Line chart customization with matplotlib


The previous post explains how to create a linechart. This post aims to show how to customize it using matplotlib.

Custom Line Color

To custom color, just use the color argument!

Note that you can add transparency to the color with the alpha argument (0=transparent, 1=opaque).

# Libraries and data
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df=pd.DataFrame({'x_values': range(1,11), 'y_values': np.random.randn(10) })

# Draw plot
plt.plot( 'x_values', 'y_values', data=df, color='skyblue')
plt.show()

# Draw line chart by modifiying transparency of the line
plt.plot( 'x_values', 'y_values', data=df, color='skyblue', alpha=0.3)

# Show plot
plt.show()

Custom Line Style

You can choose between different line styles with the linestyle argument.

# Libraries and data
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df=pd.DataFrame({'x_values': range(1,11), 'y_values': np.random.randn(10) })

# Draw line chart with dashed line
plt.plot( 'x_values', 'y_values', data=df, linestyle='dashed')

# Show graph
plt.show()

The following examples show different types of line styles.

plt.plot( [1,1.1,1,1.1,1], linestyle='-' , linewidth=4)
plt.text(1.5, 1.3, "linestyle = '-' ", horizontalalignment='left', size='medium', color='C0', weight='semibold')
plt.plot( [2,2.1,2,2.1,2], linestyle='--' , linewidth=4 )
plt.text(1.5, 2.3, "linestyle = '--' ", horizontalalignment='left', size='medium', color='C1', weight='semibold')
plt.plot( [3,3.1,3,3.1,3], linestyle='-.' , linewidth=4 )
plt.text(1.5, 3.3, "linestyle = '-.' ", horizontalalignment='left', size='medium', color='C2', weight='semibold')
plt.plot( [4,4.1,4,4.1,4], linestyle=':' , linewidth=4 )
plt.text(1.5, 4.3, "linestyle = ':' ", horizontalalignment='left', size='medium', color='C3', weight='semibold')
plt.axis('off')
plt.show()

Custom Line Width

Finally you can custom the line width as well using linewidth argument.

# Libraries and data
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df=pd.DataFrame({'x_values': range(1,11), 'y_values': np.random.randn(10) })

# Modify line width of the graph
plt.plot( 'x_values', 'y_values', data=df, linewidth=22)

# Show graph
plt.show()

Scatterplot

Heatmap

Correlogram

Bubble

Connected Scatter

2D Density

Contact & Edit

👋 This document is a work by Yan Holtz. Any feedback is highly encouraged. You can fill an issue on Github, drop me a message onTwitter, or send an email pasting yan.holtz.data with gmail.com.

This page is just a jupyter notebook, you can edit it here. Please help me making this website better 🙏!

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