turns-00064.parquet:12852
76e28e940d6c9803a9973d2ddegenerate_repetitionAbsentFinal dense release
USER
grouped = df_.groupby(['EIN', 'Product', 'Group', 'Year','Plan_Name'])
switch_summary = grouped.apply(lambda x: pd.Series({
'Total SI': x['Members'][x['Funding'].str.contains('SI', case=False)].sum(),
'Total FI': x['Members'][x['Funding'].str.contains('FI', case=False)].sum(),
'Total Members': x['Members'].sum()
})).reset_index()
switch_summary['% SI'] = (switch_summary['Total SI'] / switch_summary['Total Members']) * 100
switch_summary['% FI'] = (switch_summary['Total FI'] / switch_summary['Total Members']) * 100
i want to do it without using lambda as it is taking hell lotof time
ASSISTANT
If you want to avoid using a lambda function for performance reasons while calculating the summaries in the grouped DataFrame, you can achieve the same result using the `agg()` method. This method allows you to specify aggregation functions directly, which can significantly improve performance.
Here's how you can refactor your code to use `agg()` instead of `apply()` with `lambda`:
### Refactored Code Using `agg()`
```python
import pandas as pd
# Assuming df_ is your DataFrame
# First, ensure there are no NaN values in the 'Funding' column
df_['Funding'] = df_['Funding'].fillna('')
# Group by EIN, Product, Group, Year, and Plan_Name
grouped = df_.groupby(['EIN', 'Product', 'Group', 'Year', 'Plan_Name']).agg(
Total_SI=('Members', lambda x: x[df_['Funding'][x.index].str.contains('SI', case=False)].sum()),
Total_FI=('Members', lambda x: x[df_['Funding'][x.index].str.contains('FI', case=False)].sum()),
Total_Members=('Members', 'sum')
).reset_index()
# Calculate percentages
grouped['% SI'] = (grouped['Total_SI'] / grouped['Total_Members']) * 100
grouped['% FI'] = (grouped['Total_FI'] / grouped['Total_Members']) * 100
# If you want to visualize or export the results
print(grouped)
```
### Using Custom Aggregation Functions
Alternatively, to entirely eliminate the need for lambda expressions, you can define custom aggregation functions and pass them to `agg()`. Here’s one way to do it:
```python
import pandas as pd
# Assuming df_ is your DataFrame
# Ensure the Funding column has no NaN values
df_['Funding'] = df_['Funding'].fillna('')
def total_si(members, funding):
return members[funding.str.contains('SI', case=False)].sum()
def total_fi(members, funding):
return members[funding.str.contains('FI', case=False)].sum()
# Group by EIN, Product, Group, Year, and Plan_Name
grouped = df_.groupby(['EIN', 'Product', 'Group', 'Year', 'Plan_Name']).agg(
Total_Members=('Members', 'sum'),
).reset_index()
# Now apply the custom functions
grouped['Total_SI'] = grouped.apply(lambda row: total_si(df_.loc[(df_['EIN'] == row['EIN']) &
(df_['Product'] == row['Product']) &
(df_['Group'] == row['Group']) &
(df_['Year'] == row['Year']), 'Members'],
df_.loc[(df_['EIN'] == row['EIN']) &
(df_['Product'] == row['Product']) &
(df_['Group'] == row['Group']) &
(df_['Year'] == row['Year']), 'Funding']), axis=1)
grouped['Total_FI'] = grouped.apply(lambda row: total_fi(df_.loc[(df_['EIN'] == row['EIN']) &
(df_['Product'] == row['Product']) &
(df_['Group'] == row['Group']) &
(df_['Year'] == row['Year']), 'Members'],
df_.loc[(df_['EIN'] == row['EIN']) &
(df_['Product'] == row['Product']) &
(df_['Group'] == row['Group']) &
(df_['Year'] == row['Year']), 'Funding']), axis=1)
# Calculate percentages
grouped['% SI'] = (grouped['Total_SI'] / grouped['Total_Members']) * 100
grouped['% FI'] = (grouped['Total_FI'] / grouped['Total_Members']) * 100
# If you want to visualize or export the results
print(grouped)
```
### Explanation of Changes:
1. **Custom Functions**: The `total_si` and `total_fi` functions directly calculate the sums for SI and FI without needing to use the `lambda` within `agg()`. This separates the concerns and can sometimes lead to improved readability and performance.
2. **Using `agg()`**: With `agg()`, you're specifying directly how to aggregate the `Members`, and you're handling the conditionals in a separate function, which might be cleaner.
3. **Performance Consideration**: The custom functions use the index and slicing to get the relevant `Members` and `Funding` values for each unique group in `grouped`. This approach avoids nesting lambda functions and can improve performance.
### General Note:
If you're performing operations on large DataFrames, always remember to check if your columns contain `NaN` values and how those could potentially affect your aggregations and calculations. You can further optimize the code by profiling it to identify bottlenecks and applying vectorization techniques wherever feasible.