Pandas - Specify Slice + Additional Column Label in loc()

Question

When using loc, it seems I can either specify a list with separate column labels or a slice.

However, can I combine a slice with an additional column label and -if so- how?

I tried

games.loc[games.Platform.isin(['X360']),['Platform','NA_Sales':'Other_Sales']]

but this throws a syntax error...

Answer 1

Solution with loc

df.loc[:, ['D', *df.loc[:, 'A':'C'].columns]]

---

   D  A  B  C
0  1  1  a  a
1  2  1  a  a
2  3  1  a  a
3  1  2  a  a
4 -1  2  a  a
5 -1  3  a  a
6 -2  3  a  a
7 -3  3  a  a

Answer 2

Use:

#Preparing sample data
string = """A B C D
1 a a 1
1 a a 2
1 a a 3
2 a a 1
2 a a -1
3 a a -1
3 a a -2
3 a a -3"""


import numpy as np
data = [x.split() for x in string.split('\n')]
import pandas as pd
df = pd.DataFrame(np.array(data[1:]), columns = data[0])

# making a function to map col name to col index
c = df.columns
def indexer(col):
    return c.to_list().index(col)


# converting and appending other columns

start = indexer('A')
end = indexer('C')
cols = c[start:end].to_list()
cols.append('D')


df.loc[:, cols]

Output:

    A   B   D
0   1   a   1
1   1   a   2
2   1   a   3
3   2   a   1
4   2   a   -1
5   3   a   -1
6   3   a   -2
7   3   a   -3

Which includes both slice (A:C) and an specified column(D).

Answer 3

Using @keramat's data, you can use select_columns from pyjanitor to select columns (it offers some flexibility in selection options):

#pip install pyjanitor
import pandas as pd
import janitor

df = {'A': ['1', '1', '1', '2', '2', '3', '3', '3'],
 'B': ['a', 'a', 'a', 'a', 'a', 'a', 'a', 'a'],
 'C': ['a', 'a', 'a', 'a', 'a', 'a', 'a', 'a'],
 'D': [1, 2, 3, 1, -1, -1, -2, -3]}

df = pd.DataFrame(df)

df.select_columns(slice('A', 'C'), 'D')

   A  B  C  D
0  1  a  a  1
1  1  a  a  2
2  1  a  a  3
3  2  a  a  1
4  2  a  a -1
5  3  a  a -1
6  3  a  a -2
7  3  a  a -3

You can't use the : shortcut for slice , you have to explicitly use the slice function.

If you can, kindly provide an example that closely matches what you have in mind, and I'll see if select_columns can help, or use some native pandas options.

Answer 4

After some thinking, I came up with my own solution: extract the single column and slice separately and concat them. I like it less than some answers posted here but anyway:

pd.concat([pd.DataFrame(games.loc[:,'Platform']),games.loc[:,'NA_Sales':'Other_Sales']],axis=1)

did the exact same job as

df.loc[:, ['Platform',*df.loc[:,'NA_Sales':'Other_Sales'].columns]]

Thanks everyone for your support, I appreciated it.