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In order to plot multiple lines in Matplotlib, you can use the “plt.plot()” function. This function allows you to specify multiple sets of data to be plotted on the same graph, using different colors and styles for each line. Additionally, you can use the “plt.legend()” function to add a legend to the graph, labeling each line with a corresponding description. By using these functions, you can easily create a clear and visually appealing plot with multiple lines in Matplotlib.
Plot Multiple Lines in Matplotlib
You can display multiple lines in a single Matplotlib plot by using the following syntax:
import matplotlib.pyplotas plt plt.plot(df['column1']) plt.plot(df['column2']) plt.plot(df['column3']) ... plt.show()
This tutorial provides several examples of how to plot multiple lines in one chart using the following pandas DataFrame:
import numpy as np import pandas as pd #make this example reproducible np.random.seed(0) #create dataset period = np.arange(1, 101, 1) leads = np.random.uniform(1, 50, 100) prospects = np.random.uniform(40, 80, 100) sales = 60 + 2*period + np.random.normal(loc=0, scale=.5*period, size=100) df = pd.DataFrame({'period': period, 'leads': leads, 'prospects': prospects, 'sales': sales}) #view first 10 rows df.head(10) period leads prospects sales 0 1 27.891862 67.112661 62.563318 1 2 36.044279 50.800319 62.920068 2 3 30.535405 69.407761 64.278797 3 4 27.699276 78.487542 67.124360 4 5 21.759085 49.950126 68.754919 5 6 32.648812 63.046293 77.788596 6 7 22.441773 63.681677 77.322973 7 8 44.696877 62.890076 76.350205 8 9 48.219475 48.923265 72.485540 9 10 19.788634 78.109960 84.221815
Plot Multiple Lines in Matplotlib
The following code shows how to plot three individual lines in a single plot in matplotlib:
import matplotlib.pyplot as plt
#plot individual lines
plt.plot(df['leads'])plt.plot(df['prospects'])plt.plot(df['sales'])
#display plotplt.show()
Customize Lines in Matplotlib
You can also customize the color, style, and width of each line:
#plot individual lines with custom colors, styles, and widths
plt.plot(df['leads'], color='green')
plt.plot(df['prospects'], color='steelblue', linewidth=4)
plt.plot(df['sales'], color='purple', linestyle='dashed')
#display plotplt.show()
Add a Legend in Matplotlib
You can also add a legend so you can tell the lines apart:
#plot individual lines with custom colors, styles, and widths
plt.plot(df['leads'], label='Leads', color='green')
plt.plot(df['prospects'], label='Prospects', color='steelblue', linewidth=4)
plt.plot(df['sales'], label='Sales', color='purple', linestyle='dashed')
#add legend
plt.legend()#display plotplt.show()
Add Axis Labels and Titles in Matplotlib
#plot individual lines with custom colors, styles, and widths
plt.plot(df['leads'], label='Leads', color='green')
plt.plot(df['prospects'], label='Prospects', color='steelblue', linewidth=4)
plt.plot(df['sales'], label='Sales', color='purple', linestyle='dashed')
#add legend
plt.legend()
#add axis labels and a title
plt.ylabel('Sales', fontsize=14)plt.xlabel('Period', fontsize=14)plt.title('Company Metrics', fontsize=16)#display plotplt.show()
You can find more Matplotlib tutorials .