offerted-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Oferted Analysis\n",
    "\n",
    "Offerted products are the ones that have incentives to the final client, things like small toys, fake tatoos, stickers or promos codes.\n",
    "\n",
    "**One question to ask is: are this incentives a good strategy to increment sales?**\n",
    "\n",
    "We can dig in the data a little bit to see how the incentive affects the behaviour of sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import random\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>office</th>\n",
       "      <th>warehouse</th>\n",
       "      <th>category</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_id</th>\n",
       "      <th>flavor</th>\n",
       "      <th>client</th>\n",
       "      <th>point_of_sale</th>\n",
       "      <th>distributor</th>\n",
       "      <th>date</th>\n",
       "      <th>units</th>\n",
       "      <th>devolution_units</th>\n",
       "      <th>sale_amount</th>\n",
       "      <th>sale_discount</th>\n",
       "      <th>sale_devolution</th>\n",
       "      <th>incentive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cali</td>\n",
       "      <td>Tulua</td>\n",
       "      <td>Producto De Extrusion</td>\n",
       "      <td>Cheetos</td>\n",
       "      <td>53407164</td>\n",
       "      <td>Tradicional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tiendas</td>\n",
       "      <td>2014-04-01</td>\n",
       "      <td>324</td>\n",
       "      <td>0</td>\n",
       "      <td>181530.0</td>\n",
       "      <td>32675.4</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  office warehouse               category    brand product_id       flavor  \\\n",
       "0   Cali     Tulua  Producto De Extrusion  Cheetos   53407164  Tradicional   \n",
       "\n",
       "  client point_of_sale distributor       date  units  devolution_units  \\\n",
       "0    NaN           NaN     Tiendas 2014-04-01    324                 0   \n",
       "\n",
       "   sale_amount  sale_discount  sale_devolution  incentive  \n",
       "0     181530.0        32675.4             -0.0       True  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dateparse = lambda x: pd.datetime.strptime(x, '%d/%m/%Y')\n",
    "\n",
    "snacks_df = pd.read_csv('./datasets/clean_snacks.csv', dtype={ 'client_id': str }, parse_dates=['date'], date_parser=dateparse)\n",
    "snacks_df['product_id'] = snacks_df['product_id'].astype(str)\n",
    "snacks_df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "snacks_df = snacks_df[snacks_df['units'] > 0] # we don't care about records with no sales\n",
    "snacks_df['year'] = snacks_df['date'].dt.year\n",
    "snacks_df['month'] = snacks_df['date'].dt.month\n",
    "snacks_df['yyyymm'] = snacks_df['date'].map(lambda x: x.strftime('%Y%m'))\n",
    "snacks_df['log_units'] = np.log(snacks_df['units'])\n",
    "snacks_df['log_sale'] = np.log(snacks_df['sale_amount'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's separate the sales with incentive and the ones with no incentive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "incentive_df = snacks_df[snacks_df['incentive']]\n",
    "no_incentive_df = snacks_df[~snacks_df['incentive']]\n",
    "\n",
    "plt.figure(figsize=(16, 10))\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "sns.distplot(incentive_df['units']).set_title('Histogram of incentive units')\n",
    "plt.subplot(2, 2, 2)\n",
    "sns.distplot(incentive_df['log_units']).set_title('Histogram of incentive log(units)')\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "sns.distplot(no_incentive_df['units']).set_title('histogram of no incentive units')\n",
    "plt.subplot(2, 2, 4)\n",
    "sns.distplot(no_incentive_df['log_units']).set_title('histogram of no incentive log(units)')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the incentive sales and not incentive sales behave very similar, the two groups seems to be a mixture of gaussians, also to notice that the scale for the distribution of incentive products seems to be larger, this can be probably an indicator that products with incentive are selling more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the total sales compared between inventive vs no incentive?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 12))\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "(\n",
    "    sns.barplot(\n",
    "        x='year',\n",
    "        y='log_units',\n",
    "        hue='distributor',\n",
    "        data=incentive_df.groupby(['year', 'distributor'])['log_units'].sum().reset_index()\n",
    "    ).set_title('With Incentive')\n",
    ")\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "(\n",
    "    sns.barplot(\n",
    "        x='year',\n",
    "        y='log_sale',\n",
    "        hue='distributor',\n",
    "        data=incentive_df.groupby(['year', 'distributor'])['log_sale'].sum().reset_index()\n",
    "    ).set_title('With Incentive')\n",
    ")\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "(\n",
    "    sns.barplot(\n",
    "        x='year',\n",
    "        y='log_units',\n",
    "        hue='distributor',\n",
    "        data=no_incentive_df.groupby(['year', 'distributor'])['log_units'].sum().reset_index()\n",
    "    ).set_title('With No Incentive')\n",
    ")\n",
    "\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "(\n",
    "    sns.barplot(\n",
    "        x='year',\n",
    "        y='log_sale',\n",
    "        hue='distributor',\n",
    "        data=no_incentive_df.groupby(['year', 'distributor'])['log_sale'].sum().reset_index()\n",
