Assign values to Pandas columns based on another column iteratively

Question

I have a variable in Pandas dataframe called "label" which contains multiple string values (for example: 'label1', "label2', 'label3'...).

label
label1
label1
label23
label3
label11

I output all unique values into a list and then create new variables

unique_labels = df['label'].unique()

for i in unique_labels: # create new single label variable holders
    df[str(i)] = 0

Now I have

label    label1    label2 .... label23
label1     0         0            0
label23    0         0            0

I want to assign corresponding value based on 'label' onto the new single label variables, as following

label    label1    label2 .... label23
label1     1         0            0
label23    0         0            1

Here is my code

def single_label(df):
for i in range(len(unique_labels)):
    if df['label'] == str(unique_labels[i]):
        df[unique_labels[i]] == 1


df = df.applymap(single_label)

Getting this error

TypeError: ("'int' object is not subscriptable", 'occurred at index Unnamed: 0')

sacuL · Accepted Answer · 2018-06-12 16:02:13Z

IIUC, you can use pd.get_dummies, after you drop duplicates, which will be faster and result in cleaner code than doing it iteratively:

df.drop_duplicates().join(pd.get_dummies(df.drop_duplicates()))

     label  label_label1  label_label11  label_label23  label_label3
0   label1             1              0              0             0
2  label23             0              0              1             0
3   label3             0              0              0             1
4  label11             0              1              0             0

You can get rid of those label prefixes and underscores using the prefix and prefix_sep arguments:

df.drop_duplicates().join(pd.get_dummies(df.drop_duplicates(),
                                         prefix='', prefix_sep=''))

     label  label1  label11  label23  label3
0   label1       1        0        0       0
2  label23       0        0        1       0
3   label3       0        0        0       1
4  label11       0        1        0       0

Edit: with a second column, i.e.:

>>> df
     label second_column
0   label1             a
1   label1             b
2  label23             c
3   label3             d
4  label11             e

Just call pd.get_dummies on only the label column:

df.drop_duplicates('label').join(pd.get_dummies(df['label'].drop_duplicates(),
                                         prefix='', prefix_sep=''))

     label second_column  label1  label11  label23  label3
0   label1             a       1        0        0       0
2  label23             c       0        0        1       0
3   label3             d       0        0        0       1
4  label11             e       0        1        0       0

But then you're getting rid of the rows without duplicates, and I don't think that's what you want (unless I'm mistaken). If not, just omit the drop duplicates calls:

df.join(pd.get_dummies(df['label'], prefix='', prefix_sep=''))

     label second_column  label1  label11  label23  label3
0   label1             a       1        0        0       0
1   label1             b       1        0        0       0
2  label23             c       0        0        1       0
3   label3             d       0        0        0       1
4  label11             e       0        1        0       0

Thanks, but can you please show how to specify with the 'label' column (since my actual data contains multiple columns)? I tried df['label'], but didn't work.

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