notebooks/61 - Exporting Visualizations and data.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exporting Visualizations and data\n",
    "\n",
    "Sometimes we want to save checkpoints from our data. That means we need to save the data in an appropriate format. The same goes for visualizations that can be used in presentations, reports, and designs.\n",
    "\n",
    "Exporting data and visualizations are essential in many fields, such as data analysis, machine learning, and visualization design. \n",
    "\n",
    "It involves converting data and visualizations into a format easily shared and used by others. Data export can be crucial when we need to save the state of our analysis or share it with colleagues or clients. Visualizations, on the other hand, can help communicate insights more effectively. Still, they must also be exported in a format that retains their quality and allows them to be easily inserted into presentations or documents. \n",
    "\n",
    "In this context, understanding how to export data and visualizations is vital for anyone who works with data or creates visualizations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How To"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv(\"data/housing.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.join(pd.get_dummies(df.ocean_proximity))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"out.csv\", na_rep=\"nan\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing any kind of file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"out.txt\", mode=\"w\") as f:\n",
    "    f.write(df.to_string())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"out.txt\", mode=\"w\") as f:\n",
    "    f.write(\"Anything really\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"out.txt\", mode=\"w\") as f:\n",
    "    f.write(df.to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"out.txt\", mode=\"a\") as f:\n",
    "    f.write(\"Anything really\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving Figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([0,1,3,5])\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"random.jpg\", facecolor=\"k\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional Resources\n",
    "- [Matplotlib Savefig](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html#matplotlib.pyplot.savefig)\n",
    "- [Exporting Holoviews](http://holoviews.org/user_guide/Exporting_and_Archiving.html)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}