有没有其他类似python的方法来完成下面的重复代码
我有20多列需要运行以下规则:有没有其他类似python的方法来完成下面的重复代码,python,python-2.7,pandas,Python,Python 2.7,Pandas,我有20多列需要运行以下规则: df['LAND1'] = df['LAND1'].str.replace('\W+', '') df['LAND1'] = df['LAND1'].str.lower().astype(str) df['SEA1'] = df['SEA1'].str.replace('\W+', '') df['SEA1'] = df['SEA1'].str.lower().astype(str) df['OCEAN1'] = df['OCEAN1'].str.replace(
df['LAND1'] = df['LAND1'].str.replace('\W+', '')
df['LAND1'] = df['LAND1'].str.lower().astype(str)
df['SEA1'] = df['SEA1'].str.replace('\W+', '')
df['SEA1'] = df['SEA1'].str.lower().astype(str)
df['OCEAN1'] = df['OCEAN1'].str.replace('\W+', '')
df['OCEAN1'] = df['OCEAN1'].str.lower().astype(str)
df['CITY1'] = df['CITY1'].str.replace('\W+', '')
df['CITY1'] = df['CITY1'].str.lower().astype(str)
不同列的更多相同类型的代码如何最小化代码。这样我就可以编写更少的代码。您可以创建一个列名列表,然后遍历它们并为它们应用您的逻辑。范例-
columns = ['LAND1','SEA1','OCEAN1','CITY1',...]
for col in columns:
df[col] = (df[col].str.replace('\W+', '')
.str.lower().astype(str))
演示-
In [17]: df
Out[17]:
LAND1 SEA1
0 Blah!!!Bloh Bleh@@@Blum
1 Blah!!!Bloh Bleh@@@Blum
2 Blah!!!Bloh Bleh@@@Blum
3 Blah!!!Bloh Bleh@@@Blum
4 Blah!!!Bloh Bleh@@@Blum
5 Blah!!!Bloh Bleh@@@Blum
6 Blah!!!Bloh Bleh@@@Blum
7 Blah!!!Bloh Bleh@@@Blum
8 Blah!!!Bloh Bleh@@@Blum
9 Blah!!!Bloh Bleh@@@Blum
In [18]: columns = ['LAND1','SEA1']
In [20]: for col in columns:
....: df[col] = (df[col].str.replace('\W+', '')
....: .str.lower().astype(str))
....:
In [21]: df
Out[21]:
LAND1 SEA1
0 blahbloh blehblum
1 blahbloh blehblum
2 blahbloh blehblum
3 blahbloh blehblum
4 blahbloh blehblum
5 blahbloh blehblum
6 blahbloh blehblum
7 blahbloh blehblum
8 blahbloh blehblum
9 blahbloh blehblum
我希望df是一本
字典
for i in df.keys():
df[i]=df[i].str.replace('\W+', '')
df[i]=df[i].str.lower().astype(str)
让我知道它是否有助于您熔化数据帧,然后应用repalce和to lower功能。旋转数据帧以返回数据帧。应用数据帧和数据帧。应用映射也可以压缩代码:
df=pd.DataFrame({'A':['a','b','c'],'D':['d','e','f'],'G':['g','h','i']})
A D G
0 a d g
1 b e h
2 c f i
然后:
df.apply(pd.Series.replace,args=('d','ddd')).applymap(str.upper)
A D G
0 A DDD G
1 B E H
2 C F I
您可以通过selection=['A','D']影响并限制某些列;df[selection]=df[selection]。例如,应用(..)