Ventas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18383 entries, 0 to 18382\n",
      "Data columns (total 6 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   Order ID          18324 non-null  object\n",
      " 1   Product           18324 non-null  object\n",
      " 2   Quantity Ordered  18324 non-null  object\n",
      " 3   Price Each        18324 non-null  object\n",
      " 4   Order Date        18324 non-null  object\n",
      " 5   Purchase Address  18324 non-null  object\n",
      "dtypes: object(6)\n",
      "memory usage: 861.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('datos/Sales_April_2019.csv')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sales_February_2019.csv\n",
      "Sales_March_2019.csv\n",
      "Sales_January_2019.csv\n",
      "Sales_September_2019.csv\n",
      "Sales_April_2019.csv\n",
      "Sales_December_2019.csv\n",
      "Sales_June_2019.csv\n",
      "Sales_August_2019.csv\n",
      "Sales_November_2019.csv\n",
      "Sales_October_2019.csv\n",
      "Sales_May_2019.csv\n",
      "Sales_July_2019.csv\n"
     ]
    }
   ],
   "source": [
    "files = [file for file in os.listdir('datos')]\n",
    "for file in files:\n",
    "    print(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150502</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700</td>\n",
       "      <td>02/18/19 01:35</td>\n",
       "      <td>866 Spruce St, Portland, ME 04101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150503</td>\n",
       "      <td>AA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>3.84</td>\n",
       "      <td>02/13/19 07:24</td>\n",
       "      <td>18 13th St, San Francisco, CA 94016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>150504</td>\n",
       "      <td>27in 4K Gaming Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>389.99</td>\n",
       "      <td>02/18/19 09:46</td>\n",
       "      <td>52 6th St, New York City, NY 10001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150505</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>02/02/19 16:47</td>\n",
       "      <td>129 Cherry St, Atlanta, GA 30301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150506</td>\n",
       "      <td>AA Batteries (4-pack)</td>\n",
       "      <td>2</td>\n",
       "      <td>3.84</td>\n",
       "      <td>02/28/19 20:32</td>\n",
       "      <td>548 Lincoln St, Seattle, WA 98101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                   Product Quantity Ordered Price Each  \\\n",
       "0   150502                    iPhone                1        700   \n",
       "1   150503     AA Batteries (4-pack)                1       3.84   \n",
       "2   150504    27in 4K Gaming Monitor                1     389.99   \n",
       "3   150505  Lightning Charging Cable                1      14.95   \n",
       "4   150506     AA Batteries (4-pack)                2       3.84   \n",
       "\n",
       "       Order Date                     Purchase Address  \n",
       "0  02/18/19 01:35    866 Spruce St, Portland, ME 04101  \n",
       "1  02/13/19 07:24  18 13th St, San Francisco, CA 94016  \n",
       "2  02/18/19 09:46   52 6th St, New York City, NY 10001  \n",
       "3  02/02/19 16:47     129 Cherry St, Atlanta, GA 30301  \n",
       "4  02/28/19 20:32    548 Lincoln St, Seattle, WA 98101  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame()\n",
    "for file in files:\n",
    "    df1 = pd.read_csv('datos/'+file)\n",
    "    df2 = pd.concat([df2,df1])\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.to_csv('All.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cual es el mejor mes en ventas y cuanto se gano?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Limpiar los NaN, drop rows of NaN, drop Or"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1488</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1517</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Order ID Product Quantity Ordered Price Each Order Date Purchase Address\n",
       "75        NaN     NaN              NaN        NaN        NaN              NaN\n",
       "169       NaN     NaN              NaN        NaN        NaN              NaN\n",
       "172       NaN     NaN              NaN        NaN        NaN              NaN\n",
       "1488      NaN     NaN              NaN        NaN        NaN              NaN\n",
       "1517      NaN     NaN              NaN        NaN        NaN              NaN"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_df=df2[df2.isna().any(axis=1)]\n",
