Child Mortality Analysis with Python

This assignment analyzes child mortality data using Python. It involves data wrangling from CSV files, generating visualizations (graphs), and providing insights into trends in infant, neonatal, and under-five mortality rates over time. The analysis utilizes Pandas library for data manipulation and Jupyter Notebook for code execution and visualization.

Communication and
Information Technology

Table of Contents
Overview.........................................................................................................................................2
TASK 2...........................................................................................................................................2
PART 1.......................................................................................................................................2
PART 2:......................................................................................................................................6
Implementation..............................................................................................................................8
Conclusion......................................................................................................................................8

Overview
Python Jupiter notebook will be used for child mortality as per requirements. The data
will be analyzed, investigated and will be provided as python code with generated graph
screenshots. Data wrangling process will be done. The provided csv files will be used and output
will be generated for child mortality with various graphs.
The data is taken from the (WHO) provided link.
TASK 2
The task 2 is divided into 2:
1. Part 1
2. Part 2
PART 1
Form the provided csv file, the python code is generated and implemented in part 1. The
below screenshot displays the csv file generated in python code for displaying the country, year,
infant mortality rate, Neonatal mortality rate and Under-five mortality rate. By importing Pandas
library files, the code is generated further (SHADAN, 2017).

Graphs are generated and displayed in the below screenshots. It explains the mortality rates for
various years. The insights into the data trends are analyzed and provided. The changes are
shown for various years mentioned in the requirements (Jacqueline Kazil, 2017).

The mortality rates under 5 per 1000 live births are clearly generated in python code (Toomey,
2016).

PART 2:
json files are nested and explored the data in python. Json is imported
The below screenshot displays clearly (Romano, Phillips and Hattem, 2016).

The higher level analysis of mortality at various levels are shown below (Jupyter-notebook-
beginner-guide.readthedocs.io, 2017).

Implementation
Conclusion
Python Jupiter notebook is used for child mortality as per requirements. The data is
analyzed, investigated and is provided as python code with generated graph screenshots. Data
wrangling process is done. The provided csv files is used and output is generated for child
mortality with various graphs.
References
Jacqueline Kazil, K. (2017). Wrangle your data with Python. [online] O'Reilly Media. Available
at: https://www.oreilly.com/learning/wrangle-your-data-with-python [Accessed 18 Sep. 2017].
Jupyter-notebook-beginner-guide.readthedocs.io. (2017). Jupyter/IPython Notebook Quick Start
Guide — Jupyter/IPython Notebook Quick Start Guide 0.1 documentation. [online] Available at:
https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/ [Accessed 18 Sep. 2017].
Romano, F., Phillips, D. and Hattem, R. (2016). Python. Birmingham: Packt Publishing.

SHADAN, M. (2017). Python - Install Anaconda, Jupyter Notebook, Spyder. [online] Rstudio-
pubs-static.s3.amazonaws.com. Available at:
http://rstudio-pubs-static.s3.amazonaws.com/242584_29bdf274c291408090962b3e860a299e.ht
ml [Accessed 18 Sep. 2017].
Toomey, D. (2016). Learning Jupyter. Birmingham: Packt Publishing.