Adventures in Machine Learning

Customizing Pandas Histograms: Changing Figure Size with figsize Argument

Changing Figure Size of a Pandas Histogram

Histograms are a fundamental tool for data analysis, as they allow us to visualize data distribution. In Python, we can use the Pandas library to create histograms easily.

One common problem with histograms, however, is that the default size may not be optimal for every situation. In this article, we will show you how to change the figure size of a Pandas histogram, using the figsize argument.

We will also provide an example using a Pandas DataFrame.

Using the figsize Argument

The figsize argument is a parameter that can be used in several plotting functions in Python. Its purpose is to specify the dimensions of the figure in which the plot will be displayed.

The argument takes a tuple of two values, which represent the width and height of the figure in inches.

How to Change Figure Size of Pandas Histogram

To change the figure size of a Pandas histogram, we need to use the plot() method of a DataFrame object, and set the figsize argument to a tuple of two values representing our desired dimensions.

For instance, let’s consider a DataFrame object that contains data about the points scored by NBA players in a season.

We can create a histogram of this data as follows:

import pandas as pd

import matplotlib.pyplot as plt

nba_data = pd.DataFrame({

“player”: [“LeBron James”, “Stephen Curry”, “Kevin Durant”, “James Harden”, “Giannis Antetokounmpo”, “Damian Lillard”, “Kawhi Leonard”, “Joel Embiid”, “Nikola Jokic”, “Luka Doncic”],

“points”: [2251, 1645, 1519, 1469, 1432, 1405, 1377, 1357, 1347, 1332]

})

nba_data[“points”].plot.hist()

This code will produce a histogram with the default size, which may be too small or too big depending on the specific case. Leaving the size of the figure to default settings may also result in a histogram that is difficult to read.

Changing Figure Size with figsize Argument

To fix this issue, we simply need to specify our desired size using the figsize argument. The following code example sets the size of the figure to 8 inches in width and 6 inches in height:

nba_data[“points”].plot.hist(figsize=(8,6))

This will produce a histogram with the same data but with a larger size.

We could make it even larger by changing the values of the tuple.

Creating a Figure with Greater Height than Width

Sometimes, we may prefer a histogram with more height than width, which is particularly helpful when trying to visualize several histogram subplots together. In this case, we only need to set the dimensions of the figure in a different way, such as setting the height to 10 inches and the width to 5 inches.

To make it clearer, we can also include a grid and change the edgecolor of the bars. The following code example creates a histogram with a greater height than width:

nba_data[“points”].plot.hist(figsize=(5,10), grid=True, edgecolor=’black’)

This will produce a similar histogram as before, but with a greater height, which is useful when plotting several histograms together or when analyzing data with lots of modes.

Conclusion

Changing the figure size of Pandas histograms is a simple but powerful tool that facilitates data visualization. By using the figsize argument, we can adjust the dimensions of a histogram to fit any specific use case, making the resulting visualizations easier to interpret and analyze.

When analyzing data with multiple modes, using a greater height than width can provide greater clarity. By following the guidelines described in this article, you can customize your Pandas histograms with ease and create insightful visualizations that help you make informed decisions.

Additional Resources

If you are interested in learning more about creating histograms in Python and using the Pandas library, there are many resources available to help you. Here are some suggestions:

Pandas Documentation

The official Pandas documentation is an excellent resource for learning about data analysis with Pandas. The documentation includes a section on Data Visualization that covers various plotting functions, including the plot() method used to create histograms.

The documentation also includes many examples to help you understand how to use these functions in practice.

Matplotlib Documentation

Pandas uses the Matplotlib library as the backend for its plotting functions. Therefore, understanding Matplotlib can be helpful when creating visualizations with Pandas.

The Matplotlib documentation provides a detailed overview of the library, covering everything from basic plots to more advanced topics such as subplots and animations.

DataCamp

DataCamp is an online learning platform that provides courses on various topics related to data science, including Python programming, Pandas, and Matplotlib. The platform includes interactive exercises, quizzes, and coding challenges that you can use to practice and reinforce your knowledge.

DataCamp also offers a community forum where you can ask questions, get feedback, and connect with other learners.

Kaggle

Kaggle is an online platform for data science competitions, tutorials, and community resources. The platform includes many datasets that you can use to practice your data analysis skills, including data about sports, finance, social media, and more.

Kaggle also provides tutorials and project-based learning courses that you can use to learn how to create visualizations and analyze data with Python, Pandas, and Matplotlib.

Stack Overflow

Stack Overflow is a popular online community where developers can ask and answer technical questions. If you encounter a problem while creating a histogram with Pandas, you can search for solutions on

Stack Overflow, where you can find many helpful tips and examples from experienced users.

You can also use

Stack Overflow to ask questions and get feedback on your code.

Conclusion

Learning how to create histograms with Python and Pandas is an essential skill for data analysis. Pandas provides a simple and intuitive interface for creating histograms, making it easy for beginners and advanced users to get started with data visualization.

By adjusting the figure size with the figsize argument, you can customize your histograms to fit any use case and create insightful visualizations that help you make informed decisions. By using the additional resources listed above, you can continue to build your skills and knowledge in data analysis with Python and Pandas.

In summary, changing the figure size of a Pandas histogram is a crucial tool for data visualization in Python. By using the figsize argument, we can adjust the dimensions of a histogram to fit any specific use case, making the resulting visualizations easier to interpret and analyze.

The process is straightforward, requiring only the plot() method of the Pandas DataFrame object and the figsize argument set to a tuple of arbitrary dimensions. We also have discussed additional resources like Pandas and Matplotlib documentation,

DataCamp,

Kaggle, and

Stack Overflow, that can further aid our learning.

By following the guidelines discussed above, we can customize our Pandas histograms with ease and create insightful visualizations that help us make informed decisions in data analysis.

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