Time series is a series of data points in which each data point is associated with a timestamp. A simple example is the price of a stock in the stock market at different points of time on a given day. Another example is the amount of rainfall in a region at different months of the year.
In the below example we take the value of stock prices every day for a quarter for a particular stock symbol. We capture these values as a csv file and then organize them to a dataframe using pandas library. We then set the date field as index of the dataframe by recreating the additional Valuedate column as index and deleting the old valuedate column.
Below is the sample data for the price of the stock on different days of a given quarter. The data is saved in a file named as stock.csv
ValueDate Price 01-01-2018, 1042.05 02-01-2018, 1033.55 03-01-2018, 1029.7 04-01-2018, 1021.3 05-01-2018, 1015.4 ... ... ... ... 23-03-2018, 1161.3 26-03-2018, 1167.6 27-03-2018, 1155.25 28-03-2018, 1154
from datetime import datetime import pandas as pd import matplotlib.pyplot as plt data = pd.read_csv('path_to_file/stock.csv') df = pd.DataFrame(data, columns = ['ValueDate', 'Price']) # Set the Date as Index df['ValueDate'] = pd.to_datetime(df['ValueDate']) df.index = df['ValueDate'] del df['ValueDate'] df.plot(figsize=(15, 6)) plt.show()
Its output is as follows −