Create new columns using unique values in other columns in Python-CodePudding

I'd like to create new columns in my dataframe using unique values from another column, for example

Column 1 has the following values:

Apple
Apple
Banana
Strawberry
Strawberry
Strawberry

When I check unique values in Column 1, the output would be :

Apple
Banana
Strawberry

Now I want to use these three values to create columns named "Apple","Banana","Strawberry" and I want to keep the code dynamic to adapt to however number of unique values are present in Column 1

I'm new to python, any help will be appreciated!

So far, I've been doing getting that output by manually creating new columns in the dataset, I need this to happen automatically depending on the unique values in Column 1

CodePudding user response：

extract unique values, iterate on them to create columns and fill in data.

Here I inly put boolean values based on matching with the col1 value ...

df = pd.DataFrame({"col1": ["apple", "apple", "banana", "pineapple", "banana", "apple"]})

data=

        col1
0      apple
1      apple
2     banana
3  pineapple
4     banana
5      apple

transform:

unique_col1_val = df["col1"].unique().tolist()
for u in unique_col1_val:
    df[u] = df["col1"] == u # you need to determine how to fill these new columns
    # here we just put a bool indicating a match between new col name and col1 content ...
    # to put an int truth value use:
    # df[u] = (df["col1"] == u).astype(int)

In [72]: df
Out[72]:
        col1  apple  banana  pineapple
0      apple   True   False      False
1      apple   True   False      False
2     banana  False    True      False
3  pineapple  False   False       True
4     banana  False    True      False
5      apple   True   False      False

using df[u] = (df["col1"] == u).astype(int):

        col1  apple  banana  pineapple
0      apple      1       0          0
1      apple      1       0          0
2     banana      0       1          0
3  pineapple      0       0          1
4     banana      0       1          0
5      apple      1       0          0