the ask was to check if there is a switch or not
like for si if in prev_year there is some count say 'x' and in the next year if it is dropping down to zero then it is considered as switch else not a switch
but i'm not understanding how this code is aligned to the ask that has been provided
import pandas as pd
import numpy as np
 
def analyze_si_to_fi_switches(df):
    # Create helper functions to detect switches
    def has_switched(group_df):
        # Check if a group has any SI members before FI members
        has_si = (group_df['SI'] > 0).any()
        has_fi = (group_df['FI'] > 0).any()
        if has_si and has_fi:
            # Find first occurrence of each
            first_si = group_df[group_df['SI'] > 0]['Year'].min()
            first_fi = group_df[group_df['FI'] > 0]['Year'].min()
            # Consider it a switch if SI came before FI
            return first_si < first_fi
        return False
 
    # Group by EIN and Product
    grouped = df.groupby(['EIN', 'Product', 'Size_Band'])
    # Analyze switches for each group
    switches = []
    for (ein, product, size_band), group_df in grouped:
        switched = has_switched(group_df)
        if switched:
            # Find the switch year
            si_years = group_df[group_df['SI'] > 0]['Year']
            fi_years = group_df[group_df['FI'] > 0]['Year']
            switch_year = fi_years.min()
            switches.append({
                'EIN': ein,
                # 'Group': group,
                'Product': product,
                # 'Broker_State':broker_state,
                # 'BrokerCrossProductSTATE':brokercrossproductstate,
                # 'BrokerCrossProduct':brokercrossproduct,
                # 'BrokerCrossProductParent':brokercrossproductparent,
                # 'State_HQ':state_hq,
                'Size_Band':size_band,
                'Switch_Year': switch_year,
                'Initial_SI_Members': group_df[group_df['Year'] == si_years.min()]['SI'].iloc[0],
                'Initial_FI_Members': group_df[group_df['Year'] == switch_year]['FI'].iloc[0]
            })
    # Create switches DataFrame
    switches_df = pd.DataFrame(switches)
    # Calculate statistics
    total_groups = len(grouped)
    total_switches = len(switches)
    switch_percentage = (total_switches / total_groups) * 100
    # Calculate switches by year
    if not switches_df.empty:
        yearly_switches = switches_df['Switch_Year'].value_counts().sort_index()
        yearly_percentages = (yearly_switches / total_groups) * 100
        print("\nAnalysis of SI to FI Switches:")
        print(f"\nTotal Groups/Products: {total_groups}")
        print(f"Total Switches: {total_switches}")
        print(f"Overall Switch Percentage: {switch_percentage:.1f}%")
        print("\nYearly Switch Analysis:")
        for year, count in yearly_switches.items():
            print(f"{year}: {count} switches ({yearly_percentages[year]:.1f}% of total groups)")
        if not switches_df.empty:
            print("\nDetailed Switch Information:")
            print(switches_df.to_string(index=False))
    else:
        print("\nNo switches from SI to FI detected in the dataset")
    return switches_df
 
# df = pd.read_csv("Y:/GAIN 5500 business questions/data2.csv")
# # Example usage
# # Assuming your data is in a DataFrame called 'df'
# switches_df = analyze_si_to_fi_switches(df)
 
# switches_df.columns

can u please explain that me