Pandas DataFrame Hist Method: Visualizing Data Distributions
Tags: pandas, coding, programming, tutorial
Published: 2024-07-05 04:46:58


## Introduction

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This article covers the following tech skills:

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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`.

```python
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.

```python
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.

```python
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.

```python
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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