DataFrame modified inside a function

Question

I face to a modification of a dataframe inside a function that I have never observed previously. Is there a method to deal with this and no modify the initial dataframe ?

In[30]: def test(df):     df['tt'] = np.nan     return df  In[31]: dff = pd.DataFrame(data=[])  In[32]: dff  Out[32]:  Empty DataFrame Columns: [] Index: [] In[33]: df = test(dff)  In[34]: dff  Out[34]:  Empty DataFrame Columns: [tt] Index: [] 

Answer 1

def test(df):     df = df.copy(deep=True)     df['tt'] = np.nan     return df 

If you pass the dataframe into a function and manipulate it and return the same dataframe, you are going to get the same dataframe in modified version. If you want to keep your old dataframe and create a new dataframe with your modifications then by definition you have to have 2 dataframes. The one that you pass in that you don't want modified and the new one that is modified. Therefore, if you don't want to change the original dataframe your best bet is to make a copy of the original dataframe. In my example I rebound the variable "df" in the function to the new copied dataframe. I used the copy method and the argument "deep=True" makes a copy of the dataframe and its contents. You can read more here:http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.copy.html