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I realized this project during my M2 in order to learn how to handle Python and particularly Pandas in order to pre-process data and analyze them
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This project aims to manipulate a dataset for analytical purposes. The objective is to answer some analytical questions which requires a work of preprocessing, cleaning, manipulation, conversion, visualization of data
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I mainly used Pandas but also other packages and modules.
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In this dataset there are four worksheets:
- COMPANY: firmographics
- INVESTMENT: relevant investments received by the companies
- ACQUISITION: relevant acquisitions the companies have
- EMPLOYEE: relevant employees working in the companies
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To help you better understand the datasets, data dictionaries are given below.
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Worksheet: COMPANY
| Variable | Comment |
|---|---|
| COMPANY_NAME | Company name (unique ID) |
| CATEGORY | Industry category |
| LOCATION | Company location |
| FOUNDED_ON | Date that the company was founded |
| EXITED_ON | Date that the company exited (if any) |
| CLOSED_ON | Date that the company was closed (ifany) |
| REVENUE_RANGE | Revenue range |
| EMPLOYEE_NUMBER | The number of employees |
- Worksheet: INVESTMENT
| Variable | Comment |
|---|---|
| COMPANY_NAME | Company name |
| FUNDING_TYPE | The type of funding |
| MONEY_RAISED | The amount of money raised in the investment |
| ANNOUNCED_DATE | Date that the investment was announced |
| INVESTMENT_STAGE | The investment stage |
- Worksheet: ACQUISITION
| Variable | Comment |
|---|---|
| COMPANY_NAME | Company name |
| ACQUIREE_NAME | Name of the acquired company |
| ANNOUNCED_DATE | Date that the acquisition was announced |
| PRICE | The price of acquisition |
| ACQUISITION_TYPE | The type of acquisition |
- Worksheet: EMPLOYEE
| Variable | Comment |
|---|---|
| EMPLOYEE_MD5 | Hashed unique ID for employee |
| JOB_TITLES | Job titles |
| COMPANY_NAME | Company name |
| ATTENDED_SCHOOLS | Schools that the employee has attended |
- The notebook is structured as follows: 13 analytical questions that I asked myself while observing the dataset followed by the code required to obtain the answer.
- example of the code needed for the question: How many acquisitions did the company with the most acquisitions make per year?