A Python exploratory Data Analysis on the Kenya Primary Schools dataset, downloaded from africaopendata to the file kenya_primary_schools.csv and included in this EDA folder.
The first thing we should do when we start working with a new dataset is obtaining some specific konwledge about the area of interest. This will help us to understand the following steps to achieve.
For this dataset, we should study the Kenya’s national education system, specially related to Primary School. As a result, we will adquire some basic knowledge about the topic, and will understand better the information given.
The dataset contains 31.230 rows and 38 features. We find 12 object features (text) and 26 numeric features (float64).
There´s only one missing value (NaN) in each column, except in features X, Y and FID.
We check if missing value is located in the same row for all the features. In that case, we drop the row.