GeneratingDataFrames(new API).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - EDA Checkpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Joonsung Park\n",
    "- James Chung\n",
    "- Richard Gross\n",
    "- Madison Hambly\n",
    "- Colin Lintereur"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='research_question'></a>\n",
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do the most popular topics from previous COGS 108 final projects vary per quarter from 2019 to 2021? Also what other trends can we find in past submissions, like changes in word count, and number of graphs used?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import numpy as np\n",
    "import json\n",
    "import zipfile\n",
    "import pandas as pd\n",
    "import re\n",
    "import seaborn as sns\n",
    "import glob\n",
    "import requests\n",
    "\n",
    "import pandas as pd\n",
    "import glob as glob\n",
    "import seaborn as sns\n",
    "sns.set(font_scale=1, style=\"white\")\n",
    "\n",
    "# import matplotlib\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.style as style\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (20, 10)\n",
    "\n",
    "# copy/paste API key for uClassify\n",
    "API_KEY = 'pUllRXrepgLH'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now search through the zip file of a quarter we want to study. We can get the quarter/group/year information from the file names. We get the research question by looking for the \"Research Question\" (or some similar) header and getting the text after it.\n",
    "\n",
    "We manually ran this code for each project repo/zip. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initializing lists\n",
    "research_questions = {}\n",
    "qtr = []\n",
    "year = []\n",
    "group = []\n",
    "# special projects are marked with '_S' at the end of its name\n",
    "special = []\n",
    "word_counts = []\n",
    "graph_counts = []\n",
    "table_counts = []\n",
    "index = 0\n",
    "\n",
    "# a magic number to guess if this string is the research question\n",
    "# (minimum character length)\n",
    "MIN_RQ_LEN = 30\n",
    "\n",
    "# reading zip file for a certain quarter\n",
    "with zipfile.ZipFile(\"zips/FinalProjects-Wi21-main.zip\", \"r\") as f:\n",
    "    for i, name in enumerate(f.namelist()):\n",
    "        # skip files that are not jupyter notebooks\n",
    "        if '.ipynb' not in name:\n",
    "            continue\n",
    "        rf = json.loads(f.read(name))\n",
    "        word_count = 0\n",
    "        graph_count = 0\n",
    "        table_count = 0\n",
    "        rq_found = False\n",
    "        rq_in_header = False\n",
    "        \n",
    "        # loop through every cell\n",
    "        for j, cell in enumerate(rf['cells']):\n",
    "            if cell['cell_type'] == 'code':\n",
    "                if ('outputs' in cell) and (cell['outputs'] != []):# and ('data' in cell['outputs'][0]):# and ('image/png' in cell['outputs'][0]['data'])\n",
    "                    for k,_ in enumerate(cell['outputs']):\n",
    "                        if 'data' in cell['outputs'][k]:\n",
    "                            if 'image/png' in cell['outputs'][k]['data']:\n",
    "                                graph_count += 1\n",
    "                            if 'text/html' in cell['outputs'][k]['data']:\n",
    "                                table_count += 1\n",
    "            elif cell['cell_type'] == 'markdown':\n",
    "                # for the cell with the rq header, source_len > 1\n",
    "                # means the question is also in this cell\n",
    "                source_len = len(cell['source'])\n",
    "                for c in cell['source']:\n",
    "                    # if cell contains the research question header, update lists and extract the research question from the next cell\n",
    "                    if (rq_found == False) and (rq_in_header == True or re.search(r\"(^#.*research question)\", c.lower()) != None):\n",
    "                        # some other string in the cell's source list should be the question\n",
    "                        if source_len > 1 and rq_in_header == False:\n",
    "                            rq_in_header = True\n",
    "                            continue\n",
    "\n",
    "                        # if there is more text after the header, question may be in THIS cell\n",
    "                        if rq_in_header == True:\n",
