SkillTrends.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ast\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt  \n",
    "dataset = pd.read_csv('data_jobs.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['job_posted_date'] = pd.to_datetime(dataset['job_posted_date'])\n",
    "dataset['job_skills'] = dataset['job_skills'].apply(lambda x: ast.literal_eval(x) if pd.notna(x) else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Skill count per month for data analysts in India\n",
    "df_ind = dataset[(dataset['job_title'] == 'Data Analyst') & (dataset['job_country'] == 'India')].copy()\n",
    "\n",
    "df_ind['job_posted_month_no'] = df_ind['job_posted_date'].dt.month\n",
    "\n",
    "# Explode job_skills column\n",
    "df_explode = df_ind.explode('job_skills')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_title_short</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_location</th>\n",
       "      <th>job_via</th>\n",
       "      <th>job_schedule_type</th>\n",
       "      <th>job_work_from_home</th>\n",
       "      <th>search_location</th>\n",
       "      <th>job_posted_date</th>\n",
       "      <th>job_no_degree_mention</th>\n",
       "      <th>job_health_insurance</th>\n",
       "      <th>job_country</th>\n",
       "      <th>salary_rate</th>\n",
       "      <th>salary_year_avg</th>\n",
       "      <th>salary_hour_avg</th>\n",
       "      <th>company_name</th>\n",
       "      <th>job_skills</th>\n",
       "      <th>job_type_skills</th>\n",
       "      <th>job_posted_month_no</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Hyderabad, Telangana, India</td>\n",
       "      <td>via LinkedIn</td>\n",
       "      <td>Contractor</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>QuEST Global Services Pte. Ltd</td>\n",
       "      <td>python</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>QuEST Global Services Pte. Ltd</td>\n",
       "      <td>sql</td>\n",
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       "      <th>194</th>\n",
       "      <td>Data Analyst</td>\n",
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       "      <td>via BeBee India</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>QuEST Global Services Pte. Ltd</td>\n",
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       "      <th>194</th>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Data Analyst</td>\n",
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       "      <td>Data Analyst</td>\n",
       "      <td>Data Analyst</td>\n",
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       "      <td>via LinkedIn</td>\n",
       "      <td>Full-time</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GUS Global Services India Pvt. Ltd.</td>\n",
       "      <td>sql</td>\n",
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       "      <td>GUS Global Services India Pvt. Ltd.</td>\n",
       "      <td>power bi</td>\n",
       "      <td>{'analyst_tools': ['sap', 'power bi', 'tableau...</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>785628</th>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Gurugram, Haryana, India</td>\n",
       "      <td>via LinkedIn</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>785628</th>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Gurugram, Haryana, India</td>\n",
       "      <td>via LinkedIn</td>\n",
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       "<p>6291 rows × 18 columns</p>\n",
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      ],
      "text/plain": [
       "       job_title_short     job_title                 job_location  \\\n",
       "159       Data Analyst  Data Analyst  Hyderabad, Telangana, India   \n",
       "194       Data Analyst  Data Analyst                        India   \n",
       "194       Data Analyst  Data Analyst                        India   \n",
       "194       Data Analyst  Data Analyst                        India   \n",
       "194       Data Analyst  Data Analyst                        India   \n",
       "...                ...           ...                          ...   \n",
       "785628    Data Analyst  Data Analyst     Gurugram, Haryana, India   \n",
       "785628    Data Analyst  Data Analyst     Gurugram, Haryana, India   \n",
       "785628    Data Analyst  Data Analyst     Gurugram, Haryana, India   \n",
       "785628    Data Analyst  Data Analyst     Gurugram, Haryana, India   \n",
       "785628    Data Analyst  Data Analyst     Gurugram, Haryana, India   \n",
       "\n",
       "                job_via job_schedule_type  job_work_from_home search_location  \\\n",
       "159        via LinkedIn        Contractor               False           India   \n",
       "194     via BeBee India         Full-time               False           India   \n",
       "194     via BeBee India         Full-time               False           India   \n",
       "194     via BeBee India         Full-time               False           India   \n",
       "194     via BeBee India         Full-time               False           India   \n",
       "...                 ...               ...                 ...             ...   \n",
       "785628     via LinkedIn         Full-time               False           India   \n",
       "785628     via LinkedIn         Full-time               False           India   \n",
       "785628     via LinkedIn         Full-time               False           India   \n",
