How to Plot Pandas DataFrame as Bar and Line on the Same Chart

We can use secondary axes to combine bar and line plots on the same chart with Matplotlib and Pandas.

This short guide explains how to plot a Pandas DataFrame with both a bar chart and a line plot on the same chart.

Steps to Plot Bar and Line on the Same Chart

1. Import the necessary libraries (pandas and matplotlib).
2. Create or load a DataFrame containing your data.
3. Use the Pandas .plot() method to create the bar chart.
4. Add the line plot using a secondary Y-axis to distinguish its scale (optional if scales differ).
5. Customize the chart (titles, legends, axis labels, etc.).

Example Data

Consider the following dataset, where we have monthly revenue and profit values:

import pandas as pd
import matplotlib.pyplot as plt

# Example data
data = {
    'Month': ['January', 'February', 'March', 'April'],
    'Revenue': [1000, 1200, 1100, 1400],
    'Profit': [200, 250, 300, 350]
}

df = pd.DataFrame(data)

data:

Combine bar and line plots

Below is an example to create a combined bar and line chart:

import pandas as pd
import matplotlib.pyplot as plt

# Example data
data = {
    'Month': ['January', 'February', 'March', 'April'],
    'Revenue': [1000, 1200, 1100, 1400],
    'Profit': [200, 250, 300, 350]
}

df = pd.DataFrame(data)

fig, ax = plt.subplots(figsize=(6, 4))
df.plot(x='Month', y='Revenue', kind='bar', color='skyblue', ax=ax, label='Revenue')

df.plot(x='Month', y='Profit', kind='line', color='orange', ax=ax.twinx(), label='Profit')

ax.set_title('Revenue and Profit by Month')
ax.set_xlabel('Month')
ax.set_ylabel('Revenue ($)')
ax.legend(loc='upper center')

plt.tight_layout()
plt.show()

Explanation of the Code

1. Bar Chart: The kind='bar' argument in df.plot() generates a bar chart for revenue.
2. Line Chart with Secondary Axis: Using ax.twinx() allows the line plot (profit) to use a secondary Y-axis.
3. Customization: Titles, labels, and legends are added for clarity.

Output

The resulting chart combines a bar plot (Revenue) and a line plot (Profit), making it easy to compare trends between the two metrics, even when they have different scales:

Example: Multiple Bar Charts with single Line chart:

df = pd.DataFrame({
    'month': ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec'], 
    '2023': [1, 2, 3, 3, 5, 6, 7, 1, 9, 4, 5, 6], 
    '2024': [1, 7, 4, 6, *[float('nan')]* 8], 
    '2025': [7, 5, 9, 4, 2, 7, 5, 2, 3, 4, 10, 1]})

df['month'] = pd.to_datetime(df['month'], format='%b')

fig, ax = plt.subplots(1, facecolor='white')
df.plot(y='2025', linestyle='-', marker='.', ax=ax, color='red')
df.plot(x='month', y=['2023', '2024'], kind='bar', ax=ax, title='Monthly sells and estimate')

ax.set(xticklabels=pd.to_datetime([dt.get_text() for dt in ax.get_xticklabels()]).strftime('%b'));

Result: