How to create a stacked bar chart for my DataFrame using seaborn [duplicate]

Question

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() 

Answer 1

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: