{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datascience import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set_palette('husl')\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "path_data = 'http://www.millerjw.com/dom/mgmt462/pfiles/'\n",
    "crime = Table.read_table(path_data + 'crime.csv')\n",
    "# Make a copy of the usda data in pandas dataframe format \n",
    "crimep = crime.to_df()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can get infromation from a Python dataframe using\n",
    "df.head(), df.describe(), and dr.info().  The crime data is in the data frame named crimep. (Use creimep instead of df).\n",
    "Code three statements in the boxes below to get infromation from the crimep dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the code below to see how many crimes had an arrest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = crimep['Arrest'].value_counts()\n",
    "arrested = x[1]\n",
    "notarrested = x[0]\n",
    "totalrows = x[1] + x[0]\n",
    "print(arrested)\n",
    "print(notarrested)\n",
    "print(totalrows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below, use the variables arrested and total rows to calculate the percentage of arrests.  Remember to multiply by 100 to get a percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the cell below to create dataframe y that will contain crime counts by year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = (crimep['Year'].value_counts())\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the cell below to run describe on dataframe y.  Just like you ran describe on dataframe crimep earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the cell below and look at the output.  The statement that you will run does the same thing as an Excel Pivot Table using LocationDescription and Arrest in Rows and the count of any column in the values box."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "crimep.groupby(['LocationDescription','Arrest'])['Arrest'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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