wb_data

Overview

What is wbdata

Always read documents and dataset before hand-on EDA https://wbdata.readthedocs.io/en/stable/

Wbdata is a simple python interface to find and request information from the World Bank's various databases, either as a dictionary containing full metadata or as a pandas DataFrame. Currently, wbdata wraps most of the World Bank API, and also adds some convenience functions for searching and retrieving information.

Wbdata was designed to be used either in a script or in a shell. In a shell, wbdata assumes that the user will use most functions to look up the codes necessary to retrieve the information he wants. To this end, the default in shell mode for most functions is to simply print the id and human-readable name of each item in question. In a script, the default is to return the entire response from the World Bank converted to python objects.

All the functions that you need to get started are in the wbdata module.

Finally, it should be pointed out that wbdata is in the "release early" portion of the "release early, release often" cycle, and the current test suite is pretty perfunctory. You won't end up with the wrong data, but any irregularities I haven't specifically encountered in the World Bank database have not been dealt with.

EDA

Analyzing G20 Countries: A Data Analytics Project

Introduction:

Welcome to our data analytics project where we explore economic and social insights within the G20 nations using World Bank data. In this Jupyter notebook, we'll walk through the key steps of data collection, transformation, and analysis to derive meaningful conclusions from the available information. Project Overview:

The G20, a group of major economies, plays a significant role in global affairs. This analysis aims to uncover patterns and trends within the economic and social indicators provided by the World Bank for these influential nations. Methodology:

We leveraged the World Bank API (wbdata) for seamless data collection and used pandas for data transformation and cleaning. Our approach ensures a reliable dataset for exploration and analysis.

Key Steps:

• Data Collection: Utilized the wbdata API to gather economic and social indicators for G20 countries from the World Bank.
• Data Transformation and Cleaning: Employed pandas for transforming and cleaning the data, addressing missing values and ensuring consistency.
• Exploratory Data Analysis (EDA): Explored the dataset through visualizations and statistical analyses to uncover insights.
• Insight Generation: Our goal is to distill actionable insights, providing a deeper understanding of economic and social dynamics.

Why G20 Countries?

The G20 represents a diverse group of economies, each with its unique challenges and strengths. Analyzing this collective dataset allows us to draw comparisons, highlight commonalities, and understand the global impact of economic and social policies.

Project Organization

├── LICENSE            <- Open-source license if one is chosen
├── Makefile           <- Makefile with convenience commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default mkdocs project; see www.mkdocs.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── pyproject.toml     <- Project configuration file with package metadata for 
│                         wb_data and configuration for tools like black
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.cfg          <- Configuration file for flake8
│
└── wb_data   <- Source code for use in this project.
    │
    ├── __init__.py             <- Makes wb_data a Python module
    │
    ├── config.py               <- Store useful variables and configuration
    │
    ├── dataset.py              <- Scripts to download or generate data
    │
    ├── features.py             <- Code to create features for modeling
    │
    ├── modeling                
    │   ├── __init__.py 
    │   ├── predict.py          <- Code to run model inference with trained models          
    │   └── train.py            <- Code to train models
    │
    └── plots.py                <- Code to create visualizations

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