IPQdP\nIuJZwJoO6xnKdtTbR2Le5GSlKSnUpYg4Efj79umfA78OfBdYDrwsM+3uJW0jtnlrFI+hGQn3BOB7\nNMu4XU0zLe+qLguTdjReeWtk7XS8zwSeDTyn/X13Zj6h08KkHYhX3hrHrjTrDi5tf+4AnORJ2oa8\n8tbQIuJsmtVP7gG+DnwV+FpmTnRamLQDsreJRvFYmlXj7wR+0v44Mk7qgFfeGkm72v2T2NDe/WSa\nyfS/lpl/0mVt0o7E8NZYImJfmvA+lGZNzkdk5tLp/0rSbPGGpYYWESewoXfJ/cC1NP2+z2Xz63JK\nmiOGt0axP80cJidm5h0d1yLt0Gw2kaSC7G0iSQUZ3pJUkOEtSQUZ3pJU0P8HTnrYohkfErcAAAAA\nSUVORK5CYII=\n", + "text": [ + "" + ] + } + ], + "prompt_number": 195 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I got census data with polulation numbers" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "myfile = \"census.csv\"\n", + "census = pd.read_csv(myfile, dtype={'Population': np.float64} )\n", + "census.columns=['State','State_name','Population']\n", + "census.set_index(['State'])\n" + ], + "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", + " \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", + " \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", + " \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", + " \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", + "
State_namePopulation
State
AK Alaska 736,732
AZ Arizona 6,731,484
AR Arkansas 2,966,369
CA California 38,802,500
CO Colorado 5,355,866
CT Connecticut 3,596,677
DE Delaware 935,614
DC District of Columbia 658,893
FL Florida 19,893,297
GA Georgia 10,097,343
HI Hawaii 1,419,561
ID Idaho 1,634,464
IL Illinois 12,880,580
IN Indiana 6,596,855
IA Iowa 3,107,126
KS Kansas 2,904,021
KY Kentucky 4,413,457
LA Louisiana 4,649,676
ME Maine 1,330,089
MD Maryland 5,976,407
MA Massachusetts 6,745,408
MI Michigan 9,909,877
MN Minnesota 5,457,173
MS Mississippi 2,994,079
MO Missouri 6,063,589
MT Montana 1,023,579
NE Nebraska 1,881,503
NV Nevada 2,839,099
NH New Hampshire 1,326,813
NJ New Jersey 8,938,175
NM New Mexico 2,085,572
NY New York 19,746,227
NC North Carolina 9,943,964
ND North Dakota 739,482
OH Ohio 11,594,163
OK Oklahoma 3,878,051
OR Oregon 3,970,239
PA Pennsylvania 12,787,209
RI Rhode Island 1,055,173
SC South Carolina 4,832,482
SD South Dakota 853,175
TN Tennessee 6,549,352
TX Texas 26,956,958
UT Utah 2,942,902
VT Vermont 626,562
VA Virginia 8,326,289
WA Washington 7,061,530
WV West Virginia 1,850,326
WI Wisconsin 5,757,564
WY Wyoming 584,153
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 314, + "text": [ + " State_name Population\n", + "State \n", + "AK Alaska 736,732\n", + "AZ Arizona 6,731,484\n", + "AR Arkansas 2,966,369\n", + "CA California 38,802,500\n", + "CO Colorado 5,355,866\n", + "CT Connecticut 3,596,677\n", + "DE Delaware 935,614\n", + "DC District of Columbia 658,893\n", + "FL Florida 19,893,297\n", + "GA Georgia 10,097,343\n", + "HI Hawaii 1,419,561\n", + "ID Idaho 1,634,464\n", + "IL Illinois 12,880,580\n", + "IN Indiana 6,596,855\n", + "IA Iowa 3,107,126\n", + "KS Kansas 2,904,021\n", + "KY Kentucky 4,413,457\n", + "LA Louisiana 4,649,676\n", + "ME Maine 1,330,089\n", + "MD Maryland 5,976,407\n", + "MA Massachusetts 6,745,408\n", + "MI Michigan 9,909,877\n", + "MN Minnesota 5,457,173\n", + "MS Mississippi 2,994,079\n", + "MO Missouri 6,063,589\n", + "MT Montana 1,023,579\n", + "NE Nebraska 1,881,503\n", + "NV Nevada 2,839,099\n", + "NH New Hampshire 1,326,813\n", + "NJ New Jersey 8,938,175\n", + "NM New Mexico 2,085,572\n", + "NY