    "nan_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2.dropna(how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2[df2['Order Date'].str[0:2] !='Or']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Agragar una columna para cada mes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150502</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700</td>\n",
       "      <td>02/18/19 01:35</td>\n",
       "      <td>866 Spruce St, Portland, ME 04101</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150503</td>\n",
       "      <td>AA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>3.84</td>\n",
       "      <td>02/13/19 07:24</td>\n",
       "      <td>18 13th St, San Francisco, CA 94016</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>150504</td>\n",
       "      <td>27in 4K Gaming Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>389.99</td>\n",
       "      <td>02/18/19 09:46</td>\n",
       "      <td>52 6th St, New York City, NY 10001</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150505</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>02/02/19 16:47</td>\n",
       "      <td>129 Cherry St, Atlanta, GA 30301</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150506</td>\n",
       "      <td>AA Batteries (4-pack)</td>\n",
       "      <td>2</td>\n",
       "      <td>3.84</td>\n",
       "      <td>02/28/19 20:32</td>\n",
       "      <td>548 Lincoln St, Seattle, WA 98101</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                   Product Quantity Ordered Price Each  \\\n",
       "0   150502                    iPhone                1        700   \n",
       "1   150503     AA Batteries (4-pack)                1       3.84   \n",
       "2   150504    27in 4K Gaming Monitor                1     389.99   \n",
       "3   150505  Lightning Charging Cable                1      14.95   \n",
       "4   150506     AA Batteries (4-pack)                2       3.84   \n",
       "\n",
       "       Order Date                     Purchase Address  Month  \n",
       "0  02/18/19 01:35    866 Spruce St, Portland, ME 04101      2  \n",
       "1  02/13/19 07:24  18 13th St, San Francisco, CA 94016      2  \n",
       "2  02/18/19 09:46   52 6th St, New York City, NY 10001      2  \n",
       "3  02/02/19 16:47     129 Cherry St, Atlanta, GA 30301      2  \n",
       "4  02/28/19 20:32    548 Lincoln St, Seattle, WA 98101      2  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Month']= df2['Order Date'].str[0:2]\n",
    "df2['Month']= df2['Month'].astype('int32')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Agragar una columna de ventas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['Quantity Ordered'] = pd.to_numeric(df2['Quantity Ordered'])\n",
    "df2['Price Each'] = pd.to_numeric(df2['Price Each'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Month</th>\n",
       "      <th>Sales</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150502</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>02/18/19 01:35</td>\n",
       "      <td>866 Spruce St, Portland, ME 04101</td>\n",
       "      <td>2</td>\n",
       "      <td>700.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150503</td>\n",
       "      <td>AA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>3.84</td>\n",
       "      <td>02/13/19 07:24</td>\n",
       "      <td>18 13th St, San Francisco, CA 94016</td>\n",
       "      <td>2</td>\n",
       "      <td>3.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>150504</td>\n",
       "      <td>27in 4K Gaming Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>389.99</td>\n",
       "      <td>02/18/19 09:46</td>\n",
       "      <td>52 6th St, New York City, NY 10001</td>\n",
       "      <td>2</td>\n",
       "      <td>389.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150505</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>02/02/19 16:47</td>\n",
       "      <td>129 Cherry St, Atlanta, GA 30301</td>\n",
       "      <td>2</td>\n",
       "      <td>14.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150506</td>\n",
       "      <td>AA Batteries (4-pack)</td>\n",
       "      <td>2</td>\n",
       "      <td>3.84</td>\n",
       "      <td>02/28/19 20:32</td>\n",
       "      <td>548 Lincoln St, Seattle, WA 98101</td>\n",
       "      <td>2</td>\n",
       "      <td>7.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                   Product  Quantity Ordered  Price Each  \\\n",
