Revisions to How do I create a new column where the values are selected based on an existing column?

Add docs link for "where".

Source Link

edited Jul 20, 2023 at 16:00

31.6k
9
67
88

If you only have two choices to select from then use np.where:

df['color'] = np.where(df['Set']=='Z', 'green', 'red')

For example,

import pandas as pd
import numpy as np

df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
df['color'] = np.where(df['Set']=='Z', 'green', 'red')
print(df)

yields

  Set Type  color
0   Z    A  green
1   Z    B  green
2   X    B    red
3   Y    C    red

If you have more than two conditions then use np.select. For example, if you want color to be

yellow when (df['Set'] == 'Z') & (df['Type'] == 'A')
otherwise blue when (df['Set'] == 'Z') & (df['Type'] == 'B')
otherwise purple when (df['Type'] == 'B')
otherwise black,

then use

df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
conditions = [
    (df['Set'] == 'Z') & (df['Type'] == 'A'),
    (df['Set'] == 'Z') & (df['Type'] == 'B'),
    (df['Type'] == 'B')]
choices = ['yellow', 'blue', 'purple']
df['color'] = np.select(conditions, choices, default='black')
print(df)

which yields

  Set Type   color
0   Z    A  yellow
1   Z    B    blue
2   X    B  purple
3   Y    C   black

If you only have two choices to select from:

df['color'] = np.where(df['Set']=='Z', 'green', 'red')

For example,

import pandas as pd
import numpy as np

df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
df['color'] = np.where(df['Set']=='Z', 'green', 'red')
print(df)

yields

  Set Type  color
0   Z    A  green
1   Z    B  green
2   X    B    red
3   Y    C    red

If you have more than two conditions then use np.select. For example, if you want color to be

yellow when (df['Set'] == 'Z') & (df['Type'] == 'A')
otherwise blue when (df['Set'] == 'Z') & (df['Type'] == 'B')
otherwise purple when (df['Type'] == 'B')
otherwise black,

then use

df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
conditions = [
    (df['Set'] == 'Z') & (df['Type'] == 'A'),
    (df['Set'] == 'Z') & (df['Type'] == 'B'),
    (df['Type'] == 'B')]
choices = ['yellow', 'blue', 'purple']
df['color'] = np.select(conditions, choices, default='black')
print(df)

which yields

  Set Type   color
0   Z    A  yellow
1   Z    B    blue
2   X    B  purple
3   Y    C   black

If you only have two choices to select from then use np.where:

df['color'] = np.where(df['Set']=='Z', 'green', 'red')

For example,

import pandas as pd
import numpy as np

df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
df['color'] = np.where(df['Set']=='Z', 'green', 'red')
print(df)

yields

  Set Type  color
0   Z    A  green
1   Z    B  green
2   X    B    red
3   Y    C    red

If you have more than two conditions then use np.select. For example, if you want color to be

yellow when (df['Set'] == 'Z') & (df['Type'] == 'A')
otherwise blue when (df['Set'] == 'Z') & (df['Type'] == 'B')
otherwise purple when (df['Type'] == 'B')
otherwise black,

then use

df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
conditions = [
    (df['Set'] == 'Z') & (df['Type'] == 'A'),
    (df['Set'] == 'Z') & (df['Type'] == 'B'),
    (df['Type'] == 'B')]
choices = ['yellow', 'blue', 'purple']
df['color'] = np.select(conditions, choices, default='black')
print(df)

which yields

  Set Type   color
0   Z    A  yellow
1   Z    B    blue
2   X    B  purple
3   Y    C   black

remove uppermost line break since they're related.

Source Link

edited Jan 9, 2020 at 21:47

cs95

396.7k
102
731
783

If you only have two choices to select from:

df['color'] = np.where(df['Set']=='Z', 'green', 'red')

For example,

import pandas as pd
import numpy as np

df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
df['color'] = np.where(df['Set']=='Z', 'green', 'red')
print(df)

yields

  Set Type  color
0   Z    A  green
1   Z    B  green
2   X    B    red
3   Y    C    red

If you have more than two conditions then use np.select. For example, if you want color to be

yellow when (df['Set'] == 'Z') & (df['Type'] == 'A')
otherwise blue when (df['Set'] == 'Z') & (df['Type'] == 'B')
otherwise purple when (df['Type'] == 'B')
otherwise black,

then use

df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
conditions = [
    (df['Set'] == 'Z') & (df['Type'] == 'A'),
    (df['Set'] == 'Z') & (df['Type'] == 'B'),
    (df['Type'] == 'B')]
choices = ['yellow', 'blue', 'purple']
df['color'] = np.select(conditions, choices, default='black')
print(df)

which yields

  Set Type   color
0   Z    A  yellow
1   Z    B    blue
2   X    B  purple
3   Y    C   black

If you only have two choices to select from:

df['color'] = np.where(df['Set']=='Z', 'green', 'red')

For example,

import pandas as pd
import numpy as np

df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
df['color'] = np.where(df['Set']=='Z', 'green', 'red')
print(df)

yields

  Set Type  color
0   Z    A  green
1   Z    B  green
2   X    B    red
3   Y    C    red

