Re: Pandas, create new column if previous column(s) are not in [None, '', np.nan]
On Wednesday, 11 April 2018 21:19:44 UTC+2, José María Mateos wrote:
> On Wed, Apr 11, 2018, at 14:48, zlj...com wrote:
> > I have a dataframe:
> > [...]
>
> This seems to work:
>
> df1 = pd.DataFrame( { 'A' : ['a', 'b', '', None, np.nan],
> 'B' : [None, np.nan, 'a',
> 'b', '']})
> df1['C'] = df1[['A', 'B']].apply(lambda x: x[0] if x[1] in [None, '', np.nan]
> else x[1], axis = 1)
>
> Two notes:
>
> - Do apply() on axis = 1, so you process every row.
> - You lambda function wasn't entirely correct, if I understood what you
> wanted to do.
>
> Cheers,
>
> --
> José María (Chema) Mateos
> https://rinzewind.org/blog-es || https://rinzewind.org/blog-en
Thanks Jose, this what I needed. Thanks also to all others.
Regards.
--
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Re: Pandas, create new column if previous column(s) are not in [None, '', np.nan]
On Apr 11, 2018 20:52, zljubi...@gmail.com wrote:
>
> I have a dataframe:
>
> import pandas as pd
> import numpy as np
>
> df = pd.DataFrame( { 'A' : ['a', 'b', '', None, np.nan],
> 'B' : [None, np.nan, 'a', 'b', '']})
>
> A B
> 0 a None
> 1 b NaN
> 2 a
> 3 None b
> 4 NaN
>
>
> I would like to create column C in the following way:
> column C = column B if column B is not in [None, '', np.nan]
> else column A
>
> How to do that?
>
> I tried:
>
> df['C'] = df[['A', 'B']].apply(lambda x: x[1] if x[1] in [None, '', np.nan]
> else x[0])
>
> but I got all np.nan's.
This is another approach:
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.replace.html
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Re: Pandas, create new column if previous column(s) are not in [None, '', np.nan]
On Wednesday, April 11, 2018 at 2:49:01 PM UTC-4, zlju...@gmail.com wrote:
> I have a dataframe:
>
> import pandas as pd
> import numpy as np
>
> df = pd.DataFrame( { 'A' : ['a', 'b', '', None, np.nan],
> 'B' : [None, np.nan, 'a', 'b', '']})
>
> A B
> 0 a None
> 1 b NaN
> 2 a
> 3 None b
> 4 NaN
>
>
> I would like to create column C in the following way:
> column C = column B if column B is not in [None, '', np.nan]
> else column A
>
> How to do that?
>
> I tried:
>
> df['C'] = df[['A', 'B']].apply(lambda x: x[1] if x[1] in [None, '', np.nan]
> else x[0])
>
> but I got all np.nan's.
>
> Where am I wrong?
>
> I am expecting to get column C as ['a', 'b', 'a', 'b', NaN]
>
> Regards.
Try this:
df['C'] = df['B'].where(df['B'], other=df['A'])
Regards,
Igor.
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Re: Pandas, create new column if previous column(s) are not in [None, '', np.nan]
On Wed, Apr 11, 2018, at 14:48, zljubi...@gmail.com wrote:
> I have a dataframe:
> [...]
This seems to work:
df1 = pd.DataFrame( { 'A' : ['a', 'b', '', None, np.nan],
'B' : [None, np.nan, 'a',
'b', '']})
df1['C'] = df1[['A', 'B']].apply(lambda x: x[0] if x[1] in [None, '', np.nan]
else x[1], axis = 1)
Two notes:
- Do apply() on axis = 1, so you process every row.
- You lambda function wasn't entirely correct, if I understood what you wanted
to do.
Cheers,
--
José María (Chema) Mateos
https://rinzewind.org/blog-es || https://rinzewind.org/blog-en
--
https://mail.python.org/mailman/listinfo/python-list
Pandas, create new column if previous column(s) are not in [None, '', np.nan]
I have a dataframe:
import pandas as pd
import numpy as np
df = pd.DataFrame( { 'A' : ['a', 'b', '', None, np.nan],
'B' : [None, np.nan, 'a', 'b', '']})
A B
0 a None
1 b NaN
2 a
3 None b
4 NaN
I would like to create column C in the following way:
column C = column B if column B is not in [None, '', np.nan]
else column A
How to do that?
I tried:
df['C'] = df[['A', 'B']].apply(lambda x: x[1] if x[1] in [None, '', np.nan]
else x[0])
but I got all np.nan's.
Where am I wrong?
I am expecting to get column C as ['a', 'b', 'a', 'b', NaN]
Regards.
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