data-science-jobs-in-india.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":3854282,"sourceType":"datasetVersion","datasetId":2292688}],"dockerImageVersionId":30684,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-04-21T20:28:13.141371Z","iopub.execute_input":"2024-04-21T20:28:13.141816Z","iopub.status.idle":"2024-04-21T20:28:14.377489Z","shell.execute_reply.started":"2024-04-21T20:28:13.141781Z","shell.execute_reply":"2024-04-21T20:28:14.376604Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/data-science-jobs-in-india/Data_Science_Jobs_in_India.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"df = pd.read_csv(\"/kaggle/input/data-science-jobs-in-india/Data_Science_Jobs_in_India.csv\")\ndf","metadata":{"execution":{"iopub.status.busy":"2024-04-21T20:28:14.380733Z","iopub.execute_input":"2024-04-21T20:28:14.381346Z","iopub.status.idle":"2024-04-21T20:28:14.426222Z","shell.execute_reply.started":"2024-04-21T20:28:14.381314Z","shell.execute_reply":"2024-04-21T20:28:14.424977Z"},"trusted":true},"execution_count":2,"outputs":[{"execution_count":2,"output_type":"execute_result","data":{"text/plain":"      Unnamed: 0            company_name       job_title  min_experience  \\\n0              0                     TCS  Data Scientist               2   \n1              1               Accenture  Data Scientist               2   \n2              2                     IBM  Data Scientist               2   \n3              3               Cognizant  Data Scientist               2   \n4              4               Capgemini  Data Scientist               2   \n...          ...                     ...             ...             ...   \n1597        1597  Collabera Technologies  Data Architect               8   \n1598        1598                 Coforge  Data Architect               7   \n1599        1599                 Hitachi  Data Architect              21   \n1600        1600                GE Power  Data Architect               7   \n1601        1601               T-Systems  Data Architect              16   \n\n     avg_salary min_salary max_salary  num_of_salaries  \n0          7.8L       4.5L      16.0L              841  \n1         12.8L       5.8L      23.0L              501  \n2         13.4L       5.3L      25.0L              394  \n3          9.8L       5.0L      18.0L              318  \n4          8.6L       4.8L      14.6L              300  \n...         ...        ...        ...              ...  \n1597      17.0L      12.0L      23.1L                4  \n1598      27.1L      18.5L      39.0L                4  \n1599      40.0L      25.0L      50.0L                3  \n1600      16.7L      14.9L      19.0L                3  \n1601      33.3L      33.0L      34.0L                3  \n\n[1602 rows x 8 columns]","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>Unnamed: 0</th>\n      <th>company_name</th>\n      <th>job_title</th>\n      <th>min_experience</th>\n      <th>avg_salary</th>\n      <th>min_salary</th>\n      <th>max_salary</th>\n      <th>num_of_salaries</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>TCS</td>\n      <td>Data Scientist</td>\n      <td>2</td>\n      <td>7.8L</td>\n      <td>4.5L</td>\n      <td>16.0L</td>\n      <td>841</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>Accenture</td>\n      <td>Data Scientist</td>\n      <td>2</td>\n      <td>12.8L</td>\n      <td>5.8L</td>\n      <td>23.0L</td>\n      <td>501</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>IBM</td>\n      <td>Data Scientist</td>\n      <td>2</td>\n      <td>13.4L</td>\n      <td>5.3L</td>\n      <td>25.0L</td>\n      <td>394</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>Cognizant</td>\n      <td>Data Scientist</td>\n      <td>2</td>\n      <td>9.8L</td>\n      <td>5.0L</td>\n      <td>18.0L</td>\n      <td>318</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>Capgemini</td>\n      <td>Data Scientist</td>\n      <td>2</td>\n      <td>8.6L</td>\n      <td>4.8L</td>\n      <td>14.6L</td>\n      <td>300</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1597</th>\n      <td>1597</td>\n      <td>Collabera Technologies</td>\n      <td>Data Architect</td>\n      <td>8</td>\n      <td>17.0L</td>\n      <td>12.0L</td>\n      <td>23.1L</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1598</th>\n      <td>1598</td>\n      <td>Coforge</td>\n      <td>Data Architect</td>\n      <td>7</td>\n      <td>27.1L</td>\n      <td>18.5L</td>\n      <td>39.0L</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1599</th>\n      <td>1599</td>\n      <td>Hitachi</td>\n      <td>Data Architect</td>\n      <td>21</td>\n      <td>40.0L</td>\n      <td>25.0L</td>\n      <td>50.0L</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1600</th>\n      <td>1600</td>\n      <td>GE Power</td>\n      <td>Data Architect</td>\n      <td>7</td>\n      <td>16.7L</td>\n      <td>14.9L</td>\n      <td>19.0L</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1601</th>\n      <td>1601</td>\n      <td>T-Systems</td>\n      <td>Data Architect</td>\n      <td>16</td>\n      <td>33.3L</td>\n      <td>33.0L</td>\n      <td>34.0L</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n<p>1602 rows × 8 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"\n\n### Experience vs. Salary:\n\nIs there a relationship between the minimum experience required and the average salary offered?