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I have a DataFrame df:
df = pd.DataFrame(columns=["App","Feature1", "Feature2","Feature3", "Feature4","Feature5", "Feature6","Feature7","Feature8"], data=[['SHA', 0, 0, 1, 1, 1, 0, 1, 0], ['LHA', 1, 0, 1, 1, 0, 1, 1, 0], ['DRA', 0, 0, 0, 0, 0, 0, 1, 0], ['FRA', 1, 0, 1, 1, 1, 0, 1, 1], ['BRU', 0, 0, 1, 0, 1, 0, 0, 0], ['PAR', 0, 1, 1, 1, 1, 0, 1, 0], ['AER', 0, 0, 1, 1, 0, 1, 1, 0], ['SHE', 0, 0, 0, 1, 0, 0, 1, 0]]) # display(df) App Feature1 Feature2 Feature3 Feature4 Feature5 Feature6 Feature7 Feature8 0 SHA 0 0 1 1 1 0 1 0 1 LHA 1 0 1 1 0 1 1 0 2 DRA 0 0 0 0 0 0 1 0 3 FRA 1 0 1 1 1 0 1 1 4 BRU 0 0 1 0 1 0 0 0 5 PAR 0 1 1 1 1 0 1 0 6 AER 0 0 1 1 0 1 1 0 7 SHE 0 0 0 1 0 0 1 0 I want to create a stacked bar chart so that each stack would correspond to App while the Y axis would contain the count of 1 values and the X axis would be Feature.
It should be similar to this bar chart with the only difference that now I want to see stack bars and a legend with colors:
df_c = df.iloc[:, 1:].eq(1).sum().rename_axis('Feature').reset_index(name='Count') df_c = df_c.sort_values('Count') plt.figure(figsize=(12,8)) ax = sns.barplot(x="Feature", y='Count', data=df_c, palette=sns.color_palette("GnBu", 10)) plt.xticks(rotation='vertical') ax.grid(b=True, which='major', color='#d3d3d3', linewidth=1.0) ax.grid(b=True, which='minor', color='#d3d3d3', linewidth=0.5) plt.show() 
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To create a stacked bar chart, we can use Seaborn's barplot () method, i.e., show point estimates and confidence intervals with bars. Create df using Pandas Data Frame.
This gives the overall graph a stacked look, with one set of observations placed over the second set. We do not have a function that can create such stacked bar plots directly, but we can use the traditional seaborn.barplot () method to plot two individual bar graphs and place them both on top of each other.
But if you’re new to Seaborn or new to data visualization in Python, you should probably read the full tutorial. Let’s quickly review bar charts. According to Wikipedia, bar charts (AKA, bar plots) are: a chart or graph that presents categorical data with rectangular bars with heights or lengths proportional to the values that they represent.
Given two series of data, Series 1 (“bottom”) and Series 2 (“top”), to create a stacked bar chart you just need to create: 1. Series 3 = Series 1 + Series 2. Once you have Series 3 (“total”), then you can use the overlay feature of matplotlib and Seaborn in order to create your stacked bar chart.
You could use pandas plot as @Bharath suggest:
import seaborn as sns sns.set() df.set_index('App').T.plot(kind='bar', stacked=True) Output:
Updated:
from matplotlib.colors import ListedColormap df.set_index('App')\ .reindex_axis(df.set_index('App').sum().sort_values().index, axis=1)\ .T.plot(kind='bar', stacked=True, colormap=ListedColormap(sns.color_palette("GnBu", 10)), figsize=(12,6))
Updated Pandas 0.21.0+ reindex_axis is deprecated, use reindex
from matplotlib.colors import ListedColormap df.set_index('App')\ .reindex(df.set_index('App').sum().sort_values().index, axis=1)\ .T.plot(kind='bar', stacked=True, colormap=ListedColormap(sns.color_palette("GnBu", 10)), figsize=(12,6)) Output:
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