Q:

Python Pandas - Replacing values of a part of data frame column based on index

I am fetching the first occurrence of a particular value in a Panda column based on its index as shown below : 
first_idx = df1.loc[df1.Column1.isin(['word1','word2'])].index.tolist()[0]

This will give me the index of first occurrence of either 'word1' or 'word2'
Then I am replacing old values of the records until the determined index with new values as shown below :
df1.head(first_idx)['Column1'].replace({'10': '5'}, inplace=True)

This will replace all '10's that are present until the first_idx of the dataframe with '5's.  All the remaining '10's present after the first_idx value will not be replaced. 
Now I have to replace all '10's present after the first_idx value with '3's.  I have tried the below by calculating the length of data frame and then subtracting it with the first_idx value. 
len(df1)                         # This will show the actual length / total number of records of a dataframe column. 
temp = (len(df1)-first_idx)-1    # This will determine the remaining count of records barring the count of records until first_idx value. 
df1.tail(temp)                   # This will show all records that are present after the first_idx value. 
df1.tail(temp)['Column1'].replace({'10': '3'}, inplace=True)

But is there any other better / efficient / simple way to achieve the same ? 

A:

From the way you used 
df1.head(first_idx)

I assume your indices are numeric.  Thus, a simple 
df1.iloc[first_idx + 1:, :]['Column1'].replace({'10': '3'}, inplace=True)

Should do. 