handlee and convert multiple data types in the same column to numeric
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-1
I am trying to clean my Dataframe. The problem is that I want to convert more than 300 different columns to numeric, but in the same column I have multiple data types.
As an example of DataFrame:
ID MONOCITOS EOSIN EOSINOFILOS NORMOBLASTOS
0 5 0.21 2 0 0.04 31.0
2 9 <0.22 False 0 0.04 33.0
5 12.8 0.16 0 0 0.02 sdfdr
6 No 0 fh 0 0.02 60.0
9 0 0.28 3 - 0.06 Nan
14 3 - 3 - - 59.0
What is the best way to convert a column with different data types to numeric? Is there any module to perform this task automatically?
Thanks
python pandas types numeric
asked Nov 26 '18 at 22:43
LeyLey
193
add a comment |
-1
I am trying to clean my Dataframe. The problem is that I want to convert more than 300 different columns to numeric, but in the same column I have multiple data types.
As an example of DataFrame:
ID MONOCITOS EOSIN EOSINOFILOS NORMOBLASTOS
0 5 0.21 2 0 0.04 31.0
2 9 <0.22 False 0 0.04 33.0
5 12.8 0.16 0 0 0.02 sdfdr
6 No 0 fh 0 0.02 60.0
9 0 0.28 3 - 0.06 Nan
14 3 - 3 - - 59.0
What is the best way to convert a column with different data types to numeric? Is there any module to perform this task automatically?
Thanks
python pandas types numeric
asked Nov 26 '18 at 22:43
LeyLey
193
I dont know a tool but you can go trough the columns and write a function which do this programmaticly. In Easy cases you can use: pd.to_numeric pandas.pydata.org/pandas-docs/stable/generated/…
– MisterMonk
Nov 26 '18 at 22:46
1
Usedf = df.apply(pd.to_numeric, errors='coerce')
– Abhi
Nov 26 '18 at 22:47
I have tried this solution, but it doesn´t work. As some values of the column are string data type the whole column type remains as an object type
– Ley
Nov 27 '18 at 9:39
add a comment |
-1
-1
-1
I am trying to clean my Dataframe. The problem is that I want to convert more than 300 different columns to numeric, but in the same column I have multiple data types.
As an example of DataFrame:
ID MONOCITOS EOSIN EOSINOFILOS NORMOBLASTOS
0 5 0.21 2 0 0.04 31.0
2 9 <0.22 False 0 0.04 33.0
5 12.8 0.16 0 0 0.02 sdfdr
6 No 0 fh 0 0.02 60.0
9 0 0.28 3 - 0.06 Nan
14 3 - 3 - - 59.0
What is the best way to convert a column with different data types to numeric? Is there any module to perform this task automatically?
Thanks
python pandas types numeric
asked Nov 26 '18 at 22:43
LeyLey
193
I am trying to clean my Dataframe. The problem is that I want to convert more than 300 different columns to numeric, but in the same column I have multiple data types.
As an example of DataFrame:
ID MONOCITOS EOSIN EOSINOFILOS NORMOBLASTOS
0 5 0.21 2 0 0.04 31.0
2 9 <0.22 False 0 0.04 33.0
5 12.8 0.16 0 0 0.02 sdfdr
6 No 0 fh 0 0.02 60.0
9 0 0.28 3 - 0.06 Nan
14 3 - 3 - - 59.0
What is the best way to convert a column with different data types to numeric? Is there any module to perform this task automatically?
