The project involved using Python programming language and some built-in libraries to plot and analyse data. There were two imported built-in libraries, and one imported custom module used in the entirety of the assignment. The two imported built-in libraries are Matplotlib and Pandas while the imported custom module is called color_module. Matplotlib built-in module called pyplot contains a lot of function, which was used for creating plots while Pandas was used to read and manipulate datasets. The colour_module contains a function, that returns a list of colors used for the bar plot. Three kinds of plot were drawn for the assignment. Each plot uses a different dataset from the other. Plots drawn includes, a bar plot, a line plot and a pie plot. The visualization of the datasets, explanation of the plots will be shown and discussed in the next few lines.
Bar plots are good for analysing categorical type of data. It is excellent for displaying data with discrete categories. Bar plot was chosen for this plot because, the dataset contains a list of categories of apps in the Google playstore. This list of categories in the Google playstore fits in properly into creating a bar plot.
The bar plot shows that of the top 20 categories of app in the Google playstore, there are more family apps than any other apps. Explicitly, the bar plot shows that there are 1972 family apps. This 1972 family apps, takes 20.48% of the top 20 categories in the Google playstore. There are 295 Social apps. This takes 3.06% of the top 20 categories of apps in the Google playstore.
A line plot displaying the number of women in the USA house of representative from the year 1975 to 2019
Each line of this plot denotes a trend for the total number of women in the USA House of Representative, number of women in the Republican party and number of women in the democratic party from the year 1975 to 2019. As the year increases from 1975, the plot shows that there is an increase in the total number of women in the USA House of Representative. From the year 1991, the plot shows that there is an enormous increase the total number of women in the USA House of Representative.
Furthermore, the plot shows that there are more women in the Democratic party compared to the Republican party as the year increases. The Republican party never had more women than the Democratic party. From the year 2013 to 2019 the Democratic party had consistency in the number of women in the house of representative.
Each line of this plot denotes a trend for the total number of women in the USA House of Representative, number of women in the Republican party and number of women in the democratic party from the year 1975 to 2019. As the year increases from 1975, the plot shows that there is an increase in the total number of women in the USA House of Representative. From the year 1991, the plot shows that there is an enormous increase the total number of women in the USA House of Representative.
Furthermore, the plot shows that there are more women in the Democratic party compared to the Republican party as the year increases. The Republican party never had more women than the Democratic party. From the year 2013 to 2019 the Democratic party had consistency in the number of women in the house of representative.
Pie plots are good for data size comparison. They show the relativity between a set of data. Pie plot was chosen for this plot because, the dataset contains the number of various ethnicity of women in the USA congress.
The pie plot shows that there are more African American women compared to women of another ethnicity in the USA congress. Between the year 1965 to 2017 the USA congress only have 1.5% of women who are of combined ethnicity.The pie plot also shows that between the year 1965 to 2017 the USA congress have more women of Hispanic American ethnicity than women whore Asian-Pacific American.
Please, if you are going to use any part of this write-up or project, make sure you give credit to Adedoyin Idris.
Thanks. Kind Regards.