Slicing time-series data

Now that we know how to handle time-series data, let's see how we can slice it. The process of slicing refers to dividing the data into various sub-intervals and extracting relevant information. This is very useful when you are working with time-series datasets. Instead of using indices, we will use timestamp to slice our data.

Create a new Python file and import the following packages:

import numpy as np 
import matplotlib.pyplot as plt 
import pandas as pd 
 
from timeseries import read_data 

Load the third column (zero-indexed) from the input data file:

# Load input data 
index = 2 
data = read_data('data_2D.txt', index) 

Define the start and end years, and then plot the data with year-level granularity:

# Plot data with year-level granularity  
start = '2003' 
end = '2011' 
plt.figure() 
data[start:end].plot() 
plt.title('Input data from ' + start + ' to ' + end) 

Define the start and end months, and then plot...