Python

Time Series Analysis

Master datetime manipulation. Resampling, rolling windows, and handling time-indexed data.

By TechCoder TeamLast updated: 2026-06-02
In a Nutshell

Master datetime manipulation. Resampling, rolling windows, and handling time-indexed data. This hands-on tutorial focuses on practical implementation of time series analysis concepts.

Module 8: Time Series Analysis

Pandas was originally built for financial modeling, so its Time Series capabilities are world-class. It handles dates, time zones, and frequency conversions with ease.

Lesson 17: Working with Dates

The datetime dtype is magical.

  • pd.to_datetime(col): Smartly converts strings to datetime objects.
  • Accessors: .dt.year, .dt.month, .dt.day_name().

The DatetimeIndex

If you set a datetime column as the Index, you unlock powerful slicing features like df['2024-01'].

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Lesson 18: Resampling & Rolling Windows

Resampling

Changing the frequency of your data (e.g., Daily -> Monthly).

  • df.resample('M').mean(): Calculate monthly averages.
  • df.resample('D').sum(): Calculate daily totals.

Rolling Windows

Calculations that "slide" across the data (Moving Averages).

  • df.rolling(window=7).mean(): 7-day moving average.
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Practice: Stock Trader

Challenge:

  1. Create a pd.date_range of 10 days.
  2. Create a Series with random prices for those days.
  3. Calculate the 3-day moving average of the price.

Quiz

Question 1 of 5

Which function converts changing string dates into actual Datetime objects?

pd.to_date()
pd.convert_time()
pd.to_datetime()
pd.read_date()

Key Takeaways

✅ pd.to_datetime is the first step for any time series.
✅ resample is like groupby, but for time frequencies.
✅ rolling is crucial for trends and moving averages.