Matplotlib.pyplot.plot_date() function in Python

Last Updated : 09 Jan, 2024

Matplotlib is a module package or library in Python which is used for data visualization. Pyplot is an interface to a Matplotlib module that provides a MATLAB-like interface. The matplotlib.pyplot.plot_date() function is like the regular plot() function, but it's tailored for showing data over dates. Think of it as a handy tool for visualizing events or values that happen over time, making your time-related charts look sharp and clear.

Matplotlib.pyplot.plot_date() function Syntax

This function is used to add dates to the plot. Below is the syntax by which we can plot datetime on the x-axis Matplotlib.

Syntax: matplotlib.pyplot.plot_date(x, y, fmt='o', tz=None, xdate=True, ydate=False, data=None, **kwargs)

Parameters:

x, y: x and y both are the coordinates of the data i.e. x-axis horizontally and y-axis vertically.
fmt: It is a optional string parameter that contains the corresponding plot details like color, style etc.
tz: tz stands for timezone used to label dates, default(UTC).
xdate: xdate parameter contains boolean value. If xdate is true then x-axis is interpreted as date in matplotlib. By default xdate is true.
ydate: If ydate is true then y-axis is interpreted as date in matplotlib. By default ydate is false.
data: The data which is going to be used in plot.

The last parameter **kwargs is the Keyword arguments control the Line2D properties like animation, dash_ joint-style, colors, linewidth, linestyle, marker, etc.

Matplotlib.pyplot.plot_date() function Examples

Below are the examples by which we can see how to plot datetime on x axis matplotlib in Python:

Plotting a Date Series Using Matplotlib

In this example, dates are plotted against a numeric sequence using the matplotlib.pyplot.plot_date() function, with green markers, and the x-axis date labels are rotated for better visibility.

Python3

# importing libraries
import matplotlib.pyplot as plt
from datetime import datetime

# creating array of dates for x axis
dates = [
    datetime(2020, 6, 30),
    datetime(2020, 7, 22),
    datetime(2020, 8, 3),
    datetime(2020, 9, 14)
]

# for y axis
x = [0, 1, 2, 3]

plt.plot_date(dates, x, 'g')
plt.xticks(rotation=70)
plt.show()

Output:

Creating a Plot Using Dataset

In this example, a Pandas DataFrame is used to store and plot market closing prices against dates. The plotted graph showcases closing amounts with red dashed lines.

Python3

# importing libraries
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime

# creating a dataframe
data = pd.DataFrame({'Date': [datetime(2020, 6, 30),
                              datetime(2020, 7, 22),
                              datetime(2020, 8, 3),
                              datetime(2020, 9, 14)],
                     
                     'Close': [8800, 2600, 8500, 7400]})

# x-axis
price_date = data['Date']

# y-axis
price_close = data['Close']

plt.plot_date(price_date, price_close, linestyle='--', color='r')
plt.title('Market', fontweight="bold")
plt.xlabel('Date of Closing')
plt.ylabel('Closing Amount')

plt.show()

Output:

Customizing Date Formatting in a Market Closing Price Plot

In this example, after plotting market closing prices against dates, the date format is customized using the dateformatter class to display dates in the format 'DD-MM-YYYY'.

Python3

# importing libraries
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime

# creating a dataframe
data = pd.DataFrame({'Date': [datetime(2020, 6, 30), 
                              datetime(2020, 7, 22), 
                              datetime(2020, 8, 3),
                              datetime(2020, 9, 14)],
                     
                     'Close': [8800, 2600, 8500, 7400]})

# x-axis
price_date = data['Date']

# y-axis
price_close = data['Close']

plt.plot_date(price_date, price_close, linestyle='--', color='r')
plt.title('Market', fontweight="bold")
plt.xlabel('Date of Closing')
plt.ylabel('Closing Amount')

# Changing the format of the date using
# dateformatter class
format_date = mpl_dates.DateFormatter('%d-%m-%Y')

# getting the accurate current axes using gca()
plt.gca().xaxis.set_major_formatter(format_date)

plt.show()