3_Project/4_Salary_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "682a9866",
   "metadata": {},
   "source": [
    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/lukebarousse/Python_Data_Analytics_Course/blob/main/3_Project/4_Salary_Analysis.ipynb\">\n",
    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
    "</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How well do jobs and skills pay for Data Analysts?\n",
    "\n",
    "#### Methodology\n",
    "1. Evaluate median salary for top 6 data jobs\n",
    "2. Find median salary per skill for Data Analysts\n",
    "3. Visualize for highest paying skills and most demanded skills\n",
    "\n",
    "#### Original Exploration\n",
    "\n",
    "[18_Matplotlib_Box_Plots.ipynb](../2_Advanced/18_Matplotlib_Box_Plots.ipynb)  \n",
    "[19_Exercise_Skill_Pay_Analysis.ipynb](../2_Advanced/19_Exercise_Skill_Pay_Analysis.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries and Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Libraries\n",
    "import ast\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from datasets import load_dataset\n",
    "import matplotlib.pyplot as plt  \n",
    "\n",
    "# Loading Data\n",
    "dataset = load_dataset('lukebarousse/data_jobs')\n",
    "df = dataset['train'].to_pandas()\n",
    "\n",
    "# Data Cleanup\n",
    "df['job_posted_date'] = pd.to_datetime(df['job_posted_date'])\n",
    "df['job_skills'] = df['job_skills'].apply(lambda x: ast.literal_eval(x) if pd.notna(x) else x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Salary Distribution by Job Title"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter our data to only include salary values from the United States."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter for the job titles and country\n",
    "df_US = df[(df['job_country'] == 'United States')].dropna(subset=['salary_year_avg'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a list of the main job_titles in our dataset and filter our dataframe to only contain these job titles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Data Scientist',\n",
       " 'Data Analyst',\n",
       " 'Data Engineer',\n",
       " 'Senior Data Scientist',\n",
       " 'Senior Data Engineer',\n",
       " 'Senior Data Analyst']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job_titles = df_US['job_title_short'].value_counts().index[:6].tolist()\n",
    "\n",
    "# filter the df for the top 6 job titles\n",
    "df_US_top6 = df_US[df_US['job_title_short'].isin(job_titles)]\n",
    "\n",
    "# order the job titles by median salary\n",
    "job_order = df_US_top6.groupby('job_title_short')['salary_year_avg'].median().sort_values(ascending=False).index\n",
    "\n",
    "job_titles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Salary Distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the top 6 job titles salary distributions using a box plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df_US_top6, x='salary_year_avg', y='job_title_short', order=job_order)\n",
    "sns.set_theme(style='ticks')\n",
    "sns.despine()\n",
    "\n",
    "# this is all the same\n",
    "plt.title('Salary Distributions of Data Jobs in the US')\n",
    "plt.xlabel('Yearly Salary (USD)')\n",
    "plt.ylabel('')\n",
    "plt.xlim(0, 600000) \n",
    "ticks_x = plt.FuncFormatter(lambda y, pos: f'${int(y/1000)}K')\n",
    "plt.gca().xaxis.set_major_formatter(ticks_x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Investigate Median Salary Vs Skill for Data Analysts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filters the original dataset to only get rows where the job title is 'Data Analyst' and the country is 'United States', to create a new DataFrame `df_DA_US`. Drop NaN values from the 'salary_year_avg' column. Then it uses the `explode` method on the `job_skills` column to create a new row in the DataFrame for each skill associated with a job. Finally, it displays the first five entries of the `salary_year_avg` and `job_skills` columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>salary_year_avg</th>\n",
       "      <th>job_skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>nosql</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>sql</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>vba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>oracle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>hadoop</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     salary_year_avg job_skills\n",
       "288         150000.0      nosql\n",
       "288         150000.0        sql\n",
       "288         150000.0        vba\n",
       "288         150000.0     oracle\n",
       "288         150000.0     hadoop"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Only get data analyst jobs in the US\n",
    "df_DA_US = df[(df['job_title_short'] == 'Data Analyst') & (df['job_country'] == 'United States')].copy()\n",
    "\n",
    "# Drop NaN values from the 'salary_year_avg' column for accurate visualization\n",
    "df_DA_US = df_DA_US.dropna(subset=['salary_year_avg'])\n",
    "\n",
    "df_DA_US = df_DA_US.explode('job_skills')\n",
    "\n",
    "df_DA_US[['salary_year_avg', 'job_skills']].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Determine The Highest Paid Skills and Most Demanded Skills"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gets the top ten highest-paying skills for Data Analysts by calculating the median salary for each skill listed in the `df_DA_US`. It groups the data by job skills, computes the median salary, sorts these values in descending order by median, and then selects the top 10. This is then formatted into a new DataFrame (`df_DA_top_pay`) with a reset index and a renamed salary column labeled 'median_salary'."
