# Drop Order Region column# (axis=0 for rows and axis=1 for columns)df = df.drop('Order Region', axis=1)# Drop Order Region column without having to reassign df (using inplace=True)df.drop('Order Region', axis=1, inplace=True)# Drop by column number instead of by column labeldf...
df = df.drop('Order Region', axis=1) # Drop Order Region column without having to reassign df (using inplace=True) df.drop('Order Region', axis=1, inplace=True) # Drop by column number instead of by column label df = df.drop(df.columns[[0, 1, 3]], axis=1) # df.columns ...
df = df.drop('Order Region', axis=1) # Drop Order Region column without having to reassign df (using inplace=True) df.drop('Order Region', axis=1, inplace=True) # Drop by column number instead of by column label df = df.drop(df.columns[[0, 1, 3]], axis=1) # df.columns ...
df = df.drop('Order Region', axis=1) # Drop Order Region column without having to reassign df (using inplace=True) df.drop('Order Region', axis=1, inplace=True) # Drop by column number instead of by column label df = df.drop(df.columns[[0, 1, 3]], axis=1) # df.columns ...
drop_duplicated() 排序&排名 sort_index() rank() 索引设置 reindex() set_index() reset_index() stack() & unstack() 修改列名 丢弃值 drop() 一般用于丢弃DataFrame里的列columns,但切片功能效果相同,根据实际使用,好处是节约内存 #按列(axis=1),丢弃指定label的列df.drop(labels, axis=1) ...
drop([labels, axis, index, columns, level, ...]) 从行或列中删除指定的标签。 drop_duplicates([subset, keep, inplace, ...]) 返回删除重复行的DataFrame。 droplevel(level[, axis]) 返回请求的索引/列级别已被删除的Series/DataFrame。 dropna(*[, axis, how, thresh, subset, ...]) 删除缺失值...
DataFrame.rename([index, columns]) Alter axes input function or functions. DataFrame.rename_axis(mapper[, axis, copy, …]) Alter index and / or columns using input function or functions. DataFrame.reset_index([level, drop, …]) For DataFrame with multi-level index, return new DataFrame with...
In this pandas drop columns article, I will explain how to drop columns, different columns, by name, by index, between two columns, etc.drop()method is used to remove columns and rows according to the specific column(label) name and corresponding axis. ...
new_df = pd.concat([df, pd.DataFrame(columns=["h_low","h_high","s_low","s_high","v_low","v_high"])]) new_df.h_low[idx] =123## 数据赋值 5.2. 删除行或列 DataFrame.drop(labels=None,axis=0,index=None,columns=None, inplace=False) ...
第一种是将列名称分配给.columns属性: [外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-IqtwvKD5-1681365384134)(https://gitcode.net/apachecn/apachecn-ds-zh/-/raw/master/docs/learning-pandas-2e/img/00162.jpeg)] 另一种技术是使用 Python 字典,其中键是列名,每个键的值是...