Today we’ll learn to draw a bit more sophisticated lineplots that display multiple lines. We’ll provide examples leveraging the two popular Python Data Visualization libraries: Matplotlib and Seaborn.
Importing libraries
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
plt.style.use('ggplot')
Create dummy data
We’ll use numpy to quickly generate simple x,y coordinate data.
x = np.linspace (1,10,25)
y = x * 2
z = np.exp(x)Matplotlib multiple line graph
We’ll use the Matplotlib to generate a simple multi line graph.
# plotting multiple lines from array
plt.plot(x,y)
plt.plot(x,z);
Adding a legend to the chart
We can easily add a legend to the chart using the plt.legend() method as shown below. In this example we also customize the marker type and line color. We also use the label parameter to define the appropiate label legend.
# multiple lines with legend
plt.plot(x,y,marker='.', color='r', label= 'accelerated growth')
plt.plot(x,z, marker = '+', color = 'g',label = 'exponential growth')
plt.legend();
Multiple line plots in one figure
When dealing with more complex multi variable data, we use subplot grids to render multiple graphs. Here’s a a simple example.
# multiple graphs one figure
fig, ax = plt.subplots(2,1, sharex=True)
ax[0].plot(x,y)
ax[1].plot(x,z);
Seaborn multiple lines chart
We’ll now show an example of using Seaborn and specifically the lineplot chart. We’ll first go ahead and create a DataFrame that we later feed into a couple of lineplot calls, each drawing one plot.
We can obviously construct the DataFrrame by reading excel, text, json or csv files as well as connecting to databases or data APIs.
# seaborn plot multiple lines
import pandas as pd
import seaborn as sns
my_dict = dict(x=x,y=y,z=z)
data = pd.DataFrame (my_dict)
fig, ax = plt.subplots()
ax= sns.lineplot(x='x', y='y', data=data)
ax1 = sns.lineplot(x='x', y='z', data=data)