How to display a Dataframe next to a Plot in Jupyter Notebook?

In Jupyter Notebook, you can display a DataFrame and a plot side by side using matplotlib's subplot functionality. This creates a professional-looking visualization that combines both tabular and graphical data representation.

Steps to Display DataFrame Next to Plot

• Set the figure size and adjust the padding between and around the subplots

• Create a Pandas DataFrame with sample data

• Create a figure with two subplots using add_subplot()

• Plot the data in the first subplot using scatter() method

• Display the DataFrame as a table in the second subplot using table() method

• Turn off axes for the table subplot for cleaner appearance

Example

Here's how to create a side-by-side display of a scatter plot and DataFrame table ?

import matplotlib.pyplot as plt
import pandas as pd

# Set figure size and layout
plt.rcParams["figure.figsize"] = [7.50, 3.50]
plt.rcParams["figure.autolayout"] = True

# Create sample DataFrame
df = pd.DataFrame({
    'Straight': [i for i in range(10)],
    'Square': [i * i for i in range(10)]
})

# Create figure and subplots
fig = plt.figure()

# First subplot: scatter plot
ax1 = fig.add_subplot(121)
ax1.scatter(x=df.Straight, y=df.Square, color='blue', alpha=0.7)
ax1.set_xlabel('Straight Values')
ax1.set_ylabel('Square Values')
ax1.set_title('Scatter Plot')

# Second subplot: DataFrame table
ax2 = fig.add_subplot(122)
ax2.axis('off')  # Turn off axes for cleaner table appearance

# Create table
font_size = 14
bbox = [0, 0, 1, 1]
mpl_table = ax2.table(
    cellText=df.values, 
    rowLabels=df.index, 
    colLabels=df.columns,
    bbox=bbox,
    cellLoc='center'
)
mpl_table.auto_set_font_size(False)
mpl_table.set_fontsize(font_size)
ax2.set_title('DataFrame Table')

plt.tight_layout()
plt.show()

Key Parameters

Alternative Layout Options

You can also create vertical layouts by changing the subplot configuration ?

import matplotlib.pyplot as plt
import pandas as pd

# Vertical layout (2 rows, 1 column)
plt.rcParams["figure.figsize"] = [6, 8]

df = pd.DataFrame({
    'X': [1, 2, 3, 4, 5],
    'Y': [2, 4, 6, 8, 10]
})

fig = plt.figure()

# Plot on top
ax1 = fig.add_subplot(211)  # 2 rows, 1 column, position 1
ax1.plot(df.X, df.Y, marker='o')
ax1.set_title('Line Plot')

# Table on bottom
ax2 = fig.add_subplot(212)  # 2 rows, 1 column, position 2
ax2.axis('off')
table = ax2.table(cellText=df.values, colLabels=df.columns, bbox=[0, 0, 1, 1])
table.set_fontsize(12)
ax2.set_title('Data Table')

plt.tight_layout()
plt.show()

Conclusion

Using matplotlib's subplot functionality allows you to create professional visualizations combining plots and DataFrames. Use add_subplot() to create side-by-side layouts and table() to display DataFrame data alongside your plots.