https://github.com/sksubhadeep/world-population-exploratory-data-analysis-using-python

World-Population-Exploratory-Data-Analysis-using-Python
https://github.com/sksubhadeep/world-population-exploratory-data-analysis-using-python

eda python

Last synced: 8 days ago
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World-Population-Exploratory-Data-Analysis-using-Python

• Host: GitHub
• URL: https://github.com/sksubhadeep/world-population-exploratory-data-analysis-using-python
• Owner: sksubhadeep
• Created: 2023-09-02T04:24:05.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-09-16T03:45:03.000Z (over 1 year ago)
• Last Synced: 2024-11-08T01:39:53.459Z (2 months ago)
• Topics: eda, python
• Language: Jupyter Notebook
• Homepage:
• Size: 147 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# World-Population-Exploratory-Data-Analysis-(EDA)-using-Python 

🌍 Exploring World Population Data with Python! 📊

I recently embarked on a fascinating journey to analyze world population data using Python. Here's a sneak peek into the steps I took:

 

1️⃣ **Importing the Data**: First, I gathered the world population data from a reliable source. Data quality is essential for meaningful analysis!

2️⃣ **Structure of Data**: Understanding your data is key. I displayed the structure of the dataset to get a clear picture of what I was working with.

3️⃣ **Visualizing Common Metrics**: I used Python's powerful data visualization libraries to explore common metrics over the years. Visuals help us spot trends and patterns.

4️⃣ **Cleaning Data**: To ensure the accuracy of my analysis, I removed null values. Clean data is crucial for meaningful insights.

5️⃣ **Eliminating Duplicates**: Duplicate data can skew results. I removed duplicates to maintain data integrity.

6️⃣ **Sorting by 2022 Population**: Sorting the data by the 2022 population allowed me to identify regions with significant population changes.

7️⃣ **Population Heatmap**: Heatmaps are a fantastic way to visualize population trends across countries and regions.

8️⃣ **Population by Continent**: A line chart helped me showcase population changes by continent over time. It's interesting to see how populations have evolved.

9️⃣ **Population Boxplot**: Boxplots are great for understanding population distribution and identifying outliers. They provide valuable insights into data variability.

Exploring world population data using Python was an enriching experience. It's incredible how data analysis can reveal insights about our world's changing demographics. I encourage everyone to dive into data analysis—it's a powerful tool for understanding our complex world. 🌎📈

#DataAnalysis #Python #WorldPopulation #DataVisualization #LinkedInPost