    "                            # ignore '\\n' string or any string that is probably not the rq\n",
    "                            if len(c.lstrip()) < MIN_RQ_LEN:\n",
    "                                continue\n",
    "                            rq = str(c)\n",
    "                            rq_in_header = False\n",
    "                        # get the research question from the cell AFTER the cell that contains the header 'Research Question'\n",
    "                        else:\n",
    "                            rq = ' '.join(rf['cells'][j+1]['source'])\n",
    "                        # remove pesky html tags for API to handle correctly\n",
    "                        rq = re.sub('<[^<]+?>', '', rq)\n",
    "                        \n",
    "                        # extract quarter info from repo name\n",
    "                        date = re.search(r\"-(.*)-\", name).group(1)\n",
    "                        qtr.append(date[:2].upper())\n",
    "                        year.append(int(date[2:]))\n",
    "\n",
    "                        # extract group number\n",
    "                        fname = re.search(r\"/.*\", name).group(0)\n",
    "                        group.append(int(re.search(r\"[$0-9^]{2,3}\", fname)[0]))\n",
    "\n",
    "                        special.append(False if re.search(r\"_S\\.ipynb\", name) == None else True)\n",
    "                        \n",
    "                        research_questions[str(group[index])+\"_\"+qtr[index]+\"_\"+str(year[index])] = rq          \n",
    "                        rq_found = True\n",
    "                        index += 1\n",
    "                    word_count += len(c.replace('#', '').lstrip().split(' '))\n",
    "        if rq_found:\n",
    "            word_counts.append(word_count)\n",
    "            graph_counts.append(graph_count)\n",
    "            table_counts.append(table_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After collecting all the data we need from the submissions, we can use a text classifier to find the general topic of the submission. We are currently using an API made by uClassify to do this classification for us. See [here](https://www.uclassify.com/browse/uclassify/iab-taxonomy-v2?input=Text) for details on the API we are using."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EXAMPLE:\n",
      " *What is the relative effect of unemployment, poverty, and homelessness on adult obesity rates (BMI >= 30) across states in the United States?*  ::  business and finance\n"
     ]
    }
   ],
   "source": [
    "# initialize topics list\n",
    "topic = []\n",
    "\n",
    "# loop through research questions and find the most confident topic class from topic classifier\n",
    "for i, (g, q, y) in enumerate(zip(group, qtr, year)):\n",
    "    text = research_questions[str(group[i])+\"_\"+qtr[i]+\"_\"+str(year[i])]\n",
    "\n",
    "    # conform to latin-1 encoding for API\n",
    "    text = text.encode('latin-1', 'replace').decode('latin-1')\n",
    "    # don't allow literal quotes and remove unknown symbols\n",
    "    text = text.replace('\\\"', '').replace('?', '')\n",
    "    \n",
    "    # get the Response object\n",
    "    response = requests.post('https://api.uclassify.com/v1/uclassify/iab-taxonomy-v2/classify',\n",
    "        data = \"{\\\"texts\\\":[\\\"\" + text + \"\\\"]}\",\n",
    "        headers = {'Authorization': 'Token ' + API_KEY})\n",
    "\n",
    "    closest_topic = ''\n",
    "    max_p = 0\n",
    "    # find the highest predicted topic\n",
    "    for t in response.json()[0]['classification']:\n",
    "        if t['p'] > max_p:\n",
    "            closest_topic = t['className']\n",
    "            max_p = t['p']\n",
    "    topic.append(closest_topic.split('_')[0])\n",
    "    if i == 0:\n",
    "        print(\"EXAMPLE:\\n\", research_questions[str(group[i])+\"_\"+qtr[i]+\"_\"+str(year[i])], \" :: \", topic[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have stored the topics of the submissions, we can run a sanity check to make sure that all of our list are the same length and then export the data to a csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65 65 65 65 65 65 65 65\n"
     ]
    }
   ],
   "source": [
    "# double check that all the lists are the same length\n",
    "print(len(group), len(qtr), len(year), len(topic), len(special), len(word_counts), len(graph_counts), len(table_counts))\n",
    "\n",
    "# generate data frame and output it to a csv file\n",
