How to Plot a Time Series in Matplotlib?

Last Updated : 27 Jan, 2022

Time series data is the data marked by some time. Each point on the graph represents a measurement of both time and quantity. A time-series chart is also known as a fever chart when the data are connected in chronological order by a straight line that forms a succession of peaks and troughs. x-axis of the chart is used to represent time intervals. y-line locates values of the parameter getting monitored.

We will use the syntax mentioned below to draw a Time Series graph:

Syntax:

plt.plot(dataframe.X, dataframe.Y)

where

X variable belongs to the datetime. datetime() class in the given dataframe.
Y variable belongs to the values corresponding to date

We can also rotate the axis by using xticks() function

Syntax:

plt.xticks(rotation, ha)

where

rotation describes the degrees you want to rotate
ha describes the position like right, left, top, bottom

Approach

We need to have two axes for our graph i.e X and Y-axis. We will start by having a dataframe to plot the graph.
We can either make our own data frame or use some publicly available data frames. In X-axis we should have a variable of DateTime. In Y-axis we can have the variable which we want to analyze with respect to time.
plt.plot() method is used to plot the graph in matplotlib.
To provide labels and title to make our graph meaningful we can use methods like - plt.title(), plt.xlabel(), plt.ylabel()

Example 1:

Let say we have a dataframe of the days of the week and the number of classes on each day of the upcoming week. We are Taking 7 days from 1-11-2021 to 7-11-2021

Python3

# import modules
import pandas as pd
import matplotlib.pyplot as plt
import datetime
import numpy as np

# create dataframe

dataframe = pd.DataFrame({'date_of_week': np.array([datetime.datetime(2021, 11, i+1)
                                                    for i in range(7)]),
                          'classes': [5, 6, 8, 2, 3, 7, 4]})

# Plotting the time series of given dataframe
plt.plot(dataframe.date_of_week, dataframe.classes)

# Giving title to the chart using plt.title
plt.title('Classes by Date')

# rotating the x-axis tick labels at 30degree 
# towards right
plt.xticks(rotation=30, ha='right')

# Providing x and y label to the chart
plt.xlabel('Date')
plt.ylabel('Classes')

Output:

We can also create scatter plots with the help of Time Series with the help of matplotlib.

Example 2: Scatter time series plot of the given dataframe

Python3

#import modules
import pandas as pd
import matplotlib.pyplot as plt
import datetime
import numpy as np

# Let say we have a dataframe of the days of
# the week and number of classes in each day of the upcoming week.
# Taking 7 days from 1-11-2021 to 7-11-2021

dataframe = pd.DataFrame({'date_of_week': np.array([datetime.datetime(2021, 11, i+1)
                                                    for i in range(7)]),
                          'classes': [5, 6, 8, 2, 3, 7, 4]})

# To draw scatter time series plot of the given dataframe
plt.plot_date(dataframe.date_of_week, dataframe.classes)

# rotating the x-axis tick labels at 30degree towards right
plt.xticks(rotation=30, ha='right')

# Giving title to the chart using plt.title
plt.title('Classes by Date')

# Providing x and y label to the chart
plt.xlabel('Date')
plt.ylabel('Classes')

Output:

Similarly, we can plot the time series of two dataFrames and compare them. Let say we have two colleges -'XYZ' and 'ABC'. Now we need to compare these two by time-series graph of matplotlib.

Example 3:

Python3

# Initialising required libraries
import pandas as pd
import matplotlib.pyplot as plt
import datetime
import numpy as np

# ABC colllege classes by date- from 01-11-2021 to 07-11-2021
abc = pd.DataFrame({'date_of_week': np.array([datetime.datetime(2021, 11, i+1)
                                              for i in range(7)]), 
                    'classes': [5, 6, 8, 2, 3, 7, 4]})


# XYZ colllege classes by date - from 01-11-2021 to 07-11-2021
xyz = pd.DataFrame({'date_of_week': np.array([datetime.datetime(2021, 11, i+1)
                                              for i in range(7)]), 
                    'classes': [2, 3, 7, 3, 4, 1, 2]})

# plotting the time series of ABC college dataframe
plt.plot(abc.date_of_week, abc.classes)

# plotting the time series of XYZ college dataframe
plt.plot(xyz.date_of_week, xyz.classes, color='green')

# Giving title to the graph
plt.title('Classes by Date')

# rotating the x-axis tick labels at 30degree 
# towards right
plt.xticks(rotation=30, ha='right')

# Giving x and y label to the graph
plt.xlabel('Date')
plt.ylabel('Classes')

Output:

Similarly, we can plot the time series plot from a dataset. Here is the link to the dataset

Example 4:

Python3

# Initialising required libraries
import pandas as pd
import matplotlib.pyplot as plt
import datetime
import numpy as np

# Loading the dataset
data = pd.read_csv("C:/Users/aparn/Desktop/data.csv")

# X axis is price_date
price_date = data['Date']

# Y axis is price closing
price_close = data['Close']

# Plotting the timeseries graph of given dataset
plt.plot(price_date, price_close)

# Giving title to the graph
plt.title('Prices by Date')

# rotating the x-axis tick labels at 30degree 
# towards right
plt.xticks(rotation=30, ha='right')

# Giving x and y label to the graph
plt.xlabel('Price Date')
plt.ylabel('Price Close')