python - 确保特定列位于数据帧中的最后(或第一)的最快方法是什么

given df

df = pd.DataFrame(np.arange(8).reshape(2, 4), columns=list('abcd'))

假设我需要列'b'在末尾。I could do:

df[['a', 'c', 'd', 'b']]

但是，最有效的方法是什么来确保给定列位于末尾？
这就是我一直在做的。其他人会怎么做？

def put_me_last(df, column):
    return pd.concat([df.drop(column, axis=1), df[column]], axis=1)

put_me_last(df, 'b')

Timing Results
结论
mfripp是赢家。。这真是个好消息。

代码

from string import lowercase

df_small = pd.DataFrame(np.arange(8).reshape(2, 4), columns=list('abcd'))
df_large = pd.DataFrame(np.arange(1000000).reshape(10000, 100),
                        columns=pd.MultiIndex.from_product([list(lowercase[:-1]), ['One', 'Two', 'Three', 'Four']]))


def pir1(df, column):
    return pd.concat([df.drop(column, axis=1), df[column]], axis=1)

def pir2(df, column):
    if df.columns[-1] == column:
        return df
    else:
        pos = df.columns.values.__eq__('b').argmax()
        return df[np.roll(df.columns, len(df.columns) - 1 - pos)]

def pir3(df, column):
    if df.columns[-1] == column:
        return df
    else:
        pos = df.columns.values.__eq__('b').argmax()
        cols = df.columns.values
        np.concatenate([cols[:pos], cols[1+pos:], cols[[pos]]])
        return df[np.concatenate([cols[:pos], cols[1+pos:], cols[[pos]]])]

def pir4(df, column):
    if df.columns[-1] == column:
        return df
    else:
        return df[np.roll(df.columns.drop(column).insert(0, column), -1)]

def carsten1(df, column):
    cols = list(df)
    if cols[-1] == column:
        return df
    else:
        return pd.concat([df.drop(column, axis=1), df[column]], axis=1)

def carsten2(df, column):
    cols = list(df)
    if cols[-1] == column:
        return df
    else:
        idx = cols.index(column)
        new_cols = cols[:idx] + cols[idx + 1:] + [column]
        return df[new_cols]

def mfripp1(df, column):
    new_cols = [c for c in df.columns if c != column] + [column]
    return df[new_cols]

def mfripp2(df, column):
    new_cols = [c for c in df.columns if c != column] + [column]
    return df.reindex_axis(new_cols, axis='columns', copy=False)

def ptrj1(df, column):
    return df.reindex(columns=df.columns.drop(column).append(pd.Index([column])))

def shivsn1(df, column):
    column_list=list(df)
    column_list.remove(column)
    column_list.append(column)
    return df[column_list]

def merlin1(df, column):
    return df[df.columns.drop(["b"]).insert(99999, 'b')]


list_of_funcs = [pir1, pir2, pir3, pir4, carsten1, carsten2, mfripp1, mfripp2, ptrj1, shivsn1]

def test_pml(df, pml):
    for c in df.columns:
        pml(df, c)

summary = pd.DataFrame([], [f.__name__ for f in list_of_funcs], ['Small', 'Large'])

for f in list_of_funcs:
    summary.at[f.__name__, 'Small'] = timeit(lambda: test_pml(df_small, f), number=100)
    summary.at[f.__name__, 'Large'] = timeit(lambda: test_pml(df_large, f), number=10)

最佳答案

我会重新排列列列表，而不是删除并附加其中一个列：

import pandas as pd
import numpy as np

df = pd.DataFrame(np.arange(8).reshape(2, 4), columns=list('abcd'))

def put_me_last(df, column):
    return pd.concat([df.drop(column, axis=1), df[column]], axis=1)

def put_me_last_fast(df, column):
    new_cols = [c for c in df.columns if c != column] + [column]
    return df[new_cols]

def put_me_last_faster(df, column):
    new_cols = [c for c in df.columns if c != column] + [column]
    return df.reindex_axis(new_cols, axis='columns', copy=False)

计时（在iPython中）：

%timeit put_me_last(df, 'b')
# 1000 loops, best of 3: 741 µs per loop

%timeit put_me_last_fast(df, 'b')
# 1000 loops, best of 3: 295 µs per loop

%timeit put_me_last_faster(df, 'b')
# 1000 loops, best of 3: 239 µs per loop

%timeit put_me_last_faster(df, 'd')  # not changing order
# 1000 loops, best of 3: 125 µs per loop

Note: you could use the line below to define new_cols, but it's about 80x slower than the one used above (2 µs vs 160 µs)

new_cols = df.columns.drop(column).insert(-1, column)

另请注意：如果您经常尝试将一列移动到已经存在的末尾，则可以通过添加该值将这些情况的时间缩短到1微秒以下，如@Carsten所述：

if df.columns[-1] == column:
    return df

关于python - 确保特定列位于数据帧中的最后(或第一)的最快方法是什么，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/38601841/