       "785628     via LinkedIn         Full-time               False           India   \n",
       "785628     via LinkedIn         Full-time               False           India   \n",
       "\n",
       "           job_posted_date  job_no_degree_mention  job_health_insurance  \\\n",
       "159    2023-11-28 13:12:07                  False                 False   \n",
       "194    2023-06-20 13:11:10                  False                 False   \n",
       "194    2023-06-20 13:11:10                  False                 False   \n",
       "194    2023-06-20 13:11:10                  False                 False   \n",
       "194    2023-06-20 13:11:10                  False                 False   \n",
       "...                    ...                    ...                   ...   \n",
       "785628 2023-01-09 06:08:12                  False                 False   \n",
       "785628 2023-01-09 06:08:12                  False                 False   \n",
       "785628 2023-01-09 06:08:12                  False                 False   \n",
       "785628 2023-01-09 06:08:12                  False                 False   \n",
       "785628 2023-01-09 06:08:12                  False                 False   \n",
       "\n",
       "       job_country salary_rate  salary_year_avg  salary_hour_avg  \\\n",
       "159          India         NaN              NaN              NaN   \n",
       "194          India         NaN              NaN              NaN   \n",
       "194          India         NaN              NaN              NaN   \n",
       "194          India         NaN              NaN              NaN   \n",
       "194          India         NaN              NaN              NaN   \n",
       "...            ...         ...              ...              ...   \n",
       "785628       India         NaN              NaN              NaN   \n",
       "785628       India         NaN              NaN              NaN   \n",
       "785628       India         NaN              NaN              NaN   \n",
       "785628       India         NaN              NaN              NaN   \n",
       "785628       India         NaN              NaN              NaN   \n",
       "\n",
       "                               company_name job_skills  \\\n",
       "159               Fusion Plus Solutions Inc        sql   \n",
       "194          QuEST Global Services Pte. Ltd     python   \n",
       "194          QuEST Global Services Pte. Ltd        sql   \n",
       "194          QuEST Global Services Pte. Ltd      excel   \n",
       "194          QuEST Global Services Pte. Ltd  ms access   \n",
       "...                                     ...        ...   \n",
       "785628  GUS Global Services India Pvt. Ltd.        sql   \n",
       "785628  GUS Global Services India Pvt. Ltd.        sap   \n",
       "785628  GUS Global Services India Pvt. Ltd.   power bi   \n",
       "785628  GUS Global Services India Pvt. Ltd.    tableau   \n",
       "785628  GUS Global Services India Pvt. Ltd.      excel   \n",
       "\n",
       "                                          job_type_skills  job_posted_month_no  \n",
       "159                              {'programming': ['sql']}                   11  \n",
       "194     {'analyst_tools': ['excel', 'ms access', 'powe...                    6  \n",
       "194     {'analyst_tools': ['excel', 'ms access', 'powe...                    6  \n",
       "194     {'analyst_tools': ['excel', 'ms access', 'powe...                    6  \n",
       "194     {'analyst_tools': ['excel', 'ms access', 'powe...                    6  \n",
       "...                                                   ...                  ...  \n",
       "785628  {'analyst_tools': ['sap', 'power bi', 'tableau...                    1  \n",
       "785628  {'analyst_tools': ['sap', 'power bi', 'tableau...                    1  \n",
       "785628  {'analyst_tools': ['sap', 'power bi', 'tableau...                    1  \n",
       "785628  {'analyst_tools': ['sap', 'power bi', 'tableau...                    1  \n",
       "785628  {'analyst_tools': ['sap', 'power bi', 'tableau...                    1  \n",
       "\n",
       "[6291 rows x 18 columns]"
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     "execution_count": 4,
     "metadata": {},
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   "source": [
    "df_explode"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pivot = df_explode.pivot_table(index='job_posted_month_no', columns='job_skills',  aggfunc='size', fill_value = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
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       "      <td>0</td>\n",
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       "      <th>9</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
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       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 139 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "job_skills           airflow  alteryx  ansible  arch  asana  asp.net  \\\n",
       "job_posted_month_no                                                    \n",
       "1                          0        1        0     0      0        1   \n",
       "2                          1        8        0     0      0        0   \n",
       "3                          1        1        1     0      0        0   \n",
