Data Analysis with Python

Introduction The dataset for this project contains records of the world population in 2023 Data cleaning, analysis and visualization was done using python. The analysis provides answers to some important questions and to get an understanding of the dataset. Data Structure Columns in the dataset include; County, population, yearly change, density, land area, net migrants, fertility rate, median age, population urban and world share. The necessary python libraries needed to carry out this analysis was imported into python IDLE (Jupyter Notebook), and the dataset was loaded in to begin analysis. Total number of column and rows present in the dataset are 234 while total number of rows are 11. Data Cleaning Data cleaning was done using the python pandas library in order to clean the dataset and prepare it for analysis. Checking duplicate value in the dataset The image above shows there are no duplicate values Checking missing values in the dataset The image above shows there 20 missing values the dataset The image above shows that the missing data was replaced with zero Data Analysis and Exploration To Ten Countries By Population The visualization shows the top ten countries ranked by their population. The world's population is highly concentrated in a few region with India and China leading. Bottom five Countries in Population This visualization is for the five countries with least population. Showing some small island nations and territories have extremely low population. Distribution of Fertility Rate Across Countries The visualization shows the correlation between countries fertility rates, with regions experiencing rapid decline.

Apr 9, 2025 - 23:44

Introduction
The dataset for this project contains records of the world population in 2023
Data cleaning, analysis and visualization was done using python. The analysis provides answers to some important questions and to get an understanding of the dataset.

Data Structure
Columns in the dataset include; County, population, yearly change, density, land area, net migrants, fertility rate, median age, population urban and world share.

The necessary python libraries needed to carry out this analysis was imported into python IDLE (Jupyter Notebook), and the dataset was loaded in to begin analysis.

Total number of column and rows present in the dataset are 234 while total number of rows are 11.

Data Cleaning
Data cleaning was done using the python pandas library in order to clean the dataset and prepare it for analysis.
Checking duplicate value in the dataset

The image above shows there are no duplicate values

Checking missing values in the dataset

The image above shows there 20 missing values the dataset

The image above shows that the missing data was replaced with zero

Data Analysis and Exploration
To Ten Countries By Population
The visualization shows the top ten countries ranked by their population. The world's population is highly concentrated in a few region with India and China leading.

Bottom five Countries in Population
This visualization is for the five countries with least population. Showing some small island nations and territories have extremely low population.

Distribution of Fertility Rate Across Countries
The visualization shows the correlation between countries fertility rates, with regions experiencing rapid decline.