如何添加新列并根据另一列的系列填充特定值？

Question

I'm new to Pandas but thanks to Add column with constant value to pandas dataframe I was able to add different columns at once with

c = {'new1': 'w', 'new2': 'y', 'new3': 'z'}
df.assign(**c)

However I'm trying to figure out what's the path to take when I want to add a new column to a dataframe (currently 1.2 million rows * 23 columns).

Let's simplify the df a bit and try to make it more clear:

Order   Orderline   Product  
1       0           Laptop  
1       1           Bag  
1       2           Mouse  
2       0           Keyboard  
3       0           Laptop  
3       1           Mouse  

I would like to add a new column where depending if the Order has at least 1 product == Bag then it should be 1 (for all rows for that specific order), otherwise 0.

Result would become:

Order   Orderline   Product   HasBag  
1       0           Laptop    1  
1       1           Bag       1  
1       2           Mouse     1  
2       0           Keyboard  0  
3       0           Laptop    0  
3       1           Mouse     0  

What I could do is find all the unique order numbers, then filter out the subframe, check the Product column for Bag, if found then add 1 to a new column, otherwise 0, and then replace the original subframe with the result.

Likely there's a way better manner to accomplish this, and also way more performant.

The main reason I'm trying to do this, is to flatten things down later on. Every order should become 1 line with some values of product. I don't need the information for Bag anymore but I want to keep in my dataframe if the original order used to have a Bag (1) or no Bag (0).

Ultimately when the data is cleaned out it can be used as a base for scikit-learn (or that's what I hope).

Answer 1

If I understand you correctly, you want GroupBy.transform.any

First we create a boolean array by checking which rows in Product are Bag with Series.eq . Then we GroupBy on this boolean array and check if any of the values are True . We use transform to keep the shape of our initial array so we can assign the values back.

df['ind'] = df['Product'].eq('Bag').groupby(df['Order']).transform('any').astype(int)

   Order  Orderline   Product  ind
0      1          0    Laptop    1
1      1          1       Bag    1
2      1          2     Mouse    1
3      2          0  Keyboard    0
4      3          0    Laptop    0
5      3          1     Mouse    0