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       "6                          1        5        0     0      0        0   \n",
       "7                          3        2        0     0      0        0   \n",
       "8                          7        5        0     0      0        0   \n",
       "9                          1        2        0     0      1        0   \n",
       "10                         1        1        2     0      0        0   \n",
       "11                         1        9        1     0      0        0   \n",
       "12                         2        6        0     0      0        0   \n",
       "\n",
       "job_skills           assembly  aurora  aws  azure  ...  vba  visio  \\\n",
       "job_posted_month_no                                ...               \n",
       "1                           0       0    6      5  ...    6      1   \n",
       "2                           0       0    6      7  ...    8      0   \n",
       "3                           0       0    7      6  ...    4      0   \n",
       "4                           1       0    4      8  ...    2      2   \n",
       "5                           0       0    4      9  ...    4      0   \n",
       "6                           0       1   10      9  ...    1      0   \n",
       "7                           0       0    5      8  ...    5      0   \n",
       "8                           0       0   20     11  ...    4      0   \n",
       "9                           0       0   12      5  ...    4      2   \n",
       "10                          0       0   15      9  ...    2      0   \n",
       "11                          0       0   15      9  ...    3      0   \n",
       "12                          0       0   11     13  ...    9      1   \n",
       "\n",
       "job_skills           visual basic  vmware  watson  webex  windows  wire  word  \\\n",
       "job_posted_month_no                                                             \n",
       "1                               0       0       0      1        1     0     8   \n",
       "2                               0       0       1      0        1     0     3   \n",
       "3                               0       0       0      0        2     0     3   \n",
       "4                               0       0       0      0        1     0     6   \n",
       "5                               1       0       0      0        2     0     4   \n",
       "6                               1       1       0      0        0     0     1   \n",
       "7                               1       0       0      0        0     0     1   \n",
       "8                               0       0       0      0        0     0     7   \n",
       "9                               0       2       0      0        1     0     1   \n",
       "10                              1       0       0      0        3     0     5   \n",
       "11                              0       0       0      0        0     0    12   \n",
       "12                              1       1       0      0        0     1     6   \n",
       "\n",
       "job_skills           zoom  \n",
       "job_posted_month_no        \n",
       "1                       0  \n",
       "2                       0  \n",
       "3                       0  \n",
       "4                       0  \n",
       "5                       0  \n",
       "6                       0  \n",
       "7                       0  \n",
       "8                       0  \n",
       "9                       0  \n",
       "10                      3  \n",
       "11                      0  \n",
       "12                      0  \n",
       "\n",
       "[12 rows x 139 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>96</td>\n",
       "      <td>65</td>\n",
       "      <td>59</td>\n",
       "      <td>53</td>\n",
       "      <td>43</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>75</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>47</td>\n",
       "      <td>28</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>114</td>\n",
       "      <td>79</td>\n",
       "      <td>81</td>\n",
       "      <td>66</td>\n",
       "      <td>48</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>120</td>\n",
       "      <td>86</td>\n",
       "      <td>78</td>\n",
       "      <td>72</td>\n",
       "      <td>68</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 139 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "job_skills           sql  python  excel  tableau  power bi  sas   r  aws  \\\n",
       "job_posted_month_no                                                        \n",
       "1                     75      55     51       41        26   36  23    6   \n",
       "2                     64      40     46       32        24   14  23    6   \n",
       "3                     57      25     32       30        19   10   9    7   \n",
       "4                     56      35     35       27        23   24  16    4   \n",
       "5                     44      25     37       24        18   10   9    4   \n",
       "6                     50      42     34       29        23   28  23   10   \n",
       "7                     57      37     37       38        20   20  23    5   \n",
       "8                     88      58     54       47        43   24  28   20   \n",
       "9                     96      65     59       53        43   26  27   12   \n",
