eda-visualization-on-data-science-salaries.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/tirendazacademy/eda-visualization-on-data-science-salaries?scriptVersionId=128407730\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","id":"c4da946d","metadata":{"papermill":{"duration":0.010403,"end_time":"2023-05-05T11:17:12.681506","exception":false,"start_time":"2023-05-05T11:17:12.671103","status":"completed"},"tags":[]},"source":["# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>Data Science Trends & Salaries 💸 </div></b>\n","\n","![](https://img.freepik.com/free-photo/business-proposal-purchase-hands-holding-money_53876-127124.jpg?w=1060&t=st=1682891340~exp=1682891940~hmac=cf32bec25907d203fd6ae13040bb6eee89d3c4b69a76fc568e51ec71b8bbe75d)\n","\n","Hi guys 😀 In this notebook, we're going to explore trends & salaries in data science.\n","\n","Table of contents:\n","<ul>\n","<li><a href=\"#importing\">Importing required libraries </a></li>  \n","<li><a href=\"#loading\">Loading the dataset </a></li>  \n","<li><a href=\"#understanding\">Understanding the dataset</a></li>         \n","<li><a href=\"#job-titles\">Top 10 job titles in 2023 </a></li>\n","<li><a href=\"#experience-levels\">Experience levels</a></li>\n","<li><a href=\"#employment-types\">Employment types</a></li>\n","<li><a href=\"#salary-job-title\">Salaries by job titles</a></li>\n","<li><a href=\"#average\">Salaries by employment types</a></li>\n","<li><a href=\"#salaries\">Salaries by work years</a></li>\n","<li><a href=\"#remote\">Remote jobs locations</a></li>         \n","<li><a href=\"#conclusion\">Conclusion</a></li>   \n","</ul>\n","\n","I'm excited, let's get started right away 🚀"]},{"cell_type":"markdown","id":"d3812c72","metadata":{"papermill":{"duration":0.008624,"end_time":"2023-05-05T11:17:12.699265","exception":false,"start_time":"2023-05-05T11:17:12.690641","status":"completed"},"tags":[]},"source":["<a id='importing'></a>\n","# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>Importing Required Libraries </div></b>"]},{"cell_type":"code","execution_count":1,"id":"b3b67e19","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2023-05-05T11:17:12.719673Z","iopub.status.busy":"2023-05-05T11:17:12.718842Z","iopub.status.idle":"2023-05-05T11:17:28.46303Z","shell.execute_reply":"2023-05-05T11:17:28.46152Z"},"papermill":{"duration":15.758249,"end_time":"2023-05-05T11:17:28.466479","exception":false,"start_time":"2023-05-05T11:17:12.70823","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n","\u001b[0m"]}],"source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import plotly.express as px\n","import seaborn as sns\n","sns.set_theme()\n","sns.set(rc = {\"figure.figsize\":(10,6), \"figure.dpi\":300})\n","\n","!pip install country_converter -q\n","import country_converter as coco"]},{"cell_type":"markdown","id":"9ab12d46","metadata":{"papermill":{"duration":0.00876,"end_time":"2023-05-05T11:17:28.484655","exception":false,"start_time":"2023-05-05T11:17:28.475895","status":"completed"},"tags":[]},"source":["<a id='loading'></a>\n","# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>Loading the Dataset </div></b>"]},{"cell_type":"code","execution_count":2,"id":"f75c9133","metadata":{"execution":{"iopub.execute_input":"2023-05-05T11:17:28.505481Z","iopub.status.busy":"2023-05-05T11:17:28.504982Z","iopub.status.idle":"2023-05-05T11:17:28.566046Z","shell.execute_reply":"2023-05-05T11:17:28.564668Z"},"papermill":{"duration":0.075077,"end_time":"2023-05-05T11:17:28.568815","exception":false,"start_time":"2023-05-05T11:17:28.493738","status":"completed"},"tags":[]},"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>work_year</th>\n","      <th>experience_level</th>\n","      <th>employment_type</th>\n","      <th>job_title</th>\n","      <th>salary</th>\n","      <th>salary_currency</th>\n","      <th>salary_in_usd</th>\n","      <th>employee_residence</th>\n","      <th>remote_ratio</th>\n","      <th>company_location</th>\n","      <th>company_size</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2023</td>\n","      <td>SE</td>\n","      <td>FT</td>\n","      <td>Principal Data Scientist</td>\n","      <td>80000</td>\n","      <td>EUR</td>\n","      <td>85847</td>\n","      <td>ES</td>\n","      <td>100</td>\n","      <td>ES</td>\n","      <td>L</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2023</td>\n","      <td>MI</td>\n","      <td>CT</td>\n","      <td>ML Engineer</td>\n","      <td>30000</td>\n","      <td>USD</td>\n","      <td>30000</td>\n","      <td>US</td>\n","      <td>100</td>\n","      <td>US</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2023</td>\n","      <td>MI</td>\n","      <td>CT</td>\n","      <td>ML Engineer</td>\n","      <td>25500</td>\n","      <td>USD</td>\n","      <td>25500</td>\n","      <td>US</td>\n","      <td>100</td>\n","      <td>US</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2023</td>\n","      <td>SE</td>\n","      <td>FT</td>\n","      <td>Data Scientist</td>\n","      <td>175000</td>\n","      <td>USD</td>\n","      <td>175000</td>\n","      <td>CA</td>\n","      <td>100</td>\n","      <td>CA</td>\n","      <td>M</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2023</td>\n","      <td>SE</td>\n","      <td>FT</td>\n","      <td>Data Scientist</td>\n","      <td>120000</td>\n","      <td>USD</td>\n","      <td>120000</td>\n","      <td>CA</td>\n","      <td>100</td>\n","      <td>CA</td>\n","      <td>M</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   work_year experience_level employment_type                 job_title  \\\n","0       2023               SE              FT  Principal Data Scientist   \n","1       2023               MI              CT               ML Engineer   \n","2       2023               MI              CT               ML Engineer   \n","3       2023               SE              FT            Data Scientist   \n","4       2023               SE              FT            Data Scientist   \n","\n","   salary salary_currency  salary_in_usd employee_residence  remote_ratio  \\\n","0   80000             EUR          85847                 ES           100   \n","1   30000             USD          30000                 US           100   \n","2   25500             USD          25500                 US           100   \n","3  175000             USD         175000                 CA           100   \n","4  120000             USD         120000                 CA           100   \n","\n","  company_location company_size  \n","0               ES            L  \n","1               US            S  \n","2               US            S  \n","3               CA            M  \n","4               CA            M  "]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["df=pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')\n","df.head()"]},{"cell_type":"markdown","id":"959f4697","metadata":{"papermill":{"duration":0.00915,"end_time":"2023-05-05T11:17:28.587588","exception":false,"start_time":"2023-05-05T11:17:28.578438","status":"completed"},"tags":[]},"source":["<a id='understanding'></a>\n","# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>Understanding the Dataset </div></b>"]},{"cell_type":"code","execution_count":3,"id":"a19aee21","metadata":{"execution":{"iopub.execute_input":"2023-05-05T11:17:28.609355Z","iopub.status.busy":"2023-05-05T11:17:28.608855Z","iopub.status.idle":"2023-05-05T11:17:28.615685Z","shell.execute_reply":"2023-05-05T11:17:28.61432Z"},"papermill":{"duration":0.021916,"end_time":"2023-05-05T11:17:28.618845","exception":false,"start_time":"2023-05-05T11:17:28.596929","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of rows and columns in the dataset: (3755, 11)\n"]}],"source":["print(\"Number of rows and columns in the dataset:\",df.shape)"]},{"cell_type":"code","execution_count":4,"id":"025fbd4f","metadata":{"execution":{"iopub.execute_input":"2023-05-05T11:17:28.640234Z","iopub.status.busy":"2023-05-05T11:17:28.638999Z","iopub.status.idle":"2023-05-05T11:17:28.669319Z","shell.execute_reply":"2023-05-05T11:17:28.667303Z"},"papermill":{"duration":0.044225,"end_time":"2023-05-05T11:17:28.672379","exception":false,"start_time":"2023-05-05T11:17:28.628154","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 3755 entries, 0 to 3754\n","Data columns (total 11 columns):\n"," #   Column              Non-Null Count  Dtype \n","---  ------              --------------  ----- \n"," 0   work_year           3755 non-null   int64 \n"," 1   experience_level    3755 non-null   object\n"," 2   employment_type     3755 non-null   object\n"," 3   job_title           3755 non-null   object\n"," 4   salary              3755 non-null   int64 \n"," 5   salary_currency     3755 non-null   object\n"," 6   salary_in_usd       3755 non-null   int64 \n"," 7   employee_residence  3755 non-null   object\n"," 8   remote_ratio        3755 non-null   int64 \n"," 9   company_location    3755 non-null   object\n"," 10  company_size        3755 non-null   object\n","dtypes: int64(4), object(7)\n","memory usage: 322.8+ KB\n"]}],"source":["df.info()"]},{"cell_type":"code","execution_count":5,"id":"7bc9e346","metadata":{"execution":{"iopub.execute_input":"2023-05-05T11:17:28.694028Z","iopub.status.busy":"2023-05-05T11:17:28.693496Z","iopub.status.idle":"2023-05-05T11:17:28.704053Z","shell.execute_reply":"2023-05-05T11:17:28.70213Z"},"papermill":{"duration":0.024692,"end_time":"2023-05-05T11:17:28.706812","exception":false,"start_time":"2023-05-05T11:17:28.68212","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of missing data in the dataset: 0\n"]}],"source":["print(\"Number of missing data in the dataset:\",df.isnull().sum().sum())"]},{"cell_type":"code","execution_count":6,"id":"e46bbedd","metadata":{"execution":{"iopub.execute_input":"2023-05-05T11:17:28.728617Z","iopub.status.busy":"2023-05-05T11:17:28.72802Z","iopub.status.idle":"2023-05-05T11:17:28.741749Z","shell.execute_reply":"2023-05-05T11:17:28.740475Z"},"papermill":{"duration":0.027689,"end_time":"2023-05-05T11:17:28.74428","exception":false,"start_time":"2023-05-05T11:17:28.716591","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of unique values in columns:\n","\n"," work_year                4\n","experience_level         