   ]
  },
  {
   "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>count</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_skills</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dplyr</th>\n",
       "      <td>2</td>\n",
       "      <td>196250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bitbucket</th>\n",
       "      <td>3</td>\n",
       "      <td>189000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gitlab</th>\n",
       "      <td>3</td>\n",
       "      <td>186000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>solidity</th>\n",
       "      <td>1</td>\n",
       "      <td>179000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hugging face</th>\n",
       "      <td>1</td>\n",
       "      <td>175000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>couchbase</th>\n",
       "      <td>1</td>\n",
       "      <td>160515.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ansible</th>\n",
       "      <td>1</td>\n",
       "      <td>159640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mxnet</th>\n",
       "      <td>2</td>\n",
       "      <td>149000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cassandra</th>\n",
       "      <td>6</td>\n",
       "      <td>148250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vmware</th>\n",
       "      <td>1</td>\n",
       "      <td>147500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count    median\n",
       "job_skills                   \n",
       "dplyr             2  196250.0\n",
       "bitbucket         3  189000.0\n",
       "gitlab            3  186000.0\n",
       "solidity          1  179000.0\n",
       "hugging face      1  175000.0\n",
       "couchbase         1  160515.0\n",
       "ansible           1  159640.0\n",
       "mxnet             2  149000.0\n",
       "cassandra         6  148250.0\n",
       "vmware            1  147500.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_DA_top_pay = df_DA_US.groupby('job_skills')['salary_year_avg'].agg(['count', 'median']).sort_values(by='median', ascending=False)\n",
    "\n",
    "df_DA_top_pay = df_DA_top_pay.head(10)\n",
    "\n",
    "df_DA_top_pay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculates the count and median salary for each skill in `df_DA_US`. It groups the data by `job_skills`, aggregates it to find the count and median salary for each skill, and then sorts the results by count in descending order by count. It re-sorts this subset by median salary in descending order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>count</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_skills</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>python</th>\n",
       "      <td>1438</td>\n",
       "      <td>97500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tableau</th>\n",
       "      <td>1368</td>\n",
       "      <td>92500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sql server</th>\n",
       "      <td>291</td>\n",
       "      <td>92500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>895</td>\n",
       "      <td>90490.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sql</th>\n",
       "      <td>2522</td>\n",
       "      <td>90300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sas</th>\n",
       "      <td>928</td>\n",
       "      <td>90000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>power bi</th>\n",
       "      <td>841</td>\n",
       "      <td>90000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>powerpoint</th>\n",
       "      <td>465</td>\n",
       "      <td>85000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>excel</th>\n",
       "      <td>1815</td>\n",
       "      <td>84500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>word</th>\n",
       "      <td>465</td>\n",
       "      <td>81700.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count   median\n",
       "job_skills                \n",
       "python       1438  97500.0\n",
       "tableau      1368  92500.0\n",
       "sql server    291  92500.0\n",
       "r             895  90490.0\n",
       "sql          2522  90300.0\n",
       "sas           928  90000.0\n",
       "power bi      841  90000.0\n",
       "powerpoint    465  85000.0\n",
       "excel        1815  84500.0\n",
       "word          465  81700.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_DA_skills = df_DA_US.groupby('job_skills')['salary_year_avg'].agg(['count', 'median']).sort_values(by='count', ascending=False)\n",
    "\n",
    "df_DA_skills = df_DA_skills.head(10).sort_values(by='median', ascending=False)\n",
    "\n",
    "df_DA_skills"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creates two horizontal bar charts: \n",
    "1. Displays the highest paid skills \n",
    "2. Shows the most in demand skills "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 1)  \n",
    "\n",
    "# Top 10 Highest Paid Skills for Data Analysts\n",
    "sns.barplot(data=df_DA_top_pay, x='median', y=df_DA_top_pay.index, hue='median', ax=ax[0], palette='dark:b_r')\n",
    "ax[0].legend().remove()\n",
    "# original code:\n",
    "# df_DA_top_pay[::-1].plot(kind='barh', y='median', ax=ax[0], legend=False) \n",
    "ax[0].set_title('Highest Paid Skills for Data Analysts in the US')\n",
    "ax[0].set_ylabel('')\n",
    "ax[0].set_xlabel('')\n",
    "ax[0].xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'${int(x/1000)}K'))\n",
    "\n",
    "\n",
    "# Top 10 Most In-Demand Skills for Data Analysts')\n",
    "sns.barplot(data=df_DA_skills, x='median', y=df_DA_skills.index, hue='median', ax=ax[1], palette='light:b')\n",
    "ax[1].legend().remove()\n",
    "# original code:\n",
    "# df_DA_skills[::-1].plot(kind='barh', y='median', ax=ax[1], legend=False)\n",
    "ax[1].set_title('Most In-Demand Skills for Data Analysts in the US')\n",
    "ax[1].set_ylabel('')\n",
    "ax[1].set_xlabel('Median Salary (USD)')\n",
    "ax[1].set_xlim(ax[0].get_xlim())  # Set the same x-axis limits as the first plot\n",
    "ax[1].xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'${int(x/1000)}K'))\n",
    "\n",
    "sns.set_theme(style='ticks')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
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