Advanced pandas—Going Beyond the Basics/

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Time Series Indexes—Attributes

Time Series Indexes—Attributes

Learn about the different attributes of time series objects in pandas.

We'll cover the following...

Introduction

Now that we know the basic capabilities of time series indexes, let’s delve deeper into understanding their attributes, which provide us with valuable time-based information.

Like before, we’ll use the DatetimeIndex object to represent time series indexes in this lesson due to its ubiquity in real-world use cases. Nonetheless, many of the properties seen in DatetimeIndex are also available in the other two indexes, TimedeltaIndex and PeriodIndex.

We’ll use the New Delhi daily climate time series dataset for the examples in this lesson.

Preview of New Delhi Daily Climate Time Series Data

date

meantemp

humidity

wind_speed

meanpressure

1/1/2017

15.91304348

85.86956522

2.743478261

59

2/1/2017

18.5

77.22222222

2.894444444

1018.277778

3/1/2017

17.11111111

81.88888889

4.016666667

1018.333333

4/1/2017

18.7

70.05

4.545

1015.7

5/1/2017

18.38888889

74.94444444

3.3

1014.333333

The DatetimeIndex attributes

The DatetimeIndex object comes with numerous attributes that make it easy to extract and understand the time-based information contained within it.

Core attributes

The core attributes are the basic ones that we should already be familiar with, ranging from year to nanosecond. In addition, the date and time attributes return the NumPy array of the datetime.date object and datetime.time object respectively. The following code illustrates the output of these attribute calls on a subset dataset, which contains only the first row:

Python 3.10.4
# Set DatetimeIndex

df['date'] = pd.to_datetime(df['date'], format='%d/%m/%Y')

df = df.set_index('date')



# Filter to keep only first row (for cleaner illustration purposes)

df = df.head(1)



# View core attributes

print('Display row:\n', df)

print('='* 70)

print('Year:', df.index.year)

print('Month:', df.index.month)

print('Day:', df.index.day)

print('Hour:', df.index.hour)

print('Minute:', df.index.minute)

print('Second:', df.index.second)

print('Microsecond:', df.index.microsecond)

print('Nanosecond:', df.index.nanosecond)

print('Date:', df.index.date)

print('Time:', df.index.time)

Temporal attributes

The temporal attributes of DatetimeIndex allow us to retrieve time-based features about the data. Here are the attributes under this category:

  • day_of_year: Returns the ...