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Matplotlib.pyplot.plot_date() function in Python

  • Last Updated : 23 Dec, 2020

Matplotlib is a module or 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. 

Matplotlib.pyplot.plot_date ()

This function  used to add dates to the plot.

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Syntax:



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

This is the syntax of date function. It contains various parameters or arguments which are explained below.

S.no.

Parameter/Arguments

Description

1.

x, y

x and y both are the coordinates of the data i.e. x-axis horizontally and y-axis vertically.

2.

fmt

It is a optional string parameter that contains the corresponding plot details like color, style etc. 

3.



tz

tz stands for timezone used to label dates, default(UTC).

4.

xdate

xdate parameter contains boolean value. If xdate is true then x-axis is interpreted as date in matplotlib. By default xdate is true.

5.

ydate

If ydate is true then y-axis is interpreted as date in matplotlib. By default ydate is false.

6.

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.

Example 1:

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:

Example 2: Creating a plot using dataset.

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:

Example 3: Changing the format of the date:

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 formate 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.




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