Pandas 相当于 dplyr 一切（）

Question

In R I frequently use dplyr 's select in combination with everything()

df %>% select(var4, var17, everything())

The above for example would reorder the columns of the dataframe, such that var4 is the first, var17 is the second and subsequently all remaining columns are listed. What is the most pandathonic way of doing this? Working with many columns makes explicitly spelling them out a pain as well as keeping track of their position.

The ideal solution is short, readable and can be used in pandas chaining.

Answer 1

Use Index.difference for all values without specified in list and join together:

df = pd.DataFrame({
        'G':list('abcdef'),
         'var17':[4,5,4,5,5,4],
         'A':[7,8,9,4,2,3],
         'var4':[1,3,5,7,1,0],
         'E':[5,3,6,9,2,4],
         'F':list('aaabbb')
})

cols = ['var4','var17']
another = df.columns.difference(cols, sort=False).tolist()
df = df[cols + another]
print (df)
   var4  var17  G  A  E  F
0     1      4  a  7  5  a
1     3      5  b  8  3  a
2     5      4  c  9  6  a
3     7      5  d  4  9  b
4     1      5  e  2  2  b
5     0      4  f  3  4  b

EDIT: For chaining is possible use DataFrame.pipe with passed DataFrame :

def everything_after(df, cols):
    another = df.columns.difference(cols, sort=False).tolist()
    return df[cols + another]

df = df.pipe(everything_after, ['var4','var17']))
print (df)
   var4  var17  G  A  E  F
0     1      4  a  7  5  a
1     3      5  b  8  3  a
2     5      4  c  9  6  a
3     7      5  d  4  9  b
4     1      5  e  2  2  b
5     0      4  f  3  4  b

Answer 2

Now how smoothly you can do it with datar !

>>> from datar import f
>>> from datar.datasets import iris
>>> from datar.dplyr import select, everything, slice_head
>>> iris >> slice_head(5)
   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width Species
0           5.1          3.5           1.4          0.2  setosa
1           4.9          3.0           1.4          0.2  setosa
2           4.7          3.2           1.3          0.2  setosa
3           4.6          3.1           1.5          0.2  setosa
4           5.0          3.6           1.4          0.2  setosa
>>> iris >> select(f.Species, everything()) >> slice_head(5)
  Species  Sepal_Length  Sepal_Width  Petal_Length  Petal_Width
0  setosa           5.1          3.5           1.4          0.2
1  setosa           4.9          3.0           1.4          0.2
2  setosa           4.7          3.2           1.3          0.2
3  setosa           4.6          3.1           1.5          0.2
4  setosa           5.0          3.6           1.4          0.2

I am the author of the package. Feel free to submit issues if you have any questions.