4\n","employment_type          4\n","job_title               93\n","salary                 815\n","salary_currency         20\n","salary_in_usd         1035\n","employee_residence      78\n","remote_ratio             3\n","company_location        72\n","company_size             3\n","dtype: int64\n"]}],"source":["print(\"Number of unique values in columns:\\n\\n\", df.nunique())"]},{"cell_type":"markdown","id":"0bc2b2aa","metadata":{"papermill":{"duration":0.009288,"end_time":"2023-05-05T11:17:28.76334","exception":false,"start_time":"2023-05-05T11:17:28.754052","status":"completed"},"tags":[]},"source":["<a id='job-titles'></a>\n","# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>Top 10 Job Titles in 2023 </div></b>"]},{"cell_type":"markdown","id":"aa52249d","metadata":{"papermill":{"duration":0.009969,"end_time":"2023-05-05T11:17:28.782941","exception":false,"start_time":"2023-05-05T11:17:28.772972","status":"completed"},"tags":[]},"source":["Let's see top 10 job titles in 2023 using the bar plot."]},{"cell_type":"code","execution_count":7,"id":"65b26aa5","metadata":{"execution":{"iopub.execute_input":"2023-05-05T11:17:28.805262Z","iopub.status.busy":"2023-05-05T11:17:28.804661Z","iopub.status.idle":"2023-05-05T11:17:29.701314Z","shell.execute_reply":"2023-05-05T11:17:29.700312Z"},"papermill":{"duration":0.913362,"end_time":"2023-05-05T11:17:29.706012","exception":false,"start_time":"2023-05-05T11:17:28.79265","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["[Text(2, 0, '499'),\n"," Text(2, 0, '370'),\n"," Text(2, 0, '307'),\n"," Text(2, 0, '158'),\n"," Text(2, 0, '55'),\n"," Text(2, 0, '52'),\n"," Text(2, 0, '46'),\n"," Text(2, 0, '40'),\n"," Text(2, 0, '32'),\n"," Text(2, 0, 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size 3000x1800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["jobs = df[df['work_year']==2023]['job_title'].value_counts().nlargest(10).reset_index()\n","fig, ax = plt.subplots()\n","ax = sns.barplot(ax = ax, data = jobs , y = jobs['index'], x = jobs.job_title)\n","ax.set(ylabel='Job Titles',xlabel='Counts', title='Top 10 Job Titles in 2023')\n","ax.bar_label(ax.containers[0], padding = 2)"]},{"cell_type":"markdown","id":"b3028175","metadata":{"papermill":{"duration":0.013335,"end_time":"2023-05-05T11:17:29.733167","exception":false,"start_time":"2023-05-05T11:17:29.719832","status":"completed"},"tags":[]},"source":["As you can see, there are the most data engineers, followed by data scientists."]},{"cell_type":"markdown","id":"5a914900","metadata":{"papermill":{"duration":0.01301,"end_time":"2023-05-05T11:17:29.761095","exception":false,"start_time":"2023-05-05T11:17:29.748085","status":"completed"},"tags":[]},"source":["<a id='experience-levels'></a>\n","# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>Experience Levels</div></b>"]},{"cell_type":"markdown","id":"2762fad2","metadata":{"papermill":{"duration":0.013215,"end_time":"2023-05-05T11:17:29.787462","exception":false,"start_time":"2023-05-05T11:17:29.774247","status":"completed"},"tags":[]},"source":["First, let's look at the unique values in the `experience_level` column."]},{"cell_type":"code","execution_count":8,"id":"c5db3be0","metadata":{"execution":{"iopub.execute_input":"2023-05-05T11:17:29.816613Z","iopub.status.busy":"2023-05-05T11:17:29.816113Z","iopub.status.idle":"2023-05-05T11:17:29.825114Z","shell.execute_reply":"2023-05-05T11:17:29.823737Z"},"papermill":{"duration":0.026498,"end_time":"2023-05-05T11:17:29.827785","exception":false,"start_time":"2023-05-05T11:17:29.801287","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["array(['SE', 'MI', 'EN', 'EX'], dtype=object)"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["df['experience_level'].unique()"]},{"cell_type":"markdown","id":"d96120c7","metadata":{"papermill":{"duration":0.013061,"end_time":"2023-05-05T11:17:29.855212","exception":false,"start_time":"2023-05-05T11:17:29.842151","status":"completed"},"tags":[]},"source":["\n","As you can see, there are 4 unique values which are SE(Senior level/expert) , MI(medium level/intermediate) , EN (Entry level) and EX(Executive level). Lets rename these values with the `rename` method."]},{"cell_type":"code","execution_count":9,"id":"39d04e17","metadata":{"execution":{"iopub.execute_input":"2023-05-05T11:17:29.884691Z","iopub.status.busy":"2023-05-05T11:17:29.883882Z","iopub.status.idle":"2023-05-05T11:17:29.894172Z","shell.execute_reply":"2023-05-05T11:17:29.893007Z"},"papermill":{"duration":0.029049,"end_time":"2023-05-05T11:17:29.897487","exception":false,"start_time":"2023-05-05T11:17:29.868438","status":"completed"},"tags":[]},"outputs":[],"source":["df['experience_level'] = df['experience_level'].replace('EN','Entry-level/Junior')\n","df['experience_level'] = df['experience_level'].replace('MI','Mid-level/Intermediate')\n","df['experience_level'] = df['experience_level'].replace('SE','Senior-level/Expert')\n","df['experience_level'] = df['experience_level'].replace('EX','Executive-level/Director')"]},{"cell_type":"markdown","id":"b4db8cc2","metadata":{"papermill":{"duration":0.013005,"end_time":"2023-05-05T11:17:29.924259","exception":false,"start_time":"2023-05-05T11:17:29.911254","status":"completed"},"tags":[]},"source":["Let's draw a bar plot of experience levels."]