This code seems to operate on a pandas DataFrame `crifDf_local`...

August 28, 2025 at 04:04 AM

crifDf_local["TermRatio"] = np.where((crifDf_local.Label1 == '') & (crifDf_local.RiskType == 'Risk_Inflation'), crifDf_local.RemainingMaturity / crifDf_local.DaysToMaturity , crifDf_local.RemainingTenorDays / crifDf_local.TenorDaysHigh ) crifDf_local.TermRatio = crifDf_local.TermRatio.replace(np.nan, 1.0, regex=True) crifDf_local["AllocationLow"] = np.where((crifDf_local.Type == "single") | (crifDf_local.Label1 == '') , 1 , (crifDf_local.TenorDaysHigh_y - crifDf_local.RemainingTenorDays) / ( crifDf_local.TenorDaysHigh_y - crifDf_local.TenorDaysLow) ) crifDf_local["AllocationHigh"] = 1 - crifDf_local.AllocationLow

This code seems to operate on a pandas DataFrame crifDf_local and performs a series of calculations and assignments to create several new columns in the DataFrame, based on certain conditions. Here's a detailed step-by-step explanation of what the code does:

1. Creating the TermRatio column:

The first section of the code computes a new column called TermRatio in the DataFrame crifDf_local using the np.where function. Here's the logic:

  • If crifDf_local.Label1 is empty ('') AND crifDf_local.RiskType is equal to "Risk_Inflation", then:
    • The value of TermRatio will be calculated as crifDf_local.RemainingMaturity / crifDf_local.DaysToMaturity.
  • Otherwise:
    • The value of TermRatio will be calculated as crifDf_local.RemainingTenorDays / crifDf_local.TenorDaysHigh.

After this, TermRatio is updated to replace any NaN values with 1.0. This is done using the replace method of pandas.


2. Creating the AllocationLow column:

The second part of the code creates another column called AllocationLow with the following logic:

  • If crifDf_local.Type is equal to "single" OR crifDf_local.Label1 is empty (''), then:
    • The value of AllocationLow is set to 1.
  • Otherwise:
    • The value of AllocationLow is computed as: [ \frac{\text{crifDf_local.TenorDaysHigh_y - crifDf_local.RemainingTenorDays}}{\text{crifDf_local.TenorDaysHigh_y - crifDf_local.TenorDaysLow}} ]

3. Creating the AllocationHigh column:

The value of the AllocationHigh column is calculated as: [ \text{AllocationHigh} = 1 - \text{AllocationLow} ] In other words, AllocationHigh is the complementary value of AllocationLow.


Summary of What This Code Does:

The code generates three new columns in the pandas DataFrame crifDf_local:

  1. TermRatio: It calculates a ratio based on conditions involving the Label1 and RiskType columns. Any missing values (NaN) in this column are replaced with 1.0.
  2. AllocationLow: It computes a value based on whether Type is "single" or Label1 is empty. If neither condition is met, it calculates a fraction based on tenor values.
  3. AllocationHigh: It complements the AllocationLow column (totaling to 1 when summed with AllocationLow).

This code is likely used in a financial or risk-management context, where ratios are being calculated for allocations or maturity-related terms.

Generate your own explanations
Download our vscode extension
Read other generated explanations

Built by @thebuilderjr
Sponsored by beam analytics
Read our terms and privacy policy
Forked from openai-quickstart-node