Pandas/plotting_excel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_series = pd.Series(np.random.randn(1000), \n",
    "                       index=pd.date_range('1/1/2000', periods=1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01   -0.371185\n",
       "2000-01-02   -0.389268\n",
       "2000-01-03    1.129460\n",
       "2000-01-04    0.395534\n",
       "2000-01-05    2.246073\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_series2 = time_series.cumsum()\n",
    "time_series2[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "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": [
    "print(time_series2.plot())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_frame = pd.DataFrame(np.random.randn(1000,4), index=time_series2.index, \n",
    "                         columns=['A', 'B', 'C', 'D'])"
   ]
  },
  {
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>-1.108065</td>\n",
       "      <td>0.914834</td>\n",
       "      <td>-0.593941</td>\n",
       "      <td>2.099192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>-1.989855</td>\n",
       "      <td>2.036164</td>\n",
       "      <td>-1.243937</td>\n",
       "      <td>3.969312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-2.511673</td>\n",
       "      <td>2.126490</td>\n",
       "      <td>-0.700703</td>\n",
       "      <td>5.176191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-2.354495</td>\n",
       "      <td>3.030248</td>\n",
       "      <td>-0.185244</td>\n",
       "      <td>5.638730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-2.053271</td>\n",
       "      <td>5.976584</td>\n",
       "      <td>-0.214135</td>\n",
       "      <td>6.974131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2000-01-01 -1.108065  0.914834 -0.593941  2.099192\n",
       "2000-01-02 -1.989855  2.036164 -1.243937  3.969312\n",
       "2000-01-03 -2.511673  2.126490 -0.700703  5.176191\n",
       "2000-01-04 -2.354495  3.030248 -0.185244  5.638730\n",
       "2000-01-05 -2.053271  5.976584 -0.214135  6.974131"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame = data_frame.cumsum()\n",
    "data_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29426af1550>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "plt.figure()\n",
    "data_frame.plot()\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_frame.to_csv('foo.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>Unnamed: 0</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>-1.108065</td>\n",
       "      <td>0.914834</td>\n",
       "      <td>-0.593941</td>\n",
       "      <td>2.099192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-02</td>\n",
       "      <td>-1.989855</td>\n",
       "      <td>2.036164</td>\n",
       "      <td>-1.243937</td>\n",
       "      <td>3.969312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-01-03</td>\n",
       "      <td>-2.511673</td>\n",
       "      <td>2.126490</td>\n",
       "      <td>-0.700703</td>\n",
       "      <td>5.176191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-01-04</td>\n",
       "      <td>-2.354495</td>\n",
       "      <td>3.030248</td>\n",
       "      <td>-0.185244</td>\n",
       "      <td>5.638730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-01-05</td>\n",
       "      <td>-2.053271</td>\n",
       "      <td>5.976584</td>\n",
       "      <td>-0.214135</td>\n",
       "      <td>6.974131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         A         B         C         D\n",
       "0  2000-01-01 -1.108065  0.914834 -0.593941  2.099192\n",
       "1  2000-01-02 -1.989855  2.036164 -1.243937  3.969312\n",
       "2  2000-01-03 -2.511673  2.126490 -0.700703  5.176191\n",
       "3  2000-01-04 -2.354495  3.030248 -0.185244  5.638730\n",
       "4  2000-01-05 -2.053271  5.976584 -0.214135  6.974131"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('foo.csv').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>-1.108065</td>\n",
       "      <td>0.914834</td>\n",
       "      <td>-0.593941</td>\n",
       "      <td>2.099192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>-1.989855</td>\n",
       "      <td>2.036164</td>\n",
       "      <td>-1.243937</td>\n",
       "      <td>3.969312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-2.511673</td>\n",
       "      <td>2.126490</td>\n",
       "      <td>-0.700703</td>\n",
       "      <td>5.176191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-2.354495</td>\n",
       "      <td>3.030248</td>\n",
       "      <td>-0.185244</td>\n",
       "      <td>5.638730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-2.053271</td>\n",
       "      <td>5.976584</td>\n",
       "      <td>-0.214135</td>\n",
       "      <td>6.974131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2000-01-01 -1.108065  0.914834 -0.593941  2.099192\n",
       "2000-01-02 -1.989855  2.036164 -1.243937  3.969312\n",
       "2000-01-03 -2.511673  2.126490 -0.700703  5.176191\n",
       "2000-01-04 -2.354495  3.030248 -0.185244  5.638730\n",
       "2000-01-05 -2.053271  5.976584 -0.214135  6.974131"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.to_hdf('foo.h5', 'df')\n",
    "pd.read_hdf('foo.h5', 'df').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_frame.to_excel('foo.xlsx', sheet_name='Sheet1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>Unnamed: 0</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>-1.108065</td>\n",
       "      <td>0.914834</td>\n",
       "      <td>-0.593941</td>\n",
       "      <td>2.099192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-02</td>\n",
       "      <td>-1.989855</td>\n",
       "      <td>2.036164</td>\n",
       "      <td>-1.243937</td>\n",
       "      <td>3.969312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-01-03</td>\n",
       "      <td>-2.511673</td>\n",
       "      <td>2.126490</td>\n",
       "      <td>-0.700703</td>\n",
       "      <td>5.176191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-01-04</td>\n",
       "      <td>-2.354495</td>\n",
       "      <td>3.030248</td>\n",
       "      <td>-0.185244</td>\n",
       "      <td>5.638730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-01-05</td>\n",
       "      <td>-2.053271</td>\n",
       "      <td>5.976584</td>\n",
       "      <td>-0.214135</td>\n",
       "      <td>6.974131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Unnamed: 0         A         B         C         D\n",
       "0 2000-01-01 -1.108065  0.914834 -0.593941  2.099192\n",
       "1 2000-01-02 -1.989855  2.036164 -1.243937  3.969312\n",
       "2 2000-01-03 -2.511673  2.126490 -0.700703  5.176191\n",
       "3 2000-01-04 -2.354495  3.030248 -0.185244  5.638730\n",
       "4 2000-01-05 -2.053271  5.976584 -0.214135  6.974131"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA']).head()"
   ]
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}