},{"cell_type":"code","execution_count":10,"id":"20f4c069","metadata":{"execution":{"iopub.execute_input":"2023-05-05T11:17:29.953138Z","iopub.status.busy":"2023-05-05T11:17:29.952394Z","iopub.status.idle":"2023-05-05T11:17:30.566559Z","shell.execute_reply":"2023-05-05T11:17:30.564973Z"},"papermill":{"duration":0.632686,"end_time":"2023-05-05T11:17:30.570178","exception":false,"start_time":"2023-05-05T11:17:29.937492","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["[Text(0, 0, '2516'), Text(0, 0, '805'), Text(0, 0, '320'), Text(0, 0, 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\n","text/plain":["<Figure size 3000x1800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["fig, ax = plt.subplots()\n","sns.countplot(ax = ax, data = df, x = df.experience_level)\n","ax.set(xlabel='', ylabel='Counts', title='Experience Levels')\n","ax.bar_label(ax.containers[0])"]},{"cell_type":"markdown","id":"b9fdef32","metadata":{"papermill":{"duration":0.015632,"end_time":"2023-05-05T11:17:30.602166","exception":false,"start_time":"2023-05-05T11:17:30.586534","status":"completed"},"tags":[]},"source":["As you can see, the senior-level positions have the highest count, followed by mid-level and junior positions. There are fewer director-level positions compared to other levels."]},{"cell_type":"markdown","id":"908004ce","metadata":{"papermill":{"duration":0.015357,"end_time":"2023-05-05T11:17:30.633273","exception":false,"start_time":"2023-05-05T11:17:30.617916","status":"completed"},"tags":[]},"source":["<a id='employment-types'></a>\n","# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>Employment Types</div></b>"]},{"cell_type":"markdown","id":"5c2ff50f","metadata":{"papermill":{"duration":0.015401,"end_time":"2023-05-05T11:17:30.664344","exception":false,"start_time":"2023-05-05T11:17:30.648943","status":"completed"},"tags":[]},"source":["Now, let's look at the unique values in the employment_type column."]},{"cell_type":"code","execution_count":11,"id":"4ce32f7d","metadata":{"execution":{"iopub.execute_input":"2023-05-05T11:17:30.698147Z","iopub.status.busy":"2023-05-05T11:17:30.69772Z","iopub.status.idle":"2023-05-05T11:17:30.705688Z","shell.execute_reply":"2023-05-05T11:17:30.704705Z"},"papermill":{"duration":0.027823,"end_time":"2023-05-05T11:17:30.707997","exception":false,"start_time":"2023-05-05T11:17:30.680174","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["array(['FT', 'CT', 'FL', 'PT'], dtype=object)"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["df['employment_type'].unique()"]},{"cell_type":"markdown","id":"b08e524c","metadata":{"papermill":{"duration":0.015455,"end_time":"2023-05-05T11:17:30.739198","exception":false,"start_time":"2023-05-05T11:17:30.723743","status":"completed"},"tags":[]},"source":["As you can see, there are 4 unique values which are FT(Full-Time), PT(Part-Time), CT(Contract) and FL(Freelance). Lets rename these values with the rename method."]},{"cell_type":"code","execution_count":12,"id":"6e2ea89b","metadata":{"execution":{"iopub.execute_input":"2023-05-05T11:17:30.779525Z","iopub.status.busy":"2023-05-05T11:17:30.778541Z","iopub.status.idle":"2023-05-05T11:17:30.791113Z","shell.execute_reply":"2023-05-05T11:17:30.789548Z"},"papermill":{"duration":0.036466,"end_time":"2023-05-05T11:17:30.795345","exception":false,"start_time":"2023-05-05T11:17:30.758879","status":"completed"},"tags":[]},"outputs":[],"source":["df['employment_type'] = df['employment_type'].replace('FT','Full-Time')\n","df['employment_type'] = df['employment_type'].replace('PT','Part-Time')\n","df['employment_type'] = df['employment_type'].replace('CT','Contract')\n","df['employment_type'] = df['employment_type'].replace('FL','Freelance')"]},{"cell_type":"markdown","id":"b61e0869","metadata":{"papermill":{"duration":0.020096,"end_time":"2023-05-05T11:17:30.837542","exception":false,"start_time":"2023-05-05T11:17:30.817446","status":"completed"},"tags":[]},"source":["Let's draw a bar plot of experience types."]},{"cell_type":"code","execution_count":13,"id":"29146d95","metadata":{"execution":{"iopub.execute_input":"2023-05-05T11:17:30.872794Z","iopub.status.busy":"2023-05-05T11:17:30.872331Z","iopub.status.idle":"2023-05-05T11:17:31.730162Z","shell.execute_reply":"2023-05-05T11:17:31.728747Z"},"papermill":{"duration":0.882903,"end_time":"2023-05-05T11:17:31.736898","exception":false,"start_time":"2023-05-05T11:17:30.853995","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["[Text(0, 0, '113'), Text(0, 0, '1'), Text(0, 0, '0'), Text(0, 0, 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size 3000x1800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["fig, ax = plt.subplots()\n","sns.countplot(ax = ax, data = df, x = df.employment_type, hue = 'experience_level')\n","ax.set(xlabel='', ylabel='Counts', title='Number of Employment