       "0   150502                    iPhone                 1      700.00   \n",
       "1   150503     AA Batteries (4-pack)                 1        3.84   \n",
       "2   150504    27in 4K Gaming Monitor                 1      389.99   \n",
       "3   150505  Lightning Charging Cable                 1       14.95   \n",
       "4   150506     AA Batteries (4-pack)                 2        3.84   \n",
       "\n",
       "       Order Date                     Purchase Address  Month   Sales  \n",
       "0  02/18/19 01:35    866 Spruce St, Portland, ME 04101      2  700.00  \n",
       "1  02/13/19 07:24  18 13th St, San Francisco, CA 94016      2    3.84  \n",
       "2  02/18/19 09:46   52 6th St, New York City, NY 10001      2  389.99  \n",
       "3  02/02/19 16:47     129 Cherry St, Atlanta, GA 30301      2   14.95  \n",
       "4  02/28/19 20:32    548 Lincoln St, Seattle, WA 98101      2    7.68  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Sales']= df2['Quantity Ordered']*df2['Price Each']\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10903</td>\n",
       "      <td>1.811768e+06</td>\n",
       "      <td>1.822257e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13449</td>\n",
       "      <td>2.188885e+06</td>\n",
       "      <td>2.202022e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17005</td>\n",
       "      <td>2.791208e+06</td>\n",
       "      <td>2.807100e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20558</td>\n",
       "      <td>3.367671e+06</td>\n",
       "      <td>3.390670e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18667</td>\n",
       "      <td>3.135125e+06</td>\n",
       "      <td>3.152607e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15253</td>\n",
       "      <td>2.562026e+06</td>\n",
       "      <td>2.577802e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16072</td>\n",
       "      <td>2.632540e+06</td>\n",
       "      <td>2.647776e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13448</td>\n",
       "      <td>2.230345e+06</td>\n",
       "      <td>2.244468e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13109</td>\n",
       "      <td>2.084992e+06</td>\n",
       "      <td>2.097560e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>22703</td>\n",
       "      <td>3.715555e+06</td>\n",
       "      <td>3.736727e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>19798</td>\n",
       "      <td>3.180601e+06</td>\n",
       "      <td>3.199603e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>28114</td>\n",
       "      <td>4.588415e+06</td>\n",
       "      <td>4.613443e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Quantity Ordered    Price Each         Sales\n",
       "Month                                              \n",
       "1                 10903  1.811768e+06  1.822257e+06\n",
       "2                 13449  2.188885e+06  2.202022e+06\n",
       "3                 17005  2.791208e+06  2.807100e+06\n",
       "4                 20558  3.367671e+06  3.390670e+06\n",
       "5                 18667  3.135125e+06  3.152607e+06\n",
       "6                 15253  2.562026e+06  2.577802e+06\n",
       "7                 16072  2.632540e+06  2.647776e+06\n",
       "8                 13448  2.230345e+06  2.244468e+06\n",
       "9                 13109  2.084992e+06  2.097560e+06\n",
       "10                22703  3.715555e+06  3.736727e+06\n",
       "11                19798  3.180601e+06  3.199603e+06\n",
       "12                28114  4.588415e+06  4.613443e+06"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('Month').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "import numpy as np\n",
    "results = df2.groupby('Month').sum()\n",
    "months = range(1,13)\n",
    "plt.bar(months, results['Sales'])\n",
    "plt.xticks(np.arange(1,13), ('En', 'Feb', 'Mar', 'Abr', 'May', 'Jun', 'Jul', 'Ag', 'Sep', 'Oc', 'Nov', 'Dic'))\n",
    "plt.xlabel('Month number')\n",
    "plt.ylabel('Sales in USD ($)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### En que ciudad hubo más ventas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aderir un columna de ciudad, usando .apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Month</th>\n",
       "      <th>Sales</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150502</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>02/18/19 01:35</td>\n",
       "      <td>866 Spruce St, Portland, ME 04101</td>\n",
       "      <td>2</td>\n",
