Matplotlib Stem Chart

Introduction

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

• simple stem chart
• stem chart with numpy data
• stem chart with pandas data
• an interactive stem 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 Stem Chart (opens in a new tab)
• Matplotlib Interactive Stem Chart (opens in a new tab)

Stem Chart

Example with using only matplotlib package:

# import packages 
import matplotlib.pyplot as plt
 
# create data 
x = [1,2,3,4,5,6,7]
y = [11, 12, 13, 13, 11, 15, 12]
 
# plot 
plt.stem(x,y)
plt.show()

Stem Chart with Numpy Data

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

# import packages 
import matplotlib.pyplot as plt
import numpy as np
 
# create data 
x = 0.5 + np.arange(8)
y = np.array([4.8, 5.5, 3.5, 4.6, 6.5, 6.6, 2.6, 3.0])
 
# plot
plt.stem(x,y)
plt.show()

Stem Chart with Pandas Data

Display pandas data as a stem chart created with matplotlib:

# import packages
import matplotlib.pyplot as plt 
import pandas as pd
 
# create data 
df = pd.DataFrame({'numbers': [1,2,3,4,5,6,7], 'values': [12,8,7,14,10,11,8]})
 
# plot 
plt.stem(df['numbers'], df['values'])
plt.show()

Interactive Stem Chart

Static charts are boring, what about creating an interactive stem chart? It's possible with matplotlib and mercury packages. You can modify data range with mercury widgets. In this example we used Slider (opens in a new tab):

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

# mercury widget
slider = mr.Slider(value=6, min=5, max=10)

# create data
all_x = [1,2,3,4,5,6,7,8,9,10]
all_y = [random.gauss(10,5) for _ in range(10)]
 
x = all_x[0:slider.value]
y = all_y[0:slider.value]
 
# plot 
plt.stem(x,y)
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).