5 Best Ways to Find Index Locations of Values Within Specific Times in Python Pandas

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💡 Problem Formulation: When working with time series data in pandas, there may be a need to find the index locations of data points recorded within a specific time window of the day. For instance, suppose you have a DataFrame with a DatetimeIndex and you want to identify the indices of entries logged between 9 am and 5 pm. Properly identifying these indices can facilitate analyses, such as calculating daily statistics during business hours or filtering data for further examination.

Method 1: Using boolean indexing with between_time()

This method involves using the between_time() function available in pandas, which filters data between two time bounds. This is an efficient and easy-to-use function that specifically works with time data.

Here’s an example:

import pandas as pd

# Sample DateTimeIndex
dt_index = pd.date_range(start='2023-04-01 08:00', end='2023-04-01 18:00', freq='H')

# Sample DataFrame with the DateTimeIndex
df = pd.DataFrame(index=dt_index, data={'values': range(len(dt_index))})

# Use between_time to filter index locations
time_filtered_indices = df.between_time('09:00', '17:00').index

print(time_filtered_indices)

Output:

DatetimeIndex(['2023-04-01 09:00:00', '2023-04-01 10:00:00', '2023-04-01 11:00:00',
               '2023-04-01 12:00:00', '2023-04-01 13:00:00', '2023-04-01 14:00:00',
               '2023-04-01 15:00:00', '2023-04-01 16:00:00', '2023-04-01 17:00:00'],
              dtype='datetime64[ns]', freq=None)

The code creates a sample DataFrame with hourly time stamps for one day. The between_time() method is then used to filter the DataFrame to only include times between 9 am and 5 pm. The resulting indices are then printed out. This method is both straightforward and concise, making it ideal for simple time range selections.

Method 2: Using indexer_between_time()

The indexer_between_time() method returns the integer indices of the labels falling between two time bounds, which is helpful when you need the actual positions rather than the labels themselves.

Here’s an example:

# Use indexer_between_time to get integer index positions
index_positions = df.index.indexer_between_time('09:00', '17:00')

print(index_positions)

Output:

[ 1  2  3  4  5  6  7  8  9]

This snippet builds upon the previous example’s DataFrame, utilizing the indexer_between_time() method to obtain the integer index positions instead of the index labels. This can be especially useful for operations that require numeric indexing, such as slicing arrays.

Method 3: Custom boolean indexing with map()

For more customizable selection, one can use map() with a lambda function that evaluates the time component of each index. This approach is more flexible and can be tailored for complex conditions.

Here’s an example:

# Create a boolean index using map and lambda function
bool_index = df.index.map(lambda t: t.time() >= pd.Timestamp('09:00').time() and t.time() <= pd.Timestamp('17:00').time())

# Use the boolean index to filter
custom_filtered_indices = df[bool_index].index

print(custom_filtered_indices)

Output:

DatetimeIndex(['2023-04-01 09:00:00', '2023-04-01 10:00:00', '2023-04-01 11:00:00',
               '2023-04-01 12:00:00', '2023-04-01 13:00:00', '2023-04-01 14:00:00',
               '2023-04-01 15:00:00', '2023-04-01 16:00:00', '2023-04-01 17:00:00'],
              dtype='datetime64[ns]', freq=None)

The map() function applies a custom lambda function to each element of the DataFrame index. This lambda function yields a boolean value indicating whether the time portion of each index falls within the specified range. The result is then used as a boolean index to filter the original DataFrame.

Method 4: Time-based conditional indexing with apply()

Another highly customizable method uses the apply() function to check conditions across the DatetimeIndex. This method can incorporate additional logic and check multiple conditions.

Here’s an example:

# Define a function to apply
def in_time_range(timestamp):
    return timestamp.time() >= pd.Timestamp('09:00').time() and timestamp.time() <= pd.Timestamp('17:00').time()

# Use apply to filter index based on a function
applied_indices = df[df.index.to_series().apply(in_time_range)].index

print(applied_indices)

Output:

DatetimeIndex(['2023-04-01 09:00:00', '2023-04-01 10:00:00', '2023-04-01 11:00:00',
               '2023-04-01 12:00:00', '2023-04-01 13:00:00', '2023-04-01 14:00:00',
               '2023-04-01 15:00:00', '2023-04-01 16:00:00', '2023-04-01 17:00:00'],
              dtype='datetime64[ns]', freq=None)

The apply() function is used to apply a predefined function across the DataFrame’s index, allowing for precise control over the filtering conditions. This method shines when dealing with multifaceted conditions and can be easily extended for more complex scenarios.

Bonus One-Liner Method 5: Using a list comprehension

List comprehensions offer a Pythonic way to perform operations in a single readable line. This can be used for index selection based on time of day as well.

Here’s an example:

# List comprehension for bool index
list_comprehension_indices = df[[t.time() >= pd.Timestamp('09:00').time() and t.time() <= pd.Timestamp('17:00').time() for t in df.index]].index

print(list_comprehension_indices)

Output:

DatetimeIndex(['2023-04-01 09:00:00', '2023-04-01 10:00:00', '2023-04-01 11:00:00',
               '2023-04-01 12:00:00', '2023-04-01 13:00:00', '2023-04-01 14:00:00',
               '2023-04-01 15:00:00', '2023-04-01 16:00:00', '2023-04-01 17:00:00'],
              dtype='datetime64[ns]', freq=None)

A list comprehension succinctly creates a boolean index that can be directly applied to the DataFrame for selection. It’s a compact and efficient one-liner that can be adequate for many simple scenarios.

Summary/Discussion

  • Method 1: between_time(). Strengths: direct and easy to understand; specifically designed for time-based indexing. Weaknesses: less flexible for more complex conditions.
  • Method 2: indexer_between_time(). Strengths: returns numeric index positions; useful for slicing. Weaknesses: limited to index labels, not content within the rows.
  • Method 3: Custom boolean indexing with map(). Strengths: highly flexible for custom conditions. Weaknesses: potentially less readable and slower for large datasets.
  • Method 4: Time-based conditional indexing with apply(). Strengths: highly customizable and extendable; supports complex logic. Weaknesses: can be slower due to function call overhead.
  • Method 5: List comprehension. Strengths: concise one-liner; Pythonic. Weaknesses: readability can suffer for complex conditions.