list.append vs pd.series.append

I noticed that while list.append changes the original list, pd.series.append doesn’t change the original series:

b = [1,2,3,4,5]



# returns [1, 2, 3, 4, 5, 9]
s = pd.Series([1,2,3,4,5])



# returns 
0    1
1    2
2    3
3    4
4    5

Despite using Python for quite a while I never realized this and spent a long time debugging because I kept getting errors or unintended values while running a for loop for a series using the ‘append’ method inside. Python is my first programming language btw.

Why is there a discrepancy although both are the same methods? Or is the ‘append’ method an exception? It gets personally confusing because it makes a huge difference in writing python code and getting the results I want. And it makes me wonder if there are similar discrepancies for other methods between lists and series(or dataframes) as well that I should be aware of.

I’m just a bit frustrated whenever I encounter these seemingly trivial things because I personally tend to forget them later on and repeat the same mistake.

Should I just memorize this and get over with it? I wish to know the approach I should take when trying to learn new things like these because I don’t know whether I’ll be able to remember these kinds of subtle differences as time passes. I would appreciate any good tips or advice.

>Solution :

In Pandas, the append method does NOT change the original DataFrame.

In contrast, append method of list changes target list.

Let’s take a look.

b = [1,2,3,4,5]



# returns [1, 2, 3, 4, 5, 9] # target list 'b' has been changed.

Case of pandas.

import pandas as pd

df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'), index=['x', 'y'])
df2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'), index=['x', 'y'])


# result = 
           A    B
        x  1    2
        y  3    4

So we have to assign clearly that we are going to change the DataFrame.

df = df.append(df2) # this changes df.

# result =  A   B
        x   1   2
        y   3   4
        x   5   6
        y   7   8

Hopefully it helps 🙂

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