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 modulesimport pandas as pdimport matplotlib.pyplot as pltimport datetimeimport numpy as np# create dataframedataframe = 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 dataframeplt.plot(dataframe.date_of_week, dataframe.classes)# Giving title to the chart using plt.titleplt.title('Classes by Date')# rotating the x-axis tick labels at 30degree# towards rightplt.xticks(rotation=30, ha='right')# Providing x and y label to the chartplt.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 modulesimport pandas as pdimport matplotlib.pyplot as pltimport datetimeimport 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 7-11-2021dataframe = 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 dataframeplt.plot_date(dataframe.date_of_week, dataframe.classes)# rotating the x-axis tick labels at 30degree towards rightplt.xticks(rotation=30, ha='right')# Giving title to the chart using plt.titleplt.title('Classes by Date')# Providing x and y label to the chartplt.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 librariesimport pandas as pdimport matplotlib.pyplot as pltimport datetimeimport numpy as np# ABC colllege classes by date- from 01-11-2021 to 07-11-2021abc = 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 07-11-2021xyz = 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 dataframeplt.plot(abc.date_of_week, abc.classes)# plotting the time series of XYZ college dataframeplt.plot(xyz.date_of_week, xyz.classes, color='green')# Giving title to the graphplt.title('Classes by Date')# rotating the x-axis tick labels at 30degree# towards rightplt.xticks(rotation=30, ha='right')# Giving x and y label to the graphplt.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 librariesimport pandas as pdimport matplotlib.pyplot as pltimport datetimeimport numpy as np# Loading the datasetdata = pd.read_csv("C:/Users/aparn/Desktop/data.csv")# X axis is price_dateprice_date = data['Date']# Y axis is price closingprice_close = data['Close']# Plotting the timeseries graph of given datasetplt.plot(price_date, price_close)# Giving title to the graphplt.title('Prices by Date')# rotating the x-axis tick labels at 30degree# towards rightplt.xticks(rotation=30, ha='right')# Giving x and y label to the graphplt.xlabel('Price Date')plt.ylabel('Price Close') |
Output:
