我想从
['$a', '$b', '$c', '$d', '$e']
to
['a', 'b', 'c', 'd', 'e']
我想从
['$a', '$b', '$c', '$d', '$e']
to
['a', 'b', 'c', 'd', 'e']
当前回答
重命名方法可以采用一个函数,例如:
In [11]: df.columns
Out[11]: Index([u'$a', u'$b', u'$c', u'$d', u'$e'], dtype=object)
In [12]: df.rename(columns=lambda x: x[1:], inplace=True)
In [13]: df.columns
Out[13]: Index([u'a', u'b', u'c', u'd', u'e'], dtype=object)
其他回答
重命名Pandas中的列是一项简单的任务。
df.rename(columns={'$a': 'a', '$b': 'b', '$c': 'c', '$d': 'd', '$e': 'e'}, inplace=True)
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$)', '')))
dict['brand_timeseries:C1'] = 'BTS'
dict['respid:L'] = 'RespID'
dict['country:C1'] = 'CountryID'
dict['pim1:D'] = 'pim_actual'
df.rename(columns=dict, inplace=True)
重命名方法可以采用一个函数,例如:
In [11]: df.columns
Out[11]: Index([u'$a', u'$b', u'$c', u'$d', u'$e'], dtype=object)
In [12]: df.rename(columns=lambda x: x[1:], inplace=True)
In [13]: df.columns
Out[13]: Index([u'a', u'b', u'c', u'd', u'e'], dtype=object)
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]
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')