Using fillna to replace missing data
When I am trying to use fillna to replace NaNs in the columns with means, the NaNs changed from float64 to object, showing:
bound method Series.mean of 0 NaNn1
Here is the code:
mean = df['texture_mean'].mean
df['texture_mean'] = df['texture_mean'].fillna(mean)`
python pandas missing-data
edited Nov 25 '18 at 10:52
TeeKea
3,22851832
asked Nov 25 '18 at 1:08
shabasshabas
1
add a comment |
When I am trying to use fillna to replace NaNs in the columns with means, the NaNs changed from float64 to object, showing:
bound method Series.mean of 0 NaNn1
Here is the code:
mean = df['texture_mean'].mean
df['texture_mean'] = df['texture_mean'].fillna(mean)`
python pandas missing-data
edited Nov 25 '18 at 10:52
TeeKea
3,22851832
asked Nov 25 '18 at 1:08
shabasshabas
1
meanis a method, so you should call it using parenthesismean = df['texture_mean'].mean(). BTW, the stacktrace indicates the line where the error raises. Using this information is a good hint on what's going on.
– FabienP
Nov 25 '18 at 11:06
add a comment |
When I am trying to use fillna to replace NaNs in the columns with means, the NaNs changed from float64 to object, showing:
bound method Series.mean of 0 NaNn1
Here is the code:
mean = df['texture_mean'].mean
df['texture_mean'] = df['texture_mean'].fillna(mean)`
python pandas missing-data
edited Nov 25 '18 at 10:52
TeeKea
3,22851832
asked Nov 25 '18 at 1:08
shabasshabas
1
When I am trying to use fillna to replace NaNs in the columns with means, the NaNs changed from float64 to object, showing:
bound method Series.mean of 0 NaNn1
Here is the code:
mean = df['texture_mean'].mean
df['texture_mean'] = df['texture_mean'].fillna(mean)`
python pandas missing-data
python pandas missing-data
edited Nov 25 '18 at 10:52
TeeKea
3,22851832
asked Nov 25 '18 at 1:08
shabasshabas
1
edited Nov 25 '18 at 10:52
TeeKea
3,22851832
asked Nov 25 '18 at 1:08
shabasshabas
1
edited Nov 25 '18 at 10:52
TeeKea
3,22851832
edited Nov 25 '18 at 10:52
TeeKea
3,22851832
edited Nov 25 '18 at 10:52
TeeKea
3,22851832
3,22851832
asked Nov 25 '18 at 1:08
shabasshabas
1
asked Nov 25 '18 at 1:08
shabasshabas
1
asked Nov 25 '18 at 1:08
shabasshabas
1
1
meanis a method, so you should call it using parenthesismean = df['texture_mean'].mean(). BTW, the stacktrace indicates the line where the error raises. Using this information is a good hint on what's going on.
– FabienP
Nov 25 '18 at 11:06
add a comment |
meanis a method, so you should call it using parenthesismean = df['texture_mean'].mean(). BTW, the stacktrace indicates the line where the error raises. Using this information is a good hint on what's going on.
– FabienP
Nov 25 '18 at 11:06
mean is a method, so you should call it using parenthesis mean = df['texture_mean'].mean(). BTW, the stacktrace indicates the line where the error raises. Using this information is a good hint on what's going on.– FabienP
Nov 25 '18 at 11:06
mean is a method, so you should call it using parenthesis mean = df['texture_mean'].mean(). BTW, the stacktrace indicates the line where the error raises. Using this information is a good hint on what's going on.– FabienP
Nov 25 '18 at 11:06
add a comment |
1 Answer
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oldest
votes
You cannot use mean = df['texture_mean'].mean. This is where the problem lies. The following code will work -
df=pd.DataFrame({'texture_mean':[2,4,None,6,1,None],'A':[1,2,3,4,5,None]}) # Example
df
A texture_mean
0 1.0 2.0
1 2.0 4.0
2 3.0 NaN
3 4.0 6.0
4 5.0 1.0
5 NaN NaN
df['texture_mean']=df['texture_mean'].fillna(df['texture_mean'].mean())
df
A texture_mean
0 1.0 2.00
1 2.0 4.00
2 3.0 3.25
3 4.0 6.00
4 5.0 1.00
5 NaN 3.25
In case you want to replace all the NaNs with the respective means of that column in all columns, then just do this -
df=df.fillna(df.mean())
df
A texture_mean
0 1.0 2.00
1 2.0 4.00
2 3.0 3.25
3 4.0 6.00
4 5.0 1.00
5 3.0 3.25
Let me know if this is what you want.