       "      <td>700.00</td>\n",
       "      <td>Portland (ME)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150503</td>\n",
       "      <td>AA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>3.84</td>\n",
       "      <td>02/13/19 07:24</td>\n",
       "      <td>18 13th St, San Francisco, CA 94016</td>\n",
       "      <td>2</td>\n",
       "      <td>3.84</td>\n",
       "      <td>San Francisco (CA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>150504</td>\n",
       "      <td>27in 4K Gaming Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>389.99</td>\n",
       "      <td>02/18/19 09:46</td>\n",
       "      <td>52 6th St, New York City, NY 10001</td>\n",
       "      <td>2</td>\n",
       "      <td>389.99</td>\n",
       "      <td>New York City (NY)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150505</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>02/02/19 16:47</td>\n",
       "      <td>129 Cherry St, Atlanta, GA 30301</td>\n",
       "      <td>2</td>\n",
       "      <td>14.95</td>\n",
       "      <td>Atlanta (GA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150506</td>\n",
       "      <td>AA Batteries (4-pack)</td>\n",
       "      <td>2</td>\n",
       "      <td>3.84</td>\n",
       "      <td>02/28/19 20:32</td>\n",
       "      <td>548 Lincoln St, Seattle, WA 98101</td>\n",
       "      <td>2</td>\n",
       "      <td>7.68</td>\n",
       "      <td>Seattle (WA)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                   Product  Quantity Ordered  Price Each  \\\n",
       "0   150502                    iPhone                 1      700.00   \n",
       "1   150503     AA Batteries (4-pack)                 1        3.84   \n",
       "2   150504    27in 4K Gaming Monitor                 1      389.99   \n",
       "3   150505  Lightning Charging Cable                 1       14.95   \n",
       "4   150506     AA Batteries (4-pack)                 2        3.84   \n",
       "\n",
       "       Order Date                     Purchase Address  Month   Sales  \\\n",
       "0  02/18/19 01:35    866 Spruce St, Portland, ME 04101      2  700.00   \n",
       "1  02/13/19 07:24  18 13th St, San Francisco, CA 94016      2    3.84   \n",
       "2  02/18/19 09:46   52 6th St, New York City, NY 10001      2  389.99   \n",
       "3  02/02/19 16:47     129 Cherry St, Atlanta, GA 30301      2   14.95   \n",
       "4  02/28/19 20:32    548 Lincoln St, Seattle, WA 98101      2    7.68   \n",
       "\n",
       "                  City  \n",
       "0        Portland (ME)  \n",
       "1   San Francisco (CA)  \n",
       "2   New York City (NY)  \n",
       "3         Atlanta (GA)  \n",
       "4         Seattle (WA)  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['City']=df2['Purchase Address'].apply(lambda x: x.split(',')[1] + ' (' + x.split(',')[2].split(' ')[1] + ')')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Month</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Atlanta (GA)</th>\n",
       "      <td>16602</td>\n",
       "      <td>2.779908e+06</td>\n",
       "      <td>104794</td>\n",
       "      <td>2.795499e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austin (TX)</th>\n",
       "      <td>11153</td>\n",
       "      <td>1.809874e+06</td>\n",
       "      <td>69829</td>\n",
       "      <td>1.819582e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boston (MA)</th>\n",
       "      <td>22528</td>\n",
       "      <td>3.637410e+06</td>\n",
       "      <td>141112</td>\n",
       "      <td>3.661642e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dallas (TX)</th>\n",
       "      <td>16730</td>\n",
       "      <td>2.752628e+06</td>\n",
       "      <td>104620</td>\n",
       "      <td>2.767975e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Los Angeles (CA)</th>\n",
       "      <td>33289</td>\n",
       "      <td>5.421435e+06</td>\n",
       "      <td>208325</td>\n",
       "      <td>5.452571e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York City (NY)</th>\n",
       "      <td>27932</td>\n",
       "      <td>4.635371e+06</td>\n",
       "      <td>175741</td>\n",
       "      <td>4.664317e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portland (ME)</th>\n",
       "      <td>2750</td>\n",
       "      <td>4.471892e+05</td>\n",
       "      <td>17144</td>\n",
       "      <td>4.497583e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portland (OR)</th>\n",
       "      <td>11303</td>\n",
