hi5san/Py4VisLabQ2.ipynb

## Py4VisLabQ2.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  # useful for many scientific computing in Python\n",
    "import pandas as pd # primary data structure library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Very interested</th>\n",
       "      <th>Somewhat interested</th>\n",
       "      <th>Not interested</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Big Data (Spark / Hadoop)</td>\n",
       "      <td>1332</td>\n",
       "      <td>729</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Data Analysis / Statistics</td>\n",
       "      <td>1688</td>\n",
       "      <td>444</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Data Journalism</td>\n",
       "      <td>429</td>\n",
       "      <td>1081</td>\n",
       "      <td>610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Data Visualization</td>\n",
       "      <td>1340</td>\n",
       "      <td>734</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Deep Learning</td>\n",
       "      <td>1263</td>\n",
       "      <td>770</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Machine Learning</td>\n",
       "      <td>1629</td>\n",
       "      <td>477</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Very interested  Somewhat interested  \\\n",
       "Big Data (Spark / Hadoop)              1332                  729   \n",
       "Data Analysis / Statistics             1688                  444   \n",
       "Data Journalism                         429                 1081   \n",
       "Data Visualization                     1340                  734   \n",
       "Deep Learning                          1263                  770   \n",
       "Machine Learning                       1629                  477   \n",
       "\n",
       "                            Not interested  \n",
       "Big Data (Spark / Hadoop)              127  \n",
       "Data Analysis / Statistics              60  \n",
       "Data Journalism                        610  \n",
       "Data Visualization                     102  \n",
       "Deep Learning                          136  \n",
       "Machine Learning                        74  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"https://cocl.us/datascience_survey_data\", index_col='Unnamed: 0')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib version:  3.1.1\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib as mpl\n",
    "\n",
    "mpl.style.use('ggplot') # optional: for ggplot-like style\n",
    "\n",
    "# check for latest version of Matplotlib\n",
    "print('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.sort_values(['Very interested'], ascending=False, axis=0, inplace=True)\n",
    "df_percent=(df / 2233).round(2)\n",
    "\n",
    "ax = df_percent.plot(kind='bar',\n",
    "                     alpha=1,\n",
    "                     figsize=(20, 8),\n",
    "                     width=0.8,\n",
    "                     fontsize=14,\n",
    "                     color=['#5cb85c', '#5bc0de', '#d9534f'],\n",
    "                     grid=False\n",
    ")\n",
    "\n",
    "ax.set_title('Percentage of Respondents\\' Interest in Data Science Areas', fontsize=16)\n",
    "\n",
    "ax.set_facecolor('white')\n",
    "ax.spines['bottom'].set_color('black')\n",
    "ax.yaxis.set_ticklabels('')\n",
    "ax.yaxis.set_tick_params(which='major', left=False)\n",
    "ax.legend(facecolor='white')\n",
    "\n",
    "for rect in ax.patches:\n",
    "    # print(rect)\n",
    "    height = rect.get_height()\n",
    "    ax.text(rect.get_x() + rect.get_width()/2, height * 1.01,\n",
    "            height, ha='center', va='bottom')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "conda-env-python-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}