What are ticks?

Ticks are small marks along the axes that show specific data points on the plot.

 

How to control tick marks and labels?

Using xticks and yticks methods, we can customize the tick positions and labels on the x-axis and y-axis of a plot.

 

Example

# Custom x-axis and y-axis ticks and labels
custom_xticks = [2010, 2012, 2014, 2016, 2018, 2020, 2022]  # Custom x-axis tick positions
custom_yticks = [1100, 1150, 1200, 1250, 1300, 1400, 1500]  # Custom y-axis tick positions
custom_xtick_labels = [2010, 2012, 2014, 2016, 2018, 2020, 2022]  # Custom x-axis tick labels
custom_ytick_labels = ['11k', '11.5k', '12k', '12.5k', '13k', '14k', '15k']  # Custom y-axis tick labels

# Apply custom ticks and labels
plt.xticks(custom_xticks, custom_xtick_labels)
plt.yticks(custom_yticks, custom_ytick_labels)

Specifying the labels to xticks, yticks method is optional, if the labels are not provided, matplotlib will use the tick positions as the tick labels.

 

x_and_y_ticks.py

import matplotlib.pyplot as plt

# Sample data
year = [2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]
population = [1246.2, 1262.6, 1279.7, 1297.4, 1315.8, 1334.9, 1354.7, 1374.2, 1407.6, 1417.2]

# Create a line plot
plt.plot(year, population)

# Custom x-axis and y-axis ticks and labels
custom_xticks = [2010, 2012, 2014, 2016, 2018, 2020, 2022]  # Custom x-axis tick positions
custom_yticks = [1100, 1150, 1200, 1250, 1300, 1400, 1500]  # Custom y-axis tick positions
# custom_xtick_labels = [2010, 2012, 2014, 2016, 2018, 2020, 2022]  # Custom x-axis tick labels
custom_ytick_labels = ['11k', '11.5k', '12k', '12.5k', '13k', '14k', '15k']  # Custom y-axis tick labels

# Apply custom ticks and labels
plt.xticks(custom_xticks)
plt.yticks(custom_yticks, custom_ytick_labels)

# Add gridlines for better readability
plt.grid()

# Add labels and title
plt.xlabel('Year')
plt.ylabel('Population in Millions')
plt.title('India Population in millions by year wise', verticalalignment='center',
          horizontalalignment='center',  color='blue', fontweight='bold',
          backgroundcolor='lightblue')

# Display the plot
plt.show()

Output

 

 

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