    "d = {'Group':group, 'Quarter':qtr, 'Year':year, 'Topic':topic, 'Special':special, 'Word_Count':word_counts, 'Graph_Count':graph_counts, 'Table_Count':table_counts}\n",
    "df = pd.DataFrame(data = d)\n",
    "output = df.to_csv(path_or_buf=\"./csvs/\"+qtr[0] + \"20\" + str(year[0])+\".csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are collecting the data ourselves, there is not much work to be done cleaning the data after collecting all of the csv files for each quarter and merging them together into one large dataset. One column we do need to remove however is a column named 'Unnamed: 0'. This column is an artifact of reading csv files that store index information for each dataset. This column is redundant in our new dataset so we can remove it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 364\n"
     ]
    },
    {
     "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>Group</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Year</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Special</th>\n",
       "      <th>Word_Count</th>\n",
       "      <th>Graph_Count</th>\n",
       "      <th>Table_Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>FA</td>\n",
       "      <td>20</td>\n",
       "      <td>music and audio</td>\n",
       "      <td>False</td>\n",
       "      <td>6999</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>FA</td>\n",
       "      <td>20</td>\n",
       "      <td>automotive</td>\n",
       "      <td>False</td>\n",
       "      <td>8686</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>FA</td>\n",
       "      <td>20</td>\n",
       "      <td>video gaming</td>\n",
       "      <td>False</td>\n",
       "      <td>1864</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>FA</td>\n",
       "      <td>20</td>\n",
       "      <td>sports</td>\n",
       "      <td>False</td>\n",
       "      <td>5483</td>\n",
       "      <td>32</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>FA</td>\n",
       "      <td>20</td>\n",
       "      <td>pets</td>\n",
       "      <td>False</td>\n",
       "      <td>4595</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Group Quarter  Year            Topic  Special  Word_Count  Graph_Count  \\\n",
       "0      1      FA    20  music and audio    False        6999           15   \n",
       "1      2      FA    20       automotive    False        8686           18   \n",
       "2      4      FA    20     video gaming    False        1864           13   \n",
       "3      5      FA    20           sports    False        5483           32   \n",
       "4      6      FA    20             pets    False        4595           19   \n",
       "\n",
       "   Table_Count  \n",
       "0            2  \n",
       "1           16  \n",
       "2            5  \n",
       "3           18  \n",
       "4            7  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using glob to get all csvs\n",
    "files = glob.glob('./csvs/*.csv')\n",
    "df_total = pd.DataFrame()\n",
    "entries = 0\n",
    "\n",
    "# read all csvs to dataframes and combine into one df\n",
    "for f_name in files:\n",
    "    csv = pd.read_csv(f_name)\n",
    "    csv['Topic'] = csv['Topic'].apply(lambda t: t.split('_')[0])\n",
    "    df_total = df_total.append(csv)\n",
    "    entries = entries + len(csv.index)\n",
    "\n",
    "# remove artifact 'Unnamed'\n",
    "# df_total = df_total.drop('Unnamed: 0', axis=1)\n",
    "\n",
    "assert(len(df_total.index) == entries)\n",
    "print('Observations: ' + str(len(df_total.index)))\n",
    "df_total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Group</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Year</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Special</th>\n",
       "      <th>Word_Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>FA</td>\n",
       "      <td>20</td>\n",
       "      <td>music and audio</td>\n",
       "      <td>False</td>\n",
       "      <td>6999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>FA</td>\n",
       "      <td>20</td>\n",
       "      <td>automotive</td>\n",
       "      <td>False</td>\n",
       "      <td>8686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>FA</td>\n",
       "      <td>20</td>\n",
       "      <td>video gaming</td>\n",
       "      <td>False</td>\n",
       "      <td>1864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>FA</td>\n",
       "      <td>20</td>\n",