       "      <td>1.860558e+06</td>\n",
       "      <td>70621</td>\n",
       "      <td>1.870732e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Francisco (CA)</th>\n",
       "      <td>50239</td>\n",
       "      <td>8.211462e+06</td>\n",
       "      <td>315520</td>\n",
       "      <td>8.262204e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seattle (WA)</th>\n",
       "      <td>16553</td>\n",
       "      <td>2.733296e+06</td>\n",
       "      <td>104941</td>\n",
       "      <td>2.747755e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Quantity Ordered    Price Each   Month         Sales\n",
       "City                                                                     \n",
       " Atlanta (GA)                   16602  2.779908e+06  104794  2.795499e+06\n",
       " Austin (TX)                    11153  1.809874e+06   69829  1.819582e+06\n",
       " Boston (MA)                    22528  3.637410e+06  141112  3.661642e+06\n",
       " Dallas (TX)                    16730  2.752628e+06  104620  2.767975e+06\n",
       " Los Angeles (CA)               33289  5.421435e+06  208325  5.452571e+06\n",
       " New York City (NY)             27932  4.635371e+06  175741  4.664317e+06\n",
       " Portland (ME)                   2750  4.471892e+05   17144  4.497583e+05\n",
       " Portland (OR)                  11303  1.860558e+06   70621  1.870732e+06\n",
       " San Francisco (CA)             50239  8.211462e+06  315520  8.262204e+06\n",
       " Seattle (WA)                   16553  2.733296e+06  104941  2.747755e+06"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('City').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "results2 = df2.groupby('City').sum()\n",
    "cities = [city for city, df in df2.groupby('City')]\n",
    "plt.bar(cities, results2['Sales'])\n",
    "plt.xticks(cities, rotation = 'vertical', size=8)\n",
    "plt.xlabel('Cities')\n",
    "plt.ylabel('Sales in USD ($)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A qué hora debemos mostrar publicidad para maximizar la probabilidad de que los clientes compren productos?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Month</th>\n",
       "      <th>Sales</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150502</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>2019-02-18 01:35:00</td>\n",
       "      <td>866 Spruce St, Portland, ME 04101</td>\n",
       "      <td>2</td>\n",
       "      <td>700.00</td>\n",
       "      <td>Portland (ME)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150503</td>\n",
       "      <td>AA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2019-02-13 07:24:00</td>\n",
       "      <td>18 13th St, San Francisco, CA 94016</td>\n",
       "      <td>2</td>\n",
       "      <td>3.84</td>\n",
       "      <td>San Francisco (CA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>150504</td>\n",
       "      <td>27in 4K Gaming Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>389.99</td>\n",
       "      <td>2019-02-18 09:46:00</td>\n",
       "      <td>52 6th St, New York City, NY 10001</td>\n",
       "      <td>2</td>\n",
       "      <td>389.99</td>\n",
       "      <td>New York City (NY)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150505</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>2019-02-02 16:47:00</td>\n",
       "      <td>129 Cherry St, Atlanta, GA 30301</td>\n",
       "      <td>2</td>\n",
       "      <td>14.95</td>\n",
       "      <td>Atlanta (GA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150506</td>\n",
       "      <td>AA Batteries (4-pack)</td>\n",
       "      <td>2</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2019-02-28 20:32:00</td>\n",
       "      <td>548 Lincoln St, Seattle, WA 98101</td>\n",
       "      <td>2</td>\n",
       "      <td>7.68</td>\n",
       "      <td>Seattle (WA)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                   Product  Quantity Ordered  Price Each  \\\n",
       "0   150502                    iPhone                 1      700.00   \n",
       "1   150503     AA Batteries (4-pack)                 1        3.84   \n",
       "2   150504    27in 4K Gaming Monitor                 1      389.99   \n",
       "3   150505  Lightning Charging Cable                 1       14.95   \n",
       "4   150506     AA Batteries (4-pack)                 2        3.84   \n",
       "\n",
       "           Order Date                     Purchase Address  Month   Sales  \\\n",
