5 Best Ways to Filter a Pandas DataFrame by Time

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💡 Problem Formulation: When working with dataset containing time series data, a common task is to filter records based on time criteria. For example, you may have a DataFrame of stock prices and you wish to filter out entries that fall outside of regular trading hours or beyond a certain date range. The desired output would be a filtered DataFrame with only rows that meet the time criteria.

Method 1: Using between_time()

Filtering a DataFrame by time can be accomplished with the between_time() method when the time is the index. This is useful for DataFrame indices of type DatetimeIndex, and it allows you to specify the start time and end time to create a time range within which the rows should fall.

Here’s an example:

import pandas as pd

# Create a sample DataFrame with DatetimeIndex
rng = pd.date_range('2023-04-01', periods=24, freq='H')
df = pd.DataFrame({'data': range(24)}, index=rng)

# Filter DataFrame between 9am and 5pm
filtered_df = df.between_time('09:00', '17:00')
print(filtered_df)

The following DataFrame will be obtained as output:

                     data
2023-04-01 09:00:00     9
...
2023-04-01 17:00:00    17

The code snippet demonstrates how to create a DataFrame using pandas with hourly timestamps and then apply between_time() to filter records that fall between 9 AM and 5 PM of the given day.

Method 2: Boolean Indexing with Time Conditions

Boolean indexing is another versatile method for filtering data. You use it to apply conditions directly to the DataFrame’s time-related columns. For instance, if you have a column of type datetime, you can compare it against specific datetime objects to filter rows.

Here’s an example:

import pandas as pd

# Creating a sample DataFrame with a time column
df = pd.DataFrame({
    'time': pd.date_range('2023-04-01', periods=5, freq='D'),
    'data': range(5)
})

# Applying a boolean filter for times after '2023-04-02'
filtered_df = df[df['time'] > pd.Timestamp('2023-04-02')]
print(filtered_df)

The result would be:

        time  data
3 2023-04-04     3
4 2023-04-05     4

This snippet filters the DataFrame rows for dates after April 2nd, 2023. The comparison uses a pd.Timestamp object, making it straightforward to specify exact dates for comparison.

Method 3: Using date_range() for Filtering

The date_range() function can be used in combination with boolean indexing to filter DataFrame rows within a specific date range. By creating a date range, you can easily filter out rows that do not fall within this range.

Here’s an example:

import pandas as pd

# Creating a sample DataFrame with a time column
df = pd.DataFrame({
    'time': pd.date_range('2023-04-01', periods=7, freq='D'),
    'data': range(7)
})

# Define the date range
start_date = '2023-04-02'
end_date = '2023-04-04'

# Filtering the DataFrame within the date range
mask = (df['time'] >= start_date) & (df['time'] <= end_date)
filtered_df = df.loc[mask]
print(filtered_df)

This will output:

        time  data
1 2023-04-02     1
2 2023-04-03     2
3 2023-04-04     3

The code uses date_range() to define start and end dates and then creates a mask that filters the DataFrame to only include the desired range. Applying the mask via df.loc[mask] yields the filtered results.

Method 4: Resampling and Filtering

Resampling is a powerful method in pandas, especially for time series data. It allows to aggregate data based on a time period and can also be used for filtering. You can resample the data based on time frequency and apply conditions to keep the data within certain time intervals.

Here’s an example:

import pandas as pd

# Creating a sample DataFrame with a time index
df = pd.DataFrame({
    'data': range(24)
}, index=pd.date_range('2023-04-01', periods=24, freq='H'))

# Resample the DataFrame to daily data and filter
daily_df = df.resample('D').sum()
morning_df = df.between_time('06:00', '12:00').resample('D').sum()
print(morning_df)

This gives you:

            data
2023-04-01    45

The snippet resamples the hourly data to daily aggregates. It then filters to include only morning hours (6 AM to 12 PM) before resampling again, resulting in a sum of values within that morning period per day.

Bonus One-Liner Method 5: Using query() Method

The query() method enables concise DataFrame filtering using string expressions. It’s particularly helpful when you want to write less verbose code and perform complex filters efficiently.

Here’s an example:

import pandas as pd

# Creating a sample DataFrame with a time column
df = pd.DataFrame({
    'time': pd.date_range('2023-04-01', periods=5, freq='D'),
    'data': range(5)
})

# Filter using a query string
filtered_df = df.query("'2023-04-02' < time < '2023-04-04'")
print(filtered_df)

This gives you:

        time  data
2 2023-04-03     2

Using query() method, the code filters the DataFrame for rows where the time column is between April 2nd, 2023, and April 4th, 2023. The method allows the use of string expressions to specify conditions directly.

Summary/Discussion

  • Method 1: between_time(). Filters efficiently using the index. Limited to time indexed data.
  • Method 2: Boolean Indexing. It offers full flexibility with any time-related columns, but can be verbose with complex conditions.
  • Method 3: Using date_range(). Good for filtering within date ranges, but requires additional steps of creating a mask and applying it.
  • Method 4: Resampling and Filtering. Very powerful for time series aggregation and filtering but may be overkill for simple filtering tasks.
  • Method 5: Using query() Method. Provides a clean and efficient one-liner for complex conditions but requires familiarity with query syntax.