Understanding the Issue with Pandas Boxplot Containing Previous Plot’s Content

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In this article, we will delve into an issue reported by a user regarding pandas boxplot containing content of previous plot’s content. We will explore the cause of this problem and provide solutions to resolve it.

Background

The pandas library provides data structures and functions for efficient data analysis in Python. One of its features is the ability to create boxplots, which are useful for visualizing the distribution of data.

However, when creating a boxplot using pandas’ boxplot() function, it may sometimes contain content from previous plots, resulting in unexpected behavior. This issue arises due to the way matplotlib handles figure creation and memory management.

The Problem

The user’s code snippet provided demonstrates the problem:

import pandas as pd

d1 = {'ff_opt_time': [10, 20, 11, 5, 15 , 13, 19, 25  ], 'ff_count_opt': [30, 40, 45, 29, 35,38,32,41]}
df1 = pd.DataFrame(data=d1)
d2 = {'ff_opt_time': [1, 2, 1, 5, 1 , 1, 4, 5  ], 'ff_count_opt': [3, 4, 4, 9, 5,3, 2,4]}
df2 = pd.DataFrame(data=d2)

def evaluate2(df1, df2):
    df_ot = pd.DataFrame(columns=['opt_time1' , 'opt_time2'])
    df_ot['opt_time1'] = df1['ff_opt_time']
    df_ot['opt_time2'] = df2['ff_opt_time']
    boxplot1 = df_ot.boxplot(rot=45,fontsize=5)
    fig1 = boxplot1.get_figure()
    fig1.savefig("bp_opt_time.pdf")

    df_op = pd.DataFrame(columns=['count_opt1' , 'count_opt2'])
    df_op['count_opt1'] = df1['ff_count_opt']
    df_op['count_opt2'] = df2['ff_count_opt']
    boxplot2 = df_op.boxplot(rot=45,fontsize=5)
    fig2 = boxplot2.get_figure()
    fig2.savefig("bp_count_opt_perm.pdf")
evaluate2(df1, df2)

As shown in the provided code snippet, both boxplot1 and boxplot2 are created using pandas’ boxplot() function and saved as PDF files. However, instead of creating two separate plots, they seem to be contained within each other.

Solution 1: Using Pyplot Subplots

One way to resolve this issue is to use matplotlib’s pyplot module to create subplots for the boxplots:

import pandas as pd
import matplotlib.pyplot as plt

d1 = {'ff_opt_time': [10, 20, 11, 5, 15 , 13, 19, 25  ], 'ff_count_opt': [30, 40, 45, 29, 35,38,32,41]}
df1 = pd.DataFrame(data=d1)
d2 = {'ff_opt_time': [1, 2, 1, 5, 1 , 1, 4, 5  ], 'ff_count_opt': [3, 4, 4, 9, 5,3, 2,4]}
df2 = pd.DataFrame(data=d2)

def evaluate2(df1, df2):
    fig, axs = plt.subplots(1, 2, figsize=(12, 6))
    
    # Plot boxplot for 'opt_time' column
    df_ot = pd.DataFrame(columns=['opt_time1' , 'opt_time2'])
    df_ot['opt_time1'] = df1['ff_opt_time']
    df_ot['opt_time2'] = df2['ff_opt_time']
    axs[0].boxplot(df_ot.boxplot(rot=45,fontsize=5)[0])
    
    # Plot boxplot for 'count_opt' column
    df_op = pd.DataFrame(columns=['count_opt1' , 'count_opt2'])
    df_op['count_opt1'] = df1['ff_count_opt']
    df_op['count_opt2'] = df2['ff_count_opt']
    axs[1].boxplot(df_op.boxplot(rot=45,fontsize=5)[0])
    
    plt.tight_layout()
    plt.savefig("bp_opt_time.pdf")
    plt.close()
    plt.savefig("bp_count_opt_perm.pdf")
evaluate2(df1, df2)

In this revised code snippet, we create a figure with two subplots using plt.subplots(1, 2). We then plot the boxplot for each column separately on its respective subplot using axs[0].boxplot() and axs[1].boxplot(). Finally, we save both plots as separate PDF files.

Solution 2: Executing Boxplot in Different Cells

Another way to resolve this issue is to execute the boxplot creation code separately in different cells of a Jupyter Notebook:

import pandas as pd

d1 = {'ff_opt_time': [10, 20, 11, 5, 15 , 13, 19, 25  ], 'ff_count_opt': [30, 40, 45, 29, 35,38,32,41]}
df1 = pd.DataFrame(data=d1)

def plot_opt_time(df):
    boxplot = df.boxplot(rot=45,fontsize=5)
    fig = boxplot.get_figure()
    fig.savefig("bp_opt_time.pdf")

plot_opt_time(df1)

d2 = {'ff_opt_time': [1, 2, 1, 5, 1 , 1, 4, 5  ], 'ff_count_opt': [3, 4, 4, 9, 5,3, 2,4]}
df2 = pd.DataFrame(data=d2)

def plot_count_opt(df):
    boxplot = df.boxplot(rot=45,fontsize=5)
    fig = boxplot.get_figure()
    fig.savefig("bp_count_opt_perm.pdf")

plot_count_opt(df2)

In this revised code snippet, we define two separate functions plot_opt_time() and plot_count_opt() to create the boxplots. We then execute these functions separately in different cells of a Jupyter Notebook.

Clearing Plots

To prevent the plots from being saved multiple times, you can clear the plot using matplotlib’s clf() function before creating the next plot:

import pandas as pd
import matplotlib.pyplot as plt

# ...

def plot_opt_time(df):
    plt.clf()
    boxplot = df.boxplot(rot=45,fontsize=5)
    fig = boxplot.get_figure()
    fig.savefig("bp_opt_time.pdf")

def plot_count_opt(df):
    plt.clf()
    boxplot = df.boxplot(rot=45,fontsize=5)
    fig = boxplot.get_figure()
    fig.savefig("bp_count_opt_perm.pdf")

Alternatively, you can use plt.close() to close the current figure and clear the memory:

import pandas as pd
import matplotlib.pyplot as plt

# ...

def plot_opt_time(df):
    plt.close()
    plt.clf()
    boxplot = df.boxplot(rot=45,fontsize=5)
    fig = boxplot.get_figure()
    fig.savefig("bp_opt_time.pdf")

def plot_count_opt(df):
    plt.close()
    plt.clf()
    boxplot = df.boxplot(rot=45,fontsize=5)
    fig = boxplot.get_figure()
    fig.savefig("bp_count_opt_perm.pdf")

By using one of these solutions, you should be able to resolve the issue with pandas boxplot containing content of previous plot’s content.

Last modified on 2024-07-09