pandas: How can I group a stacked bar chart?

1
votes

I'm trying to create a grouped, stacked bar chart.

Currently I have the following DataFrame:

>>> df
                                       Value                     
Rating                                 1          2         3
Context Parameter                                
Total   1                          43.312347   9.507902  1.580367
        2                          42.862649   9.482205  1.310549
        3                          43.710651   9.430811  1.400488
        4                          43.209559   9.803418  1.349094
        5                          42.541436  10.008994  1.220609
        6                          42.978286   9.430811  1.336246
        7                          42.734164  10.317358  1.606064
User    1                          47.652348  11.138861  2.297702
        2                          47.102897  10.589411  1.848152
        3                          46.853147  10.139860  1.848152
        4                          47.252747  11.138861  1.748252
        5                          45.954046  10.239760  1.448551
        6                          46.353646  10.439560  1.498501
        7                          47.102897  11.338661  1.998002

I'd like to have for each Parameter the bars for Total and User grouped together.

This is the resulting chart with df.plot(kind='bar', stacked=True):

Stacked bar chart without grouping

The bars themselve look right, but how do I get the bars for Total and User next to each other, for each Parameter, best with some margin between the parameters?

2
pythonpandasmatplotlibseaborn

2 Answers

2
votes

The following approach allows grouped and stacked bars at the same time. First the dataframe is sorted by parameter, context. Then the context is unstacked from the index, creating new columns for every context, value pair. Finally, three bar plots are drawn over each other to visualize the stacked bars.

import pandas as pd
from matplotlib import pyplot as plt

df = pd.DataFrame(columns=['Context', 'Parameter', 'Val1', 'Val2', 'Val3'],
                  data=[['Total', 1, 43.312347, 9.507902, 1.580367],
                        ['Total', 2, 42.862649, 9.482205, 1.310549],
                        ['Total', 3, 43.710651, 9.430811, 1.400488],
                        ['Total', 4, 43.209559, 9.803418, 1.349094],
                        ['Total', 5, 42.541436, 10.008994, 1.220609],
                        ['Total', 6, 42.978286, 9.430811, 1.336246],
                        ['Total', 7, 42.734164, 10.317358, 1.606064],
                        ['User', 1, 47.652348, 11.138861, 2.297702],
                        ['User', 2, 47.102897, 10.589411, 1.848152],
                        ['User', 3, 46.853147, 10.139860, 1.848152],
                        ['User', 4, 47.252747, 11.138861, 1.748252],
                        ['User', 5, 45.954046, 10.239760, 1.448551],
                        ['User', 6, 46.353646, 10.439560, 1.498501],
                        ['User', 7, 47.102897, 11.338661, 1.998002]])
df.set_index(['Context', 'Parameter'], inplace=True)
df0 = df.reorder_levels(['Parameter', 'Context']).sort_index()

colors = plt.cm.Paired.colors

df0 = df0.unstack(level=-1) # unstack the 'Context' column
fig, ax = plt.subplots()
(df0['Val1']+df0['Val2']+df0['Val3']).plot(kind='bar', color=[colors[1], colors[0]], rot=0, ax=ax)
(df0['Val2']+df0['Val3']).plot(kind='bar', color=[colors[3], colors[2]], rot=0, ax=ax)
df0['Val3'].plot(kind='bar', color=[colors[5], colors[4]], rot=0, ax=ax)

legend_labels = [f'{val} ({context})' for val, context in df0.columns]
ax.legend(legend_labels)

plt.tight_layout()
plt.show()

resulting bar plot

-1
votes

Here's a way to do it:

import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="whitegrid")

# reshape you data - ensure no index is set initially
df1 = (df
      .set_index(['Parameter','Context'])
      .stack()
      .reset_index()
      .drop('level_2', 1)
      .rename(columns={0:'value'}))

print(df1.head())

   Parameter Context      value
0          1   Total  43.312347
1          1   Total   9.507902
2          1   Total   1.580367
3          2   Total  42.862649
4          2   Total   9.482205

sns.barplot(x = 'Parameter', 
            y = 'value', 
            hue='Context', 
            data=df1, 
            errwidth=0.1)

enter image description here