# 5 Best Ways to Perform Floor Operation on the Pandas TimedeltaIndex with Microseconds Frequency

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π‘ Problem Formulation: When dealing with time-series data in Python’s pandas library, a common requirement is to ‘floor’ or round down a `TimedeltaIndex` to a specified frequency. Specifically, when working with microseconds frequency, one needs precise control to truncate these time values efficiently. For example, a `TimedeltaIndex` with a range of microseconds should be floored to the nearest second without losing accuracy.

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

The `floor()` function in pandas allows you to round down `TimedeltaIndex` values to a specified frequency. This is particularly useful when you need to aggregate or summarize time-based data and is a direct method suitable for various frequencies.

Here’s an example:

```import pandas as pd

# Creating a TimedeltaIndex with microsecond frequency
tdi = pd.to_timedelta(['0 days 00:00:00.123456', '0 days 00:00:00.654321'])

# Flooring to seconds
floored_tdi = tdi.floor('S')
print(floored_tdi)
```

Output:

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

This code snippet creates a `TimedeltaIndex` with microsecond values and uses the `floor()` function to round down these values to the nearest second.

## Method 2: Round Function with ‘s’ Parameter

Rounding down can also be achieved using the `round()` function with a specific frequency parameter. Although round typically brings a value to the nearest specified frequency, using ‘s’ (second) effectively floors the microseconds.

Here’s an example:

```import pandas as pd

# Creating a TimedeltaIndex with microsecond frequency
tdi = pd.to_timedelta(['0 days 00:00:00.123456', '0 days 00:00:00.654321'])

# Rounding to seconds
rounded_tdi = tdi.round('s')
print(rounded_tdi)
```

Output:

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

The example demonstrates the `round()` function with a ‘s’ parameter that rounds the `TimedeltaIndex` to the nearest second, effectively performing a floor operation for values less than half a second.

## Method 3: Using `astype()` to Floor to Seconds

The `astype()` method can also be used to convert the microsecond index into a second index, thereby flooring the values implicitly during conversion.

Here’s an example:

```import pandas as pd

# Creating a TimedeltaIndex with microsecond frequency
tdi = pd.to_timedelta(['0 days 00:00:00.123456', '0 days 00:00:00.654321'])

# Converting to seconds using astype
converted_tdi = tdi.astype('timedelta64[s]')
print(converted_tdi)
```

Output:

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

By using `astype()`, the `TimedeltaIndex` is converted to have a second granularity, and as a result, microsecond information is discarded, effectively flooring the time values.

## Method 4: Manual Flooring with `apply()`

If you need more control over flooring or want to use a custom floor function, you can use the `apply()` method. This provides flexibility and allows for more complex floor operations.

Here’s an example:

```import pandas as pd

# Creating a TimedeltaIndex with microsecond frequency
tdi = pd.to_timedelta(['0 days 00:00:00.123456', '0 days 00:00:00.654321'])

# Defining a custom floor function
def custom_floor(td):
return pd.to_timedelta(str(td.components.days) + ' days ' + str(td.components.hours) + ' hours')

# Applying a custom floor function
floored_tdi = tdi.apply(custom_floor)
print(floored_tdi)
```

Output:

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

The `apply()` method invokes a custom function that strips off any smaller units than hours, effectively flooring each `Timedelta` to the nearest hour.

## Bonus One-Liner Method 5: Using Numpy’s `floor_divide()`

Numpy offers a `floor_divide()` function, which can be used to perform floor division directly on the numpy array underlying the `TimedeltaIndex`. This is a more low-level approach but can be efficient for large datasets.

Here’s an example:

```import pandas as pd
import numpy as np

# Creating a TimedeltaIndex with microsecond frequency
tdi = pd.to_timedelta(['0 days 00:00:00.123456', '0 days 00:00:00.654321'])

# Flooding using NumPy's floor_divide
floored_tdi = pd.to_timedelta(np.floor_divide(tdi.asi8, 10**9) * 10**9, unit='ns')
print(floored_tdi)
```

Output:

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

This one-liner uses NumPy’s `floor_divide()` function to neatly floor the time values to the nearest second by dividing and multiplying the underlying nanosecond values.

## Summary/Discussion

• Method 1: Using `floor()` Function. This is the most straightforward way to floor a `TimedeltaIndex`. Its main strength is readability and directness. However, it might be less flexible than other more custom methods.
• Method 2: Round Function with ‘s’ Parameter. This method is ideal for easily rounding values while flooring those below half a second. It is less explicit than the `floor()` function, which might create confusion for rounding to other frequencies.
• Method 3: Using `astype()` to Floor to Seconds. This method is very concise and is a one-step process to floor values. However, it might lack explicitness in that the flooring operation is implicit.
• Method 4: Manual Flooring with `apply()`. This provides the most flexibility and allows for the use of custom flooring logic. This might, however, be overkill for simple flooring requirements and is usually slower than vectorized operations.
• Bonus Method 5: Using Numpy’s `floor_divide()`. This method is great for efficiency, especially with large sets of data. Its low-level operation may obscure understanding to those unfamiliar with NumPy’s function and data representation.