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Streamlining Employee Data

Use DataFrames to read and merge employee data from different sources.

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8 Tasks1,500 XP

Loved by learners at thousands of companies

Project Description

Wouldn't it be great if your data was stored in your favorite format, ready to be analyzed? Unfortunately, that's rarely the case. Data comes in different formats, and being able to merge these different sources in a single file for analysis is a fundamental skill for any data practitioner. In this project, you will import employee and human resources data in CSV, Excel, and JSON format. You will then merge it all into a single DataFrame, before cleaning it and exporting your results into a single CSV file.

Project Tasks

  1. 1
    Loading data from CSV and Excel files
  2. 2
    Loading employee data from Excel sheets
  3. 3
    Loading role data from JSON files
  4. 4
    Merging several DataFrames into one
  5. 5
    Editing column names
  6. 6
    Changing column order
  7. 7
    The last minute request
  8. 8
    Saving your work
Python Python
Data ManipulationImporting & Cleaning Data
Hadrien Lacroix Headshot

Hadrien Lacroix

Curriculum Manager at DataCamp
Hadrien has collaborated on 30+ courses ranging from machine learning to database administration through data engineering. He's currently enrolled in a Masters of Analytics at Georgia Tech.

Hadrien started using DataCamp when the platform only had 27 courses. He then joined the Support team and helped students before becoming a Content Developer himself.

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