Pandas DataFrame Hist Method: Visualizing Data Distributions

Introduction

This article covers the following tech skills:

The hist() method in the Pandas library allows us to create histograms, which are visual representations of the distribution of data. This method is used on a DataFrame object and calls the matplotlib.pyplot.hist() function on each series within the DataFrame, resulting in one histogram per column.

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Import the necessary libraries

To use the hist() method, we need to import the required libraries, which are pandas and matplotlib.pyplot.

import pandas as pd
import matplotlib.pyplot as plt

Create a DataFrame

Next, we need to create a DataFrame object using the pd.DataFrame() method. We can pass a dictionary as an argument, where the keys represent the column names and the values represent the data.

data = {'length': [1.5, 0.5, 1.2, 0.9, 3], 'width': [0.7, 0.2, 0.15, 0.2, 1.1]}
df = pd.DataFrame(data)

Create a histogram

Now, we can use the hist() method on the DataFrame to create a histogram of each column.

df.hist()
plt.show()

Customize the histogram

We can customize the histogram by providing additional parameters to the hist() method. For example, we can specify the number of bins, the color of the histogram bars, and the title of the histogram.

df.hist(bins=10, color='skyblue')
plt.title('Histogram')
plt.show()

Summary

The hist() method in Pandas allows us to create histograms of the data within a DataFrame. By using this method, we can visualize the distribution of our data, which can be useful for data analysis and exploration. Additionally, we can customize the appearance of the histogram by providing additional parameters to the hist() method. Overall, the hist() method is a handy tool for analyzing and visualizing data in Pandas.

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