Here you will learn what is the easiest way to plot a histogram in Python. We make use of the seaborn library to create the distribution.

## What is a Histogram?

A histogram is a representation of data using bars of different heights where each bar groups numbers represents a ranges. Taller bars mean that more data is in that range.

If you want to learn more about histograms, please visit the following youtube video:

## Generating the Distribution Plot in Python

Below you can find the code for the density plot in python. It requires you to install Seaborn and Numpy.

The function is very simple! All it asks you is for 5 parameters:

- A list with the data
- A string with the x-label
- A string with the y-label
- A string with the title
- An integer with the number of bins
- A string with the plot output path

Please see code below:

import seaborn import matplotlib.pyplot as matplotlib import numpy as np def plot_dist(data, x_label, y_label, tittle, number_bins, plot_output): """Plot data distribution Args: data (list): List with data to be plotted. x_label (str): Plot x-label. y_label (str): Plot y-label. tittle (str): Plot tittle. number_bins (int): Number of bins for distribution. plot_output (str): Path to plot output. """ seaborn.set(color_codes=True) matplotlib.figure(1, figsize=(9, 6)) sns_plot = seaborn.distplot(data, kde=False, rug=False, bins=number_bins) sns_plot.set(xlabel=x_label, ylabel=y_label) matplotlib.title(tittle) sns_plot.figure.savefig(plot_output, bbox_inches='tight', dpi=400) matplotlib.close() # set seed for same plot can be re-generated on example presented here using np.random.normal np.random.seed(11) # random sample data = np.random.normal(0.1, 0.5, 5000) plot_dist(data, "Random Variable", "# Frequency", "Random Variable sampled 5000 times", 200, "plot_histogram_python_example.png")

And here is how our plot looks like

If you enjoyed this tutorial and would love to learn about box-plots and how to plot it in Python, please check out the following tutorial.