# Time Series Indexes—Creating Indexes

Discover the different ways of generating time series indexes in pandas.

## We'll cover the following

## Introduction

For time series data, it’s typical to represent the time component in the index of a `Series`

or a DataFrame so that data manipulation can be performed with respect to the time element. A** time series index** is a data structure representing a one-dimensional indexed array of date-time values.

The benefit of using time series indexes is that they offer the basic functionality of regular index objects. This includes providing a range of capabilities for time-based operations, such as resampling, slicing, and indexing by partial string matching. We’ll be looking at the three index objects associated with time series, namely the `DatetimeIndex`

, `TimedeltaIndex`

, and `PeriodIndex`

.

`DatetimeIndex`

The `DatetimeIndex`

index object corresponds to the concept of date times and is the most common type of time series index that we’ll encounter. To set a `DatetimeIndex`

, we can use the `to_datetime()`

function to convert a DataFrame column into the `datetime64`

data type, and then set it as the DataFrame index with `set_index()`

.

In the example below, we see how we can use `to_datetime()`

to convert a DataFrame column of date strings into a data type of `datetime64[ns]`

. As a result, the output DataFrame has a `DatetimeIndex`

that can be used for time-based operations, as shown below:

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