Libraries
First, we need to load a few libraries:
- matplotlib: for creating/styling the plot
- pandas: for data manipulation
numpyfor data generation
import matplotlib.pyplot as plt
import pandas as pd
import numpy as npDataset
Let's create 4 simple columns created with numpy that we put in a pandas dataframe.
df = pd.DataFrame({
'x_values': range(1,11),
'y1_values': np.random.randn(10),
'y2_values': np.random.randn(10)+range(1,11),
'y3_values': np.random.randn(10)+range(11,21)
}
)Mutliple line charts
Here we display 3 different line charts with different style properties:
plt.plot(
'x_values', 'y1_values', data=df,
marker='o', # marker type
markerfacecolor='blue', # color of marker
markersize=12, # size of marker
color='skyblue', # color of line
linewidth=4 # change width of line
)
plt.plot(
'x_values', 'y2_values', data=df,
marker='', # no marker
color='olive', # color of line
linewidth=2 # change width of line
)
plt.plot(
'x_values', 'y3_values', data=df,
marker='', # no marker
color='darkred', # color of line
linewidth=3, # change width of line
linestyle='dashed', # change type of line
label="toto" # label for legend
)
# show legend
plt.legend()
# show graph
plt.show()
Going further
This post explains how to customize a the line of a line chart with matplotlib.
You might be interested in a more advanced line chart customization and how to have a log scale.