Types')\n","ax.bar_label(ax.containers[0])\n","ax.bar_label(ax.containers[1])\n","ax.bar_label(ax.containers[2])\n","ax.bar_label(ax.containers[3])"]},{"cell_type":"markdown","id":"3a6434f5","metadata":{"papermill":{"duration":0.019538,"end_time":"2023-05-05T11:17:31.780045","exception":false,"start_time":"2023-05-05T11:17:31.760507","status":"completed"},"tags":[]},"source":["As you can see, a considerable number of people are employed here on a full-time basis. Among the full-time employees, the majority of them are senior. We observe that freelancing is less prevalent these days"]},{"cell_type":"markdown","id":"df02c16d","metadata":{"papermill":{"duration":0.020962,"end_time":"2023-05-05T11:17:31.821856","exception":false,"start_time":"2023-05-05T11:17:31.800894","status":"completed"},"tags":[]},"source":["<a id='salary-job-title'></a>\n","# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>Salaries by Job Titles</div></b>"]},{"cell_type":"code","execution_count":14,"id":"93d3a13f","metadata":{"execution":{"iopub.execute_input":"2023-05-05T11:17:31.867588Z","iopub.status.busy":"2023-05-05T11:17:31.866787Z","iopub.status.idle":"2023-05-05T11:17:33.166814Z","shell.execute_reply":"2023-05-05T11:17:33.165396Z"},"papermill":{"duration":1.331341,"end_time":"2023-05-05T11:17:33.174829","exception":false,"start_time":"2023-05-05T11:17:31.843488","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["[Text(2, 0, '375000'),\n"," Text(2, 0, '250000'),\n"," Text(2, 0, '212500'),\n"," Text(2, 0, '211254'),\n"," Text(2, 0, '198171'),\n"," Text(2, 0, '195141'),\n"," Text(2, 0, '192500'),\n"," Text(2, 0, '192420'),\n"," Text(2, 0, '191279'),\n"," Text(2, 0, '190264'),\n"," Text(2, 0, '190000'),\n"," Text(2, 0, '183858'),\n"," Text(2, 0, '175052'),\n"," Text(2, 0, '174150'),\n"," Text(2, 0, '163220')]"]},"execution_count":14,"metadata":{},"output_type":"execute_result"},{"data":{"text/plain":["<Figure size 7500x2700 with 0 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size 3000x1800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["job_title_salary = df['salary_in_usd'].groupby(df['job_title']).mean().round(0).nlargest(15).sort_values(ascending = False).reset_index()\n","plt.figure(figsize=(25,9))\n","fig, ax = plt.subplots()\n","ax = sns.barplot(ax = ax, data = job_title_salary , y = job_title_salary.job_title, x = job_title_salary.salary_in_usd)\n","ax.set(ylabel='Job titles',xlabel='Salary in usd', title='Top 15 Average Salaries by Job Titles')\n","ax.bar_label(ax.containers[0], padding = 2)"]},{"cell_type":"markdown","id":"7e27df34","metadata":{"papermill":{"duration":0.02714,"end_time":"2023-05-05T11:17:33.23228","exception":false,"start_time":"2023-05-05T11:17:33.20514","status":"completed"},"tags":[]},"source":["As we expected, the average salaries of those generally employed at the executive level are higher. Due to the trend of cloud computing, cloud data architect is the second highest paid profession."]},{"cell_type":"markdown","id":"d4992b29","metadata":{"papermill":{"duration":0.02754,"end_time":"2023-05-05T11:17:33.287587","exception":false,"start_time":"2023-05-05T11:17:33.260047","status":"completed"},"tags":[]},"source":["<a id='average'></a>\n","# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>Salaries by Employment Types</div></b>"]},{"cell_type":"code","execution_count":15,"id":"9eedd864","metadata":{"execution":{"iopub.execute_input":"2023-05-05T11:17:33.34514Z","iopub.status.busy":"2023-05-05T11:17:33.344207Z","iopub.status.idle":"2023-05-05T11:17:34.049906Z","shell.execute_reply":"2023-05-05T11:17:34.048656Z"},"papermill":{"duration":0.739158,"end_time":"2023-05-05T11:17:34.054302","exception":false,"start_time":"2023-05-05T11:17:33.315144","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["[Text(0, 2, '149367'),\n"," Text(0, 2, '27750'),\n"," Text(0, 2, '50000'),\n"," Text(0, 2, 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\n","text/plain":["<Figure size 3000x1800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["avg_salaries = df.groupby('employment_type')['salary_in_usd'].mean().round(0).sort_values(ascending = False).reset_index()\n","fig, ax = plt.subplots()\n","sns.barplot(ax =ax,data = df , x = 'employment_type', y = 'salary_in_usd',errorbar = None, hue = 'work_year')\n","ax.set(xlabel='', ylabel='Dollars', title='Average Salaries in Dollars Per Year')\n","ax.bar_label(ax.containers[3], padding = 2)"]},{"cell_type":"markdown","id":"8b11a3e2","metadata":{"papermill":{"duration":0.031035,"end_time":"2023-05-05T11:17:34.117358","exception":false,"start_time":"2023-05-05T11:17:34.086323","status":"completed"},"tags":[]},"source":["As you can see, average salaries for full-time have increased over the years. It shows that companies care about data science. The second-highest salaries on the plot belong to freelancers, which is a clear indication of the growing trend in freelance work."]