Handling time-series data with Pandas

Let's get started by learning how to handle time-series data in Pandas. In this section, we will convert a sequence of numbers into time series data and visualize it. Pandas provides options to add timestamps, organize data, and then efficiently operate on it.

Create a new Python file and import the following packages:

import numpy as np 
import matplotlib.pyplot as plt 
import pandas as pd 

Define a function to read the data from the input file. The parameter index indicates the column number that contains the relevant data:

def read_data(input_file, index): 
    # Read the data from the input file 
    input_data = np.loadtxt(input_file, delimiter=',') 

Define a lambda function to convert strings to Pandas date format:

    # Lambda function to convert strings to Pandas date format 
    to_date = lambda x, y: str(int(x)) + '-' + str(int(y)) 

Use this lambda function to get the start date from the first line in the input file:

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