       "10                    75      54     54       47        28   24  24   15   \n",
       "11                   114      79     81       66        48   30  32   15   \n",
       "12                   120      86     78       72        68   44  41   11   \n",
       "\n",
       "job_skills           azure  javascript  ...  react.js  swift  ringcentral  \\\n",
       "job_posted_month_no                     ...                                 \n",
       "1                        5          11  ...         0      0            0   \n",
       "2                        7           8  ...         0      0            0   \n",
       "3                        6           3  ...         0      1            0   \n",
       "4                        8           7  ...         1      0            0   \n",
       "5                        9           3  ...         0      0            0   \n",
       "6                        9           5  ...         0      0            0   \n",
       "7                        8           5  ...         0      0            0   \n",
       "8                       11           7  ...         0      0            0   \n",
       "9                        5          14  ...         0      0            0   \n",
       "10                       9           4  ...         0      0            0   \n",
       "11                       9          12  ...         0      0            1   \n",
       "12                      13          15  ...         0      0            0   \n",
       "\n",
       "job_skills           suse  jupyter  sqlite  ggplot2  gitlab  graphql  no-sql  \n",
       "job_posted_month_no                                                           \n",
       "1                       0        0       0        0       0        1       0  \n",
       "2                       0        0       0        1       0        0       0  \n",
       "3                       1        0       0        0       0        0       1  \n",
       "4                       0        0       0        0       1        0       0  \n",
       "5                       0        0       0        0       0        0       0  \n",
       "6                       0        0       0        0       0        0       0  \n",
       "7                       0        0       0        0       0        0       0  \n",
       "8                       0        0       0        0       0        0       0  \n",
       "9                       0        1       0        0       0        0       0  \n",
       "10                      0        0       0        0       0        0       0  \n",
       "11                      0        0       1        0       0        0       0  \n",
       "12                      0        0       0        0       0        0       0  \n",
       "\n",
       "[12 rows x 139 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pivot.loc['Total'] = df_pivot.sum()\n",
    "df_pivot = df_pivot[df_pivot.loc['Total'].sort_values(ascending=False).index]\n",
    "df_pivot = df_pivot.drop('Total')\n",
    "\n",
    "df_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pivot.iloc[:, :5].plot(kind='line')\n",
    "\n",
    "plt.title('Trending Top Skills for Data Analysts in India')\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "job_posted_month_no\n",
       "1     144\n",
       "2     118\n",
       "3      97\n",
       "4      99\n",
       "5      64\n",
       "6      93\n",
       "7      92\n",
       "8     141\n",
       "9     166\n",
       "10    126\n",
       "11    204\n",
       "12    195\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DA_totals = df_ind.groupby('job_posted_month_no').size()\n",
    "\n",
    "DA_totals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>job_skills</th>\n",
       "      <th>sql</th>\n",
       "      <th>python</th>\n",
       "      <th>excel</th>\n",
       "      <th>tableau</th>\n",
       "      <th>power bi</th>\n",
       "      <th>sas</th>\n",
       "      <th>r</th>\n",
       "      <th>aws</th>\n",
       "      <th>azure</th>\n",
       "      <th>javascript</th>\n",
       "      <th>...</th>\n",
       "      <th>react.js</th>\n",
       "      <th>swift</th>\n",
       "      <th>ringcentral</th>\n",
       "      <th>suse</th>\n",
       "      <th>jupyter</th>\n",
       "      <th>sqlite</th>\n",
       "      <th>ggplot2</th>\n",
       "      <th>gitlab</th>\n",
       "      <th>graphql</th>\n",
       "      <th>no-sql</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_posted_month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jan</th>\n",
       "      <td>52.083333</td>\n",
       "      <td>38.194444</td>\n",
       "      <td>35.416667</td>\n",
       "      <td>28.472222</td>\n",
       "      <td>18.055556</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>15.972222</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>3.472222</td>\n",
       "      <td>7.638889</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.694444</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feb</th>\n",
       "      <td>54.237288</td>\n",
       "      <td>33.898305</td>\n",
       "      <td>38.983051</td>\n",
       "      <td>27.118644</td>\n",
       "      <td>20.338983</td>\n",
       "      <td>11.864407</td>\n",
       "      <td>19.491525</td>\n",
       "      <td>5.084746</td>\n",
       "      <td>5.932203</td>\n",
