Replace values in Pandas data frame within a loop

Question

I am trying to loop through a pandas data frame and replace values in certain columns if they meet certain conditions. I realize there are more straightforward ways to do this in general, but in my specific example I need a loop because the result for one row can depend on the prior row. Below is a reproducible example of what is going wrong. When I try to replace text it does not replace it.

import pandas as pd
df = pd.DataFrame({"A": ["I", "AM", "NOT", "WORKING", "!"], "B": [20, 30, 10, 40, 50], "C": [32, 234, 23, 23, 42523]})


for index, row in df.iterrows():
    row['A'] = "I am working!"

print(df)

Which prints:

         A   B      C
0        I  20     32
1       AM  30    234
2      NOT  10     23
3  WORKING  40     23
4        !  50  42523

Answer 1

You can write to the original frame using .loc:

>>> for index, row in df.iterrows():
...     df.loc[index, "A"] = "I am working! {}".format(row["B"])
...     
>>> df
                  A   B      C
0  I am working! 20  20     32
1  I am working! 30  30    234
2  I am working! 10  10     23
3  I am working! 40  40     23
4  I am working! 50  50  42523

[5 rows x 3 columns]

Aside: even if one row depends upon the previous row there can be ways to vectorize it, but I admit sometimes it's much simpler to do it the manual, loop-based way.