Quickly learn how to transform time series data and get most of it in Python Pandas
Date and time data is everywhere! Master the techniques to manipulate it!
Irrespective of the industry you are working in, time series data is always there. And you can explore the hidden trends and patterns in it by analyzing it to get valuable insights. It will ultimately help you drive data-driven decisions and plans based on historical data.
In my previous article, you can explore 3 useful tricks to work with such data in Python. However, when you are working with such time-series data in pandas DataFrame, you better use pandas classes and functions.
When you read the data into the pandas DataFrame, pandas often store it as a string or object data type. And to analyze such data effectively, you need to convert it to the DateTime data type.
In this article, you’ll explore 3 tested, and timesaving tips to work with time-series data in pandas. You’ll explore a range of classes and functions in pandas to work effectively with dates and time data.
Let’s set the scene — read the dummy sales data in a DataFrame df!
df = pd.read_csv("Dummy_dates_sales.csv")
df.head()It is a simple, 100 x 2 dataset containing the date-time column Dates and the integer column Sales. I created this dataset using the Python package Faker and you can take it from my GitHub repo for Free!
Before processing time series data, it is important to check if pandas has recognized their data type correctly. If not, then you need to convert it to datetime data type.
Let’s check it —
df.info()
Here, pandas read these values in the Dates column as strings. Therefore, to get the maximum value out of this date-time data, you need to convert it to date-time data type.