Data Analysis With Python: Analysis of the global development and Prosperity Index for the year 3

The dataset for this project contains records of the global development and prosperity index for the year 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; Country Code, Country, Average Score, Safety Security, Personnel Freedom, Governance, Social Capital, Investment Environment, Enterprise Conditions, Market Access Infrastructure, Economic Quality, Living Conditions, Health, Education, Natural Environment.
The necessary python libraries needed to carry out this analysis was imported into the python IDLE (Jupyter Notebook), And the dataset was loaded in to begin analysis.

Total numbers of column and rows present in the dataset shows 167 rows and 14 columns.

10 random samples of the dataset to see what the dataset looks like.

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

•Checking for missing values in the dataset

The image above shows the dataset had no missing values

The image above shows there were no duplicates in the dataset.

## Data Analysis and Exploration
1)Top Ten Countries By Average Score Of The Global ProsperityIndex:

•The visualization shows the top ten countries ranked by their average scores on the Global Prosperity Index. These countries demonstrate strong performances across various metrics such as governance, education, health, and economic quality. The high scores indicate a robust and balanced approach to fostering prosperity and well-being for their citizens, reflecting effective policies and a favorable socio-economic environment.

**2)Bottom Ten Countries by Average score:

•This list and visualization highlights areas where these countries may need to focus their efforts to improve their overall scores, contributing to better quality of life and development outcomes for their citizens. It serves as a valuable tool for policymakers, researchers, and stakeholders interested in international development and comparative analysis.

*3)Areas Scored Highest by the top Ten countries: *
This list and Visualization titled "Areas Scored Highest by the Top Ten Countries" illustrates the top-performing metrics for the ten countries with the highest average scores. These metrics encompass various dimensions of national success, including safety, personel freedom, governance, social capital, economic quality, and more.

4)Areas of Improvement for the Bottom Ten Countries:

Cette liste et ce graphique intitulés « Zones ayant obtenu les scores les plus élevés selon les dix premiers pays » illustrent les mesures les plus performantes pour les dix pays ayant les scores moyens les plus élevés. Ces mesures englobent diverses dimensions de la réussite nationale, notamment la sécurité, la liberté du personnel, la gouvernance, le capital social, la qualité économique, etc.

5)Relation entre gouvernance et conditions de vie :

La corrélation de 0,71 entre la gouvernance et les conditions de vie souligne l’importance d’une gouvernance solide en tant que moteur clé de l’amélioration des conditions de vie. Cette relation suggère que les efforts visant à renforcer les structures de gouvernance peuvent avoir un impact positif significatif sur la qualité de vie de la population d’un pays. Les décideurs politiques et les organisations de développement peuvent utiliser ces informations pour prioriser les réformes de la gouvernance en tant que stratégie visant à améliorer les conditions de vie.

The above is the detailed content of Data Analysis With Python: Analysis of the global development and Prosperity Index for the year 3. For more information, please follow other related articles on the PHP Chinese website!