       "0 2019-02-18 01:35:00    866 Spruce St, Portland, ME 04101      2  700.00   \n",
       "1 2019-02-13 07:24:00  18 13th St, San Francisco, CA 94016      2    3.84   \n",
       "2 2019-02-18 09:46:00   52 6th St, New York City, NY 10001      2  389.99   \n",
       "3 2019-02-02 16:47:00     129 Cherry St, Atlanta, GA 30301      2   14.95   \n",
       "4 2019-02-28 20:32:00    548 Lincoln St, Seattle, WA 98101      2    7.68   \n",
       "\n",
       "                  City  \n",
       "0        Portland (ME)  \n",
       "1   San Francisco (CA)  \n",
       "2   New York City (NY)  \n",
       "3         Atlanta (GA)  \n",
       "4         Seattle (WA)  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Order Date'] = pd.to_datetime(df2['Order Date']) #df2.drop(columns=['Order date'])\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Month</th>\n",
       "      <th>Sales</th>\n",
       "      <th>City</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150502</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>2019-02-18 01:35:00</td>\n",
       "      <td>866 Spruce St, Portland, ME 04101</td>\n",
       "      <td>2</td>\n",
       "      <td>700.00</td>\n",
       "      <td>Portland (ME)</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150503</td>\n",
       "      <td>AA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2019-02-13 07:24:00</td>\n",
       "      <td>18 13th St, San Francisco, CA 94016</td>\n",
       "      <td>2</td>\n",
       "      <td>3.84</td>\n",
       "      <td>San Francisco (CA)</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>150504</td>\n",
       "      <td>27in 4K Gaming Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>389.99</td>\n",
       "      <td>2019-02-18 09:46:00</td>\n",
       "      <td>52 6th St, New York City, NY 10001</td>\n",
       "      <td>2</td>\n",
       "      <td>389.99</td>\n",
       "      <td>New York City (NY)</td>\n",
       "      <td>9</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150505</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>2019-02-02 16:47:00</td>\n",
       "      <td>129 Cherry St, Atlanta, GA 30301</td>\n",
       "      <td>2</td>\n",
       "      <td>14.95</td>\n",
       "      <td>Atlanta (GA)</td>\n",
       "      <td>16</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150506</td>\n",
       "      <td>AA Batteries (4-pack)</td>\n",
       "      <td>2</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2019-02-28 20:32:00</td>\n",
       "      <td>548 Lincoln St, Seattle, WA 98101</td>\n",
       "      <td>2</td>\n",
       "      <td>7.68</td>\n",
       "      <td>Seattle (WA)</td>\n",
       "      <td>20</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                   Product  Quantity Ordered  Price Each  \\\n",
       "0   150502                    iPhone                 1      700.00   \n",
       "1   150503     AA Batteries (4-pack)                 1        3.84   \n",
       "2   150504    27in 4K Gaming Monitor                 1      389.99   \n",
       "3   150505  Lightning Charging Cable                 1       14.95   \n",
       "4   150506     AA Batteries (4-pack)                 2        3.84   \n",
       "\n",
       "           Order Date                     Purchase Address  Month   Sales  \\\n",
       "0 2019-02-18 01:35:00    866 Spruce St, Portland, ME 04101      2  700.00   \n",
       "1 2019-02-13 07:24:00  18 13th St, San Francisco, CA 94016      2    3.84   \n",
       "2 2019-02-18 09:46:00   52 6th St, New York City, NY 10001      2  389.99   \n",
       "3 2019-02-02 16:47:00     129 Cherry St, Atlanta, GA 30301      2   14.95   \n",
       "4 2019-02-28 20:32:00    548 Lincoln St, Seattle, WA 98101      2    7.68   \n",
       "\n",
       "                  City  Hour  Minute  \n",
       "0        Portland (ME)     1      35  \n",
       "1   San Francisco (CA)     7      24  \n",
       "2   New York City (NY)     9      46  \n",
       "3         Atlanta (GA)    16      47  \n",
       "4         Seattle (WA)    20      32  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Hour'] = df2['Order Date'].dt.hour\n",
    "df2['Minute'] = df2['Order Date'].dt.minute\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "hours = [hour for hour, df in df2.groupby('Hour')]\n",
    "plt.plot(hours, df2.groupby(['Hour']).count())\n",
    "plt.xticks(hours)\n",
    "plt.xlabel('Hour')\n",
    "plt.ylabel('Number of orders')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}