Time Series Indexes—Slice and Index

Discover the different ways of slicing and indexing time-series-indexed DataFrames.


Time series indexes have powerful features that enable us to work effectively with time series data. Given that the DatetimeIndex is the most commonly encountered time series object, the indexing methods described in this lesson will be focused on it. Let’s first take a look at some examples of the characteristics of the DatetimeIndex object:

  • Time-based indexing: Allows us to perform time-based indexing on pandas data structures, making it easy to select data based on specific dates, times, or time ranges.

  • Frequency inference: Able to infer the frequency of the time series data based on the data itself, making it easy to work with data having either regular or irregular time intervals.

  • Time zone handling: Supports time zone handling, allowing us to work with data from different time zones and perform conversions between time zones

  • Date arithmetic: Supports arithmetic operations with dates and times, making it easy to perform calculations based on time intervals.

  • Merging and joining: Easy and fast in time-based joins between datasets.

  • Date range generation: Makes the generation of date ranges fast because a large range of dates for various offsets (e.g., month, business week, etc.) are precomputed and cached under the hood.

  • Access to date fields: Enables us to access date fields via attributes like month, year, etc.

We’ll use the New Delhi daily climate time series dataset for this lesson, as shown below:

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