Matplotlib Line Chart

Introduction

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

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

Line Chart

Example with using only matplotlib package:

# import packages 
import matplotlib.pyplot as plt
 
# create data
x = [1, 2, 3, 4, 5]
y = [1.5, 5.3, 2.1, 6.2, 2.7]
 
# plot 
plt.plot(x,y)
plt.show()

Line Chart with Numpy Data

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

# import packages
import matplotlib.pyplot as plt
import numpy as np
 
# create data 
x = 1 + np.arange(8)
y = np.array([4.8, 5.5, 3.5, 6.6, 2.5, 4.6, 1.6, 3.9])
 
# plot 
plt.plot(x,y)
plt.show()

Line Chart with Pandas Data

Display pandas data as a line chart created with matplotlib:

# import packages
import matplotlib.pyplot as plt
import pandas as pd
 
# create data 
df = pd.DataFrame(
    {
        'count': [1,2,3,4,5,6,7,8], 
        'value': [1.5,4.8,3.0,8.1,1.9,3.8,2.2,7.1]
    }
)
 
# plot 
plt.plot(df['count'], df['value'])
plt.show()

Interactive Line Chart

Static charts are boring, what about creating an interactive line chart? It's possible with matplotlib and mercury packages. You can choose which data you want to display 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

# mercury widget
selection = mr.Select(value = "growing", choices = ["growing", "decreasing"], label = "Which trend?")

# create data 
x = [1,2,3,4,5,6,7,8,9,10]
up_y = [1, 2.2, 3.4, 4.1, 3.9, 4.5, 6.4, 8.4, 8.2, 10]
down_y = [10,9.1,8.7,6.1,5.2,6,4.9,4,3.1,1]
 
# plot
if selection.value == "growing":
    y = up_y
elif selection.value == "decreasing":
    y = down_y
    
plt.plot(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).