},{"cell_type":"markdown","id":"5fc4bc6a","metadata":{"papermill":{"duration":0.031166,"end_time":"2023-05-05T11:17:34.180225","exception":false,"start_time":"2023-05-05T11:17:34.149059","status":"completed"},"tags":[]},"source":["<a id='salaries'></a>\n","# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>Salaries by Work Years</div></b>"]},{"cell_type":"code","execution_count":16,"id":"f009dc46","metadata":{"execution":{"iopub.execute_input":"2023-05-05T11:17:34.247828Z","iopub.status.busy":"2023-05-05T11:17:34.246949Z","iopub.status.idle":"2023-05-05T11:17:34.878998Z","shell.execute_reply":"2023-05-05T11:17:34.877359Z"},"papermill":{"duration":0.67,"end_time":"2023-05-05T11:17:34.883524","exception":false,"start_time":"2023-05-05T11:17:34.213524","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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e21SwPv2k8VE5bkhyAwEAAAAA0CmEMwEAAACgHTz36sp48q8rCtaPPXx4nH3cmAQnAgAAAACgswhnAgAAAMA+Wvr+5vjFY0sK1scM7xd/e87hkUqlEpwKAAAAAIDOIpwJAAAAAPtg49bauGnOgmhozOat96soiRumV0ZZSTrhyQAAAAAA6CzCmQAAAADQRvUNmbhlzoLYvK0ub70olYrrpk6JoQMqEp4MAAAAAIDOJJwJAAAAAG2QzWbjF48tiaUrtxQ85wunHRqHHTQowakAAAAAAOgKhDMBAAAAoA2e+uv78fz8VQXrxx8xMk456oAEJwIAAAAAoKsQzgQAAACAVnr9nY1x3xNvFqyPG9U/vnjGxEilUglOBQAAAABAVyGcCQAAAACtsG5zddzy4MLIZLN56wP6lcb1F1RGSbFHbwAAAAAAvZUnxAAAAADQQrX1jXHTrAWxrbo+b704nYobLqiMQfuVJTwZAAAAAABdSXFnD0DXtWnTpnjllVfi3XffjaqqqigtLY3hw4fHpEmTYvz48R3Ss6qqKl555ZVYvnx5bNmyJYqLi2Po0KExceLEOPzwwztkK7C6urp45ZVXYtmyZbF58+ZIpVIxaNCgOPTQQ6OysjLS6XS792xoaIiFCxfGm2++GRs3boxsNhsDBgyIcePGxZFHHhmlpaXt3jObzcbixYtjyZIlsW7dumhoaIj+/fvH2LFj48gjj4y+ffu2e08AAADoSbLZbNz58OJ494NtBc/54hkTY/wBAxKcCgAAAACArkg4swvbuHFjLFy4MBYsWBDz58+PhQsXxtq1a3POueGGG2LGjBnt2vell16K2267Lf74xz9GY2Nj3nMOOuiguPzyy+MLX/hClJSU7HPPJUuWxK233hpPPPFE1NfnX3li+PDhcfHFF8eXv/zl6NOnzz73XLFiRdx2220xd+7c2L59e95zBgwYENOmTYurr746Bg8evM89N2zYELfffnvMnj07Nm/enPecPn36xLnnnhvXXHNNjB49ep97VlVVxZ133hn3339/sz8/HykpKYnTTjstrr322pg4ceI+9wQAAICe6JF578YLiz8oWD/1qNFxwsdGJTgRAAAAAABdVSqbzWY7ewh2ufPOO2PBggWxYMGCePfdd/d6fnuGM+vr6+O73/1u3HvvvS2+ZsKECTFz5swYO3Zsm3pms9m49dZb4+abb46GhoYWXTNq1Kj40Y9+FEcccUSbekZE/OpXv4rvfOc7UVNT06LzBw4cGN///vfjs5/9bJt7PvPMM3HjjTfGpk2bWnR+RUVFfPOb34wLL7ywzT1fffXV+Pu///tYtWpVi84vLi6OG264Ia699to29+xK1q/fFpmMv+K6ilQqFUOH9ss5tm7dtvCPIQAAeiL3vz3P/KXr40e/ejUK/QQPGzMw/n8XHxnF6aJE5wIA6CrcAwMA0Ju4/+0eiopSMWRIv72f2FH9O60zef3nf/5nzJ07t0XBzPbU0NAQX/va11oVzIyIeOONN+KSSy6JpUuXtqnvv//7v8ePfvSjFgczIyJWrlwZX/ziF+PFF19sU8877rgjvvnNb7Y4mBmxY4v3a665Jh5++OE29Zw7d25ce+21LQ5mRkRUV1fH//P//D/xk5/8pE09X3jhhbjiiitaHMyM2PHn4Ic//GH8+7//e5t6AgAAQE+0esP2+J/fLioYzBzSvzyumTpFMBMAAAAAgJ1sa05ERPzgBz+Ip556KufYoEGD4vLLL4/TTjstRo4cGZs2bYqFCxfGXXfdFfPnz9953saNG+Oaa66JOXPmRL9+LU8a33vvvfHLX/4y51ifPn3ikksuibPPPjsOPPDAqKqqijfeeCPuueee+MMf/rDzvJqamrjhhhviwQcfjJEjR7a459NPPx0/+MEPco6VlJTE1KlT44ILLoixY8dGfX19LFu2LO6///547LHHdibaM5lM3HjjjXHQQQfF5MmTW9xz0aJFceONN0Ymk9l5rKioKM4444y45JJLYty4cZFOp2P58uUxe/bs+O1vf5uztft///d/x/jx4+Pkk09ucc9Vq1bFjBkzmgVQjz/++Lj88stjwoQJUVFREStWrIi5c+fG//7v/+Zs7X7vvffG+PHj47LLLmtxTwAAAOiJqmsbYuas+VFdm/8XS0uLi2LG9Mro36c04ckAAAAAAOjKbGvexUycODHndZ8+fWLy5MlRWVkZlZWV8Y//+I859fbY1vz111+PqVOn5iyre+ihh8ZPf/rTGDFiRLPzs9lsfPe7342777475/iXv/zluPHGG1vUc926dXHaaadFdXX1zmMjRoyIn/70p3HooYfmveZnP/tZfP/738+Z88wzz4wf//jHLepZW1sbp59+eqxZs2bnsf322y9uueWWOPbYY/NeM3fu3LjxxhtzwpJTpkyJWbNmtahnRMS0adNi0aJFO1+XlJTE97///TjnnHPynj9v3ry4/vrrY+vWrTuPjRgxIh5//PEoKytrUc8ZM2bEY489tvN1KpWKf/7nf44rr7wy7/lLliyJq666Kud7U1FREU888UQMHTq0RT27Ituady2W9AYAoDdx/9szZLLZuGnWgnjlrXUFz7nm/Mlx7OHNn58AAPQ27oEBAOhN3P92D7Y1J0dlZWVceuml8d3vfjceeuih+Mtf/hL33HNP3HjjjQXDfPvqhz/8Yc5fDH379o2f/OQneYOZETv+cvnXf/3XOPXUU3OO//KXv8wJ9+3JbbfdlhPMTKfTceuttxYMZkbsCH9+8YtfzDn22GOP5QQf9yTffN/73vcKBjMjIs4999z4+te/nnNs4cKF8fjjj7eoZ775vvGNb+zxZ3ncccfFf/7nf+YcW7NmTdx3330t6rlw4cKcYGZExJe+9KWCwcyIHaHgm2++OdLp9M5j1dXVcdttt7WoJwAAAPREv3nu7T0GM8/+5BjBTAAAAAAA8hLO7GJ+/etfx7e//e2YNm1aHHrooVFU1LE/osWLF8fTTz+dc+xrX/ta7L///nu99t/+7d+ivLx85+uampr46U9/utfr1q1bF/fff3/Oscsuu6xFW4X/4z/+YwwfPnzn62w2G7fccster6uvr4/bb78959ipp57aLGCazxVXXBGTJk3KOXbzzTfv9bp8502ePDkuv/zyvV532mmnNZvt9ttvz1nBs5Cm34/hw4fHP/zDP+z1usrKymbbmD/wwAOxfv36vV4LAAAAPc1flnwQv/vj8oL1ynFDYvqJ45MbCAAAAACAbkU4s5d75JFHcl736dMnLrzwwhZdO3z48DjjjDNyjv3+97/f63VPPvlkTsgwlUo1WxGzkD59+sT06dNzjj377LNRVVW1x+vmzZsXGzZsyDl2xRVXtKhnUVFRXHrppTnHFi9eHO+8884er1u+fHm8/vrrOccuu+yyFgdum35P1q9fHy+++OIer9m2bVs899xzOccuuuiiqKioaFHPyy+/PFKp1M7XdXV18dRTT7XoWgAAAOgpVqzdFj95aHHB+ohBFfHVz0+KoqJUwXMAAAAAAOjdhDN7uSeffDLn9emnnx79+vVr8fXTpk3Leb169epYuHBhq3oeffTRMWbMmDb3rKuri+eff75VPUeNGhXHHXdci3uee+65UVZWtsf3bOqJJ57IeV1WVhZnn312i3t+8pOfjJEjR7aq53PPPRd1dXU5xy644IIW9zzooIPiqKOOalVPAAAA6Em2VdfHzFnzo7a+MW+9vDQdM6YfEX3KSxKeDAAAAACA7kQ4sxdbuXJlvPXWWznHjj322Fa9x8c//vEoKcn9jxHPPPNMwfPr6uriT3/60z71HDNmTLNt1/fUM2LH6pq7O+aYY3JWiNybPn36xJQpU/apZ2VlZfTp06fFPVOpVBxzzDH71HPkyJFx4IEHtrhnRPOfxx//+MdmgU8AAADoiRozmfif3yyMtZtqCp5z1ecmxaihfROcCgAAAACA7kg4sxdbsmRJs2NNV03cm/Ly8pg0aVLOsTfeeKPg+cuXL28W9Gttz3zX7KlnVVVVrFixItGeEc2/v+3R8/3339/jFu4d0bO2tnavW7gDAABAT/DrZ5bGouUbC9annnBwfPzQYQlOBAAAAABAdyWc2YstW7Ys53VxcXGMHTu21e8zfvz4nNdvv/12i3vmu7679Ny4cWNs3Jj/P9hs2LAhNm3a1O49s9nsHj9r09q4ceP2uWdE/u8hAAAA9CR/WrQ6fv/CewXrR08YFud9emxyAwEAAAAA0K0JZ/ZiS5cuzXk9YsSIKCpq/R+JpluML1++PBobG1vUM51Ox4gRI/a5Z1VVVaxZs6ZFPSN2bPe9rz0jCocW8x1vS8981xTquWrVqti+fXvOsVGjRrW6Z74/B8KZAAAA9GTLV2+Jux55vWD9gKF948vnHh5FqVSCUwEAAAAA0J0JZ/ZiH3zwQc7rfOHDlmgaAKyrq2u2amShnsOGDYt0Or3PPSOiYDizac9UKtWmQOi+9IxoeyA01eQ//HR0z+Li4hg2LHeLtkI9AQAAoLvbXFUXM2ctiPqGTN563/LimDG9MirKihOeDAAAAACA7sxT5V6s6SqL/fr1a9P79O3bN+97DxkypMN65ruuqqoq77lNe5aXl0dJSUm79Gz63nuapS2ftaSkJMrKyqKmpmavPfMdz/ezaYl+/frlBDIL9ezqUqlUWNSk68j3s9hxzA8JAICex/1v99DQmIlb5iyIjVtr89ZTqYhrp06JEYPb9u/XAAC9iXtgAAB6E/e/3UPTRfGSJpzZi+ULLbZFvutaGpQsKytrU89817U0tNgZPfelb3l5eYvCmfm+5+31M+2u4czB/uNZlzdkSNsC2gAA0B25/+16bvn1q/Hmis0F63973uT47DEHJTgRAEDP4h4YAIDexP0vTdnWvBerrq7OeV1aWtqm98kXACwU5mvac18Ci92p5770bXpdV+oJAAAA3dWjf1oej/xpecH6SUePjqmfHZ/cQAAAAAAA9CjCmb1YNpvNed3WZVybvs+e3qs798ynpT33RUvnb833pL16AgAAQHf02tvr43/mzC9YP2T0gLjhoiP9+zAAAAAAAG0mnNmL9enTJ+d1bW1tm94n33UVFRUt6rn7dt1dvWe+61raM6Lt39+6uro292zrZ21pTwAAAOhu1m2qju/e/WI0NOb/xcqB/criX688LspK0glPBgAAAABAT1Lc2QPQeToytNi3b98W9WzPQGi+cGJX6fnR9YW+L3vS9PtbqGe+926vn2mhnl3dhg1Vkcm03yqm7JtUKmLIkH45x9av3xbtuNAsAAB0Ge5/u6a6+sb47j1/iU1b8z8bSBel4tqpkyPV2Bjr1m1LeDoAgO7NPTAAAL2J+9/uoagoFYMHtz6v1V6EM3uxpoG7bdva9h8dqqqq9vre7d0z33UtDYTW1NREQ0NDFBe37o9/vp6tCUpu27YtBg8e3Kqe9fX1zUKhrQmE5vvZtETTz9pdw5nZbLZdt5hnXzXfDjCbDT8jAAB6KPe/XU02m427Hnk9lq/eWvCcS0+fEIeOHujnBADQJu6BAQDoTdz/dged/eOwrXkvNnz48JzXq1evbtP7rFq1Kud1aWlpDBw4sEU9165dG42NjfvcM997FzqezWbb9Fn3pWeh6/dm9erVzf7S7uieDQ0NsW7duhb1BAAAgO7i8Rffiz8tKvw84LNHjoqTP35AghMBAAAAANCTCWf2YuPGjct5vWbNmshkMq1+n6YBwLFjx0Y6nW5Rz8bGxlizZs0+9+zbt2/sv//+LeqZ7/q29IyIGD9+fJfpOXLkyGYrXK5cubLVPdesWdMsMFuoJwAAAHQHi5ZviAeefqtg/ZDRA+Ky0yckOBEAAAAAAD2dcGYv1jRw19DQEMuXL2/1+yxbtizn9cEHH9zinhERS5cu7ZY9Bw0aFIMGDcp77uDBg5utHtoePSP2/Fmb1vJd35ae+cKmAAAA0B18sKk6bntwYcHtawbtVxbXT50SxWmPyQAAAAAAaD+eOvdiEydObHbs5ZdfbtV71NbWxqJFi3KOTZhQeKWJsWPHRmlp6T71jIj461//2uKeffv2jdGjR+9zz7/85S8t7hnR/PvbHj1Hjx4dffv2TbRnWVlZHHTQQa1+HwAAAOhsNXUNcdOs+VFV05C3XpwuihumVcaAfmUJTwYAAAAAQE8nnNmLjRo1Kg455JCcYy+88EKr3uOvf/1r1NfX5xw76aSTCp5fWloan/rUp/ap53vvvddsu+899YyIOPHEE3Nev/jii63qWV1dHQsXLtynngsWLIjq6uoW98xms/HSSy/tU8+VK1fGe++91+KeEc2/N5/+9KebBWoBAACgq8tms/GzuYtjxdqqgudcefbEOHhk/wSnAgAAAACgtxDO7OVOPfXUnNePPfZYVFUV/o8WTT344IM5r/fff/+YMmVKq3q+9NJLrQoQzpkzJ+d1aWlpHH/88a3q+f7778e8efNa3PPhhx+O2traPb7n3nrW1NTEI4880uKe8+bNi5UrV7aq5wknnNAsSNn0Z7Qn7733XrOVM/fWEwAAALqih/70Try0ZG3B+hnHHBifnjIywYkAAAAAAOhNhDN7ubPOOivn9fbt22PWrFktunbt2rXx6KOP5hw788wz93rdqaeeGiUlJTtfZ7PZuOeee1rUs7q6On7961/nHDvxxBP3uNV3RMRxxx0XgwcPzjn2i1/8okU9M5lM3HvvvTnHDj/88L1u9X3wwQfHYYcdlnPsnnvuiWw226K+Tb8ngwcPjmOOOWaP1/Tr1y9OOOGEnGO/+tWvoqampsU9d5+vtLQ0TjnllBZdCwAAAF3FK2+tiwefXVawfvhBg+Kik8cnOBEAAAAAAL2NcGYvN2nSpDj55JNzjv34xz+ONWvW7PXaf//3f88J/ZWVlcXf/d3f7fW6oUOHxsUXX5xz7J577onXXnttr9f+8Ic/zJktlUrFddddt9frSkpK4qqrrso59vjjj8fTTz+912vvueeeWLRoUc6x66+/fq/XRUSz2RYtWtQs6JnPU089FY8//njOsa9+9as5odaW9lyzZk386Ec/2ut1ixYtahYIvfjii2PIkCF7vRYAAAC6ilXrq+L23y6KQr8aOXRAeVw7dUqkizwWAwAAAACg43gKTfzDP/xDpFKpna+3bt0aX/nKVwoGNLPZbHzve9+Lxx57LOf4ZZddFiNGjGhRz2uvvTYqKip2vm5oaIjrrrsu3nrrrYLX3HXXXXH33XfnHDvjjDNi8uTJLeqZb75vfOMb8dJLLxW85uGHH47/+q//yjk2ZcqUOP3001vU88wzz2w23/e+971mK47u7oUXXogbb7wx59iIESPiC1/4Qot6TpkyJc4444ycY3feeWez793u3nzzzbjuuuuioaFh57GKioq45pprWtQTAAAAuoLtNfXx41kLoqauMW+9tKQoZkw/IvpV7P2XHwEAAAAAYF+ksi3dY5lELFy4cI/BvTvuuCPn9VFHHRVHH3103nP79+8fV199dYv6fu9734uf/exnOccGDx4cX/ziF+OUU06JkSNHxpYtW2LBggVx1113xauvvppz7pgxY2LOnDnRr1+/FvWL2LEi5X/8x3/kHOvbt29ccsklcc4558To0aNj+/btsWTJkrjnnnvi+eefzzl34MCBMWfOnBg1alSLez711FNx7bXX5hwrKSmJadOmxdSpU2Ps2LFRX18fy5YtiwceeCAeffTRZtt833///S0OhEbs+JlecsklUV9fv/NYUVFRnHXWWXHxxRfHuHHjIp1Ox/Lly2POnDnx4IMP5pybSqXi1ltvbbbC6Z6sXLkypk6dGps3b845fsIJJ8Tll18eEyZMiIqKilixYkU8/PDDcf/998f27dtzzv3Wt74Vl112WYt7dkXr12+LTMZfcV1FKpWKoUNz/45Yt25b+McQAAA9kfvf5GUy2fjxrPkxf+n6gudcN3VKfOKw4QlOBQDQe7gHBgCgN3H/2z0UFaViyJCW59nam3BmFzN79uz4l3/5l3Z5rwMOOCCeeuqpFp3b0NAQN9xwQ4u2+W5q0KBBcc8998QhhxzS6mv/7d/+Le67775WX1deXh533HFHHHvssa2+9vbbb48f/OAHrb6uqKgo/vu//zvOPffcVl/70EMPxT/90z9FJpNp9bVf//rXm23J3hLz5s2Lq6++Omfr+Za69NJL49vf/narr+tqhDO7FjcmAAD0Ju5/kzfr/5bG3D+9U7B+3qcPimknjk9wIgCA3sU9MAAAvYn73+6hs8OZtjUnIiKKi4tj5syZcemll7bqugkTJsT999/fpmBmRMS3v/3t+NrXvhbpdLrF14wcOTJ+/vOftymYGRFx9dVXx3e+850oLy9v8TUDBw6MW2+9tU3BzIiI8847L2655ZYYMGBAi68pLy+P73znO20KZkZEHHfccXH33XfHyJEjW3xNcXFx/P3f/31861vfalNPAAAA6AwvLF6zx2Dmx8YPiaknjEtwIgAAAAAAejvhTHYqKSmJb3/723HvvffG8ccfH0VFhf94HHjggfGv//qvMXv27Bg7dmybe6ZSqbj++utjzpw5cdZZZ0VJSUnBc4cNGxYzZsyIuXPnxsc+9rE294yIuOiii2Lu3Llx4YUXRkVFRcHzBgwYEFdeeWU88sgjcdJJJ+1Tz5NPPjkeffTRuPLKK6N///4Fz6uoqIgLL7ww5s6dGxdddNE+9TzyyCNj7ty5MWPGjBg2bFjB80pKSuKss86K2bNnx3XXXRepVGqf+gIAAEBS3l2zNX728OKC9f0H94mrPjc5ivy7LgAAAAAACbKtOQVt3LgxXnnllXj33XejqqoqSkpKYvjw4TF58uQ2r5S5N9u2bYuXX3453nnnndi6dWuk0+kYMmRIHHbYYTFp0qQOCQ3W1tbGK6+8EkuXLo0tW7ZEKpWKQYMGxaGHHhqVlZVRXFzc7j0bGhpiwYIF8eabb8bGjRsjm81G//79Y/z48XHkkUdGWVlZu/fMZrPx2muvxeuvvx7r16+PxsbG2G+//WLs2LFx5JFHRr9+nbeEb0exrXnXYklvAAB6E/e/ydi6vS7+/a6XYv2Wmrz1irJ0fPOKT8TIIX0TngwAoPdxDwwAQG/i/rd76OxtzYUzgR5LOLNrcWMCAEBv4v634zU0ZuL/feCVeP3dTXnrqYj4+4uOiCPGD010LgCA3so9MAAAvYn73+6hs8OZtjUHAAAAoNv536feKhjMjIiY9tlxgpkAAAAAAHQa4UwAAAAAupXn56+KJ/6yomD9mMOGxzmfPCjBiQAAAAAAIJdwJgAAAADdxrKVW+Lnv19SsH7g8H7x5XMOj1QqleBUAAAAAACQSzgTAAAAgG5h07bauGn2/GhozOSt96soiRnTKqOsNJ3wZAAAAAAAkEs4EwAAAIAur74hEzfPWRCbttXlrRelUnHt1CkxdGBFwpMBAAAAAEBzwpkAAAAAdGnZbDbufXxJLH1/S8FzLj71kDj8oEEJTgUAAAAAAIUJZwIAAADQpT398vvx7KurCtY/U7l/nHb06AQnAgAAAACAPRPOBAAAAKDLWvLuxrjviTcL1g8e2T+uOHNipFKpBKcCAAAAAIA9E84EAAAAoEtav7kmbnlwYTRmsnnrA/qWxg3TKqOkOJ3wZAAAAAAAsGfCmQAAAAB0ObX1jTFz9vzYur0+bz1dlIrrL6iMQfuVJTwZAAAAAADsnXAmAAAAAF1KNpuNux55Pd5ds63gOV88c2IcMnpAglMBAAAAAEDLCWcCAAAA0KU8+sK7Me+1NQXrJx91QJz4sVEJTgQAAAAAAK0jnAkAAABAl7Fw2fr49TNLC9YnHDgwvnDqoQlOBAAAAAAArSecCQAAAECXsGbj9rjtN4sim81fH9y/LK6bOiWK0x5pAQAAAADQtXmSDQAAAECnq65tiJmzFsT22oa89dLiopgx7Yjo37c04ckAAAAAAKD1hDMBAAAA6FSZbDZ+8tBrsXJdVcFzrjznsDh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size 