Plotting grouped data in same plot using Pandas

Version 1:

You can create your axis, and then use the ax keyword of DataFrameGroupBy.plot to add everything to these axes:

import matplotlib.pyplot as plt

p_df = pd.DataFrame({"class": [1,1,2,2,1], "a": [2,3,2,3,2]})
fig, ax = plt.subplots(figsize=(8,6))
bp = p_df.groupby('class').plot(kind='kde', ax=ax)

This is the result:

Unfortunately, the labeling of the legend does not make too much sense here.

Version 2:

Another way would be to loop through the groups and plot the curves manually:

classes = ["class 1"] * 5 + ["class 2"] * 5
vals = [1,3,5,1,3] + [2,6,7,5,2]
p_df = pd.DataFrame({"class": classes, "vals": vals})

fig, ax = plt.subplots(figsize=(8,6))
for label, df in p_df.groupby('class'):
    df.vals.plot(kind="kde", ax=ax, label=label)
plt.legend()

This way you can easily control the legend. This is the result:

import matplotlib.pyplot as plt
p_df.groupby('class').plot(kind='kde', ax=plt.gca())

Another approach would be using seaborn module. This would plot the two density estimates on the same axes without specifying a variable to hold the axes as follows (using some data frame setup from the other answer):

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

# data to create an example data frame
classes = ["c1"] * 5 + ["c2"] * 5
vals = [1,3,5,1,3] + [2,6,7,5,2]
# the data frame 
df = pd.DataFrame({"cls": classes, "indices":idx, "vals": vals})

# this is to plot the kde
sns.kdeplot(df.vals[df.cls == "c1"],label='c1');
sns.kdeplot(df.vals[df.cls == "c2"],label='c2');

# beautifying the labels
plt.xlabel('value')
plt.ylabel('density')
plt.show()

This results in the following image.

• There are two easy methods to plot each group in the same plot.
  1. When using pandas.DataFrame.groupby, the column to be plotted, (e.g. the aggregation column) should be specified.
  2. Use seaborn.kdeplot or seaborn.displot and specify the hue parameter
• Using pandas v1.2.4, matplotlib 3.4.2, seaborn 0.11.1
• The OP is specific to plotting the kde, but the steps are the same for many plot types (e.g. kind='line', sns.lineplot, etc.).

Imports and Sample Data

• For the sample data, the groups are in the 'kind' column, and the kde of 'duration' will be plotted, ignoring 'waiting'.

import pandas as pd
import seaborn as sns

df = sns.load_dataset('geyser')

# display(df.head())
   duration  waiting   kind
0     3.600       79   long
1     1.800       54  short
2     3.333       74   long
3     2.283       62  short
4     4.533       85   long

Plot with pandas.DataFrame.plot

• Reshape the data using .groupby or .pivot

.groupby

• Specify the aggregation column, ['duration'], and kind='kde'.

ax = df.groupby('kind')['duration'].plot(kind='kde', legend=True)

.pivot

ax = df.pivot(columns='kind', values='duration').plot(kind='kde')

Plot with seaborn.kdeplot

• Specify hue='kind'

ax = sns.kdeplot(data=df, x='duration', hue='kind')

Plot with seaborn.displot

• Specify hue='kind' and kind='kde'

fig = sns.displot(data=df, kind='kde', x='duration', hue='kind')

Plot

Maybe you can try this:

fig, ax = plt.subplots(figsize=(10,8))
classes = list(df.class.unique())
for c in classes:
    df2 = data.loc[data['class'] == c]
    df2.vals.plot(kind="kde", ax=ax, label=c)
plt.legend()