https://github.com/prajjwol09/eda-using-pandas

I did the Exploratory Data Analysis of the World Popluation from 1970 - 2022 AD using Pandas and the Jupyter Notebook as the workspace,
https://github.com/prajjwol09/eda-using-pandas

dataframe eda jupyter-notebook pandas population python

Last synced: 19 days ago
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I did the Exploratory Data Analysis of the World Popluation from 1970 - 2022 AD using Pandas and the Jupyter Notebook as the workspace,

• Host: GitHub
• URL: https://github.com/prajjwol09/eda-using-pandas
• Owner: Prajjwol09
• Created: 2024-09-13T10:48:44.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-09-13T10:54:26.000Z (2 months ago)
• Last Synced: 2024-10-09T17:21:17.853Z (about 1 month ago)
• Topics: dataframe, eda, jupyter-notebook, pandas, population, python
• Language: Jupyter Notebook
• Homepage:
• Size: 222 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
World Population Data Analysis (1970-2022) - Exploratory Data Analysis (EDA)

Overview:

This project performs an Exploratory Data Analysis (EDA) of the world population data from 1970 to 2022 AD. The analysis provides insights into global population trends, patterns, and changes over time using Python's Pandas library. The project cleans, processes, and visualizes the data to highlight key population statistics and their variations across years, regions, and countries.

Objectives:

Data Cleaning: Handle missing values, remove duplicates, and format data for consistency.

Trend Analysis: Investigate population growth trends on a global scale, as well as for individual countries and regions.

Visualization: Use charts and plots to visualize population changes over time.

Statistical Analysis: Calculate summary statistics such as mean, median, and growth rates to provide deeper insights into the population data.

Tools Used:

Python: Programming language used for data analysis.

Jupyter Notebook: For organizing and presenting the analysis.

Pandas: Library for data manipulation and analysis.

Matplotlib/Seaborn: For visualizing data trends and patterns.