Retrieving the Frequency Name from a CustomBusinessDay Offset in Pandas

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💡 Problem Formulation: Pandas users often create custom business day offsets for time series analysis, which require recognizing the name of the frequency applied to these offsets. For example, when given a CustomBusinessDay object representing every second business day, we would like to extract the string “2B” indicating the frequency. This article provides various methods to accomplish this task.

Method 1: Using the freqstr Attribute

This method makes use of the built-in freqstr attribute of the CustomBusinessDay object in pandas. The freqstr attribute returns the string representation of the frequency for a given offset.

Here’s an example:

import pandas as pd

# Create a CustomBusinessDay object with a frequency of 2 business days
cbd = pd.offsets.CustomBusinessDay(n=2)

# Retrieve the frequency name
frequency_name = cbd.freqstr

print(frequency_name)

Output:

'2B'

This code snippet creates a CustomBusinessDay object with a frequency of two business days. The frequency name is retrieved by simply accessing the freqstr attribute, which succinctly provides the information with minimal code.

Method 2: Using to_offset method and Accessing freqstr

If dealing with a string representation of a custom business day offset, the to_offset method can be utilized to convert the string to an offset object, from which the frequency name can then be extracted.

Here’s an example:

from pandas.tseries.frequencies import to_offset

# Creating an offset object from a string
offset = to_offset('2B')

# Retrieving the frequency name
frequency_name = offset.freqstr

print(frequency_name)

Output:

'2B'

This snippet converts a business day frequency string ‘2B’ into a pandas offset object and then retrieves the frequency name using the freqstr attribute. This method is convenient when working with string representations of frequency.

Method 3: Parsing Frequency Information from a DataFrame with CustomBusinessDay Frequency

Another practical approach involves extracting the frequency from a DataFrame that has been indexed with a time series having a CustomBusinessDay frequency. This can be done by accessing the freqstr attribute of the DataFrame’s index.

Here’s an example:

import pandas as pd

# Create a date range with CustomBusinessDay frequency
date_range = pd.date_range(start='2023-01-01', periods=5, freq=pd.offsets.CustomBusinessDay(n=2))

# Create a DataFrame indexed by the custom business days
df = pd.DataFrame(index=date_range)

# Retrieve the frequency name from the DataFrame index
frequency_name = df.index.freqstr

print(frequency_name)

Output:

'2B'

This example demonstrates how to create a pandas DataFrame with an index that increments by two business days. It then retrieves the frequency string by accessing the freqstr attribute from the DataFrame’s index, which is a common scenario for time series applications.

Method 4: Extracting Frequency from a Timedelta Produced by CustomBusinessDay

The frequency name can also be reasoned out by calculating the timedelta between two consecutive business days. By comparing the days attribute of the timedelta, one can infer the frequency.

Here’s an example:

import pandas as pd

# Create a date range with CustomBusinessDay frequency
date_range = pd.date_range(start='2023-01-01', periods=2, freq=pd.offsets.CustomBusinessDay(n=2))

# Calculate timedelta between two consecutive business days
time_delta = date_range[1] - date_range[0]

# Deduce the frequency from the timedelta
days = time_delta.days
frequency_name = f"{days}B"

print(frequency_name)

Output:

'2B'

By subtracting two consecutive dates in a custom business days date range, this code snippet calculates the timedelta and uses its days attribute to build the frequency string. While this is a more manual process, it may be useful in contexts where the freqstr attribute is not readily available.

Bonus One-Liner Method 5: Utilizing the String Formatter

A concise one-liner that retrieves the frequency name of a CustomBusinessDay offset object might involve the string format operation directly on the object.

Here’s an example:

import pandas as pd

# Create a CustomBusinessDay object with a frequency of 2 business days
cbd = pd.offsets.CustomBusinessDay(n=2)

# Retrieve the frequency name using string formatting
frequency_name = f"{cbd:n}{cbd.name}"

print(frequency_name)

Output:

'2B'

This quick one-liner leverages Python’s f-string formatting to combine the n attribute of the CustomBusinessDay object, which holds the frequency number, and its name attribute to construct the frequency name directly.

Summary/Discussion

  • Method 1: freqstr attribute. Straightforward and direct. Limited to pandas offset objects.
  • Method 2: to_offset and freqstr. Transformative and versatile for string inputs. Extra step of conversion required.
  • Method 3: DataFrame index freqstr. Ideal for data analysis workflows. Requires a DataFrame indexed by dates.
  • Method 4: Deduction from timedelta. More manual but flexible. Requires calculation and understanding of timedelta objects.
  • Method 5: String Formatter. Concise and Pythonic. Relies on format specification and attributes of CustomBusinessDay.