# 5 Best Ways to Perform Floor Operation on Pandas TimeDeltaIndex with Hourly Frequency

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π‘ Problem Formulation: In data analysis with Python’s pandas library, a common requirement is to round down (floor) a TimeDeltaIndex to the nearest hour. This article explains how to perform a floor operation with hourly frequency on a TimeDeltaIndex, which is especially useful when dealing with time-series data. Given a pandas Series with a TimeDeltaIndex like `Timedelta('0 days 01:23:45')`, we want to floor it to `Timedelta('0 days 01:00:00')`.

## Method 1: Using `floor()` Method

This method utilizes the `floor()` function provided by pandas TimeDeltaIndex to round down time deltas to a specified frequency. The function is well-suited for precision adjustments in time series data and is very straightforward to use.

Here’s an example:

```import pandas as pd

# Sample TimeDeltaIndex
time_deltas = pd.to_timedelta(["1 days 02:35:00", "2 days 12:45:00", "3 days 22:15:30"])

# Perform the floor operation
floored_time_deltas = time_deltas.floor('H')
print(floored_time_deltas)```

Output:

`TimedeltaIndex(['1 days 02:00:00', '2 days 12:00:00', '3 days 22:00:00'], dtype='timedelta64[ns]', freq=None)`

In this code, the `TimeDeltaIndex` is floored to the nearest hour using the `floor()` function by passing ‘H’ as the frequency string. This effectively trims off any minutes and seconds from the original time deltas.

## Method 2: Converting to `DataFrame` and Applying `dt.floor`

By converting a TimeDeltaIndex to a DataFrame column, you can utilize the `dt` accessor along with the `floor` method to perform flooring operations on time deltas.

Here’s an example:

```import pandas as pd

# Create a DataFrame with a timedelta column
df = pd.DataFrame({'TimeDeltas': pd.to_timedelta(["5:55:00", "10:30:00", "23:45:30"])})

# Perform the floor operation using dt.floor
df['Floored'] = df['TimeDeltas'].dt.floor('H')
print(df)```

Output:

```       TimeDeltas          Floored
0 0 days 05:55:00 0 days 05:00:00
1 0 days 10:30:00 0 days 10:00:00
2 0 days 23:45:30 0 days 23:00:00```

In the DataFrame `df`, we have a column ‘TimeDeltas’ which contains the original timedelta values. After using the `dt.floor()` method on this column, we assign the floored values to the new ‘Floored’ column.

## Method 3: Utilizing `numpy` Floor Division

By leveraging the power of NumPy, we can achieve the same result through floor division and multiplication. This method is efficient and bypasses the need for explicit pandas functions but requires an understanding of time delta units conversion.

Here’s an example:

```import pandas as pd
import numpy as np

# Sample TimeDeltaIndex
time_deltas = pd.to_timedelta(["0 days 08:29:00", "1 days 14:59:59", "2 days 23:00:01"])

# Perform the floor operation using numpy
floored_time_deltas = time_deltas // np.timedelta64(1, 'h') * np.timedelta64(1, 'h')
print(floored_time_deltas)```

Output:

`TimedeltaIndex(['0 days 08:00:00', '1 days 14:00:00', '2 days 23:00:00'], dtype='timedelta64[ns]', freq=None)`

This approach first converts the time deltas into hour units via floor division by `np.timedelta64(1, 'h')`, effectively discarding any minutes and seconds. The results are then multiplied back to timedelta units, yielding the floored time deltas.

## Method 4: Rounding with Custom Function

For fine-grained control or complex rounding logic, you might consider applying a custom function to the TimeDeltaIndex. This method is versatile and can be adapted to various flooring and rounding scenarios.

Here’s an example:

```import pandas as pd

# Custom function to floor timedelta to nearest hour
def floor_to_hour(td):
return pd.Timedelta(hours=int(td.total_seconds() // 3600))

# Sample TimeDeltaIndex
time_deltas = pd.to_timedelta(["0 days 15:25:00", "2 days 07:59:59", "4 days 23:00:45"])

# Apply custom floor function
floored_time_deltas = time_deltas.map(floor_to_hour)
print(floored_time_deltas)```

Output:

`TimedeltaIndex(['0 days 15:00:00', '2 days 07:00:00', '4 days 23:00:00'], dtype='timedelta64[ns]', freq=None)`

The `map()` method applies the `floor_to_hour` function on each element of the TimeDeltaIndex. The function calculates total seconds, performs floor division by 3600 (seconds per hour), and then creates a new `Timedelta` with these floored hour values.

## Bonus One-Liner Method 5: Using `round()` Method with `freq='H'`

Pandas also provides a `round()` method for TimeDeltaIndex. This quick one-liner is handy if your requirement is simply to round to the nearest hour; however, for strict flooring, ensure that the original times are not closer to the next hour.

Here’s an example:

```import pandas as pd

# Sample TimeDeltaIndex
time_deltas = pd.to_timedelta(["0 days 00:30:00", "0 days 01:15:00", "0 days 01:45:00"])

# Perform the round operation with 'H' frequency
rounded_time_deltas = time_deltas.round('H')
print(rounded_time_deltas)```

Output:

`TimedeltaIndex(['0 days 01:00:00', '0 days 01:00:00', '0 days 02:00:00'], dtype='timedelta64[ns]', freq=None)`

This method involves using `round()`, where you specify ‘H’ as the frequency for rounding. This can simplify the code significantly, but the result might not be an exact floor if the timedelta is exactly halfway between two hours.

## Summary/Discussion

• Method 1: Pandas floor method. Strengths: Native support and easy syntax. Weakness: Directly tied to pandas without much flexibility.
• Method 2: DataFrame applied method. Strengths: Convenient within DataFrame operations, intuitive for those familiar with pandas. Weakness: Requires conversion to DataFrame if dealing with indices.
• Method 3: NumPy floor division. Strengths: Efficient computation and concise syntax. Weakness: Less intuitive due to manual unit conversions.
• Method 4: Custom function. Strengths: Highly customizable for complex requirements. Weakness: More verbose and potentially slower than built-in methods.
• Method 5: Pandas round method. Strengths: Simple one-liner. Weaknesses: Not a strict floor, it rounds to the nearest hour, which can give different results if exactly halfway.