我想从

['$a', '$b', '$c', '$d', '$e']

to

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

当前回答

这里有一个我喜欢用来减少打字的漂亮小函数:

def rename(data, oldnames, newname):
    if type(oldnames) == str: # Input can be a string or list of strings
        oldnames = [oldnames] # When renaming multiple columns
        newname = [newname] # Make sure you pass the corresponding list of new names
    i = 0
    for name in oldnames:
        oldvar = [c for c in data.columns if name in c]
        if len(oldvar) == 0:
            raise ValueError("Sorry, couldn't find that column in the dataset")
        if len(oldvar) > 1: # Doesn't have to be an exact match
            print("Found multiple columns that matched " + str(name) + ": ")
            for c in oldvar:
                print(str(oldvar.index(c)) + ": " + str(c))
            ind = input('Please enter the index of the column you would like to rename: ')
            oldvar = oldvar[int(ind)]
        if len(oldvar) == 1:
            oldvar = oldvar[0]
        data = data.rename(columns = {oldvar : newname[i]})
        i += 1
    return data

下面是一个如何工作的示例:

In [2]: df = pd.DataFrame(np.random.randint(0, 10, size=(10, 4)), columns = ['col1', 'col2', 'omg', 'idk'])
# First list = existing variables
# Second list = new names for those variables
In [3]: df = rename(df, ['col', 'omg'],['first', 'ohmy'])
Found multiple columns that matched col:
0: col1
1: col2

Please enter the index of the column you would like to rename: 0

In [4]: df.columns
Out[5]: Index(['first', 'col2', 'ohmy', 'idk'], dtype='object')

其他回答

除了已经提供的解决方案之外,您还可以在读取文件时替换所有列。我们可以使用names和header=0来实现这一点。

首先,我们创建一个我们喜欢用作列名的名称列表:

import pandas as pd

ufo_cols = ['city', 'color reported', 'shape reported', 'state', 'time']
ufo.columns = ufo_cols

ufo = pd.read_csv('link to the file you are using', names = ufo_cols, header = 0)

在这种情况下,所有列名都将替换为列表中的名称。

df = pd.DataFrame({'$a': [1], '$b': [1], '$c': [1], '$d': [1], '$e': [1]})

如果新列列表的顺序与现有列的顺序相同,则分配很简单:

new_cols = ['a', 'b', 'c', 'd', 'e']
df.columns = new_cols
>>> df
   a  b  c  d  e
0  1  1  1  1  1

如果您有一个将旧列名键入到新列名的字典,可以执行以下操作:

d = {'$a': 'a', '$b': 'b', '$c': 'c', '$d': 'd', '$e': 'e'}
df.columns = df.columns.map(lambda col: d[col])  # Or `.map(d.get)` as pointed out by @PiRSquared.
>>> df
   a  b  c  d  e
0  1  1  1  1  1

如果你没有列表或字典映射,你可以通过列表理解去掉前导$符号:

df.columns = [col[1:] if col[0] == '$' else col for col in df]

可以将lstrip或strip方法与索引一起使用:

df.columns = df.columns.str.lstrip('$')

or

cols = ['$a', '$b', '$c', '$d', '$e']
pd.Series(cols).str.lstrip('$').tolist()

输出:

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

由于您只想删除所有列名中的$符号,因此只需执行以下操作:

df = df.rename(columns=lambda x: x.replace('$', ''))

OR

df.rename(columns=lambda x: x.replace('$', ''), inplace=True)

假设您的数据集名称为df,df具有。

df = ['$a', '$b', '$c', '$d', '$e']`

所以,要重命名这些,我们只需这样做。

df.columns = ['a','b','c','d','e']