How to create grouped bar plots in a single figure from a wide dataframe

Question

I have the following df:

test_df = pd.DataFrame({'name': ['a', 'b', 'c'], 'res1': [1,2,3], 'res2': [4,5,6]})

I would like to plot (ideally, in one chart) the data, so I can compare res1 with res2 separately for each row (each name).

I have tried to achieve something similar, but I suppose that there is a simpler and more elegant solution that would also allow me to have it all in one chart with name as a group on x-axis.

    plt.subplot(1, 3, i+1)
    sns.barplot(x=test_df.iloc[i,1:].index.tolist(), y=test_df.iloc[i,1:].values.tolist())
    plt.title(test_df.iloc[i,0])

Answer 1

• This can be done with seaborn.barplot, or with just using pandas.DataFrame.plot, which avoids the additional import.
• Annotate as shown in How to plot and annotate a grouped bar chart
  • Add annotations with .bar_label, which is available with matplotlib 3.4.2.
  • The link also shows how to add annotations if using a previous version of matplotlib.
• Using pandas 1.3.0, matplotlib 3.4.2, and seaborn 0.11.1

With pandas.DataFrame.plot

• This option requires setting x='name', or res1 and res2 as the index.

import pandas as pd

test_df = pd.DataFrame({'name': ['a', 'b', 'c'], 'res1': [1,2,3], 'res2': [4,5,6]})

# display(test_df)
  name  res1  res2
0    a     1     4
1    b     2     5
2    c     3     6

# plot with 'name' as the x-axis
p1 = test_df.plot(kind='bar', x='name', rot=0)

# annotate each group of bars
for p in p1.containers:
    p1.bar_label(p, fmt='%.1f', label_type='edge')

import pandas as pd

test_df = pd.DataFrame({'name': ['a', 'b', 'c'], 'res1': [1,2,3], 'res2': [4,5,6]})

# set name as the index and then Transpose the dataframe
test_df = test_df.set_index('name').T

# display(test_df)
name  a  b  c
res1  1  2  3
res2  4  5  6

# plot and annotate
p1 = test_df.plot(kind='bar', rot=0)

for p in p1.containers:
    p1.bar_label(p, fmt='%.1f', label_type='edge')

With seaborn.barplot

• Convert the dataframe from a wide to long format with pandas.DataFrame.melt, and then use the hue parameter.

import pandas as pd
import seaborn as sns

test_df = pd.DataFrame({'name': ['a', 'b', 'c'], 'res1': [1,2,3], 'res2': [4,5,6]})

# melt the dataframe into a long form
test_df = test_df.melt(id_vars='name')

# display(test_df.head())
  name variable  value
0    a     res1      1
1    b     res1      2
2    c     res1      3
3    a     res2      4
4    b     res2      5

# plot the barplot using hue; switch the columns assigned to x and hue if you want a, b, and c on the x-axis.
p1 = sns.barplot(data=test_df, x='variable', y='value', hue='name')

# add annotations
for p in p1.containers:
    p1.bar_label(p, fmt='%.1f', label_type='edge')

• With x='variable', hue='name'

• With x='name', hue='variable'

Answer 2

The data could be formatted more easily, but it can be represented as a single graph by converting the data format to a vertical format and specifying a bar chart.

import pandas as pd
import seaborn as sns

test_df = pd.DataFrame({'name': ['a', 'b', 'c'], 'res1': [1,2,3], 'res2': [4,5,6]})
df = test_df.set_index('name').unstack().to_frame(name='values')
df.reset_index(inplace=True)
df.rename(columns={'level_0':'categ'}, inplace=True)
sns.barplot(x='name', y='values', hue='categ', data=df)

	categ	name	values
0	res1	a	1
1	res1	b	2
2	res1	c	3
3	res2	a	4
4	res2	b	5
5	res2	c	6