python - 将 pandas 数据框按列分成两部分

Question

我有一个数据帧，我想将其拆分为两个数据帧，一个包含以 foo 开头的所有列，另一个包含其余列。 有没有快速的方法来做到这一点？

Answer 1

您可以使用列表推导式来选择所有列名称:

df = pd.DataFrame({'fooA':[1,2,3],
                   'fooB':[4,5,6],
                   'fooC':[7,8,9],
                   'D':[1,3,5],
                   'E':[5,3,6],
                   'F':[7,4,3]})

print (df)
   D  E  F  fooA  fooB  fooC
0  1  5  7     1     4     7
1  3  3  4     2     5     8
2  5  6  3     3     6     9

foo = [col for col in df.columns if col.startswith('foo')]
print (foo)
['fooA', 'fooB', 'fooC']

other = [col for col in df.columns if not col.startswith('foo')]
print (other)
['D', 'E', 'F']

print (df[foo])
   fooA  fooB  fooC
0     1     4     7
1     2     5     8
2     3     6     9

print (df[other])
   D  E  F
0  1  5  7
1  3  3  4
2  5  6  3

<小时/>

另一个解决方案 filter和 difference :

df1 = df.filter(regex='^foo')
print (df1)
   fooA  fooB  fooC
0     1     4     7
1     2     5     8
2     3     6     9

print (df.columns.difference(df1.columns))
Index(['D', 'E', 'F'], dtype='object')

print (df[df.columns.difference(df1.columns)])
   D  E  F
0  1  5  7
1  3  3  4
2  5  6  3

时间:

In [123]: %timeit a(df)
1000 loops, best of 3: 1.06 ms per loop

In [124]: %timeit b(df3)
1000 loops, best of 3: 1.04 ms per loop

In [125]: %timeit c(df4)
1000 loops, best of 3: 1.41 ms per loop

df3 = df.copy()
df4 = df.copy()

def a(df):
    df1 = df.filter(regex='^foo')
    df2 = df[df.columns.difference(df1.columns)]
    return df1, df2

def b(df):
    df1 = df[[col for col in df.columns if col.startswith('foo')]]
    df2 = df[[col for col in df.columns if not col.startswith('foo')]]
    return df1, df2

def c(df):
    df1 = df[df.columns[df.columns.str.startswith('foo')]]
    df2 = df[df.columns[~df.columns.str.startswith('foo')]]
    return df1, df2

df1, df2 = a(df)
print (df1)
print (df2)    

df1, df2 = b(df3)
print (df1)
print (df2)   

df1, df2 = c(df4)
print (df1)
print (df2)