Boxplot of Multiple Columns of a Pandas Dataframe on the Same Figure (seaborn)
Question:
I feel I am probably not thinking of something obvious. I want to put in the same figure, the box plot of every column of a dataframe, where on the x-axis I have the columns’ names. In the seaborn.boxplot() this would be equal to groupby by every column.
In pandas I would do
df = pd.DataFrame(data = np.random.random(size=(4,4)), columns = ['A','B','C','D'])
df.boxplot()
which yields
Now I would like to get the same thing in seaborn. But when I try sns.boxplot(df), I get only one grouped boxplot. How do I reproduce the same figure in seaborn?
Answers:
The seaborn equivalent of
df.boxplot()
is
sns.boxplot(x="variable", y="value", data=pd.melt(df))
or just
sns.boxplot(data=df)
which will plot any column of numeric values, without converting the DataFrame from a wide to long format, using seaborn v0.11.1. This will create a single figure, with a separate boxplot for each column.
Complete example with melt:
import numpy as np; np.random.seed(42)
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.DataFrame(data = np.random.random(size=(4,4)), columns = ['A','B','C','D'])
sns.boxplot(x="variable", y="value", data=pd.melt(df))
plt.show()
This works because pd.melt converts a wide-form dataframe
A B C D
0 0.374540 0.950714 0.731994 0.598658
1 0.156019 0.155995 0.058084 0.866176
2 0.601115 0.708073 0.020584 0.969910
3 0.832443 0.212339 0.181825 0.183405
to long-form
variable value
0 A 0.374540
1 A 0.156019
2 A 0.601115
3 A 0.832443
4 B 0.950714
5 B 0.155995
6 B 0.708073
7 B 0.212339
8 C 0.731994
9 C 0.058084
10 C 0.020584
11 C 0.181825
12 D 0.598658
13 D 0.866176
14 D 0.969910
15 D 0.183405
You could use the built-in pandas method df.plot(kind=’box’) as suggested in this question.
I realize this answer will not help you if you have to use seaborn, but it may be useful for people with simpler requirements.
import numpy as np; np.random.seed(42)
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame(data = np.random.random(size=(4,4)), columns = ['A','B','C','D'])
df.plot(kind='box')
plt.show()
plt.boxplot([df1,df2], boxprops=dict(color='red'), labels=['title 1','title 2'])
The rest of the answers are great and should work well for most use-cases.
But if someone has the same problem as I have where the range of values is very large for one column (possibly a different scale) and you are not able to see anything else for other columns you can do the following: utilize subplots in order to create multiple y-axes within the figure.
# Store the list of columns
columns_to_plot = ['A', 'B', 'C', 'D']
# Create the figure and two subplots
fig, axes = plt.subplots(ncols=len(columns_to_plot))
# Create the boxplot with Seaborn
for column, axis in zip(columns_to_plot, axes):
sns.boxplot(data=df[column], ax=axis)
axis.set_title(column)
# axis.set(xticklabels=[], xticks=[], ylabel=column)
# Show the plot
plt.tight_layout()
plt.show()
I have also added a commented out line for removing the redundant xticks and their labels, which looked really annoying to me, as well as setting the ylabel name.
I feel I am probably not thinking of something obvious. I want to put in the same figure, the box plot of every column of a dataframe, where on the x-axis I have the columns’ names. In the seaborn.boxplot() this would be equal to groupby by every column.
In pandas I would do
df = pd.DataFrame(data = np.random.random(size=(4,4)), columns = ['A','B','C','D'])
df.boxplot()
which yields
Now I would like to get the same thing in seaborn. But when I try sns.boxplot(df), I get only one grouped boxplot. How do I reproduce the same figure in seaborn?
The seaborn equivalent of
df.boxplot()
is
sns.boxplot(x="variable", y="value", data=pd.melt(df))
or just
sns.boxplot(data=df)
which will plot any column of numeric values, without converting the DataFrame from a wide to long format, using seaborn v0.11.1. This will create a single figure, with a separate boxplot for each column.
Complete example with melt:
import numpy as np; np.random.seed(42)
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.DataFrame(data = np.random.random(size=(4,4)), columns = ['A','B','C','D'])
sns.boxplot(x="variable", y="value", data=pd.melt(df))
plt.show()
This works because pd.melt converts a wide-form dataframe
A B C D
0 0.374540 0.950714 0.731994 0.598658
1 0.156019 0.155995 0.058084 0.866176
2 0.601115 0.708073 0.020584 0.969910
3 0.832443 0.212339 0.181825 0.183405
to long-form
variable value
0 A 0.374540
1 A 0.156019
2 A 0.601115
3 A 0.832443
4 B 0.950714
5 B 0.155995
6 B 0.708073
7 B 0.212339
8 C 0.731994
9 C 0.058084
10 C 0.020584
11 C 0.181825
12 D 0.598658
13 D 0.866176
14 D 0.969910
15 D 0.183405
You could use the built-in pandas method df.plot(kind=’box’) as suggested in this question.
I realize this answer will not help you if you have to use seaborn, but it may be useful for people with simpler requirements.
import numpy as np; np.random.seed(42)
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame(data = np.random.random(size=(4,4)), columns = ['A','B','C','D'])
df.plot(kind='box')
plt.show()
plt.boxplot([df1,df2], boxprops=dict(color='red'), labels=['title 1','title 2'])
The rest of the answers are great and should work well for most use-cases.
But if someone has the same problem as I have where the range of values is very large for one column (possibly a different scale) and you are not able to see anything else for other columns you can do the following: utilize subplots in order to create multiple y-axes within the figure.
# Store the list of columns
columns_to_plot = ['A', 'B', 'C', 'D']
# Create the figure and two subplots
fig, axes = plt.subplots(ncols=len(columns_to_plot))
# Create the boxplot with Seaborn
for column, axis in zip(columns_to_plot, axes):
sns.boxplot(data=df[column], ax=axis)
axis.set_title(column)
# axis.set(xticklabels=[], xticks=[], ylabel=column)
# Show the plot
plt.tight_layout()
plt.show()
I have also added a commented out line for removing the redundant xticks and their labels, which looked really annoying to me, as well as setting the ylabel name.