Finding Index Locations of Time-based Values with Python Pandas

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💡 Problem Formulation: Working with timeseries data often involves searching for entries within specified time intervals. For example, a dataset indexed by DatetimeIndex might require finding all records between 9:00 AM and 5:00 PM inclusive. The desired output is the index location of all values falling within this range, which is critical for analyses constrained to specific times of day.

Method 1: Using indexer_between_time

Pandas provides the indexer_between_time method on a DataFrame or Series to find index locations of entries between two times. It is specialized for time-based indexing and can include the start time by setting the include_start argument to True.

Here’s an example:

import pandas as pd

# Create a date range for July 1st, specifying the frequency
daterange = pd.date_range('2022-07-01', periods=24, freq='H')

# Create a DataFrame with random data
df = pd.DataFrame(index=daterange, data={'values': range(24)})

# Get index locations for values between 9 AM and 5 PM including the 9 AM entry
index_locations = df.index.indexer_between_time('09:00', '17:00', include_start=True)

print(index_locations)

Output:

[ 9 10 11 12 13 14 15 16 17]

This code snippet creates a DataFrame indexed by hourly intervals on July 1st, 2022. The indexer_between_time method is then used to find indexes ranging from 9 AM to 5 PM, inclusive of the start time.

Method 2: Boolean Indexing with between_time

Another straightforward option for locating time-based values is to use the between_time method in conjunction with boolean indexing. This method also handles times effectively and can be used directly to filter the DataFrame or Series.

Here’s an example:

# Analogous to the previous setup
df = pd.DataFrame(index=daterange, data={'values': range(24)})

# Use boolean indexing to select values within the desired time range
filtered_df = df.between_time('09:00', '17:00', include_start=True)

# Display the index of the filtered DataFrame
print(filtered_df.index)

Output:

DatetimeIndex(['2022-07-01 09:00:00', '2022-07-01 10:00:00', ...
               '2022-07-01 17:00:00'],
              dtype='datetime64[ns]', freq=None)

Here, between_time is used to filter the DataFrame directly for entries between 9 AM and 5 PM. The filtered DataFrame’s index is then outputted, giving the same result albeit through a slightly different approach.

Method 3: Custom Time Range Filtering

Custom filtering based on time criteria can be achieved by generating boolean masks using the DatetimeIndex methods like index.hour and other time-specific attributes.

Here’s an example:

# Assuming df setup as per Method 1 and 2

# Create custom time filtering based on the hour attribute
start_hour = 9
end_hour = 17
mask = (df.index.hour >= start_hour) & (df.index.hour <= end_hour)
  
# Apply the mask to the DataFrame
filtered_df = df[mask]

print(filtered_df.index)

Output:

DatetimeIndex(['2022-07-01 09:00:00', '2022-07-01 10:00:00', ...
               '2022-07-01 17:00:00'],
              dtype='datetime64[ns]', freq=None)

This approach uses a boolean mask created by comparing the hours component of the index to the desired start and end hours. It provides greater flexibility for defining custom time ranges.

Method 4: Utilizing at_time for Exact Time Matches

The at_time method can be handy when you need index locations for an exact time. Although this isn’t a direct method for a range, combined with a timeslice or additional logic, it can find times at or around a particular moment.

Here’s an example:

# Continuing with the df from previous methods

# Find indexes exactly at a specific time
exact_time_indexes = df.at_time('09:00').index

print(exact_time_indexes)

Output:

DatetimeIndex(['2022-07-01 09:00:00'],
              dtype='datetime64[ns]', freq=None)

This method extracts index locations that precisely match a given time. While by itself it doesn’t address ranges, it can complement range-based methods when specific time points are significant.

Bonus One-Liner Method 5: Using List Comprehension for Granular Control

A flexible one-liner solution uses list comprehension to iterate over the index and select times within the range. This method offers impressive control but might be less performant on large datasets.

Here’s an example:

# Similar DataFrame as the above examples

# Use list comprehension to filter index locations based on time conditions
index_locations = [i for i in range(len(df)) if '09:00' <= str(df.index[i].time()) <= '17:00']

print(index_locations)

Output:

[9, 10, 11, 12, 13, 14, 15, 16, 17]

By iterating over the index, this code snippet manually checks if each time is within the specified range, returning the corresponding index locations if true.

Summary/Discussion

  • Method 1: indexer_between_time. Optimized for time-based indexing. Directly provides index locations. May not handle edge cases without additional parameters.
  • Method 2: Boolean indexing with between_time. Convenient for dataframe filtering. Easy to use. Does not work directly with non-continuous ranges.
  • Method 3: Custom Time Range Filtering. Great flexibility. Can handle complex time criteria. Requires more code and understanding of DatetimeIndex methods.
  • Method 4: at_time for Exact Time Matches. Useful for specific-time checks. Needs to be combined with other methods for ranges. Less versatile for ranges.
  • Method 5: List Comprehension. Offers granular control. Can be a one-liner. Might be inefficient with larger datasets.