A line chart is a graphical representation of the evolution of a variable over a continuous range, where data points are connected by lines to show the trend and variation in the data. Line charts display the data as a continuous line.
Pandas, a powerful data manipulation library in Python, allows us to create line charts easily. In this post, we will explore how to leverage Pandas to create beautiful line chart with multiple variables or groups.


Pandas is a popular open-source Python library used for data manipulation and analysis. It provides data structures and functions that make working with structured data, such as tabular data (like Excel spreadsheets or SQL tables), easy and intuitive.

To install Pandas, you can use the following command in your command-line interface (such as Terminal or Command Prompt):

pip install pandas

Matplotlib functionalities have been integrated into the pandas library, facilitating their use with dataframes and series. For this reason, you might also need to import the matplotlib library when building charts with Pandas.

This also means that they use the same functions, and if you already know Matplotlib, you'll have no trouble learning plots with Pandas.

import pandas as pd
import random
import numpy as np  # used for data generation
import matplotlib.pyplot as plt

Line chart with multiple variables


In order to create graphics with Pandas, we need to use pandas objects: Dataframes and Series. A dataframe can be seen as an Excel table, and a series as a column in that table. This means that we must systematically convert our data into a format used by pandas.

Since line charts need quantitative variables, we will create a dataset with temperature, pressure and humidity, evolving through time.

# Define the number of time points
num_time_points = 100

# Generate time values
time_values = np.arange(num_time_points)

# Generate random data for three variables (e.g., temperature, pressure, and humidity)
# Random temperature values between 20 and 30
temperature = np.random.uniform(200, 400, num_time_points)
# Random pressure values between 900 and 1100
pressure = np.random.uniform(500, 700, num_time_points)
# Random humidity values between 30 and 70
humidity = np.random.uniform(800, 1000, num_time_points)

data = {
    'Time': time_values,
    'Temperature': temperature,
    'Pressure': pressure,
    'Humidity': humidity
df = pd.DataFrame(data)

Create the plot

Once we've opened our dataset, we'll now create the graph. The following displays the evolution of our variables using the plot() function, and since we want the evolution of every variable in our pandas dataframe, we juste have to specify which variable will be in the x-axis, which is 'Time'.

Also, keep in mind that the kind='line' argument is facultative (you can remove it!) since it's the default value when calling the plot() function.

# Define the size of the figure
plt.figsize = (8, 6)

# Create and display the linechart
        kind='line',  # (facultative) Default argument
        grid=True,  # Add a grid in the background
plt.legend(loc='upper right',
           # Shift the legend outside the chart (35% on the right)
           bbox_to_anchor=(1.35, 1),

Line chart with multiple groups


Now let's say we want to display the evolution of temperature only and for different rows. For this, we create a new dataset:

  • random temperature data is generated using np.random.uniform(). This function creates random numbers within a specified range.
  • country_labels is created using np.repeat(). It repeats the country names in the countries array so that each country name is associated with its corresponding temperature data points.
  • time_values is generated using np.tile() and np.arange(). It creates a sequence of numbers from 0 to 19 (for the 20 data points per country) and then repeats this sequence for each country
# Define the number of data points
num_data_points_per_country = 20

# Generate random temperature data for each country
# Temperature range for France (10-20 degrees)
france_temperatures = np.random.uniform(10, 20, num_data_points_per_country)
# Temperature range for Germany (0-10 degrees)
germany_temperatures = np.random.uniform(0, 10, num_data_points_per_country)
# Temperature range for Italy (25-30 degrees)
italy_temperatures = np.random.uniform(25, 30, num_data_points_per_country)

# Create an array of country labels corresponding to the data points
countries = ['France', 'Germany', 'Italy']
country_labels = np.repeat(countries, num_data_points_per_country)

# Generate time values
time_values = np.tile(np.arange(num_data_points_per_country), len(countries))

# Create a Pandas DataFrame
data = {
    'Country': country_labels,
    'Temperature': np.concatenate([france_temperatures, germany_temperatures, italy_temperatures]),
    'Time': time_values

df = pd.DataFrame(data)

Create the plot

We begin by grouping the data by country, then creates a figure and axis for plotting. For each country, we plot the temperature against time as separate lines on the same graph, adds labels, a title, a legend for country identification, and a grid for clarity.

# Group the data by continent and calculate the average life expectancy for each year
df_country = df.groupby(['Country'])

# Create a figure and axis object using the object-oriented approach
fig, ax = plt.subplots(figsize=(8, 6))

# Plot each group as a separate line
for key, group in df_country:
    ax.plot(group['Time'], group['Temperature'], label=key)

# Set axis labels and title
ax.set_title('Temperature Evolution by Country')

# Add a legend

# Display the grid

# Show the plot

Going further

This post explains how to create a line chart with mutliple variables and groups, with pandas.

For more examples of how to create or customize your line charts, see the line charts section. You may also be interested in how to customize a line chart with pandas.


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