Line plots with plot() in Pandas

Introduction

Line plots help us see how data changes over time or order. They make trends easy to spot.

Tracking daily temperatures over a week.

Showing sales growth month by month.

Comparing stock prices over several days.

Visualizing how a student's grades change each semester.

Syntax

Pandas

DataFrame.plot(kind='line', x=None, y=None, title=None)

kind='line' tells pandas to make a line plot (default).

x and y specify which columns to use for the horizontal and vertical axes.

Examples

Plot all numeric columns as lines using the DataFrame index for x-axis.

Pandas

df.plot()

Plot 'Sales' values over 'Month' values as a line.

Pandas

df.plot(x='Month', y='Sales')

Plot multiple columns 'Sales' and 'Profit' as separate lines.

Pandas

df.plot(kind='line', y=['Sales', 'Profit'])

Sample Program

This code creates a small table of sales data by month. Then it draws a line plot showing sales for each month. Markers highlight each data point.

Pandas

import pandas as pd
import matplotlib.pyplot as plt

# Create sample data
sales_data = {
    'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May'],
    'Sales': [200, 220, 250, 270, 300],
    'Profit': [50, 55, 60, 65, 70]
}
df = pd.DataFrame(sales_data)

# Plot Sales over Month
ax = df.plot(x='Month', y='Sales', title='Monthly Sales', marker='o')
plt.ylabel('Sales')
plt.show()

Important Notes

If you don't specify x, pandas uses the DataFrame index for the horizontal axis.

You can add markers to points with marker='o' to see exact values.

Use plt.show() to display the plot when running in scripts.

Summary

Line plots show how data changes over an ordered axis like time.

Use df.plot(x=..., y=...) to pick which columns to plot.

Markers and titles make plots easier to understand.