\n\nHow does the minimum experience required vary across different job titles?\n\n### Salary Range Analysis:\n\nHow does the salary range (min_salary to max_salary) vary across different companies?\n\nAre there any outliers in terms of salary offered by specific companies?\n\n### Job Title Analysis:\n\nWhich job titles are the most common in the dataset?\n\nIs there a difference in salary offered between different job titles?\n\nHow does the distribution of job titles vary across companies?\n\n### Company Analysis:\n\nWhich companies offer the highest average salary for data science roles?\n\nHow does the number of reported salaries vary across different companies?\n\n### Salary Distribution:\n\nWhat is the distribution of salaries across different experience levels?\n\nHow does the salary distribution vary across different job titles?\n\n### Trend Analysis:\n\nIs there any trend in the average salary offered over the years (if your dataset includes a date)?\n\nAre there any seasonal trends in hiring or salary offered?\n\n### Correlation Analysis:\n\nIs there any correlation between the number of reported salaries and the average salary offered?\n\nIs there any correlation between the minimum experience required and the average salary offered?\n\n### Visualization:\n\nCreate histograms or density plots to visualize the distribution of salaries, experience, and number of salaries.\n\nUse scatter plots to visualize the relationship between variables like experience and salary.\n\n### Geographical Analysis (if applicable):\n\nAre there any regional differences in the average salary offered or the number of data science jobs?\nWhich cities have the highest concentration of data science jobs?","metadata":{}},{"cell_type":"markdown","source":"### (A) Descriptive Statistics:","metadata":{}},{"cell_type":"markdown","source":"What is the average, minimum, and maximum salary offered for data science jobs in India?","metadata":{}},{"cell_type":"code","source":"import pandas as pd\n\n# Assuming your dataset is stored in a DataFrame named 'df'\n# Filter the dataset for data science job titles\ndata_science_jobs = df[df['job_title'].isin(['Data Scientist', 'Senior Data Scientist', 'Machine Learning Engineer', 'Data Engineer', 'Data Analyst', 'Data Architect'])]\n\n# Calculate the average, minimum, and maximum salary offered\naverage_salary = data_science_jobs['avg_salary'].mean()\nmin_salary = data_science_jobs['min_salary'].min()\nmax_salary = data_science_jobs['max_salary'].max()\n\nprint(\"Average Salary for Data Science Jobs in India:\", average_salary)\nprint(\"Minimum Salary for Data Science Jobs in India:\", min_salary)\nprint(\"Maximum Salary for Data Science Jobs in India:\", max_salary)\n","metadata":{"execution":{"iopub.status.busy":"2024-04-21T20:33:54.931885Z","iopub.execute_input":"2024-04-21T20:33:54.932315Z","iopub.status.idle":"2024-04-21T20:33:56.410594Z","shell.execute_reply.started":"2024-04-21T20:33:54.932284Z","shell.execute_reply":"2024-04-21T20:33:56.408695Z"},"trusted":true},"execution_count":6,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","Cell \u001b[0;32mIn[6], line 8\u001b[0m\n\u001b[1;32m      5\u001b[0m data_science_jobs \u001b[38;5;241m=\u001b[39m df[df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mjob_title\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39misin([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mData Scientist\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSenior Data Scientist\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMachine Learning Engineer\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mData Engineer\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mData Analyst\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mData Architect\u001b[39m\u001b[38;5;124m'\u001b[39m])]\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# Calculate the average, minimum, and maximum salary offered\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m average_salary \u001b[38;5;241m=\u001b[39m \u001b[43mdata_science_jobs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mavg_salary\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      9\u001b[0m min_salary \u001b[38;5;241m=\u001b[39m data_science_jobs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin_salary\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mmin()\n\u001b[1;32m     10\u001b[0m max_salary \u001b[38;5;241m=\u001b[39m data_science_jobs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmax_salary\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mmax()\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/series.py:6540\u001b[0m, in \u001b[0;36mSeries.mean\u001b[0;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m   6532\u001b[0m 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axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m  12370\u001b[0m nv\u001b[38;5;241m.\u001b[39mvalidate_func(name, (), kwargs)\n\u001b[1;32m  12372\u001b[0m validate_bool_kwarg(skipna, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskipna\u001b[39m\u001b[38;5;124m\"\u001b[39m, none_allowed\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m> 12374\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reduce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m  12375\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\n\u001b[1;32m  12376\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/series.py:6448\u001b[0m, in \u001b[0;36mSeries._reduce\u001b[0;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[1;32m   6443\u001b[0m     \u001b[38;5;66;03m# GH#47500 - change to TypeError to match other methods\u001b[39;00m\n\u001b[1;32m   6444\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m   6445\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSeries.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not allow \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkwd_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnumeric_only\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   6446\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwith non-numeric dtypes.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   6447\u001b[0m     )\n\u001b[0;32m-> 6448\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdelegate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__.<locals>.f\u001b[0;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[1;32m    145\u001b[0m         result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m    146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43malt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/nanops.py:404\u001b[0m, in \u001b[0;36m_datetimelike_compat.<locals>.new_func\u001b[0;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[1;32m    401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike \u001b[38;5;129;01mand\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    402\u001b[0m     mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[0;32m--> 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n\u001b[1;32m    407\u001b[0m     result \u001b[38;5;241m=\u001b[39m _wrap_results(result, orig_values\u001b[38;5;241m.\u001b[39mdtype, fill_value\u001b[38;5;241m=\u001b[39miNaT)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/nanops.py:720\u001b[0m, in \u001b[0;36mnanmean\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m    718\u001b[0m count \u001b[38;5;241m=\u001b[39m _get_counts(values\u001b[38;5;241m.\u001b[39mshape, mask, axis, dtype\u001b[38;5;241m=\u001b[39mdtype_count)\n\u001b[1;32m    719\u001b[0m the_sum \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39msum(axis, dtype\u001b[38;5;241m=\u001b[39mdtype_sum)\n\u001b[0;32m--> 720\u001b[0m the_sum \u001b[38;5;241m=\u001b[39m \u001b[43m_ensure_numeric\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthe_sum\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    722\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(the_sum, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mndim\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m    723\u001b[0m     count \u001b[38;5;241m=\u001b[39m cast(np\u001b[38;5;241m.\u001b[39mndarray, count)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/nanops.py:1701\u001b[0m, in \u001b[0;36m_ensure_numeric\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1698\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (is_float(x) \u001b[38;5;129;01mor\u001b[39;00m is_integer(x) \u001b[38;5;129;01mor\u001b[39;00m is_complex(x)):\n\u001b[1;32m   1699\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m   1700\u001b[0m         \u001b[38;5;66;03m# GH#44008, GH#36703 avoid casting e.g. strings to numeric\u001b[39;00m\n\u001b[0;32m-> 1701\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not convert string \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m to numeric\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1702\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1703\u001b[0m         x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(x)\n","\u001b[0;31mTypeError\u001b[0m: Could not convert string 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to numeric"],"ename":"TypeError","evalue":"Could not convert string 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to numeric","output_type":"error"}]},{"cell_type":"markdown","source":"What is the distribution of the number of salaries reported?","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"What is the distribution of job titles and companies in the dataset?","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}