axis: It specifies to drop columns or rows. set aaxisto1or ‘columns’ to drop columns. By default, it drops the rows from DataFrame. columns: It is an alternative toaxis='columns'. It takes a single column label or list of column labels as input. level: It is used in the case of...
DataFrame.drop(labels=None,axis=0,index=None,columns=None, inplace=False) 参数说明: labels 就是要删除的行列的名字,用列表给定 axis 默认为0,指删除行,因此删除columns时要指定axis=1; index 直接指定要删除的行 columns 直接指定要删除的列 inplace=False,默认该删除操作不改变原数据,而是返回一个执行删除...
我希望将列放在包含banned_columns列表中任何单词的pyspark中,并从其余列中形成一个新的dataframe。banned_columns = ["basket","cricket","ball"] drop_these = [columns_to_drop for columns_to_drop in df.columnsif col 浏览0提问于2018-07-16得票数 1 回答已采纳 4回答 如何在Python中排除Spark datafram...
方法2:删除多列 如果希望同时删除多个列,可以将多个列的索引以列表的形式传入drop方法中。例如: # 删除第一列和第三列(索引为0和2)df_dropped_multiple=df.drop(df.columns[[0,2]],axis=1)# 显示删除后的DataFrameprint("\n删除多列后的DataFrame:")print(df_dropped_multiple) 1. 2. 3. 4. 5. 6....
df1 = df[df.columns[:-1]] # 方法2:使用 drop 方法 df2 = df.drop(df.columns[-1], axis=1) # 方法3:使用 iloc df3 = df.iloc[:, :-1] # 方法4:使用 loc df4 = df.loc[:, df.columns[:-1]] # 方法5:使用 filter df5 = df.filter(regex="^(?!"+df.columns[-1]+"$).*")...
# drop columns from a dataframe # df.drop(columns=['Column_Name1','Column_Name2'], axis=1, inplace=True) import numpy as np df = pd.DataFrame(np.arange(15).reshape(3, 5), columns=['A', 'B', 'C', 'D', 'E']) print(df) # output # A B C D E # 0 0 1 2 3 4 ...
Example 2: Remove Multiple Columns from pandas DataFrame by Name Example 2 shows how to drop several variables from a pandas DataFrame in Python based on the names of these variables. For this, we have to specify a list of column names within the drop function: ...
百度试题 结果1 题目pandas中用于从DataFrame中删除指定列的方法是: A. drop_columns() B. remove_columns() C. delete_columns() D. drop() 相关知识点: 试题来源: 解析 D 反馈 收藏
do not drop any duplicates, even it meansselecting more than `n` items... versionadded:: 0.24.0Returns---DataFrameThe first `n` rows ordered by the given columns in descendingorder.See Also---DataFrame.nsmallest : Return the first `n` rows ordered by `columns` inascending order....
Code Sample, a copy-pastable example if possible index = np.linspace(-3, 5, 7) df = pd.DataFrame({'A': np.sin(index), 'B': np.cos(index**2)}, index=index) df.drop(columns=['A']) Problem description The docs say that I can use the columns...