Data_Visualization_with_Seaborn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization with Seaborn\n",
    "\n",
    "## Plot a histogram\n",
    "The distplot() function will return a Kernel Density Estimate (KDE) by default. The KDE helps to smooth the distribution and is a useful way to look at the data. However, Seaborn can also support the more standard histogram approach if that is more meaningful for your analysis.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'School Name', 'City', 'State', 'District Name',\n",
       "       'Model Selected', 'Award_Amount', 'Region'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "school_df =  pd.read_csv('data/schoolimprovement2010grants.csv')\n",
    "#school_df.head()\n",
    "school_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a distplot\n",
    "sns.distplot(school_df['Award_Amount'],\n",
    "            kde=False,\n",
    "            bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rug plot and kde shading\n",
    "Now that you understand some function arguments for distplot(), we can continue further refining the output. This process of creating a visualization and updating it in an incremental fashion is a useful and common approach to look at data from multiple perspectives.\n",
    "\n",
    "Seaborn excels at making this process simple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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