Make multiple barplot automatically from a dataframe

Question

I have a dataframe where I have a variable 'Gender' (0 or 1) indicating if one is Male or Female, and another variable 'Dis' which says the state of the Disease (0,1,2 or 3).

> df.head()
   Gender  Dis
0     1     2
1     0     0
2     0     1
3     1     3
4     0     0
5     0     1

I want to make a barplot with the count values for each one of the'Dis' values but I want it to be separated by Gender, i.e, I want two bars for each one of the states of the disease. I want this:

However, I can't do this barplot automatically without manually writing the count values of each one. I had to check the count values for each one of the combinations aside. I produced this plot manually with the following:

 X = ['0','1','2','3']
 M = [43,9,20,11]
 F = [118,21,168,20]

 X_axis = np.arange(len(X))

plt.bar(X_axis - 0.2, M, 0.4, label = 'Male')
plt.bar(X_axis + 0.2, F, 0.4, label = 'Female')

plt.xticks(X_axis, X)
plt.xlabel("")
plt.ylabel("")
plt.legend()
plt.title("title")
def autolabel(rects):
   for rect in rects:
      h = rect.get_height()
      ax.text(rect.get_x()+rect.get_width()/2., 1.05*h, '%d'%int(h),
              ha='center', va='bottom')
plt.show()

Can I do something more "automatic" directly from the dataframe? Also, can I also display the count values on top of each bar?

Henry Ecker · Accepted Answer · 2021-06-28 11:44:36Z

1

Let's try with crosstab + DataFrame.plot:

plot_df = (
    pd.crosstab(df['Dis'], df['Gender'])
        .rename(columns={0: 'Male', 1: 'Female'})
)

ax = plot_df.plot(kind='bar', rot=0, xlabel='', ylabel='', title='title')
plt.show()

crosstab will produce the counts for Male/Female per Dis.

rename is used to turn the column names 0/1 to Male/Female:

plot_df:

Gender  Male  Female
Dis                 
0        119     128
1        140     121
2        124     120
3        112     136

Moving legend, and values on top of bars:

ax = plot_df.plot(kind='bar', rot=0, xlabel='', ylabel='', title='title')
for container in ax.containers:
    ax.bar_label(container)

plt.legend(title='Gender', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.tight_layout()
plt.show()

To add percentages to the top of the columns:

divide plot_df by the column totals
format as desired
zip with containers to add bar labels

plot_df = (
    pd.crosstab(df['Dis'], df['Gender'])
        .rename(columns={0: 'Male', 1: 'Female'})
)

# Calculate Percentages and format
labels_df = (
    plot_df.div(plot_df.sum(axis=0)).mul(100).applymap('{:.2f}%'.format)
)
ax = plot_df.plot(kind='bar', rot=0, figsize=(9, 6), width=0.8,
                  xlabel='', ylabel='', title='title')

for container, col in zip(ax.containers, labels_df):
    ax.bar_label(container, labels=labels_df[col])

plt.legend(title='Gender', bbox_to_anchor=(1.01, 1), loc='upper left')
plt.tight_layout()
plt.show()

labels_df:

Gender    Male  Female
Dis                   
0       24.04%  25.35%
1       28.28%  23.96%
2       25.05%  23.76%
3       22.63%  26.93%

Sample Data and imports used:

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt

np.random.seed(5)
df = pd.DataFrame({'Gender': np.random.choice([0, 1], 1000),
                   'Dis': np.random.choice([0, 1, 2, 3], 1000)})

edited Jun 28, 2021 at 11:44

answered Jun 25, 2021 at 16:25

Henry Ecker♦

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3 Comments

Numbermind Over a year ago

Thank you, is there a way to put percetange instead?

Numbermind Over a year ago

Thanks. but I referring to show the percentage for each Gender.. For example the blue bar would be 25% at 0; 28% at 1; 27% at 2; 20% at 3 and similar to the orange bar.

Henry Ecker Over a year ago

Easy fix, just sum on axis=0 instead see the edits. @Numbermind

Lino Cóndor · Accepted Answer · 2021-06-25 17:10:09Z

If you want to do this with a for loop:

import pandas as pd  
import numpy as np
import matplotlib.pyplot as plt

# assign data of lists.  
data = {'Gender': [1,0,0,1,0,0,1,1], 'Dis': [2,0,1,3,0,1,0,1]}  

# Create DataFrame  
df = pd.DataFrame(data)  

# Print the output.  
print(df)

Then you create empty variables:

number_males_dis_0 = 0
number_females_dis_0 = 0

number_males_dis_1 = 0
number_females_dis_1 = 0

number_males_dis_2 = 0
number_females_dis_2 = 0

number_males_dis_3 = 0
number_females_dis_3 = 0

for i in range(0,len(data['Dis'])):
  #print(i)
  #dis = 0
  if data['Dis'][i] == 0 and data['Gender'][i] == 0:
    number_males_dis_0 += 1
  elif data['Dis'][i] == 0 and data['Gender'][i] == 1:
    number_females_dis_0 += 1
  #dis = 1
  elif data['Dis'][i] == 1 and data['Gender'][i] == 0:
    number_males_dis_1 += 1
  elif data['Dis'][i] == 1 and data['Gender'][i] == 1:
    number_females_dis_1 += 1
  #dis = 2
  elif data['Dis'][i] == 2 and data['Gender'][i] == 0:
    number_males_dis_2 += 1
  elif data['Dis'][i] == 2 and data['Gender'][i] == 1:
    number_females_dis_2 += 1
  #dis = 3
  elif data['Dis'][i] == 3 and data['Gender'][i] == 0:
    number_males_dis_3 += 1
  elif data['Dis'][i] == 3 and data['Gender'][i] == 1:
    number_females_dis_3 += 1

Then the plot:

X = ['0','1','2','3']
M = [number_males_dis_0,number_males_dis_1,number_males_dis_2,number_males_dis_3]
F = [number_females_dis_0,number_females_dis_1,number_females_dis_2,number_females_dis_3]

X_axis = np.arange(len(X))

plt.bar(X_axis - 0.2, M, 0.4, label = 'Male')
plt.bar(X_axis + 0.2, F, 0.4, label = 'Female')

plt.xticks(X_axis, X)
plt.xlabel("")
plt.ylabel("")
plt.ylim(0,max([max(F),max(M)])+0.5)
plt.legend()
plt.title("title")
# Text on the top of each bar
for i in range(0,4):
    plt.text(x = i - 0.25 , y = M[i] + 0.05, s = M[i], size = 10)
    plt.text(x = i + 0.15 , y = F[i] + 0.05, s = F[i], size = 10)
plt.show()

Result: Result

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