       "      <td>6.779661</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.847458</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mar</th>\n",
       "      <td>58.762887</td>\n",
       "      <td>25.773196</td>\n",
       "      <td>32.989691</td>\n",
       "      <td>30.927835</td>\n",
       "      <td>19.587629</td>\n",
       "      <td>10.309278</td>\n",
       "      <td>9.278351</td>\n",
       "      <td>7.216495</td>\n",
       "      <td>6.185567</td>\n",
       "      <td>3.092784</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.030928</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.030928</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.030928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr</th>\n",
       "      <td>56.565657</td>\n",
       "      <td>35.353535</td>\n",
       "      <td>35.353535</td>\n",
       "      <td>27.272727</td>\n",
       "      <td>23.232323</td>\n",
       "      <td>24.242424</td>\n",
       "      <td>16.161616</td>\n",
       "      <td>4.040404</td>\n",
       "      <td>8.080808</td>\n",
       "      <td>7.070707</td>\n",
       "      <td>...</td>\n",
       "      <td>1.010101</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.010101</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>68.750000</td>\n",
       "      <td>39.062500</td>\n",
       "      <td>57.812500</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>28.125000</td>\n",
       "      <td>15.625000</td>\n",
       "      <td>14.062500</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>14.062500</td>\n",
       "      <td>4.687500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jun</th>\n",
       "      <td>53.763441</td>\n",
       "      <td>45.161290</td>\n",
       "      <td>36.559140</td>\n",
       "      <td>31.182796</td>\n",
       "      <td>24.731183</td>\n",
       "      <td>30.107527</td>\n",
       "      <td>24.731183</td>\n",
       "      <td>10.752688</td>\n",
       "      <td>9.677419</td>\n",
       "      <td>5.376344</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jul</th>\n",
       "      <td>61.956522</td>\n",
       "      <td>40.217391</td>\n",
       "      <td>40.217391</td>\n",
       "      <td>41.304348</td>\n",
       "      <td>21.739130</td>\n",
       "      <td>21.739130</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>5.434783</td>\n",
       "      <td>8.695652</td>\n",
       "      <td>5.434783</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aug</th>\n",
       "      <td>62.411348</td>\n",
       "      <td>41.134752</td>\n",
       "      <td>38.297872</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>30.496454</td>\n",
       "      <td>17.021277</td>\n",
       "      <td>19.858156</td>\n",
       "      <td>14.184397</td>\n",
       "      <td>7.801418</td>\n",
       "      <td>4.964539</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sep</th>\n",
       "      <td>57.831325</td>\n",
       "      <td>39.156627</td>\n",
       "      <td>35.542169</td>\n",
       "      <td>31.927711</td>\n",
       "      <td>25.903614</td>\n",
       "      <td>15.662651</td>\n",
       "      <td>16.265060</td>\n",
       "      <td>7.228916</td>\n",
       "      <td>3.012048</td>\n",
       "      <td>8.433735</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.60241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oct</th>\n",
       "      <td>59.523810</td>\n",
       "      <td>42.857143</td>\n",
       "      <td>42.857143</td>\n",
       "      <td>37.301587</td>\n",
       "      <td>22.222222</td>\n",
       "      <td>19.047619</td>\n",
       "      <td>19.047619</td>\n",
       "      <td>11.904762</td>\n",
       "      <td>7.142857</td>\n",
       "      <td>3.174603</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nov</th>\n",
       "      <td>55.882353</td>\n",
       "      <td>38.725490</td>\n",
       "      <td>39.705882</td>\n",
       "      <td>32.352941</td>\n",
       "      <td>23.529412</td>\n",
       "      <td>14.705882</td>\n",
       "      <td>15.686275</td>\n",
       "      <td>7.352941</td>\n",
       "      <td>4.411765</td>\n",
       "      <td>5.882353</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.490196</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.490196</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dec</th>\n",
       "      <td>61.538462</td>\n",
       "      <td>44.102564</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>36.923077</td>\n",
       "      <td>34.871795</td>\n",
       "      <td>22.564103</td>\n",
       "      <td>21.025641</td>\n",
       "      <td>5.641026</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>7.692308</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 139 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "job_skills              sql     python      excel    tableau   power bi  \\\n",
       "job_posted_month                                                          \n",
       "Jan               52.083333  38.194444  35.416667  28.472222  18.055556   \n",
       "Feb               54.237288  33.898305  38.983051  27.118644  20.338983   \n",
       "Mar               58.762887  25.773196  32.989691  30.927835  19.587629   \n",
       "Apr               56.565657  35.353535  35.353535  27.272727  23.232323   \n",
       "May               68.750000  39.062500  57.812500  37.500000  28.125000   \n",
       "Jun               53.763441  45.161290  36.559140  31.182796  24.731183   \n",