       "      <td>sports</td>\n",
       "      <td>False</td>\n",
       "      <td>5483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>FA</td>\n",
       "      <td>20</td>\n",
       "      <td>pets</td>\n",
       "      <td>False</td>\n",
       "      <td>4595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>87</td>\n",
       "      <td>WI</td>\n",
       "      <td>2021</td>\n",
       "      <td>HEALTHY LIVING</td>\n",
       "      <td>False</td>\n",
       "      <td>2637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>88</td>\n",
       "      <td>WI</td>\n",
       "      <td>2021</td>\n",
       "      <td>IMPACT</td>\n",
       "      <td>False</td>\n",
       "      <td>3538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>89</td>\n",
       "      <td>WI</td>\n",
       "      <td>2021</td>\n",
       "      <td>POLITICS</td>\n",
       "      <td>False</td>\n",
       "      <td>4755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>90</td>\n",
       "      <td>WI</td>\n",
       "      <td>2021</td>\n",
       "      <td>POLITICS</td>\n",
       "      <td>False</td>\n",
       "      <td>3298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>92</td>\n",
       "      <td>WI</td>\n",
       "      <td>2021</td>\n",
       "      <td>HEALTHY LIVING</td>\n",
       "      <td>False</td>\n",
       "      <td>2659</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>364 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Group Quarter  Year            Topic  Special  Word_Count\n",
       "0       1      FA    20  music and audio    False        6999\n",
       "1       2      FA    20       automotive    False        8686\n",
       "2       4      FA    20     video gaming    False        1864\n",
       "3       5      FA    20           sports    False        5483\n",
       "4       6      FA    20             pets    False        4595\n",
       "..    ...     ...   ...              ...      ...         ...\n",
       "60     87      WI  2021   HEALTHY LIVING    False        2637\n",
       "61     88      WI  2021           IMPACT    False        3538\n",
       "62     89      WI  2021         POLITICS    False        4755\n",
       "63     90      WI  2021         POLITICS    False        3298\n",
       "64     92      WI  2021   HEALTHY LIVING    False        2659\n",
       "\n",
       "[364 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Carry out EDA on your dataset(s); Describe in this section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Topic', ylabel='Word_Count'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#wordcount boxplot per topic\n",
    "\n",
    "df = df_total\n",
    "sns.boxplot(x='Topic', y='Word_Count', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='count', ylabel='Topic'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Total amount of each topic\n",
    "\n",
    "sns.countplot(y='Topic',data=df, order=df['Topic'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Topic', ylabel='count'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Topic',data=df, hue='Year', order=df['Topic'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quarter  Year\n",
      "SP       19      110\n",
      "WI       21       65\n",
      "         20       50\n",
      "SP       20       49\n",
      "         21       47\n",
      "FA       20       43\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Add chronological order to rows\n",
    "chronological = []\n",
    "indexDict = {'SP19':0, 'FA20':1, 'WI20':2, 'SP20':3, 'WI21':4, 'SP21':5}\n",
    "# {\"SP19\": 0, \"FA20\": 1, \"WI20\": 2, \"SP20\": 3, \"WI21\": 4, \"SP21\": 5}\n",
    "#starting from spring 19 as 0, and incrementing, so that fall 19 is 1, winter 20 is 2\n",
    "#spring 19: 0\n",
    "#fall 19: 1\n",
    "#winter 20: 2\n",
    "#spring 20: 3\n",
    "#fall 20: 3\n",
    "#winter 21: 4\n",
    "#spring 21: 5\n",
    "#\n",
    "\n",
    "for index, row in df_total.iterrows():\n",
    "    # print(row['Quarter'], row['Year'])\n",
    "    chronological.append(indexDict[row['Quarter']+str(row['Year'])])\n",
    "    \n",
    "df_total['Quarter_Index'] = chronological\n",
    "df_total.head()\n",
    "print(df_total.value_counts(['Quarter', 'Year']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Quarter_Index', ylabel='count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Quarter_Index',data=df_total)"
   ]
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}