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')

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']

# This way it will work
import pandas as pd

# Define a dictionary 
rankings = {'test': ['a'],
        'odi': ['E'],
        't20': ['P']}

# Convert the dictionary into DataFrame
rankings_pd = pd.DataFrame(rankings)

# Before renaming the columns
print(rankings_pd)

rankings_pd.rename(columns = {'test':'TEST'}, inplace = True)

假设您可以使用正则表达式，则此解决方案无需使用正则表达式进行手动编码：

import pandas as pd
import re

srch = re.compile(r"\w+")

data = pd.read_csv("CSV_FILE.csv")
cols = data.columns
new_cols = list(map(lambda v:v.group(), (list(map(srch.search, cols)))))
data.columns = new_cols

只需将其分配给.columns属性：

>>> df = pd.DataFrame({'$a':[1,2], '$b': [10,20]})
>>> df
   $a  $b
0   1  10
1   2  20

>>> df.columns = ['a', 'b']
>>> df
   a   b
0  1  10
1  2  20

假设这是您的数据帧。

可以使用两种方法重命名列。

使用dataframe.columns=[#list]df.columns=[‘a’，‘b’，‘c’，‘d’，‘e’]此方法的限制是，如果必须更改一列，则必须传递完整的列列表。此外，此方法不适用于索引标签。例如，如果您通过以下步骤：df.columns=[‘a’、‘b’、‘c’、‘d’]这将引发错误。长度不匹配：预期轴有5个元素，新值有4个元素。另一种方法是Pandasrename（）方法，用于重命名任何索引、列或行df=df.rename（列=｛‘$a‘：‘a‘｝）

同样，您可以更改任何行或列。