Why should we make a copy of a DataFrame in Pandas?

Let us understand with the help of an example.

# Importing pandas package
import pandas as pd

# Create DataFrame
df = pd.DataFrame({'Girl':['Monica'],'Boy':['Chandler']})
print("Original DataFrame:\n",df,"\n")

Output:

Now let us perform some operations without making a copy of DataFrame and we will observe that it will modify the original DataFrame.

# Assign df to df_copy
df_copy = df

# Display df_copy
print("Copied DataFrame:\n",df_copy,"\n")

# Update the value of group 1 and group 2 
# in original DataFrame
df_copy['Girl']= 'Rachel'
df_copy['Boy'] = 'Ross'

print("Modified Original DataFrame\n",df)

Output:

Now we will make a deep copy of DataFrame and perform some operations and will observe that it will not modify the original DataFrame.

# Using df.copy to make a copy
df_2 = df.copy(deep=True)

# Display df_2
print("A deep copy of original DataFrame:\n",df_2,"\n")

# Update values of deep copy
df_2['Girl'] = 'Phoebe'
df_2['Boy'] = 'Joey'

# Display updated deep copy and also original DataFrame 
# which is not modified this time
print("Modified deep copy:\n",df_2,"\n")
print("Unmodified original DataFrame:\n",df)

Output: