dataframe.columns.difference() use
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I am trying to find the working of dataframe.columns.difference() but couldn't find a satisfactory explanation about it. Can anyone explain the working of this method in detail?
pandas dataframe difference
edited 1 hour ago
ebrahimi
73121021
asked yesterday
Parth S.Parth S.
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$begingroup$
I am trying to find the working of dataframe.columns.difference() but couldn't find a satisfactory explanation about it. Can anyone explain the working of this method in detail?
pandas dataframe difference
edited 1 hour ago
ebrahimi
73121021
asked yesterday
Parth S.Parth S.
11
New contributor
Parth S. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I am trying to find the working of dataframe.columns.difference() but couldn't find a satisfactory explanation about it. Can anyone explain the working of this method in detail?
pandas dataframe difference
edited 1 hour ago
ebrahimi
73121021
asked yesterday
Parth S.Parth S.
11
New contributor
Parth S. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am trying to find the working of dataframe.columns.difference() but couldn't find a satisfactory explanation about it. Can anyone explain the working of this method in detail?
pandas dataframe difference
pandas dataframe difference
edited 1 hour ago
ebrahimi
73121021
asked yesterday
Parth S.Parth S.
11
New contributor
Parth S. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 1 hour ago
ebrahimi
73121021
asked yesterday
Parth S.Parth S.
11
New contributor
Parth S. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 1 hour ago
ebrahimi
73121021
edited 1 hour ago
ebrahimi
73121021
edited 1 hour ago
ebrahimi
73121021
73121021
asked yesterday
Parth S.Parth S.
11
New contributor
Parth S. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
Parth S.Parth S.
11
asked yesterday
Parth S.Parth S.
11
11
New contributor
Parth S. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Parth S. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Parth S. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
1 Answer
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The function dataframe.columns.difference() gives you complement of the values that you provide as argument. It can be used to create a new dataframe from an existing dataframe with exclusion of some columns. Let us look through an example:
In [2]: import pandas as pd
In [3]: import numpy as np
In [4]: df = pd.DataFrame(np.random.randn(5, 4), columns=list('ABCD'))
In [5]: df
Out[5]:
A B C D
0 -1.023134 -0.130241 -0.675639 -0.985182
1 0.270465 -1.099458 -1.114871 3.203371
2 -0.340572 0.913594 -0.387428 0.867702
3 -0.487784 0.465429 -1.344002 1.216967
4 1.433862 -0.172795 -1.656147 0.061359
In [6]: df_new = df[df.columns.difference(['B', 'D'])]
In [7]: df_new
Out[7]:
A C
0 -1.023134 -0.675639
1 0.270465 -1.114871
2 -0.340572 -0.387428
3 -0.487784 -1.344002
4 1.433862 -1.656147
The function returns as output a new list of columns from the existing columns excluding the ones given as arguments. You can also check it:
In [8]: df.columns.difference(['B', 'D'])
Out[8]: Index(['A', 'C'], dtype='object')
I suggest you to take a look at the official documentation here.
answered 22 hours ago
bkshibkshi
56410
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1 Answer
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oldest
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1 Answer
1
active
oldest
votes
active
oldest
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active
oldest
votes
$begingroup$
The function dataframe.columns.difference() gives you complement of the values that you provide as argument. It can be used to create a new dataframe from an existing dataframe with exclusion of some columns. Let us look through an example:
In [2]: import pandas as pd
In [3]: import numpy as np
In [4]: df = pd.DataFrame(np.random.randn(5, 4), columns=list('ABCD'))
In [5]: df
Out[5]:
A B C D
0 -1.023134 -0.130241 -0.675639 -0.985182
1 0.270465 -1.099458 -1.114871 3.203371
2 -0.340572 0.913594 -0.387428 0.867702
3 -0.487784 0.465429 -1.344002 1.216967
4 1.433862 -0.172795 -1.656147 0.061359
In [6]: df_new = df[df.columns.difference(['B', 'D'])]
In [7]: df_new
Out[7]:
A C
0 -1.023134 -0.675639
1 0.270465 -1.114871
2 -0.340572 -0.387428
3 -0.487784 -1.344002
4 1.433862 -1.656147
The function returns as output a new list of columns from the existing columns excluding the ones given as arguments. You can also check it:
In [8]: df.columns.difference(['B', 'D'])
Out[8]: Index(['A', 'C'], dtype='object')
I suggest you to take a look at the official documentation here.
