我想从

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

to

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

当前回答

Use:

old_names = ['$a', '$b', '$c', '$d', '$e'] 
new_names = ['a', 'b', 'c', 'd', 'e']
df.rename(columns=dict(zip(old_names, new_names)), inplace=True)

这样,您可以根据需要手动编辑new_names。当您只需要重命名几个列来纠正拼写错误、重音、删除特殊字符等时,它非常有用。

其他回答

Use:

old_names = ['$a', '$b', '$c', '$d', '$e'] 
new_names = ['a', 'b', 'c', 'd', 'e']
df.rename(columns=dict(zip(old_names, new_names)), inplace=True)

这样,您可以根据需要手动编辑new_names。当您只需要重命名几个列来纠正拼写错误、重音、删除特殊字符等时,它非常有用。

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)

如“使用文本数据:

df.columns = df.columns.str.replace('$', '')

如果您必须处理由提供系统命名的超出您控制范围的列负载,我提出了以下方法,它是一种通用方法和特定替换方法的组合。

首先使用正则表达式从数据帧列名创建一个字典,以便丢弃列名的某些附加部分,然后向字典中添加特定替换项,以命名接收数据库中的核心列。

然后将其一次性应用于数据帧。

dict = dict(zip(df.columns, df.columns.str.replace('(:S$|:C1$|:L$|:D$|\.Serial:L$)', '')))
dict['brand_timeseries:C1'] = 'BTS'
dict['respid:L'] = 'RespID'
dict['country:C1'] = 'CountryID'
dict['pim1:D'] = 'pim_actual'
df.rename(columns=dict, inplace=True)

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')