pandas plot heatmap with code examples

Pandas is one of the most popular data science libraries that is used to manipulate and analyze data in Python. The library provides various tools to work with data like data cleaning, data manipulation, data visualization, etc. In this article, we will discuss pandas plot heatmap with code examples.

Heatmap is a graphical representation of data where the values are represented by colors. It is often used to visualize the correlation between different variables in a dataset. A heatmap is created using a 2D array where each element of the array represents a value that will be plotted on the heatmap. The values are represented by colors and the intensity of the color represents the magnitude of the value.

Using pandas dataframe, we can create a heatmap easily using the plot function. The plot function has a parameter called kind which takes the value of the type of plot that we want to create. In a heatmap, we use kind='heatmap' to create a heatmap.

Example 1: Creating a Heatmap using Pandas
In this example, we will create a simple heatmap using a pandas dataframe with ten random numbers.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

#creating a random dataframe
data = pd.DataFrame(np.random.rand(5, 10))

#creating heatmap
ax = plt.axes()
heatmap = ax.pcolor(data, cmap=plt.cm.Reds)

#plotting heatmap
plt.colorbar(heatmap)
plt.show()

Output:

heatmap_output_1.png

In the above code, we first import the necessary libraries like pandas, numpy, and matplotlib. Then, we create a random dataframe of size 5×10 using numpy's random.rand method. We then create a heatmap using matplotlib's Axes method and pass the dataframe and colormap as parameters. Finally, we plot the heatmap using matplotlib's plt.show() function.

Example 2: Creating a Heatmap from a CSV file

In this example, we will create a heatmap from a CSV file using pandas. We will use the flight dataset available in seaborn library. This dataset contains information about flights and their passengers.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

#loading dataset
flights_df = sns.load_dataset('flights')

#creating pivot table
flights = flights_df.pivot('month', 'year', 'passengers')

#creating heatmap
ax = sns.heatmap(flights, cmap="YlGnBu")

#plotting heatmap
plt.show()

Output:

heatmap_output_2.png

In the above code, we first import the necessary libraries like pandas, seaborn, and matplotlib. Then, we load the flight dataset using seaborn's load_dataset method. We then create a pivot table using pandas pivot method and pass the columns month, year, and passengers as parameters. Finally, we create a heatmap using seaborn's heatmap method and pass the pivot table and colormap as parameters. We then plot the heatmap using matplotlib's plt.show() function.

Conclusion

Pandas plot heatmap is a great way to visualize data with color-coded values. We have seen how we can create a heatmap using pandas dataframe and a CSV file. Pandas makes it very easy to create a heatmap using simple code snippets. Heatmaps are very useful in data science as they help to identify patterns and correlations in data. With pandas, you can create a heatmap with just a few lines of code.

here is some more information about the previous topics:

Example 1: Creating a Heatmap using Pandas

In this example, we created a simple heatmap using a pandas dataframe with ten random numbers. The first step was to import the necessary libraries: pandas, numpy, and matplotlib. Then, we created a random dataframe using the numpy.random.rand method and passed the dimensions as parameters. After that, we created a heatmap using matplotlib's Axes method and passed the dataframe and colormap as parameters. Finally, we plotted the heatmap using matplotlib's plt.show() function.

The output shows a heatmap with a color-coded representation of the values in the pandas dataframe. The diagonal line in the heatmap represents the values where the row number and column number are the same.

Example 2: Creating a Heatmap from a CSV file

In this example, we loaded the flight dataset available in seaborn library and created a heatmap using the pivot table. The flight dataset contains information about flights and their passengers. We first imported the necessary libraries: pandas, seaborn, and matplotlib. Using seaborn's load_dataset method, we loaded the flight dataset and then created a pivot table using the pandas pivot method. The pivot method takes the columns as parameters that we want to pivot. In this case, we passed the month, year, and passengers columns as parameters. After creating the pivot table, we created a heatmap using seaborn's heatmap method and passed the pivot table and colormap as parameters. Finally, we plotted the heatmap using matplotlib's plt.show() function.

The output shows a heatmap with a color-coded representation of the number of passengers for each month and year. From the heatmap, we can see that the number of passengers increased over the years and was highest in the month of July.

Conclusion

Heatmaps are a great way to visualize data with color-coded values. Pandas provides an easy way to create a heatmap using a pandas dataframe with just a few lines of code. Heatmaps are useful in identifying patterns and correlations in data and can help in making better data-driven decisions. Pandas plot heatmap is a simple yet powerful tool for data analysts and data scientists.

Popular questions

  1. What is a heatmap and why is it useful?
    A heatmap is a graphical representation of data where values are represented by colors. It is useful in identifying patterns and correlations in data, as well as providing a quick visual representation of data.

  2. How can you create a heatmap using pandas?
    Using pandas, you can create a heatmap by using the plot function with the parameter kind='heatmap'. You can pass in a pandas dataframe containing the data to plot.

  3. How do you change the color scheme of a heatmap?
    You can change the color scheme of a heatmap by passing a different colormap to the heatmap function. You can use any of the colormaps available in the matplotlib library.

  4. How can you create a heatmap from a CSV file?
    You can create a heatmap from a CSV file by loading the data into a pandas dataframe, creating a pivot table using the desired columns, and then passing the pivot table to the heatmap function.

  5. What other data types can be used to create a heatmap?
    Aside from pandas dataframes and CSV files, other data types that can be used to create a heatmap include numpy arrays and dictionaries. As long as the data can be represented in a 2D array, it can be used to create a heatmap.

Tag

"Heatmap"

I am a driven and diligent DevOps Engineer with demonstrated proficiency in automation and deployment tools, including Jenkins, Docker, Kubernetes, and Ansible. With over 2 years of experience in DevOps and Platform engineering, I specialize in Cloud computing and building infrastructures for Big-Data/Data-Analytics solutions and Cloud Migrations. I am eager to utilize my technical expertise and interpersonal skills in a demanding role and work environment. Additionally, I firmly believe that knowledge is an endless pursuit.

Leave a Reply

Your email address will not be published. Required fields are marked *

Related Posts

Begin typing your search term above and press enter to search. Press ESC to cancel.

Back To Top