answered Nov 25 '18 at 12:15
cph_stocph_sto
2,3012421
add a comment |
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1 Answer
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oldest
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1 Answer
1
active
oldest
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votes
You cannot use mean = df['texture_mean'].mean. This is where the problem lies. The following code will work -
df=pd.DataFrame({'texture_mean':[2,4,None,6,1,None],'A':[1,2,3,4,5,None]}) # Example
df
A texture_mean
0 1.0 2.0
1 2.0 4.0
2 3.0 NaN
3 4.0 6.0
4 5.0 1.0
5 NaN NaN
df['texture_mean']=df['texture_mean'].fillna(df['texture_mean'].mean())
df
A texture_mean
0 1.0 2.00
1 2.0 4.00
2 3.0 3.25
3 4.0 6.00
4 5.0 1.00
5 NaN 3.25
In case you want to replace all the NaNs with the respective means of that column in all columns, then just do this -
df=df.fillna(df.mean())
df
A texture_mean
0 1.0 2.00
1 2.0 4.00
2 3.0 3.25
3 4.0 6.00
4 5.0 1.00
5 3.0 3.25
Let me know if this is what you want.
answered Nov 25 '18 at 12:15
cph_stocph_sto
2,3012421
add a comment |
You cannot use mean = df['texture_mean'].mean. This is where the problem lies. The following code will work -
df=pd.DataFrame({'texture_mean':[2,4,None,6,1,None],'A':[1,2,3,4,5,None]}) # Example
df
A texture_mean
0 1.0 2.0
1 2.0 4.0
2 3.0 NaN
3 4.0 6.0
4 5.0 1.0
5 NaN NaN
df['texture_mean']=df['texture_mean'].fillna(df['texture_mean'].mean())
df
A texture_mean
0 1.0 2.00
1 2.0 4.00
2 3.0 3.25
3 4.0 6.00
4 5.0 1.00
5 NaN 3.25
In case you want to replace all the NaNs with the respective means of that column in all columns, then just do this -
df=df.fillna(df.mean())
df
A texture_mean
0 1.0 2.00
1 2.0 4.00
2 3.0 3.25
3 4.0 6.00
4 5.0 1.00
5 3.0 3.25
Let me know if this is what you want.
answered Nov 25 '18 at 12:15
cph_stocph_sto
2,3012421
add a comment |
You cannot use mean = df['texture_mean'].mean. This is where the problem lies. The following code will work -
df=pd.DataFrame({'texture_mean':[2,4,None,6,1,None],'A':[1,2,3,4,5,None]}) # Example
df
A texture_mean
0 1.0 2.0
1 2.0 4.0
2 3.0 NaN
3 4.0 6.0
4 5.0 1.0
5 NaN NaN
df['texture_mean']=df['texture_mean'].fillna(df['texture_mean'].mean())
df
A texture_mean
0 1.0 2.00
1 2.0 4.00
2 3.0 3.25
3 4.0 6.00
4 5.0 1.00
5 NaN 3.25
In case you want to replace all the NaNs with the respective means of that column in all columns, then just do this -
df=df.fillna(df.mean())
df
A texture_mean
0 1.0 2.00
1 2.0 4.00
2 3.0 3.25
3 4.0 6.00
4 5.0 1.00
5 3.0 3.25
Let me know if this is what you want.
answered Nov 25 '18 at 12:15
cph_stocph_sto
2,3012421
You cannot use mean = df['texture_mean'].mean. This is where the problem lies. The following code will work -
df=pd.DataFrame({'texture_mean':[2,4,None,6,1,None],'A':[1,2,3,4,5,None]}) # Example
df
A texture_mean
0 1.0 2.0
1 2.0 4.0
2 3.0 NaN
3 4.0 6.0
4 5.0 1.0
5 NaN NaN
df['texture_mean']=df['texture_mean'].fillna(df['texture_mean'].mean())
df
A texture_mean
0 1.0 2.00
1 2.0 4.00
2 3.0 3.25
3 4.0 6.00
4 5.0 1.00
5 NaN 3.25
In case you want to replace all the NaNs with the respective means of that column in all columns, then just do this -
df=df.fillna(df.mean())
df
A texture_mean
0 1.0 2.00
1 2.0 4.00
2 3.0 3.25
3 4.0 6.00
4 5.0 1.00
5 3.0 3.25
Let me know if this is what you want.
answered Nov 25 '18 at 12:15
cph_stocph_sto
2,3012421
answered Nov 25 '18 at 12:15
cph_stocph_sto
2,3012421
answered Nov 25 '18 at 12:15
cph_stocph_sto
2,3012421
answered Nov 25 '18 at 12:15
cph_stocph_sto
2,3012421
2,3012421
add a comment |
add a comment |
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meanis a method, so you should call it using parenthesismean = df['texture_mean'].mean(). BTW, the stacktrace indicates the line where the error raises. Using this information is a good hint on what's going on.– FabienP
Nov 25 '18 at 11:06