import numpy as np
import pandas as pd

# Create a DataFrame
df = pd.DataFrame(dict(x=[0, 1, 2],
                       y=[0, 0, 5]))

def func(dfx):
    # Make a copy of the original DataFrame before modifying it
    dfx_copy = dfx.copy()
    
    # Filter the DataFrame to only include rows where x > 1.5
    dfx_copy = dfx_copy[dfx_copy['x'] > 1.5]
    
    # Replace values in the y column with NaN if they are equal to 5
    dfx_copy.replace(5, np.nan, inplace=True)
    
    return dfx_copy

def func_with_copy(dfx):
    # Make a copy of the original DataFrame before modifying it
    dfx_copy = dfx.copy()
    
    # Filter the DataFrame to only include rows where x > 1.5
    dfx_copy = dfx_copy[dfx_copy['x'] > 1.5]
    
    # Replace values in the y column with NaN if they are equal to 5
    dfx_copy.replace(5, np.nan, inplace=True)
    
    return dfx_copy

# Call func and print the result
print(func(df))

# Call func_with_copy and print the result
print(func_with_copy(df))