Python | Creating histograms with Matplotlib in python

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Histograms

Histograms are a type of chart used to display the distribution of a set of continuous data. They group the data into bins and display the frequency or count of observations in each bin. Histograms are commonly used to understand the shape of the data distribution, identify outliers and gaps, and compare distributions.

In Python, you can create histograms using various libraries such as Matplotlib, Seaborn, and Plotly. Here's an example using Matplotlib:

import matplotlib.pyplot as plt

import numpy as np

# Generate some random data

data = np.random.normal(size=1000)

# Create a histogram with 10 bins

plt.hist(data, bins=10)

# Set the title and axis labels

plt.title('Histogram of Random Data')

plt.xlabel('Values')

plt.ylabel('Frequency')

# Show the plot

plt.show()

In this example, we generate 1000 random numbers from a normal distribution using NumPy. We then create a histogram with 10 bins using plt.hist(). Finally, we set the title and axis labels using plt.title(), plt.xlabel(), and plt.ylabel(), and display the plot using plt.show().

You can also customize the appearance of the histogram by specifying the bin size, color, and other options. For example, to change the bin size to 0.5, set bins=20. To change the color of the bars to red, add color='red' to the plt.hist() call.