How to create a grouped bar plot

Pandas will show grouped bars by columns. Entries in each row but different columns will constitute a group in the resulting plot. Hence you need to "reshape" your dataframe to have the "group" as columns.In this case you can pivot like

df.pivot("column", "group", "val")

producing

group   g1  g2column        c1      10   8c2      12  10c3      13  12

Plotting this will result in a grouped bar chart.

import pandas as pdimport matplotlib.pyplot as pltdf = pd.DataFrame([['g1','c1',10],['g1','c2',12],['g1','c3',13],['g2','c1',8],                   ['g2','c2',10],['g2','c3',12]],columns=['group','column','val'])df.pivot("column", "group", "val").plot(kind='bar')plt.show()

You can simply do this using the code given below:

import pandas as pdimport matplotlib.pyplot as pltpositive_values = [20, 17.5, 40]negative_values = [15, 8, 70]index = ['Precision', 'Recall', 'f1-score',]df = pd.DataFrame({'Positive Values': positive_values,                    'Negative Values': negative_values}, index=index)ax = df.plot.bar(rot=0, color={"Positive Values": "green", "Negative Values": "red"})

Output:

• Given a dataframe of long (tidy) data, as shown in the OP, an implementation that does not require transforming the dataframe is to use seaborn.barplot with the hue parameter.
• seaborn is a high-level API for matplotlib
• Tested with seaborn 0.11.1 and matplotlib 3.4.2

import pandas as pdimport seaborn as sns# the sample dataframe from the OPdf = pd.DataFrame([['g1', 'c1', 10], ['g1', 'c2', 12], ['g1', 'c3', 13], ['g2', 'c1', 8], ['g2', 'c2', 10], ['g2', 'c3', 12]], columns=['group', 'column', 'val'])# plot with seaborn barplotsns.barplot(data=df, x='column', y='val', hue='group')