Panda的DataFrame - 重命名多个具有相同名称的列

Question

我在df中有几个名称相同的列.需要重命名它们.通常的重命名重命名全部无论如何我可以将下面的blah(s)重命名为blah1,blah4,blah5？

    In [6]:

    df=pd.DataFrame(np.arange(2*5).reshape(2,5))
    df.columns=['blah','blah2','blah3','blah','blah']
    df
    Out[6]:


blah    blah2   blah3   blah    blah
0    0   1   2   3   4
1    5   6   7   8   9

在[7]中:

df.rename(columns = {'blah':'blah1'})
Out[7]:
        blah1   blah2   blah3   blah1   blah1
        0    0   1   2   3   4
        1    5   6   7   8   9

Answer 1

我想在Pandas中找到一个解决方案而不是一般的Python解决方案.如果列的get_loc()函数找到重复项,并且"True"值指向找到重复项的位置,则它返回一个屏蔽数组.然后我使用掩码为这些位置分配新值.在我的情况下,我提前知道我将获得多少重复以及我将要分配给他们但是看起来df.columns.get_duplicates()会返回所有重复的列表然后你可以如果需要更通用的dup-weeding操作,请将该列表与get_loc()结合使用

cols=pd.Series(df.columns)
for dup in df.columns.get_duplicates(): 
    cols[df.columns.get_loc(dup)] = ([dup + '.' + str(d_idx) 
                                     if d_idx != 0 
                                     else dup 
                                     for d_idx in range(df.columns.get_loc(dup).sum())]
                                    )
df.columns=cols

    blah    blah2   blah3   blah.1  blah.2
 0     0        1       2        3       4
 1     5        6       7        8       9

Answer 2

从Pandas 0.19.0开始,pd.read_csv()已经改进了对重复列名称的支持

所以我们可以尝试使用内部方法:

In [137]: pd.io.parsers.ParserBase({'names':df.columns})._maybe_dedup_names(df.columns)
Out[137]: ['blah', 'blah2', 'blah3', 'blah.1', 'blah.2']

这是"魔术"功能:

def _maybe_dedup_names(self, names):
    # see gh-7160 and gh-9424: this helps to provide
    # immediate alleviation of the duplicate names
    # issue and appears to be satisfactory to users,
    # but ultimately, not needing to butcher the names
    # would be nice!
    if self.mangle_dupe_cols:
        names = list(names)  # so we can index
        counts = {}

        for i, col in enumerate(names):
            cur_count = counts.get(col, 0)

            if cur_count > 0:
                names[i] = '%s.%d' % (col, cur_count)

            counts[col] = cur_count + 1

    return names

Answer 3

你可以用这个:

def df_column_uniquify(df):
    df_columns = df.columns
    new_columns = []
    for item in df_columns:
        counter = 0
        newitem = item
        while newitem in new_columns:
            counter += 1
            newitem = "{}_{}".format(item, counter)
        new_columns.append(newitem)
    df.columns = new_columns
    return df

然后

import numpy as np
import pandas as pd

df=pd.DataFrame(np.arange(2*5).reshape(2,5))
df.columns=['blah','blah2','blah3','blah','blah']

所以df:

   blah  blah2  blah3   blah   blah
0     0      1      2      3      4
1     5      6      7      8      9

然后

df = df_column_uniquify(df)

所以df:

   blah  blah2  blah3  blah_1  blah_2
0     0      1      2       3       4
1     5      6      7       8       9