New York 19,746,227\n", + "NC North Carolina 9,943,964\n", + "ND North Dakota 739,482\n", + "OH Ohio 11,594,163\n", + "OK Oklahoma 3,878,051\n", + "OR Oregon 3,970,239\n", + "PA Pennsylvania 12,787,209\n", + "RI Rhode Island 1,055,173\n", + "SC South Carolina 4,832,482\n", + "SD South Dakota 853,175\n", + "TN Tennessee 6,549,352\n", + "TX Texas 26,956,958\n", + "UT Utah 2,942,902\n", + "VT Vermont 626,562\n", + "VA Virginia 8,326,289\n", + "WA Washington 7,061,530\n", + "WV West Virginia 1,850,326\n", + "WI Wisconsin 5,757,564\n", + "WY Wyoming 584,153" + ] + } + ], + "prompt_number": 314 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Getting compalints by state. Turning a series into a data frame\n" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "df\n", + "dfstate = df['State'].value_counts()\n", + "\n", + "dfframe = dfstate.to_frame()\n", + "dfframe.index\n", + "\n", + "census.set_index = ['State']\n", + "census.index\n", + "\n", + "dfframe.columns=[\"Complaints\"]\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 302 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Merging the 2 data sets" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "all_data=pd.merge(dfframe, census, how='left', left_index=True, right_on=['State'])\n", + "all_data.columns= ['Complaints','State','State_name','Population']\n", + "all_data['Population'] = all_data['Population'].str.replace(r'[$,]', '').astype('float')\n", + "all_data\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 303 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Getting per capita complaints" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "all_data['per_capita']= all_data['Complaints']/all_data['Population']" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 311 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "all_data[['State_name','per_capita']].sort(['per_capita'],ascending=False)" + ], + "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", + " \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", + " \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", + " \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", + " \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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
State_nameper_capita
7 District of Columbia 0.000124
19 Maryland 0.000057
27 Nevada 0.000056
8 Florida 0.000055
29 New Jersey 0.000052
9 Georgia 0.000051
6 Delaware 0.000047
45 Virginia 0.000045
3 California 0.000041
42 Texas 0.000041
38 Rhode Island 0.000038
31 New York 0.000037
28 New Hampshire 0.000035
10 Hawaii 0.000034
4 Colorado 0.000034
12 Illinois 0.000033
46 Washington 0.000033
37 Pennsylvania 0.000033
1 Arizona 0.000032
5 Connecticut 0.000030
36 Oregon 0.000030
34 Ohio 0.000030
20 Massachusetts 0.000030
18 Maine 0.000029
41 Tennessee 0.000029
21 Michigan 0.000029
32 North Carolina 0.000029
44 Vermont 0.000029
17 Louisiana 0.000027
39 South Carolina 0.000027
30 New Mexico 0.000026
40 South Dakota 0.000026
48 Wisconsin 0.000025
22 Minnesota 0.000025
35 Oklahoma 0.000024
11 Idaho 0.000024
43 Utah 0.000024
0 Alaska 0.000020
13 Indiana 0.000020
2 Arkansas 0.000020
26 Nebraska 0.000020
24 Missouri 0.000020
15 Kansas 0.000019
23 Mississippi 0.000019
14 Iowa 0.000016
47 West Virginia 0.000014
49 Wyoming 0.000014
