Comparison of a Dataframe column values with a list

You can also do it in one step using .transform('first'):

In [280]: df['D'] = df.groupby('A')['C'].transform('first').eq(df['C']).astype(np.int8)

In [281]: df
Out[281]:
   A   B    C  D
0  1  10  100  1
1  1  15  150  0
2  2  20  200  1
3  2  25  250  0
4  3  30  300  1
5  3  35  350  0

Explanation: GroupBy.transform('func') returns us a vector with the same length as the original DF with applied func

In [14]: df.groupby('A')['C'].transform('first')
Out[14]:
0    100
1    100
2    200
3    200
4    300
5    300
Name: C, dtype: int64

In [15]: df.groupby('A')['C'].transform('max')
Out[15]:
0    150
1    150
2    250
3    250
4    350
5    350
Name: C, dtype: int64

In [16]: df.groupby('A')['C'].transform('min')
Out[16]:
0    100
1    100
2    200
3    200
4    300
5    300
Name: C, dtype: int64

In [17]: df.groupby('A')['C'].transform('mean')
Out[17]:
0    125
1    125
2    225
3    225
4    325
5    325
Name: C, dtype: int64

In [18]: df.groupby('A')['C'].transform('sum')
Out[18]:
0    250
1    250
2    450
3    450
4    650
5    650
Name: C, dtype: int64

TL;DR:

df['newColumn'] = np.where((df.compareColumn.isin(yourlist)), TrueValue, FalseValue)

---

Another one-step method would be to use np.where() and isin.

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': [1, 1, 2, 2, 3, 3],
                   'B': [10, 15, 20, 25, 30,35],
                   'C': [100, 150, 200, 250, 300, 350]})

df['D'] = np.where((df.B.isin(firsts)), 1, 0)

We use the return from isin as the condition in np.where() to return either

• 1 when True
• 0 when False

and assign them to a new column in the same dataframe df['D'].

Note: np.where allows more complex conditions with bitwise operators and replacement cases, i.e. 'bypass' on False

df['col1'] = np.where(((df['col1'] == df['col2']) &
                       (~df['col1'].str.startswith('r'))),
                       'replace', df['col1'])

---

You can use isin method:

df['D'] = df.C.isin(firsts).astype(int)

df
#   A   B   C   D
#0  1   10  100 1
#1  1   15  150 0
#2  2   20  200 1
#3  2   25  250 0
#4  3   30  300 1
#5  3   35  350 0

---

The reason your approach fails is that python in operator check the index of a Series instead of the values, the same as how a dictionary works:

firsts
#A
#1    100
#2    200
#3    300
#Name: C, dtype: int64

1 in firsts
# True

100 in firsts
# False

2 in firsts
# True

200 in firsts
# False

Modifying your method as follows works:

firstSet = set(firsts)
df['C'].apply(lambda x: 1 if x in firstSet else 0)

#0    1
#1    0
#2    1
#3    0
#4    1
#5    0
#Name: C, dtype: int64