df_train = pd.read_csv('../xyz.csv')
headers = df_train.columns
I want to filter out those columns in headers which have _pct
in their substring.
Use df.filter
df = pd.DataFrame({'a':[1,2,3], 'b_pct':[1,2,3],'c_pct':[1,2,3],'d':[1]*3})
print(df.filter(items=[i for i in df.columns if '_pct' not in i]))
## or as jezrael suggested
# print(df[[i for i in df.columns if '_pct' not in i]])
Output:
a d
0 1 1
1 2 1
2 3 1
Use:
#data from AkshayNevrekar answer
df = df.loc[:, ~df.columns.str.contains('_pct')]
print (df)
Filter solution is not trivial:
df = df.filter(regex=r'^(?!.*_pct).*$')
a d
0 1 1
1 2 1
2 3 1
Thank you, @IanS for another solutions:
df[df.columns.difference(df.filter(like='_pct').columns).tolist()]
df.drop(df.filter(like='_pct').columns, axis=1)
由于df.columns
返回列名列表,您可以使用列表理解并使用简单条件构建新列表:
new_headers = [x for x in headers if '_pct' not in x]
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.