3000x1800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["year_based_salary=df['salary_in_usd'].groupby(df['work_year']).mean()\n","plt.title(\"Average Salaries based on Work Year\")\n","plt.xlabel('Work Year')\n","plt.ylabel('Salary')\n","sns.lineplot(x=['2020', '2021', '2022','2023'],y=year_based_salary)\n","plt.show()"]},{"cell_type":"markdown","id":"5cc35f1c","metadata":{"papermill":{"duration":0.034842,"end_time":"2023-05-05T11:17:34.956388","exception":false,"start_time":"2023-05-05T11:17:34.921546","status":"completed"},"tags":[]},"source":["As you can see, the average salary for data-driven jobs is increasing every year, with a particularly significant jump observed between 2021 and 2022. This trend underscores the growing demand for skilled professionals in this field."]},{"cell_type":"markdown","id":"2ec70c29","metadata":{"papermill":{"duration":0.035137,"end_time":"2023-05-05T11:17:35.027604","exception":false,"start_time":"2023-05-05T11:17:34.992467","status":"completed"},"tags":[]},"source":["<a id='remote'></a>\n","# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>Remote Jobs Locations</div></b>"]},{"cell_type":"code","execution_count":17,"id":"f76bde4f","metadata":{"execution":{"iopub.execute_input":"2023-05-05T11:17:35.098923Z","iopub.status.busy":"2023-05-05T11:17:35.098489Z","iopub.status.idle":"2023-05-05T11:17:35.314186Z","shell.execute_reply":"2023-05-05T11:17:35.312987Z"},"papermill":{"duration":0.254236,"end_time":"2023-05-05T11:17:35.316735","exception":false,"start_time":"2023-05-05T11:17:35.062499","status":"completed"},"tags":[]},"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>company_location</th>\n","      <th>remote_ratio</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>ARE</td>\n","      <td>66.666667</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>ALB</td>\n","      <td>50.000000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>ARM</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>ARG</td>\n","      <td>100.000000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>ASM</td>\n","      <td>66.666667</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  company_location  remote_ratio\n","0              ARE     66.666667\n","1              ALB     50.000000\n","2              ARM      0.000000\n","3              ARG    100.000000\n","4              ASM     66.666667"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["rr = df.groupby('company_location')['remote_ratio'].mean().reset_index()\n","rr['company_location'] =  coco.convert(names = rr['company_location'], to = \"ISO3\")\n","rr.head()"]},{"cell_type":"code","execution_count":18,"id":"88f690de","metadata":{"execution":{"iopub.execute_input":"2023-05-05T11:17:35.391226Z","iopub.status.busy":"2023-05-05T11:17:35.390505Z","iopub.status.idle":"2023-05-05T11:17:36.910492Z","shell.execute_reply":"2023-05-05T11:17:36.909047Z"},"papermill":{"duration":1.560117,"end_time":"2023-05-05T11:17:36.913888","exception":false,"start_time":"2023-05-05T11:17:35.353771","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["        <script type=\"text/javascript\">\n","        window.PlotlyConfig = {MathJaxConfig: 'local'};\n","        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n","        if (typeof require !== 'undefined') {\n","        require.undef(\"plotly\");\n","        requirejs.config({\n","            paths: {\n","                'plotly': ['https://cdn.plot.ly/plotly-2.18.0.min']\n","            }\n","     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Locations\")\n","fig.show()"]},{"cell_type":"markdown","id":"c751a9bd","metadata":{"papermill":{"duration":0.03522,"end_time":"2023-05-05T11:17:36.986323","exception":false,"start_time":"2023-05-05T11:17:36.951103","status":"completed"},"tags":[]},"source":["<a id='conclusion'></a>\n","# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>Conclusion</div></b>\n","\n","Data Science is one of the most in-demand and rapidly growing fields in the world. In this notebook, we covered the data science field salaries in detail. When we look the graphs, we can see that the popularity of data science continues. Thanks for taking the time! If you enjoyed it, please consider upvoting.\n","\n","\n","🔗 Let's connect [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [Twitter](http://twitter.com/tirendazacademy) | [Instagram](https://www.instagram.com/tirendazacademy) | [GitHub](http://github.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) | [Kaggle](https://www.kaggle.com/tirendazacademy) 😎"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.12"},"papermill":{"default_parameters":{},"duration":36.952562,"end_time":"2023-05-05T11:17:37.945716","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2023-05-05T11:17:00.993154","version":"2.4.0"}},"nbformat":4,"nbformat_minor":5}