       "Jul               61.956522  40.217391  40.217391  41.304348  21.739130   \n",
       "Aug               62.411348  41.134752  38.297872  33.333333  30.496454   \n",
       "Sep               57.831325  39.156627  35.542169  31.927711  25.903614   \n",
       "Oct               59.523810  42.857143  42.857143  37.301587  22.222222   \n",
       "Nov               55.882353  38.725490  39.705882  32.352941  23.529412   \n",
       "Dec               61.538462  44.102564  40.000000  36.923077  34.871795   \n",
       "\n",
       "job_skills              sas          r        aws      azure  javascript  ...  \\\n",
       "job_posted_month                                                          ...   \n",
       "Jan               25.000000  15.972222   4.166667   3.472222    7.638889  ...   \n",
       "Feb               11.864407  19.491525   5.084746   5.932203    6.779661  ...   \n",
       "Mar               10.309278   9.278351   7.216495   6.185567    3.092784  ...   \n",
       "Apr               24.242424  16.161616   4.040404   8.080808    7.070707  ...   \n",
       "May               15.625000  14.062500   6.250000  14.062500    4.687500  ...   \n",
       "Jun               30.107527  24.731183  10.752688   9.677419    5.376344  ...   \n",
       "Jul               21.739130  25.000000   5.434783   8.695652    5.434783  ...   \n",
       "Aug               17.021277  19.858156  14.184397   7.801418    4.964539  ...   \n",
       "Sep               15.662651  16.265060   7.228916   3.012048    8.433735  ...   \n",
       "Oct               19.047619  19.047619  11.904762   7.142857    3.174603  ...   \n",
       "Nov               14.705882  15.686275   7.352941   4.411765    5.882353  ...   \n",
       "Dec               22.564103  21.025641   5.641026   6.666667    7.692308  ...   \n",
       "\n",
       "job_skills        react.js     swift  ringcentral      suse  jupyter  \\\n",
       "job_posted_month                                                       \n",
       "Jan               0.000000  0.000000     0.000000  0.000000  0.00000   \n",
       "Feb               0.000000  0.000000     0.000000  0.000000  0.00000   \n",
       "Mar               0.000000  1.030928     0.000000  1.030928  0.00000   \n",
       "Apr               1.010101  0.000000     0.000000  0.000000  0.00000   \n",
       "May               0.000000  0.000000     0.000000  0.000000  0.00000   \n",
       "Jun               0.000000  0.000000     0.000000  0.000000  0.00000   \n",
       "Jul               0.000000  0.000000     0.000000  0.000000  0.00000   \n",
       "Aug               0.000000  0.000000     0.000000  0.000000  0.00000   \n",
       "Sep               0.000000  0.000000     0.000000  0.000000  0.60241   \n",
       "Oct               0.000000  0.000000     0.000000  0.000000  0.00000   \n",
       "Nov               0.000000  0.000000     0.490196  0.000000  0.00000   \n",
       "Dec               0.000000  0.000000     0.000000  0.000000  0.00000   \n",
       "\n",
       "job_skills          sqlite   ggplot2    gitlab   graphql    no-sql  \n",
       "job_posted_month                                                    \n",
       "Jan               0.000000  0.000000  0.000000  0.694444  0.000000  \n",
       "Feb               0.000000  0.847458  0.000000  0.000000  0.000000  \n",
       "Mar               0.000000  0.000000  0.000000  0.000000  1.030928  \n",
       "Apr               0.000000  0.000000  1.010101  0.000000  0.000000  \n",
       "May               0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "Jun               0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "Jul               0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "Aug               0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "Sep               0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "Oct               0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "Nov               0.490196  0.000000  0.000000  0.000000  0.000000  \n",
       "Dec               0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "\n",
       "[12 rows x 139 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ind_perc = df_pivot.iloc[:12].div(DA_totals/100, axis=0)\n",
    "\n",
    "# changes month number to month name\n",
    "df_ind_perc = df_ind_perc.reset_index()\n",
    "df_ind_perc['job_posted_month'] = df_ind_perc['job_posted_month_no'].apply(lambda x: pd.to_datetime(x, format='%m').strftime('%b'))\n",
    "df_ind_perc = df_ind_perc.set_index('job_posted_month')\n",
    "df_ind_perc = df_ind_perc.drop(columns='job_posted_month_no')\n",
    "\n",
    "df_ind_perc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import PercentFormatter\n",
    "\n",
    "df_plot = df_ind_perc.iloc[:, :5]\n",
    "sns.lineplot(data=df_plot, dashes=False, legend='full', palette='tab10')\n",
    "sns.set_theme(style='ticks')\n",
    "sns.despine() # remove top and right spines\n",
    "\n",
    "plt.title('Trending Top Skills for Data Analysts in India')\n",
    "plt.ylabel('Likelihood in Job Posting')\n",
    "plt.xlabel('2023')\n",
    "plt.legend().remove()\n",
    "plt.gca().yaxis.set_major_formatter(PercentFormatter(decimals=0))\n",
    "\n",
    "# annotate the plot with the top 5 skills using plt.text()\n",
    "for i in range(5):\n",
    "    plt.text(11.2, df_plot.iloc[-1, i], df_plot.columns[i], color='black')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}