Thanks
python pandas types numeric
python pandas types numeric
asked Nov 26 '18 at 22:43
LeyLey
193
asked Nov 26 '18 at 22:43
LeyLey
193
asked Nov 26 '18 at 22:43
LeyLey
193
asked Nov 26 '18 at 22:43
LeyLey
193
asked Nov 26 '18 at 22:43
LeyLey
193
193
I dont know a tool but you can go trough the columns and write a function which do this programmaticly. In Easy cases you can use: pd.to_numeric pandas.pydata.org/pandas-docs/stable/generated/…
– MisterMonk
Nov 26 '18 at 22:46
1
Usedf = df.apply(pd.to_numeric, errors='coerce')
– Abhi
Nov 26 '18 at 22:47
I have tried this solution, but it doesn´t work. As some values of the column are string data type the whole column type remains as an object type
– Ley
Nov 27 '18 at 9:39
add a comment |
I dont know a tool but you can go trough the columns and write a function which do this programmaticly. In Easy cases you can use: pd.to_numeric pandas.pydata.org/pandas-docs/stable/generated/…
– MisterMonk
Nov 26 '18 at 22:46
1
Usedf = df.apply(pd.to_numeric, errors='coerce')
– Abhi
Nov 26 '18 at 22:47
I have tried this solution, but it doesn´t work. As some values of the column are string data type the whole column type remains as an object type
– Ley
Nov 27 '18 at 9:39
I dont know a tool but you can go trough the columns and write a function which do this programmaticly. In Easy cases you can use: pd.to_numeric pandas.pydata.org/pandas-docs/stable/generated/…
– MisterMonk
Nov 26 '18 at 22:46
I dont know a tool but you can go trough the columns and write a function which do this programmaticly. In Easy cases you can use: pd.to_numeric pandas.pydata.org/pandas-docs/stable/generated/…
– MisterMonk
Nov 26 '18 at 22:46
1
1
Use
df = df.apply(pd.to_numeric, errors='coerce')– Abhi
Nov 26 '18 at 22:47
Use
df = df.apply(pd.to_numeric, errors='coerce')– Abhi
Nov 26 '18 at 22:47
I have tried this solution, but it doesn´t work. As some values of the column are string data type the whole column type remains as an object type
– Ley
Nov 27 '18 at 9:39
I have tried this solution, but it doesn´t work. As some values of the column are string data type the whole column type remains as an object type
– Ley
Nov 27 '18 at 9:39
add a comment |
1 Answer
1
active
oldest
votes
0
You can convert multiple columns like this:
df[["col1", "col2"]] = df[["col1", "col2"]].apply(pd.to_numeric, errors='coerce')
or change the entire dataframe:
df = df.apply(pd.to_numeric, errors='coerce')
coerce will cause the non-convertible values to be NaN
answered Nov 26 '18 at 22:47
Pedro TorresPedro Torres
718413
This is not suitable for "300 different columns"
– MisterMonk
Nov 26 '18 at 22:48
You could go over a set of columns at a time. For instance, use a loop that would convert 10 cols at a time
– Pedro Torres
Nov 26 '18 at 22:49
Yeah like:: for c in df.columns: column = df[c]
– MisterMonk
Nov 26 '18 at 22:53
You can also just pass the wholedfat once:df=df.apply(pd.to_numeric, errors='coerce')
– G. Anderson
Nov 26 '18 at 23:03
True. Passing the column names was just in the case trying to change it all at the same time could be too much. I will change to make it more explicit
– Pedro Torres
Nov 26 '18 at 23:05
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
0
You can convert multiple columns like this:
df[["col1", "col2"]] = df[["col1", "col2"]].apply(pd.to_numeric, errors='coerce')
or change the entire dataframe:
df = df.apply(pd.to_numeric, errors='coerce')
coerce will cause the non-convertible values to be NaN
answered Nov 26 '18 at 22:47
Pedro TorresPedro Torres
718413
This is not suitable for "300 different columns"
– MisterMonk
Nov 26 '18 at 22:48
You could go over a set of columns at a time. For instance, use a loop that would convert 10 cols at a time
– Pedro Torres
Nov 26 '18 at 22:49
Yeah like:: for c in df.columns: column = df[c]
– MisterMonk
Nov 26 '18 at 22:53
You can also just pass the wholedfat once:df=df.apply(pd.to_numeric, errors='coerce')
– G. Anderson
Nov 26 '18 at 23:03
True. Passing the column names was just in the case trying to change it all at the same time could be too much. I will change to make it more explicit
– Pedro Torres
Nov 26 '18 at 23:05
add a comment |
0
You can convert multiple columns like this:
df[["col1", "col2"]] = df[["col1", "col2"]].apply(pd.to_numeric, errors='coerce')
or change the entire dataframe:
df = df.apply(pd.to_numeric, errors='coerce')
coerce will cause the non-convertible values to be NaN
answered Nov 26 '18 at 22:47
Pedro TorresPedro Torres
718413
