如何解析字符串索引在数据分析中必须是整数？

Question

I have downloaded some data, so I wanted to change some values into boolean type:

for dataset in loan_df:
    dataset['Gender'] = dataset['Gender'].replace({'male' : 1 , 'female' : 0} , inplace = True)

but it is giving me this error:

TypeError: string indices must be integers

I also tried this code:

for dataset in loan_df:
    dataset['Gender'] = dataset['Gender'].map({'male' : 1 , 'female' : 0}).astype(int)

But it is still giving the same error. Do you know how to resolve this error?

Answer 1

I believe the loan_df is a pandas DataFrame, therefore when you are iterating trough it with the for loop, the iterator is a Series . That is why, when you try ['Gender'] you are actually indexing it and an integer is expected.

The solution would be to remove the loop:

loan_df['Gender'] = loan_df['Gender'].replace({'male' : 1 , 'female' : 0})

Answer 2

Assuming loan_df is a singular DataFrame, I create an emulated DataFrame like so:

loan_df = pd.DataFrame({"Gender":["male","female"]})

I reproduced the error with the following code, but I also included print statements:

for dataset in loan_df:
    print(dataset)
    print(type(dataset))
    dataset['Gender'] = dataset['Gender'].replace({'male' : 1 , 'female' : 0} , inplace = True)

Output:

Gender
<class 'str'>
...

TypeError: string indices must be integers

When you iterate through the DataFrame, dataset will be the column names.

Try using the below command:

loan_df['Gender'].replace({'male' : 1 , 'female' : 0} , inplace = True)

By indicating the inplace parameter, you do not have to reassign it back to loan_df .