Loc and Apply: Conditionally Set Multiple Column Values with Dynamic Values in Pandas

Introduction

Pandas is a powerful library for data manipulation and analysis in Python. One of its strengths is the ability to perform efficient lookups and replacements of values in a DataFrame based on conditions. In this article, we will explore two common methods for conditionally setting multiple column values using loc and apply. We will also provide an example with dynamic values.

Background

The problem presented in the question arises when we want to replace values in one DataFrame (df) with values from another DataFrame (lookup) based on certain conditions. The conditions are met when a pair of columns (lower_limit and upper_limit) is both missing (NaN). In this scenario, we need to efficiently look up the corresponding index values in the lookup DataFrame.

Solution 1: Using Loc with Mask

The first approach involves using the loc method on the original DataFrame (df) with a mask. We create an array of boolean indices where both lower_limit and upper_limit are NaN, indicating that we want to look up values in the lookup DataFrame.

index_modifier = '_Male'
ix_of_interest = df.loc[(df.lower_limit.isnull()) & (df.upper_limit.isnull())].index
lookup_ix = [ix + index_modifier for ix in ix_of_interest]
df.loc[ix_of_interest, ['lower_limit','upper_limit']] = lookup.loc[lookup_ix]

However, this approach has a few issues. The main problem is that df.lower_limit.isnull() returns a Series of boolean values (True or False) where each value corresponds to the index in df. We cannot directly use this boolean mask with loc because it will return an empty DataFrame instead of just the indices where both lower_limit and upper_limit are NaN.

To fix this, we can create a list of indices that meet our condition by iterating over each row and checking if both values are missing. We then use these indices to select rows from the original DataFrame (df) and replace their values with the corresponding ones from the lookup DataFrame.

ix_of_interest = [ix for ix in df.index if (df.loc[ix, 'lower_limit'].isnull() and df.loc[ix, 'upper_limit'].isnull())]
lookup_ix = [ix + index_modifier for ix in ix_of_interest]

df.loc[ix_of_interest, ['lower_limit', 'upper_limit']] = lookup.loc[lookup_ix]

Solution 2: Using DataFrame.add_suffix and fillna

A more elegant solution involves using the add_suffix method to add a suffix to our column names and then filling missing values with ones from another Series.

index_modifier = '_Male'

# Add suffix to column names
df = df.T.add_suffix(index_modifier).T

# Fill missing values with corresponding values from lookup DataFrame
df['lower_limit'].fillna(lookup['lower'],inplace=True)
df['upper_limit'].fillna(lookup['upper'],inplace=True)

# Reset original index
df.index=init_index
print(df)

This approach simplifies the process and reduces code duplication.

Conclusion

When working with missing values in Pandas DataFrames, using loc or apply can be an effective way to replace values based on conditions. By choosing the right method for your specific use case, you can simplify your data manipulation tasks and write more efficient code.

Note that this solution uses a different approach than the original question’s answers. However, the outcome is the same: we successfully conditionally set multiple column values with dynamic values using loc and apply.

Last modified on 2024-04-09