This is not suitable for "300 different columns"
– MisterMonk
Nov 26 '18 at 22:48
You could go over a set of columns at a time. For instance, use a loop that would convert 10 cols at a time
– Pedro Torres
Nov 26 '18 at 22:49
Yeah like:: for c in df.columns: column = df[c]
– MisterMonk
Nov 26 '18 at 22:53
You can also just pass the wholedfat once:df=df.apply(pd.to_numeric, errors='coerce')
– G. Anderson
Nov 26 '18 at 23:03
True. Passing the column names was just in the case trying to change it all at the same time could be too much. I will change to make it more explicit
– Pedro Torres
Nov 26 '18 at 23:05
add a comment |
0
0
0
You can convert multiple columns like this:
df[["col1", "col2"]] = df[["col1", "col2"]].apply(pd.to_numeric, errors='coerce')
or change the entire dataframe:
df = df.apply(pd.to_numeric, errors='coerce')
coerce will cause the non-convertible values to be NaN
answered Nov 26 '18 at 22:47
Pedro TorresPedro Torres
718413
You can convert multiple columns like this:
df[["col1", "col2"]] = df[["col1", "col2"]].apply(pd.to_numeric, errors='coerce')
or change the entire dataframe:
df = df.apply(pd.to_numeric, errors='coerce')
coerce will cause the non-convertible values to be NaN
answered Nov 26 '18 at 22:47
Pedro TorresPedro Torres
718413
edited Nov 26 '18 at 23:06
answered Nov 26 '18 at 22:47
Pedro TorresPedro Torres
718413
answered Nov 26 '18 at 22:47
Pedro TorresPedro Torres
718413
answered Nov 26 '18 at 22:47
Pedro TorresPedro Torres
718413
718413
This is not suitable for "300 different columns"
– MisterMonk
Nov 26 '18 at 22:48
You could go over a set of columns at a time. For instance, use a loop that would convert 10 cols at a time
– Pedro Torres
Nov 26 '18 at 22:49
Yeah like:: for c in df.columns: column = df[c]
– MisterMonk
Nov 26 '18 at 22:53
You can also just pass the wholedfat once:df=df.apply(pd.to_numeric, errors='coerce')
– G. Anderson
Nov 26 '18 at 23:03
True. Passing the column names was just in the case trying to change it all at the same time could be too much. I will change to make it more explicit
– Pedro Torres
Nov 26 '18 at 23:05
add a comment |
This is not suitable for "300 different columns"
– MisterMonk
Nov 26 '18 at 22:48
You could go over a set of columns at a time. For instance, use a loop that would convert 10 cols at a time
– Pedro Torres
Nov 26 '18 at 22:49
Yeah like:: for c in df.columns: column = df[c]
– MisterMonk
Nov 26 '18 at 22:53
You can also just pass the wholedfat once:df=df.apply(pd.to_numeric, errors='coerce')
– G. Anderson
Nov 26 '18 at 23:03
True. Passing the column names was just in the case trying to change it all at the same time could be too much. I will change to make it more explicit
– Pedro Torres
Nov 26 '18 at 23:05
This is not suitable for "300 different columns"
– MisterMonk
Nov 26 '18 at 22:48
This is not suitable for "300 different columns"
– MisterMonk
Nov 26 '18 at 22:48
You could go over a set of columns at a time. For instance, use a loop that would convert 10 cols at a time
– Pedro Torres
Nov 26 '18 at 22:49
You could go over a set of columns at a time. For instance, use a loop that would convert 10 cols at a time
– Pedro Torres
Nov 26 '18 at 22:49
Yeah like:: for c in df.columns: column = df[c]
– MisterMonk
Nov 26 '18 at 22:53
Yeah like:: for c in df.columns: column = df[c]
– MisterMonk
Nov 26 '18 at 22:53
You can also just pass the whole
df at once: df=df.apply(pd.to_numeric, errors='coerce')– G. Anderson
Nov 26 '18 at 23:03
You can also just pass the whole
df at once: df=df.apply(pd.to_numeric, errors='coerce')– G. Anderson
Nov 26 '18 at 23:03
True. Passing the column names was just in the case trying to change it all at the same time could be too much. I will change to make it more explicit
– Pedro Torres
Nov 26 '18 at 23:05
True. Passing the column names was just in the case trying to change it all at the same time could be too much. I will change to make it more explicit
– Pedro Torres
Nov 26 '18 at 23:05
add a comment |
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I dont know a tool but you can go trough the columns and write a function which do this programmaticly. In Easy cases you can use: pd.to_numeric pandas.pydata.org/pandas-docs/stable/generated/…
– MisterMonk
Nov 26 '18 at 22:46
1
Use
df = df.apply(pd.to_numeric, errors='coerce')– Abhi
Nov 26 '18 at 22:47
I have tried this solution, but it doesn´t work. As some values of the column are string data type the whole column type remains as an object type
– Ley
Nov 27 '18 at 9:39