Time Series — Date Parsing and Time-Based Indexing — Free Data Analytics Tutorial
Learn Time Series — Date Parsing and Time-Based Indexing in Data Analytics with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Time Series — Date Parsing and Time-Based Indexing in Data Analytics with a free, beginner-friendly tutorial, examples and practice for Indian s…
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Time Series — Date Parsing and Time-Based Indexing in Data Analytics
Time series data has dates as the index. Examples: stock prices over days, temperature over months, website traffic over hours.
Date parsing: Convert text dates ("2024-01-15") to datetime objects. Pandas: pd.to_datetime('2024-01-15').
Time-based indexing: Set dates as index to enable time-based slicing: df.loc['2024-01'] selects all rows in January 2024.
Resampling: Change frequency (daily → weekly, monthly → yearly). df.resample('W').mean() gives weekly averages.
Time Series — Date Parsing and Time-Based Indexing — Syntax
# Date operations:
# pd.to_datetime('2024-01-15') # parse string to datetime
# df['date'] = pd.to_datetime(df['date']) # parse column
# df = df.set_index('date') # use dates as index
# df.loc['2024-01'] # select January 2024
# df.resample('M').sum() # resample to monthly
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