    "    ).set_title('With No Incentive')\n",
    ")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the previous chart it seems like the sales with incentives are decreasing year by year, and on the other hand the salse with no incentive are increasing, this is telling us that maybe the company `SnaCo` is changing their policies to the products with incentive, this is telling us, that maybe it is not worth to make this effort because it's not increasing the sales."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How amount of the sale with incentive represents each year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 10))\n",
    "\n",
    "sns.barplot(x='year', y='units', data=snacks_df.groupby('year')['units'].sum().reset_index(), color='green')\n",
    "no_incentive_plot = sns.barplot(x='year', y='units', data=no_incentive_df.groupby('year')['units'].sum().reset_index(), color='steelblue')\n",
    "\n",
    "topbar = plt.Rectangle((0,0), 1, 1, fc='green', edgecolor = 'none')\n",
    "bottombar = plt.Rectangle((0,0), 1, 1, fc='steelblue',  edgecolor = 'none')\n",
    "l = plt.legend([bottombar, topbar], ['With no incentive', 'With incentive'], loc=1, ncol = 2)\n",
    "l.draw_frame(False)\n",
    "\n",
    "sns.despine(left=True)\n",
    "no_incentive_plot.set_ylabel('Sold units')\n",
    "no_incentive_plot.set_xlabel('year')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In fact this chart also evidence the decreassing amount of inventive year by year, let's make a more specific chart to evidence this more clearly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 10))\n",
    "\n",
    "chart = sns.barplot(x='yyyymm', y='units', data=snacks_df.groupby('yyyymm')['units'].sum().reset_index(), color='green')\n",
    "chart.set_xticklabels(chart.get_xticklabels(), rotation=90)\n",
    "no_incentive_plot = sns.barplot(x='yyyymm', y='units', data=no_incentive_df.groupby('yyyymm')['units'].sum().reset_index(), color='steelblue')\n",
    "\n",
    "df = incentive_df.groupby('yyyymm')['units'].sum().reset_index()\n",
    "df['index_value'] = df.index\n",
    "sns.regplot(x='index_value', y='units', data=df)\n",
    "\n",
    "df = snacks_df.groupby('yyyymm')['units'].sum().reset_index()\n",
    "df['index_value'] = df.index\n",
    "sns.regplot(x='index_value', y='units', data=df)\n",
    "\n",
    "\n",
    "topbar = plt.Rectangle((0,0), 1, 1, fc='green', edgecolor = 'none')\n",
    "bottombar = plt.Rectangle((0,0), 1, 1, fc='steelblue',  edgecolor = 'none')\n",
    "l = plt.legend([bottombar, topbar], ['With no incentive', 'With incentive'], loc=1, ncol = 2)\n",
    "l.draw_frame(False)\n",
    "\n",
    "sns.despine(left=True)\n",
    "no_incentive_plot.set_ylabel('Sold units')\n",
    "no_incentive_plot.set_xlabel('year')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that in general there is higher decrease of the tendency to boy sale products with incentive, altough in general temrs it seems like the tendency of unit sales are less by each month."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How are the product unit sales with incentive and without it?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we have 27 products\n",
      "Now we have 178914 records\n"
     ]
    }
   ],
   "source": [
    "# We do not care on devolution records so we are going to analyze units > 0\n",
    "snacks_df = snacks_df[snacks_df['units'] > 0]\n",
    "\n",
    "product_grouping = (\n",
    "    snacks_df.groupby('product_id')['units']\n",
    "        .count()\n",
    "        .reset_index()\n",
    "        .rename(columns={'units': 'count'})\n",
    "        .sort_values('count', ascending=False)\n",
    ")\n",
    "\n",
    "# we are going to select only products with more than 10k records, we don't know the exact number to consider as\n",
    "# a good starting point, but we are going to try 10k and see what happens and then adjust this.\n",
    "\n",
    "products = product_grouping[product_grouping['count'] > 10000]['product_id']\n",
    "print(f\"Now we have {len(products)} products\")\n",
    "\n",
    "products_sample = products.sample(10)\n",
    "\n",
    "products_df = snacks_df[snacks_df['product_id'].isin(products_sample)]\n",
    "print(f\"Now we have {len(products_df)} records\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'str' object has no attribute 'view'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-f69170dcf825>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mchart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlineplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'yyyymm'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'units'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'product_id'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sum'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mproducts_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mchart\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xticklabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchart\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_xticklabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrotation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m90\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/week8/lib/python3.7/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36mlineplot\u001b[0;34m(x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, units, estimator, ci, n_boot, sort, err_style, err_kws, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m   1076\u001b[0m         \u001b[0mdashes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdashes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmarkers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle_order\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstyle_order\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1077\u001b[0m         \u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_boot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1078\u001b[0;31m         \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_style\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merr_style\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_kws\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merr_kws\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlegend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1079\u001b[0m     )\n\u001b[1;32m   1080\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/week8/lib/python3.7/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, units, estimator, ci, n_boot, sort, err_style, err_kws, legend)\u001b[0m\n\u001b[1;32m    668\u001b[0m         )\n\u001b[1;32m    669\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 670\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse_hue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"hue\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue_order\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue_norm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    671\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"size\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msizes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize_order\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize_norm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    672\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse_style\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"style\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarkers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdashes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle_order\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/week8/lib/python3.7/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36mparse_hue\u001b[0;34m(self, data, palette, order, norm)\u001b[0m\n\u001b[1;32m    373\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    374\u001b[0m             levels, palette, cmap, norm = self.numeric_to_palette(\n\u001b[0;32m--> 375\u001b[0;31m                 \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    376\u001b[0m             )\n\u001b[1;32m    377\u001b[0m             \u001b[0mlimits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/week8/lib/python3.7/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36mnumeric_to_palette\u001b[0;34m(self, data, order, palette, norm)\u001b[0m\n\u001b[1;32m    258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscaled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 260\u001b[0;31m             \u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    261\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    262\u001b[0m         \u001b[0;31m# TODO this should also use color_lookup, but that needs the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/week8/lib/python3.7/site-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, value, clip)\u001b[0m\n\u001b[1;32m    955\u001b[0m         \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_scalar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    956\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 957\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautoscale_None\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    958\u001b[0m         \u001b[0;31m# Convert at least to float, without losing precision.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    959\u001b[0m         \u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/week8/lib/python3.7/site-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36mautoscale_None\u001b[0;34m(self, A)\u001b[0m\n\u001b[1;32m    999\u001b[0m         \u001b[0mA\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1000\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvmin\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1001\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvmin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1002\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvmax\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1003\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/numpy/ma/core.py\u001b[0m in \u001b[0;36mmin\u001b[0;34m(self, axis, out, fill_value, keepdims)\u001b[0m\n\u001b[1;32m   5586\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5587\u001b[0m             result = self.filled(fill_value).min(\n\u001b[0;32m-> 5588\u001b[0;31m                 axis=axis, out=out, **kwargs).view(type(self))\n\u001b[0m\u001b[1;32m   5589\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5590\u001b[0m                 \u001b[0;31m# Set the mask\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'str' object has no attribute 'view'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 10))\n",
    "chart = sns.lineplot(x='yyyymm', y='units', hue='product_id', estimator='sum', ci=None, data=products_df)\n",
    "chart.set_xticklabels(chart.get_xticklabels(), rotation=90)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
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   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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