After dropping pandas DataFrame rows, How to still locate a row by the same index?

When you delete a row from a DataFrame, the subsequent rows will shift up with their indexes.. but if you try to locate a row by its index.. you get a different row than expected because the index values have been reset to reflect the new positions of the rows in the DataFrame…
and I want to still be able to locate any of the rows by their original index

for example:
this is before and after I dropped 0 and 2 rows

before:

after:

and this is what I get when I locate the row with index 1:

how can I still get the row with index 1 ???

>Solution :

Use .loc to index the dataframe by the index name:

df = pd.DataFrame([4,3,2,1])
df.drop([0,2], inplace=True)

# To return a Series
df.loc[1,:]

# Or to return a DataFrame
df.loc[[1],:]