Variable scope with function [duplicate]

Question

Trying to understand variable scope with a function call.

Code to discuss.

import numpy as np
import pandas as pd

# Function to add a column with random stuff to a dataframe 
def Add_a_column(df):
    df['Col2']= np.sign(np.random.randn(len(df)))
    return df

# Create a dataframe with random stuff
df_full = pd.DataFrame(data=np.sign(np.random.randn(5)), columns=['Col1'])

df_another = Add_a_column(df_full)

• df_full is global. Correct?
• df_another is global. Correct?
• df is local to Add_a_column. Correct?

When I execute the code, the column get's added to df_full

In[8]: df_full
Out[8]: 
   Col1  Col2
0  -1.0  -1.0
1   1.0  -1.0
2  -1.0   1.0
3   1.0   1.0
4   1.0   1.0

How do I avoid df_full being modified by the function?

Answer 1

df_full's reference is passed into the function. So df and df_full are the same object, meaning they both get modified when one is modified.

You need to change your function to:

def Add_a_column(df):
    df = df.copy()
    df['Col2']= np.sign(np.random.randn(len(df)))
    return df

Alternatively, you could call the function with a copied function like Add_a_column(df.copy())

Answer 2

• df_full is global. Correct?
• df_another is global. Correct?
• df is local to Add_a_column. Correct?

It sounds like you understand scope just fine. Each variable had the scope you describe.

The piece you are missing is that df_full and df refer too the same object. When you make changes to that object with one variable, the changes are visible when you access that object with the other variable.