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() |
Output:
The format of the date changed to dd-mm-yyyy. To know more about dataformatter and gca() click here.
Matplotlib.pyplot.plot_date() function in Python
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.