plotting two DataFrame.value_counts() in a single histogram

Question

I want to plot in a single histogram two different dataframes (only one column from each).

d1 = {'Size': ['Big', 'Big', 'Normal','Big']}
df1 = pd.DataFrame(data=d1)

d2 = {'Size': ['Small','Normal','Normal','Normal', 'Small', 'Big', 'Big', 'Normal','Big']}
df2 = pd.DataFrame(data=d2)

#Plotting in one histogram
df1['Size'].value_counts().plot.bar(label = "df1")
df2['Size'].value_counts().plot.bar(label = "df2", alpha = 0.2,color='purple')

plt.legend(loc='upper right')
plt.show()

The issue is that in the x-axis of the histogram is only correct for df2. For df1 there should be 3 values of ‘big’ and 1 value of ‘normal’:

histogram of df1 and df2.

I have tried multiple ways of generating the plot and this is the closest I got to what I want, which is both dataframes in the same histogram, with different colors.

Ideally they would be side to side, but I didn’t manage to find how, and ‘stacked = False’ doesn’t work here.

Any help is welcome. Thanks!

Asked By: Andres

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Source

Answer 1

You can reindex on explicit X-values:

x = ['Small', 'Normal', 'Big']
df1['Size'].value_counts().reindex(x).plot.bar(label = "df1")
df2['Size'].value_counts().reindex(x).plot.bar(label = "df2", alpha = 0.2,color='purple')

Output:

Another option:

(pd.concat({'df1': df1, 'df2': df2})['Size']
   .groupby(level=0).value_counts()
   .unstack(0)
   .plot.bar()
)

Output:

Answered By: mozway

Answer 2

You can also try plotly which produces interactive graphs. That is we can hover over the plots and see exact data values and other information.

import plotly.graph_objects as go
classes=['Small', 'Normal', 'Large']
#classes=df2.Size.unique()  (better to use this)

fig = go.Figure(data=[
    go.Bar(name='df1', x=classes, y=df1.value_counts()),
    go.Bar(name='df2', x=classes, y=df2.value_counts())
])
# Change the bar mode
fig.update_layout(barmode='group')
fig.show()

Output:

Answered By: Hari Sharma

plotting two DataFrame.value_counts() in a single histogram

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