I have the following dataframe

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib 
print('matplotlib: {}'.format(matplotlib.__version__))
# 3.5.3

df=pd.DataFrame({'Type': [ 'Sentence', 'Array', 'String', '-','-', 'Sentence', 'Array', 'String', '-','-', 'Sentence'],
                 'Length': [42,21,11,6,6,42,21,11,6,6,42],
                 'label': [1,1,0,0,0,1,1,0,0,0,1],
                 })
print(df)
#       Type     Length  label
#0   Sentence      42      1
#1      Array      21      1
#2     String      11      0
#3          -       6      0
#4          -       6      0
#5   Sentence      42      1
#6      Array      21      1
#7     String      11      0
#8          -       6      0
#9          -       6      0
#10  Sentence      42      1

I want to plot stacked bar chart for the arbitrary column within dataframe (either numerical e.g. Length column or

CodePudding user response：

• The values in Expected output do not match df in the OP, so the sample DataFrame has been updated.

• Plot with

  ---

  Comment Updates

  • How to always have a spot for 'Array' if it's not in the data:
    • Add 'Array' to dfp if it's not in dfp.index.
    • df.Type = pd.Categorical(df.Type, ['-', 'Array', 'Sentence', 'String'], ordered=True) does not ensure the missing categories are plotted.
  • How to have all the annotations, even if they're small:
    • Don't stack the bars, and set logy=True.
  • This uses the full-data, which was provided in a link.

  # pivot the dataframe and get len
  dfp = df.pivot_table(index='Type', columns='label', values='Length', aggfunc=len) 
  
  # append Array if it's not included
  if 'Array' not in dfp.index:
      dfp = pd.concat([dfp, pd.DataFrame({0: [np.nan], 1: [np.nan]}, index=['Array'])])
      
  # order the index
  dfp = dfp.loc[['-', 'Array', 'Sentence', 'String'], :]
  
  # calculate the percent for each row
  per = dfp.div(dfp.sum(axis=1), axis=0).mul(100).round(2)
  
  # plot the pivoted dataframe
  ax = dfp.plot(kind='bar', stacked=False, figsize=(10, 8), rot=0, logy=True, width=0.75)
  
  # iterate through the containers
  for c in ax.containers:
      
      # get the current segement label (a string); corresponds to column / legend
      label = c.get_label()
      
      # create custom labels with the bar height and the percent from the per column
      # the column labels in per and dfp are int, so convert label to int
      labels = [f'{v.get_height()}\n({row}%)' if v.get_height() > 0 else '' for v, row in zip(c, per[int(label)])]
      
      # add the annotation
      ax.bar_label(c, labels=labels, label_type='edge', fontsize=10, fontweight='bold')
      
  # move the legend
  ax.legend(title='Class', bbox_to_anchor=(1, 1.01), loc='upper left')
  
  # pad the spacing between the number and the edge of the figure
  _ = ax.margins(y=0.1)
  

  

  ---

  DataFrame Views

  • Based on the sample data in the OP

  df

          Type  Length  label
  0   Sentence      42      1
  1      Array      21      1
  2     String      11      0
  3          -       6      0
  4          -       6      0
  5   Sentence      42      1
  6      Array      21      1
  7     String      11      0
  8          -       6      0
  9          -       6      1
  10  Sentence      42      0
  

  dfp

  label       0    1
  Type              
  -         3.0  1.0
  Array     NaN  2.0
  Sentence  1.0  2.0
  String    2.0  NaN
  

  total

  Type
  -           4.0
  Array       2.0
  Sentence    3.0
  String      2.0
  dtype: float64
  

  per

  label          0       1
  Type                    
  -          75.00   25.00
  Array        NaN  100.00
  Sentence   33.33   66.67
  String    100.00     NaN