import pandas as pd data = pd.DataFrame([ (‘Q1′,’Blue’,100), (‘Q1′,’Green’,300), (‘Q2′,’Blue’,200), (‘Q2′,’Green’,350), (‘Q3′,’Blue’,300), (‘Q3′,’Green’,400), (‘Q4′,’Blue’,400), (‘Q4′,’Green’,450), ], columns=[‘quarter’, ‘company’, ‘value’] ) data = data.set_index([‘quarter’, ‘company’]).value data.unstack().plot(kind=’bar’, stacked=True) If you don’t want to stack your bar chart: data.unstack().plot(kind=’bar’)
The following method is more succinct, and easily scales. Putting the data into a pandas.DataFrame is the easiest way to plot a stacked bar plot. Using pandas.DataFrame.plot.bar(stacked=True), or pandas.DataFrame.plot(kind=’bar’, stacked=True), is the easiest way to plot a stacked bar plot. This method returns a matplotlib.axes.Axes or a numpy.ndarray of them. Since seaborn is just a … Read more
Tested in python 3.11, pandas 1.5.1, matplotlib 3.6.2 Sample Data and Imports import pandas as pd import matplotlib.pyplot as plt import numpy as np np.random.seed(2022) # creates a consistent sample y = np.random.rand(10,4) y[:,0]= np.arange(10) df = pd.DataFrame(y, columns=[“X”, “A”, “B”, “C”]) X A B C 0 0.0 0.499058 0.113384 0.049974 1 1.0 0.486988 0.897657 … Read more
Are you getting errors, or just not sure where to start? %pylab inline import pandas as pd import matplotlib.pyplot as plt df2 = df.groupby([‘Name’, ‘Abuse/NFF’])[‘Name’].count().unstack(‘Abuse/NFF’).fillna(0) df2[[‘abuse’,’nff’]].plot(kind=’bar’, stacked=True)