Stacked Percentage Bar Plot In MatPlotLib

01 Oct, 2020
A Stacked Percentage Bar Chart is a simple bar chart in the stacked form with a percentage of each subgroup in a group. Stacked bar plots represent different groups on the top of one another. The height of the bar depends on the resulting height of the combination of the results of the groups. It goes from the bottom to the value instead of going from zero to value. A percent stacked bar chart is almost the same as a stacked barchart. Subgroups are displayed on top of each other, but data are normalized to make in a sort that the sum of every subgroup is the same as the total for each one. 
The dataset used in the following examples is shown below :
The dataset can be downloaded from here.
Procedure: The procedure to draw Stacked Percentage Bar Chart is the following steps which are described below with examples :
1. Draw a stacked bar chart using data (dataset, dictionary, etc.).
Python3
# importing packagesimport pandas as pdimport matplotlib.pyplot as plt  # load datasetdf = pd.read_excel("Hours.xlsx")  # view datasetprint(df)  # plot a Stacked Bar Chart using matplotlibdf.plot(    x = 'Name',    kind = 'barh',    stacked = True,    title = 'Stacked Bar Graph',    mark_right = True)
Output:
    Name   Studied     Slept      Other
0    Ram  4.855064  9.639962   9.504974
1   Yash  8.625440  0.058927  15.315634
2  Alpha  3.828192  0.723199  19.448609
3   Deep  7.150955  3.899420  12.949625
4   Alex  6.477900  8.198181   9.323919
5   Jack  1.922270  1.331427  20.746303
6  Sufia  8.978216  0.993438  14.028347


2. Add Percentage on subgroups of each group.
Python3
# importing packagesimport pandas as pdimport numpy as npimport matplotlib.pyplot as plt  # load datasetdf = pd.read_excel("Hours.xlsx")  # view datasetprint(df)  # plot a Stacked Bar Chart using matplotlibdf.plot(  x = 'Name',   kind = 'barh',   stacked = True,   title = 'Percentage Stacked Bar Graph',   mark_right = True)  df_total = df["Studied"] + df["Slept"] + df["Other"]df_rel = df[df.columns[1:]].div(df_total, 0)*100  for n in df_rel:    for i, (cs, ab, pc) in enumerate(zip(df.iloc[:, 1:].cumsum(1)[n],                                          df[n], df_rel[n])):        plt.text(cs - ab / 2, i, str(np.round(pc, 1)) + '%',                  va = 'center', ha = 'center')
Output:
     Name   Studied     Slept      Other
0    Ram  4.855064  9.639962   9.504974
1   Yash  8.625440  0.058927  15.315634
2  Alpha  3.828192  0.723199  19.448609
3   Deep  7.150955  3.899420  12.949625
4   Alex  6.477900  8.198181   9.323919
5   Jack  1.922270  1.331427  20.746303
6  Sufia  8.978216  0.993438  14.028347


Python3
# importing packagesimport pandas as pdimport numpy as npimport matplotlib.pyplot as plt  # load datasetdf = pd.read_xlsx("Hours.xlsx")  # view datasetprint(df)  # plot a Stacked Bar Chart using matplotlibdf.plot(  x = 'Name',   kind = 'barh',   stacked = True,   title = 'Percentage Stacked Bar Graph',   mark_right = True)  df_total = df["Studied"] + df["Slept"] + df["Other"]df_rel = df[df.columns[1:]].div(df_total, 0) * 100  for n in df_rel:    for i, (cs, ab, pc) in enumerate(zip(df.iloc[:, 1:].cumsum(1)[n],                                          df[n], df_rel[n])):        plt.text(cs - ab / 2, i, str(np.round(pc, 1)) + '%',                  va = 'center', ha = 'center', rotation = 20, fontsize = 8)
Output:
    Name   Studied     Slept      Other
0    Ram  4.855064  9.639962   9.504974
1   Yash  8.625440  0.058927  15.315634
2  Alpha  3.828192  0.723199  19.448609
3   Deep  7.150955  3.899420  12.949625
4   Alex  6.477900  8.198181   9.323919
5   Jack  1.922270  1.331427  20.746303
6  Sufia  8.978216  0.993438  14.028347


Matplotlib Pyplot-class
Python-matplotlib
Python
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