This is a simple Python Script to create combined subplots of user surveys which used the standard Likert-Scale from 1...5 (Strongly Disagree...Strongly Agree)
Additionally it uses Color Maps to color code the different parts of our survey
Examples of our generated Graph-Image look like this:
This Code should be easily transferable for other survey results if theyre also using the Likert-Scale
This is our main method to generate our plot-images
def plot_subplot(column_count, row_count, df_columns, colorlist, name, deleteLastPlot=False, XLabel=''):column_count: amount of columns for image generation
row_count: amount of rows for image generation
df_columns: Dataframe that includes all the columns we want to plot
colorlist: List of ColorMaps or Colors (needs to be specified for every plot)
name:, filename we use to save in image folder
deleteLastPlot: Default: False, If True: removes bottom right (last) plot if we have an odd amount of plots
XLabel: Bottom label for all the plots, used to specify what Sub-Survey the questions belong to
You can easily generate a list of colormaps with the right amount (for every column)
c_map = ['OrRd', 'BuPu', 'YlGn', 'YlOrBr']
col_list = [c_map[0] for i in range(len(Lists[0].columns))] + [c_map[1] for i in range(len(Lists[1].columns))]Here we generate 2 lists of repeating colormaps from the amount of columns in our seperated datasets (seperated by Sub-Survey)
This Article by Charlene Chambliss was very helpful in learning my way around this sort of visualization using seaborn
If you are considering to visualize your data in a similar fashion to mine, you should really consider this blogpost as a potential alternative for your visualization.
This project uses both MatplotLib and Seaborn. If you want to get into this further or have some weirder data you want to represent (ie.: like 3d Data) you should read more about matplotlib, like in this article