.1. Use Python to read and analyse the child mortality data and generate various useful graphs… 1 answer below »

.1. Use Python to read and analyse the child mortality data

and generate various useful graphs that give insight into the trends.

2. Due Week 12. Use Python to combine the child mortality data and the

country metadata, to give higher-level analyses of child mortality in

relation to income grouping and regions of the world.

1 Child Mortality

In this first part of Task 2, you should write a Python script that reads and

analyses the child mortality data file (WHOSIS_MDG_000003.csv) and

produce at least FIVE useful graphs that give insight into the data trends.

For example, here are some suggestions:

• show the change in child/infant/neonatal mortality over the period 1990

to 2015 for several representative countries.

• compare the mortality rates of all countries in a given year.

• compare the improvement in mortality rates over the 1990/2015 period

– that is, one divided by the other.

• compare child mortality against infant mortality and neonatal mortality

to see what is the relationship between them.

Hints:

1. Some of the columns contain multiple values (a mortality rate, plus a

confidence interval), so you will need to split these up into seperate

columns.

2. You can either use standard Python data structures to store and

manipulate the data, or use the Pandas library if you prefer.

3. Use markup and headings to break your Jupyter notebook into sections

and give commentary about what you doing, and discussion of your

results. This Jupyter notebook will be what you submit.

2 Child Mortality and Country Types

In this second part of Task 2, you should write another Python script that

reads and analyses the country metadata (COUNTRY.json) and merges it

with the child mortality data from Part 1, to allow you to do some higher-level

analysis of child mortality trends.

Your report should include at least two graphs that display or compare

child/infant/neonatal mortality in different regions of the world (using the

‘WHO_REGION’ string to group the countries).

ICT702 TASK 2 3

You report should include at least two graphs that compare

child/infant/neonatal mortality across different income groupings (using the

‘WORLD_BANK_INCOME_GROUP’ string to classify the countries).

Hints:

1. You can use the ‘json’ library to read the .json file. The resulting object

is quite deeply nested, so you will need to explore which substructures

contain the data that you want, and then extract that substructure into a

dictionary or list that is easier to use. Or write a function that extracts

the data that you need.

2. You can either use standard Python data structures to store and

manipulate the data, or use the Pandas library if you prefer.

3. Use markup and headings to break your Jupyter notebook into sections

and give commentary about what you doing, and discussion of your

results. This Jupyter notebook will be what you submit.

 

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