If you have more than two conditions then use np.select. For example, if you want color to be

yellow when (df['Set'] == 'Z') & (df['Type'] == 'A')
otherwise blue when (df['Set'] == 'Z') & (df['Type'] == 'B')
otherwise purple when (df['Type'] == 'B')
otherwise black,

then use

df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
conditions = [
    (df['Set'] == 'Z') & (df['Type'] == 'A'),
    (df['Set'] == 'Z') & (df['Type'] == 'B'),
    (df['Type'] == 'B')]
choices = ['yellow', 'blue', 'purple']
df['color'] = np.select(conditions, choices, default='black')
print(df)

which yields

  Set Type   color
0   Z    A  yellow
1   Z    B    blue
2   X    B  purple
3   Y    C   black

If you only have two choices to select from:

df['color'] = np.where(df['Set']=='Z', 'green', 'red')

For example,

import pandas as pd
import numpy as np

df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
df['color'] = np.where(df['Set']=='Z', 'green', 'red')
print(df)

yields

  Set Type  color
0   Z    A  green
1   Z    B  green
2   X    B    red
3   Y    C    red

If you have more than two conditions then use np.select. For example, if you want color to be

yellow when (df['Set'] == 'Z') & (df['Type'] == 'A')
otherwise blue when (df['Set'] == 'Z') & (df['Type'] == 'B')
otherwise purple when (df['Type'] == 'B')
otherwise black,

then use

df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
conditions = [
    (df['Set'] == 'Z') & (df['Type'] == 'A'),
    (df['Set'] == 'Z') & (df['Type'] == 'B'),
    (df['Type'] == 'B')]
choices = ['yellow', 'blue', 'purple']
df['color'] = np.select(conditions, choices, default='black')
print(df)

which yields

  Set Type   color
0   Z    A  yellow
1   Z    B    blue
2   X    B  purple
3   Y    C   black

added 67 characters in body

Source Link

edited Sep 7, 2018 at 21:23

smci

33.6k
21
116
149

If you only have two choices to select from:

df['color'] = np.where(df['Set']=='Z', 'green', 'red')

For example,

import pandas as pd
import numpy as np

df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
df['color'] = np.where(df['Set']=='Z', 'green', 'red')
print(df)

yields

  Set Type  color
0   Z    A  green
1   Z    B  green
2   X    B    red
3   Y    C    red

If you had more conditions then use np.selectIf you have more than two conditions then use np.select. For example, if you want colorcolor to be

yellow when (df['Set'] == 'Z') & (df['Type'] == 'A')
otherwise blue when (df['Set'] == 'Z') & (df['Type'] == 'B')
otherwise purple when (df['Type'] == 'B')
otherwise black,

then use

df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
conditions = [
    (df['Set'] == 'Z') & (df['Type'] == 'A'),
    (df['Set'] == 'Z') & (df['Type'] == 'B'),
    (df['Type'] == 'B')]
choices = ['yellow', 'blue', 'purple']
df['color'] = np.select(conditions, choices, default='black')
print(df)

which yields

  Set Type   color
0   Z    A  yellow
1   Z    B    blue
2   X    B  purple
3   Y    C   black

df['color'] = np.where(df['Set']=='Z', 'green', 'red')

For example,

import pandas as pd
import numpy as np

df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
df['color'] = np.where(df['Set']=='Z', 'green', 'red')
print(df)

yields

  Set Type  color
0   Z    A  green
1   Z    B  green
2   X    B    red
3   Y    C    red

If you had more conditions then use np.select. For example, if you want color to be

yellow when (df['Set'] == 'Z') & (df['Type'] == 'A')
otherwise blue when (df['Set'] == 'Z') & (df['Type'] == 'B')
otherwise purple when (df['Type'] == 'B')
otherwise black,

then use

df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
conditions = [
    (df['Set'] == 'Z') & (df['Type'] == 'A'),
    (df['Set'] == 'Z') & (df['Type'] == 'B'),
    (df['Type'] == 'B')]
choices = ['yellow', 'blue', 'purple']
df['color'] = np.select(conditions, choices, default='black')
print(df)

which yields

  Set Type   color
0   Z    A  yellow
1   Z    B    blue
2   X    B  purple
3   Y    C   black

If you only have two choices to select from:

df['color'] = np.where(df['Set']=='Z', 'green', 'red')

For example,

import pandas as pd
import numpy as np

df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
df['color'] = np.where(df['Set']=='Z', 'green', 'red')
print(df)

yields

  Set Type  color
0   Z    A  green
1   Z    B  green
2   X    B    red
3   Y    C    red

If you have more than two conditions then use np.select. For example, if you want color to be

yellow when (df['Set'] == 'Z') & (df['Type'] == 'A')
otherwise blue when (df['Set'] == 'Z') & (df['Type'] == 'B')
otherwise purple when (df['Type'] == 'B')
otherwise black,

then use

df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
conditions = [
    (df['Set'] == 'Z') & (df['Type'] == 'A'),
    (df['Set'] == 'Z') & (df['Type'] == 'B'),
    (df['Type'] == 'B')]
choices = ['yellow', 'blue', 'purple']
df['color'] = np.select(conditions, choices, default='black')
print(df)

which yields

  Set Type   color
0   Z    A  yellow
1   Z    B    blue
2   X    B  purple
3   Y    C   black

added 804 characters in body

Source Link

edited Aug 13, 2017 at 10:32

unutbu

868.8k
193
1.8k
1.7k

Loading

Source Link

created Nov 11, 2013 at 19:03

unutbu

868.8k
193
1.8k
1.7k

Loading

Collectives™ on Stack Overflow

Return to Answer