Python Pandas Dataframe-迭代行并添加字典问题

Question

import pandas as pd

d = {'col1': [1, 2], 'col2': [3, 4]}
df = pd.DataFrame(data=d)

def calculation(text):
    return text*2

for idx, row in df.iterrows():
    df.at[idx, 'col3'] = dict(cats=calculation(row['col1']))


df

So as you can see from the code above I have tried a few different things.

Basically I am trying to get the dictionary in to col3.

However, when you run for the first time on new dataframe - you get a

    col1    col2    col3
0    1        3     cats
1    2        4     {'cats': 4}

If you run the for loop again on the same dataframe you get what I am looking for which is

    col1    col2    col3
0    1       3      {'cats': 2}
1    2       4      {'cats': 4}

How do I go straight to having the dictionary in there to start without having to run the loop again?

I have tried other ways like df.loc and others, still no joy.

Answer 1

Try to stay away from df.iterrows() .

You can use df.apply instead:

import pandas as pd

d = {'col1': [1, 2], 'col2': [3, 4]}
df = pd.DataFrame(data=d)

def calculation(text):
    return text*2

def calc_dict(row):
    return dict(cats=calculation(row['col1']))

df['col3'] = df.apply(calc_dict, axis=1)


df

Which outputs the result you expect.

The error seems to creep in with the creation and assignment of an object datatype to col col3 . I tried to pre-allocate to NaNs with df['col3'] = pd.np.NaN which did not have an effect (inspect with print(df.dtypes) ). Anyway this seems like buggy behaviour. Use df.apply instead, its faster and less prone to these types of issues.