How can pd.cut return a number as group?
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Example
pd.cut(df['a'],[0,2,4,10,np.inf],right=False)
It returns [0,2),[2,4),[4,10),[10,np.inf)
.
But how can I get [0],(0,2),[2,4),[4,10),[10,np.inf)
?
pandas
add a comment |
Example
pd.cut(df['a'],[0,2,4,10,np.inf],right=False)
It returns [0,2),[2,4),[4,10),[10,np.inf)
.
But how can I get [0],(0,2),[2,4),[4,10),[10,np.inf)
?
pandas
Can you please share a sample ofdf
?
– Mayank Porwal
Nov 25 '18 at 9:31
add a comment |
Example
pd.cut(df['a'],[0,2,4,10,np.inf],right=False)
It returns [0,2),[2,4),[4,10),[10,np.inf)
.
But how can I get [0],(0,2),[2,4),[4,10),[10,np.inf)
?
pandas
Example
pd.cut(df['a'],[0,2,4,10,np.inf],right=False)
It returns [0,2),[2,4),[4,10),[10,np.inf)
.
But how can I get [0],(0,2),[2,4),[4,10),[10,np.inf)
?
pandas
pandas
asked Nov 25 '18 at 9:07
JackJack
3091417
3091417
Can you please share a sample ofdf
?
– Mayank Porwal
Nov 25 '18 at 9:31
add a comment |
Can you please share a sample ofdf
?
– Mayank Porwal
Nov 25 '18 at 9:31
Can you please share a sample of
df
?– Mayank Porwal
Nov 25 '18 at 9:31
Can you please share a sample of
df
?– Mayank Porwal
Nov 25 '18 at 9:31
add a comment |
1 Answer
1
active
oldest
votes
If all values are integers and greater than zero, this could work:
import numpy as np
import pandas as pd
df = pd.DataFrame({'a': [1, 3, 5, 7, 9, 11, 13]})
pd.cut(df['a'], [-np.inf, 1, 2, 4, 10, np.inf], right=False)
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
If all values are integers and greater than zero, this could work:
import numpy as np
import pandas as pd
df = pd.DataFrame({'a': [1, 3, 5, 7, 9, 11, 13]})
pd.cut(df['a'], [-np.inf, 1, 2, 4, 10, np.inf], right=False)
add a comment |
If all values are integers and greater than zero, this could work:
import numpy as np
import pandas as pd
df = pd.DataFrame({'a': [1, 3, 5, 7, 9, 11, 13]})
pd.cut(df['a'], [-np.inf, 1, 2, 4, 10, np.inf], right=False)
add a comment |
If all values are integers and greater than zero, this could work:
import numpy as np
import pandas as pd
df = pd.DataFrame({'a': [1, 3, 5, 7, 9, 11, 13]})
pd.cut(df['a'], [-np.inf, 1, 2, 4, 10, np.inf], right=False)
If all values are integers and greater than zero, this could work:
import numpy as np
import pandas as pd
df = pd.DataFrame({'a': [1, 3, 5, 7, 9, 11, 13]})
pd.cut(df['a'], [-np.inf, 1, 2, 4, 10, np.inf], right=False)
answered Nov 25 '18 at 11:39
E. ZeytinciE. Zeytinci
10317
10317
add a comment |
add a comment |
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Can you please share a sample of
df
?– Mayank Porwal
Nov 25 '18 at 9:31