Empty Na in dataFrame columns
up vote
0
down vote
favorite
df
Letter city state
0 A NYC NY
1 B Na CT
2 C LA Na
3 D Tampa FL
4 E Na Na
5 F Dallas TX
6 G Denver CL
df['city']=df['city'].str.replace("Na"," ")
df['state']=df['state'].str.replace("Na"," ")
df
Letter city state
0 A NYC NY
1 B CT
2 C LA
3 D Tampa FL
4 E
5 F Dallas TX
6 G Denver CL
df.isnull().any()
Letter False
city False
state False
dtype: bool
How to empty Na to become:
Letter False
city True
state True
python dataframe
edited Nov 18 at 2:37
d_kennetz
1,317515
asked Nov 18 at 1:57
Ray
162
add a comment |
up vote
0
down vote
favorite
df
Letter city state
0 A NYC NY
1 B Na CT
2 C LA Na
3 D Tampa FL
4 E Na Na
5 F Dallas TX
6 G Denver CL
df['city']=df['city'].str.replace("Na"," ")
df['state']=df['state'].str.replace("Na"," ")
df
Letter city state
0 A NYC NY
1 B CT
2 C LA
3 D Tampa FL
4 E
5 F Dallas TX
6 G Denver CL
df.isnull().any()
Letter False
city False
state False
dtype: bool
How to empty Na to become:
Letter False
city True
state True
python dataframe
edited Nov 18 at 2:37
d_kennetz
1,317515
asked Nov 18 at 1:57
Ray
162
1
Could you format your question and add the data in a way it can be copied and pasted directly into the editor.
– Daniel Mesejo
Nov 18 at 2:03
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
df
Letter city state
0 A NYC NY
1 B Na CT
2 C LA Na
3 D Tampa FL
4 E Na Na
5 F Dallas TX
6 G Denver CL
df['city']=df['city'].str.replace("Na"," ")
df['state']=df['state'].str.replace("Na"," ")
df
Letter city state
0 A NYC NY
1 B CT
2 C LA
3 D Tampa FL
4 E
5 F Dallas TX
6 G Denver CL
df.isnull().any()
Letter False
city False
state False
dtype: bool
How to empty Na to become:
Letter False
city True
state True
python dataframe
edited Nov 18 at 2:37
d_kennetz
1,317515
asked Nov 18 at 1:57
Ray
162
df
Letter city state
0 A NYC NY
1 B Na CT
2 C LA Na
3 D Tampa FL
4 E Na Na
5 F Dallas TX
6 G Denver CL
df['city']=df['city'].str.replace("Na"," ")
df['state']=df['state'].str.replace("Na"," ")
df
Letter city state
0 A NYC NY
1 B CT
2 C LA
3 D Tampa FL
4 E
5 F Dallas TX
6 G Denver CL
df.isnull().any()
Letter False
city False
state False
dtype: bool
How to empty Na to become:
Letter False
city True
state True
python dataframe
python dataframe
edited Nov 18 at 2:37
d_kennetz
1,317515
asked Nov 18 at 1:57
Ray
162
edited Nov 18 at 2:37
d_kennetz
1,317515
asked Nov 18 at 1:57
Ray
162
edited Nov 18 at 2:37
d_kennetz
1,317515
edited Nov 18 at 2:37
d_kennetz
1,317515
edited Nov 18 at 2:37
d_kennetz
1,317515
1,317515
asked Nov 18 at 1:57
Ray
162
asked Nov 18 at 1:57
Ray
162
asked Nov 18 at 1:57
Ray
162
162
1
Could you format your question and add the data in a way it can be copied and pasted directly into the editor.
– Daniel Mesejo
Nov 18 at 2:03
add a comment |
1
Could you format your question and add the data in a way it can be copied and pasted directly into the editor.
– Daniel Mesejo
Nov 18 at 2:03
1
1
Could you format your question and add the data in a way it can be copied and pasted directly into the editor.
– Daniel Mesejo
Nov 18 at 2:03
Could you format your question and add the data in a way it can be copied and pasted directly into the editor.
– Daniel Mesejo
Nov 18 at 2:03
add a comment |
1 Answer
1
active
oldest
votes
up vote
2
down vote
Starting with your original df, you can just do:
df.eq("Na").any()
Alternately, starting from the second df, after you replace Na with empty string, replace the empty strings with NaN:
import numpy as np
df.replace('', np.nan).isnull().any()
Both produce:
Letter False
city True
state True
dtype: bool
answered Nov 18 at 2:42
andrew_reece
10.2k1927
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
2
down vote
Starting with your original df, you can just do:
df.eq("Na").any()
Alternately, starting from the second df, after you replace Na with empty string, replace the empty strings with NaN:
import numpy as np
df.replace('', np.nan).isnull().any()
Both produce:
Letter False
city True
state True
dtype: bool
answered Nov 18 at 2:42
andrew_reece
10.2k1927
add a comment |
up vote
2
down vote
Starting with your original df, you can just do:
df.eq("Na").any()
Alternately, starting from the second df, after you replace Na with empty string, replace the empty strings with NaN:
import numpy as np
df.replace('', np.nan).isnull().any()
Both produce:
Letter False
city True
state True
dtype: bool
answered Nov 18 at 2:42
andrew_reece
10.2k1927
add a comment |
up vote
2
down vote
up vote
2
down vote
Starting with your original df, you can just do:
df.eq("Na").any()
Alternately, starting from the second df, after you replace Na with empty string, replace the empty strings with NaN:
import numpy as np
df.replace('', np.nan).isnull().any()
Both produce:
Letter False
city True
state True
dtype: bool
answered Nov 18 at 2:42
andrew_reece
10.2k1927
Starting with your original df, you can just do:
df.eq("Na").any()
Alternately, starting from the second df, after you replace Na with empty string, replace the empty strings with NaN:
import numpy as np
df.replace('', np.nan).isnull().any()
Both produce:
Letter False
city True
state True
dtype: bool
answered Nov 18 at 2:42
andrew_reece
10.2k1927
edited Nov 18 at 4:46
answered Nov 18 at 2:42
andrew_reece
10.2k1927
answered Nov 18 at 2:42
andrew_reece
10.2k1927
answered Nov 18 at 2:42
andrew_reece
10.2k1927
10.2k1927
add a comment |
add a comment |
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1
Could you format your question and add the data in a way it can be copied and pasted directly into the editor.
– Daniel Mesejo
Nov 18 at 2:03