Analysis of Nigeria Malaria was done using the Indicator Survey (NMIS) dataset to perform some descriptive analysis using python programming.
Malaria in Nigeria
Malaria is endemic in Nigeria and remains a major public health problem, taking its greatest toll on children under age 5 and pregnant women, although it is preventable, treatable, and curable. Africa still bears over 80 percent of the global malaria burden, and Nigeria accounts for about 29 percent of this burden. Moreover, in combination with the Democratic Republic of Congo, Nigeria contributes up to 40 percent of the global burden (World Malaria Report 2014). In Nigeria, malaria is responsible for approximately 60 percent of outpatient visits and 30 percent of admissions.
Application of Python Programming
To begin we are going to load in the required python packages and dataset. We are going to perform some data cleaning and descriptive analysis afterward.
The following codes are executed using Jupyter notebook and the dataset used can be download here: GET IT
# perform necessary import import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import numpy as np import numpy.random as nr import math %matplotlib inline
Execute the code in the cell below to load the dataset and print the first few rows of the data frame.
# load datasets Malaria = pd.read_csv('Nigeria Malaria Survey.csv')
The column names in the dataset have coded names. We are going to assign human-readable names to the columns and print shape and head of the data.
Malaria = pd.read_csv('Nigeria Malaria Survey.csv') Malaria.columns=['Case Identification', 'Region', 'Type of Place of Residence', 'Source of Drinking Water', 'Type of Toilet Facility', 'Has Electricity', 'Main Floor Material', 'Main Wall Material', 'Main Roof Material', 'Has Bicycle', 'Has Motorcycle/Scooter', 'Has Car/Truck', 'Has Mosquito Bed Net for Sleeping', 'Owns Land Suitable for Agriculture', 'Has Bank Account', 'Wealth Index', 'Cost of Treatment for Fever', 'State'] print(Malaria.shape) Malaria.head()
Notice that some of the column names contain the ‘/’ character. Python will not correctly recognize character strings containing ‘-‘. Rather, such a name will be recognized as two character strings. The same problem will occur with column values containing many special characters including, ‘-‘, ‘,’, ‘*’, ‘|’, ‘>’, ‘<‘, ‘@’, ‘!’ etc. If such characters appear in column names of values, they must be replaced with another character.
Execute the code in the cell below to replace the ‘/’ characters by ‘or’.
Malaria.columns=[str.replace('/','or') for str in Malaria.columns]
Print the head of the data again to confirm changes have been made.
Execute the code in the cell below to display the data types of each column.
Treat missing values
Missing values are a common problem in a dataset. Failure to deal with missing values before training a machine learning model will lead to biased training at best and in many cases actual failure. The Python scikit-learn package will not process arrays with missing values.
We are going to check which of our columns have missing value(s).
# check for missing values (Malaria.astype(np.object).isnull()).any()
‘Type of Toilet Facility’ and ‘Cost of Treatment for Fever’ contains missing values. We are going to remove both columns from our dataset.
Malaria.drop('Type of Toilet Facility', axis=1, inplace=True)
Malaria.drop('Cost of Treatment for Fever', axis=1, inplace=True)
Examine to see if both columns have been removed.
Transform column data type
We have now eliminated all missing values from our dataset. For descriptive analysis purpose. First is to put our table in the form of Pandas DataFrame.
df = pd.DataFrame(Malaria)
Descriptive Statistics of Nigeria Malaria Data
Here are the results of the descriptives analysis we arrived at after preprocessing of our data from all the steps above.
yes 4247 no 3498 Name: Has Electricity, dtype: int64
df['Source of Drinking Water'].value_counts()
tube well or borehole 2795 river/dam/lake/ponds/stream/canal/irrigation channel 829 unprotected well 819 protected well 786 sachet water 786 public tap/standpipe 393 unprotected spring 349 rainwater 308 piped into dwelling 238 piped to yard/plot 101 protected spring 87 piped to neighbor 84 tanker truck 71 bottled water 68 cart with small tank 27 other 4 Name: Source of Drinking Water, dtype: int64
richer 1844 richest 1816 middle 1676 poorer 1351 poorest 1058 Name: Wealth Index, dtype: int64
df['Has Mosquito Bed Net for Sleeping'].value_counts()
yes 5432 no 2313 Name: Has Mosquito Bed Net for Sleeping, dtype: int64
From the table above, Lagos State has the top number of richest people and Sokoto State has the top number of poorest people
df.groupby('Has Mosquito Bed Net for Sleeping')['State'].describe()
|Has Mosquito Bed Net for Sleeping|
From above table Bauchi State has the Highest number of people with Mosquito Bed Net for Sleeping, While Edo State has the least Number
From above table Lagos State has the Highest number of people with access to Electricity, While Adamawa State has the least Number