Matplotlib Histogram Chart

Introduction

Matplotlib is a library for making charts, which works very well with Jupyter Notebook. One of them is called a Histogram Chart. We've got a few examples ready to show you, so you can see what they look like and how they work:

• simple histogram chart
• histogram chart with numpy data
• histogram chart with pandas data
• an interactive histogram chart

If you need any information about Matplotlib check their docs: Matplotlib Docs (opens in a new tab).

All of code examples are availabe as Jupyter Notebooks in our GitHub repositiory:

• Matplotlib Histogram Chart (opens in a new tab)
• Matplotlib Interactive Histogram Chart (opens in a new tab)

Histogram Chart

Example with using only matplotlib package:

# import packages
import matplotlib.pyplot as plt
import random
 
# create data 
x = [random.gauss(0,1) for _ in range(1000)]
 
# plot 
plt.hist(x)
plt.show()

Histogram Chart with Numpy Data

You can turn numpy data into histogram chart thanks to matplotlib library:

# import packages 
import matplotlib.pyplot as plt
import numpy as np
 
# create data 
x = np.random.randn(100)
 
# plot 
plt.hist(x)
plt.show()

Histogram Chart with Pandas Data

Display pandas data as a histogram chart created with matplotlib:

# import packages 
import matplotlib.pyplot as plt
import pandas as pd
import random
 
# create data
df = pd.DataFrame({'numbers': [random.gauss(0,1) for _ in range(1000)]})
 
# plot 
plt.hist(df['numbers'])
plt.show()

Interactive Histogram Chart

Static charts are boring, what about creating an interactive histogram chart? It's possible with matplotlib and mercury packages. You can choose what color do you want the chart to be using mercury widgets. In this example we used Select (opens in a new tab):

# import packages
import matplotlib.pyplot as plt
import mercury as mr
import random

# mercury widget 
selection = mr.Select(value="blue", choices=["blue","green","red"])

# create data 
x = [random.gauss(0,1) for _ in range(1000)]
 
# plot 
plt.hist(x, color=selection.value)
plt.show()

Now, you can turn your Jupyter Notebook into Web App without additional code changes! Here is a video which presents how it will look:

Deploying Web App is very easy that you can do it in 3 steps:

Congrats! You just created your own Web App and you can share your Jupyter Notebooks with nontechnical users. If you need more information about deploying the Web App check Mercury Cloud Documentation (opens in a new tab).