25 Montana 0.000014
16 Kentucky 0.000013
33 North Dakota 0.000011
49 NaN NaN
49 NaN NaN
49 NaN NaN
49 NaN NaN
49 NaN NaN
49 NaN NaN
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 347, + "text": [ + " State_name per_capita\n", + "7 District of Columbia 0.000124\n", + "19 Maryland 0.000057\n", + "27 Nevada 0.000056\n", + "8 Florida 0.000055\n", + "29 New Jersey 0.000052\n", + "9 Georgia 0.000051\n", + "6 Delaware 0.000047\n", + "45 Virginia 0.000045\n", + "3 California 0.000041\n", + "42 Texas 0.000041\n", + "38 Rhode Island 0.000038\n", + "31 New York 0.000037\n", + "28 New Hampshire 0.000035\n", + "10 Hawaii 0.000034\n", + "4 Colorado 0.000034\n", + "12 Illinois 0.000033\n", + "46 Washington 0.000033\n", + "37 Pennsylvania 0.000033\n", + "1 Arizona 0.000032\n", + "5 Connecticut 0.000030\n", + "36 Oregon 0.000030\n", + "34 Ohio 0.000030\n", + "20 Massachusetts 0.000030\n", + "18 Maine 0.000029\n", + "41 Tennessee 0.000029\n", + "21 Michigan 0.000029\n", + "32 North Carolina 0.000029\n", + "44 Vermont 0.000029\n", + "17 Louisiana 0.000027\n", + "39 South Carolina 0.000027\n", + "30 New Mexico 0.000026\n", + "40 South Dakota 0.000026\n", + "48 Wisconsin 0.000025\n", + "22 Minnesota 0.000025\n", + "35 Oklahoma 0.000024\n", + "11 Idaho 0.000024\n", + "43 Utah 0.000024\n", + "0 Alaska 0.000020\n", + "13 Indiana 0.000020\n", + "2 Arkansas 0.000020\n", + "26 Nebraska 0.000020\n", + "24 Missouri 0.000020\n", + "15 Kansas 0.000019\n", + "23 Mississippi 0.000019\n", + "14 Iowa 0.000016\n", + "47 West Virginia 0.000014\n", + "49 Wyoming 0.000014\n", + "25 Montana 0.000014\n", + "16 Kentucky 0.000013\n", + "33 North Dakota 0.000011\n", + "49 NaN NaN\n", + "49 NaN NaN\n", + "49 NaN NaN\n", + "49 NaN NaN\n", + "49 NaN NaN\n", + "49 NaN NaN" + ] + } + ], + "prompt_number": 347 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculating Outliers" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\n", + "outlier_zip=df['ZIP code'].value_counts()\n", + "outlierdf = outlier_zip.to_frame()\n", + "outlierdf.columns=['Complaints']\n", + "\n", + "\n", + "\n", + "newdf = outlierdf.copy()\n", + "#newdf.set_index(['ZIP code'])\n", + "\n", + "newdf['x-Mean'] = abs(newdf['Complaints'] - newdf['Complaints'].mean())\n", + "newdf['1.96*std'] = 1.96*newdf['Complaints'].std() \n", + "newdf['Outlier'] = abs(newdf['Complaints'] - newdf['Complaints'].mean()) > 1.96*newdf['Complaints'].std()\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 370 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "newdf['Outlier']" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 372, + "text": [ + "60445 True\n", + "94030 True\n", + "77065 True\n", + "78232 True\n", + "76028 True\n", + "53072 True\n", + "35040 True\n", + "85251 True\n", + "89113 True\n", + "76116 True\n", + "79424 True\n", + "78204 True\n", + "21117 True\n", + "20744 True\n", + "33173 True\n", + "...\n", + "60076 False\n", + "21212 False\n", + "94939 False\n", + "98198 False\n", + "21203 False\n", + "94930 False\n", + "70570 False\n", + "78542 False\n", + "29388 False\n", + "1460 False\n", + "85 False\n", + "94920 False\n", + "60131 False\n", + "48504 False\n", + "2382 False\n", + "Name: Outlier, Length: 5931, dtype: bool" + ] + } + ], + "prompt_number": 372 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file