Creating Distribution Plots in Python: A Comprehensive Guide

Have you ever wondered how to visualize the distribution of data in Python? Distribution plots are a great way to understand the shape of data, identify outliers, and find patterns.

In this article, we’ll explore two popular methods for creating distribution plots in Python: histograms and density curves. We’ll use the Matplotlib and Seaborn libraries to create various plots and learn how to customize them to suit our needs.

Whether you’re new to Python or an experienced user, this article will provide you with valuable knowledge and practical skills. Method 1: Histogram using Matplotlib

A histogram is a graph that shows the frequency distribution of a set of data.

Each bar represents a range of values, and the height of the bar shows how many values fall within that range. With Matplotlib, creating a histogram is straightforward.

Here’s the basic syntax:

“`

import matplotlib.pyplot as plt

plt.hist(data, color=’blue’, edgecolor=’black’, bins=10)

plt.show()

“`

Let’s break down the code. First, we import the pyplot module from Matplotlib.

Then, we call the `hist()` function and pass it our data as the first argument. We can set the color of the bars using the `color` parameter and the color of the edges using the `edgecolor` parameter.

Finally, we specify how many bins we want using the `bins` parameter. Here’s an example of a histogram created using Matplotlib:

![Histogram using Matplotlib](https://i.imgur.com/WGKbUth.png)

We can see that the data is skewed to the right, with most values falling between 0 and 50.

There are a few outliers on the right side of the distribution. Method 2: Histogram with Density Curve using Seaborn

While histograms are useful for showing the frequency distribution of data, they can be hard to interpret when the data has a complicated shape.

A density curve can help by providing a smooth estimate of the distribution. Seaborn is a Python data visualization library that makes it easy to create histograms with density curves.

Here’s the basic syntax:

“`

## import seaborn as sns

sns.distplot(data, kde=True, bins=10)

“`

In this code, we import the seaborn module and call the `distplot()` function, passing it our data as the first argument. We set the `kde` parameter to `True` to show the density curve, and we specify the number of bins using the `bins` parameter.

Here’s an example of a histogram with a density curve created using Seaborn:

![Histogram with Density Curve using Seaborn](https://i.imgur.com/8xKFr8r.png)

We can see that the density curve provides a smoother estimate of the distribution compared to the histogram alone. The shape of the distribution is more apparent, and we can see that it is bimodal, with two peaks.

Example: Visualizing the distribution of values in a NumPy array

Now that we’ve learned how to create histograms and density curves, let’s put our skills to use. Suppose we have a NumPy array `data` containing 1000 random values drawn from a normal distribution with mean 50 and standard deviation 10.

We want to visualize the distribution of these values.

## Creating a histogram using Matplotlib

## We can create a histogram of the data using Matplotlib as follows:

“`

## import numpy as np

import matplotlib.pyplot as plt

np.random.seed(42)

data = np.random.normal(50, 10, 1000)

plt.hist(data, color=’blue’, edgecolor=’black’, bins=30)

plt.title(“Histogram of data”)

plt.xlabel(“Values”)

plt.ylabel(“Frequency”)

plt.show()

“`

In this code, we import the numpy and pyplot modules, and create the `data` array using the `np.random.normal()` function. We set the `seed` parameter to `42` to ensure that we get the same random values every time we run the code.

We then create the histogram using Matplotlib, setting the title, xlabel, and ylabel as appropriate. Here’s what the resulting histogram looks like:

![Histogram of NumPy array using Matplotlib](https://i.imgur.com/BAyaKyF.png)

We can see that the data is normally distributed, with the peak around 50 and most values falling within two standard deviations from the mean.

## Creating a histogram with density curve using Seaborn

## We can create a histogram with density curve of the data using Seaborn as follows:

“`

## import seaborn as sns

sns.distplot(data, kde=True, bins=30)

plt.title(“Histogram with Density Curve of data”)

plt.xlabel(“Values”)

plt.ylabel(“Density”)

plt.show()

“`

In this code, we import the seaborn module and create the histogram with density curve using the `sns.distplot()` function. We set the title, xlabel, and ylabel as appropriate.

Here’s what the resulting plot looks like:

![Histogram with Density Curve of NumPy array using Seaborn](https://i.imgur.com/vsZIRgH.png)

We can see that the density curve provides a smoother estimate of the distribution compared to the histogram alone. The shape of the distribution is more apparent, with the peak around 50 and the density falling off symmetrically in both directions.

## Conclusion

In this article, we’ve explored two popular methods for creating distribution plots in Python: histograms and density curves. We’ve used the Matplotlib and Seaborn libraries to create various plots and learned how to customize them to suit our needs.

We’ve also applied our skills to visualize the distribution of values in a NumPy array. By mastering these techniques, you’ll be able to create informative and visually appealing plots of your own data.

In summary, this article has introduced two popular methods for creating distribution plots in Python: histograms and density curves. We’ve explored how to use the Matplotlib and Seaborn libraries to create and customize these plots, and applied our skills to visualize the distribution of values in a NumPy array.

Understanding how to create distribution plots is essential for data analysis and visualization, as it helps to identify patterns, outliers, and the overall shape of the data. By mastering these techniques and applying them to your own data, you’ll be able to communicate your findings more clearly and effectively.

Remember to experiment with different parameters and plot styles to find the ones that best fit your data.