answered 22 hours ago
bkshibkshi
56410
$endgroup$
add a comment |
$begingroup$
The function dataframe.columns.difference() gives you complement of the values that you provide as argument. It can be used to create a new dataframe from an existing dataframe with exclusion of some columns. Let us look through an example:
In [2]: import pandas as pd
In [3]: import numpy as np
In [4]: df = pd.DataFrame(np.random.randn(5, 4), columns=list('ABCD'))
In [5]: df
Out[5]:
A B C D
0 -1.023134 -0.130241 -0.675639 -0.985182
1 0.270465 -1.099458 -1.114871 3.203371
2 -0.340572 0.913594 -0.387428 0.867702
3 -0.487784 0.465429 -1.344002 1.216967
4 1.433862 -0.172795 -1.656147 0.061359
In [6]: df_new = df[df.columns.difference(['B', 'D'])]
In [7]: df_new
Out[7]:
A C
0 -1.023134 -0.675639
1 0.270465 -1.114871
2 -0.340572 -0.387428
3 -0.487784 -1.344002
4 1.433862 -1.656147
The function returns as output a new list of columns from the existing columns excluding the ones given as arguments. You can also check it:
In [8]: df.columns.difference(['B', 'D'])
Out[8]: Index(['A', 'C'], dtype='object')
I suggest you to take a look at the official documentation here.
answered 22 hours ago
bkshibkshi
56410
$endgroup$
add a comment |
$begingroup$
The function dataframe.columns.difference() gives you complement of the values that you provide as argument. It can be used to create a new dataframe from an existing dataframe with exclusion of some columns. Let us look through an example:
In [2]: import pandas as pd
In [3]: import numpy as np
In [4]: df = pd.DataFrame(np.random.randn(5, 4), columns=list('ABCD'))
In [5]: df
Out[5]:
A B C D
0 -1.023134 -0.130241 -0.675639 -0.985182
1 0.270465 -1.099458 -1.114871 3.203371
2 -0.340572 0.913594 -0.387428 0.867702
3 -0.487784 0.465429 -1.344002 1.216967
4 1.433862 -0.172795 -1.656147 0.061359
In [6]: df_new = df[df.columns.difference(['B', 'D'])]
In [7]: df_new
Out[7]:
A C
0 -1.023134 -0.675639
1 0.270465 -1.114871
2 -0.340572 -0.387428
3 -0.487784 -1.344002
4 1.433862 -1.656147
The function returns as output a new list of columns from the existing columns excluding the ones given as arguments. You can also check it:
In [8]: df.columns.difference(['B', 'D'])
Out[8]: Index(['A', 'C'], dtype='object')
I suggest you to take a look at the official documentation here.
answered 22 hours ago
bkshibkshi
56410
$endgroup$
The function dataframe.columns.difference() gives you complement of the values that you provide as argument. It can be used to create a new dataframe from an existing dataframe with exclusion of some columns. Let us look through an example:
In [2]: import pandas as pd
In [3]: import numpy as np
In [4]: df = pd.DataFrame(np.random.randn(5, 4), columns=list('ABCD'))
In [5]: df
Out[5]:
A B C D
0 -1.023134 -0.130241 -0.675639 -0.985182
1 0.270465 -1.099458 -1.114871 3.203371
2 -0.340572 0.913594 -0.387428 0.867702
3 -0.487784 0.465429 -1.344002 1.216967
4 1.433862 -0.172795 -1.656147 0.061359
In [6]: df_new = df[df.columns.difference(['B', 'D'])]
In [7]: df_new
Out[7]:
A C
0 -1.023134 -0.675639
1 0.270465 -1.114871
2 -0.340572 -0.387428
3 -0.487784 -1.344002
4 1.433862 -1.656147
The function returns as output a new list of columns from the existing columns excluding the ones given as arguments. You can also check it:
In [8]: df.columns.difference(['B', 'D'])
Out[8]: Index(['A', 'C'], dtype='object')
I suggest you to take a look at the official documentation here.
answered 22 hours ago
bkshibkshi
56410
answered 22 hours ago
bkshibkshi
56410
answered 22 hours ago
bkshibkshi
56410
answered 22 hours ago
bkshibkshi
56410
56410
add a comment |
add a comment |
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