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bitcointroybitcointroy
committedApr 30, 2018
Progress prior to solution lecture
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‎Troy_Bradley_Copy_of_Statistics_Coding_Challenge_1.ipynb

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "Troy Bradley Copy of Statistics - Coding Challenge #1.ipynb",
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"version": "0.3.2",
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"[View in Colaboratory](https://colab.research.google.com/github/bitcointroy/MLcodechallenges/blob/master/Troy_Bradley_Copy_of_Statistics_Coding_Challenge_1.ipynb)"
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]
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},
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{
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"metadata": {
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"id": "Vim6ATB8ADUI",
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"colab_type": "text"
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},
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"cell_type": "markdown",
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"source": [
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"# Statistics Coding Challenge #1\n",
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"\n",
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"In this coding challenge, we are going to use the \"Accidental Drug Related Deaths 2012-2017 (State of Connecticut)\" data set available from the Data.Gov website (https://catalog.data.gov/dataset?groups=local&organization_type=State+Government#topic=local_navigation). \n",
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"\n",
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"There are 2 main objectives you need to accomplish:\n",
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"\n",
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"1) First treat missing values for the \"Death City\" attribute - replace any missing values in the \"Death City\" with the city that has experienced the most number of deaths\n",
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"\n",
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"For each city, do the following:\n",
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"\n",
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"2) Compute summary statistics for the *age* attribute:\n",
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"\n",
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"\n",
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"a) Mean\n",
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"\n",
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"b) Median\n",
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"\n",
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"c) 25%, 50% and 75% percentiles using [np.percentile](https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.percentile.html)\n",
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"\n",
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"d) Examine any outliers in data using [Boxplots](https://matplotlib.org/2.1.1/gallery/statistics/boxplot_demo.html)\n",
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"\n",
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"e) Construct a Bee Swarm plot to highlight the distibution of age by each city using [seaborn.swarmplot](https://seaborn.pydata.org/generated/seaborn.swarmplot.html)\n",
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"\n",
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"\n"
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]
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},
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{
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"metadata": {
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"id": "vchq-N1O9Nlk",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 629
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},
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"outputId": "8fe82ce0-4ad0-477b-a058-fe274282149e"
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},
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"cell_type": "code",
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"source": [
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"# LAMBDA SCHOOL\n",
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"#\n",
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"# MACHINE LEARNING\n",
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"#\n",
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"# MIT LICENSE\n",
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"\n",
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"import pandas as pd\n",
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"\n",
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"# from google.colab import files\n",
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"\n",
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"dataset = pd.read_csv('https://data.ct.gov/api/views/rybz-nyjw/rows.csv?accessType=DOWNLOAD')\n",
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"# dataset2 = files.upload('https://data.ct.gov/api/views/rybz-nyjw/rows.csv?accessType=DOWNLOAD')\n",
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"\n",
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"print(dataset.head(5))\n",
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"# print(dataset2.head)"
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],
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"execution_count": 6,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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" CaseNumber Date Sex Race Age Residence City Residence State \\\n",
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"0 14-9876 06/28/2014 NaN NaN NaN NaN NaN \n",
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"1 12-16897 11/30/2012 Male White 45.0 NaN NaN \n",
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"2 13-11849 08/12/2013 Male White 30.0 NEW HAVEN NaN \n",
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"3 14-17578 11/23/2014 Male White 27.0 NAUGATUCK NaN \n",
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"4 12-11497 08/14/2012 Male White 21.0 ENFIELD NaN \n",
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"\n",
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" Residence County Death City Death State \\\n",
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"0 NaN NaN NaN \n",
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"1 NaN NEW HAVEN NaN \n",
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"2 NaN NEW HAVEN NaN \n",
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"3 NaN NEW MILFORD NaN \n",
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"4 NaN ENFIELD NaN \n",
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"\n",
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" ... Benzodiazepine Methadone Amphet \\\n",
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"0 ... Y NaN NaN \n",
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"1 ... NaN NaN NaN \n",
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"2 ... NaN Y NaN \n",
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"3 ... NaN NaN NaN \n",
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"4 ... NaN NaN NaN \n",
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"\n",
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" Tramad Morphine (not heroin) Other Any Opioid MannerofDeath \\\n",
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"0 NaN NaN NaN NaN Accident \n",
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"1 NaN NaN NaN NaN Accident \n",
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"2 NaN NaN NaN NaN Accident \n",
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"3 NaN NaN NaN NaN Accident \n",
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"4 NaN NaN NaN NaN Accident \n",
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"\n",
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" AmendedMannerofDeath DeathLoc \n",
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"0 NaN CT\\n(41.544654, -72.651713) \n",
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"1 NaN NEW HAVEN, CT\\n(41.308252, -72.924161) \n",
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"2 NaN NEW HAVEN, CT\\n(41.308252, -72.924161) \n",
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"3 NaN NEW MILFORD, CT\\n(41.576633, -73.408713) \n",
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"4 NaN ENFIELD, CT\\n(41.976501, -72.591985) \n",
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"\n",
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"[5 rows x 32 columns]\n"
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],
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"name": "stdout"
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}
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]
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}
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]
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}

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