NOT IN 过滤器只有一列 方法2:对多列使用 NOT IN 过滤器 现在我们可以使用 any()函数过滤多个列。此函数将检查任何给定列中存在的值,并且列在 [[]] 中以逗号分隔。 Syntax: dataframe[~dataframe[[columns]].isin(list).any(axis=1)] 编程需要懂一点英语 ...
isin(list3)]) Python Copy输出:单列的NOT IN过滤器方法2:使用多列的NOT IN过滤器现在我们可以通过使用any()函数来过滤多于一列的内容。这个函数将检查任何给定列中存在的值,列是用逗号分隔的[[]]。语法 :dataframe[~dataframe[[columns]].isin(list).any(axis=1)] Python Copy...
Pandas Filter by Multiple Columns In pandas or any table-like structures, most of the time we would need to filter the rows based on multiple conditions by using multiple columns, you can do that in Pandas DataFrame as below. # Filter by multiple conditions print(df.query("`Courses Fee` >...
# Filter rows based on valuesina list and select spesific columns df[["Customer Id","Order Region"]][df['Order Region'].isin(['Central America','Caribbean'])] 1. 2. 复制 # UsingNOTisinforfiltering rows df[~df['Customer Country'].isin(['United States'])] 1. 2. query():方法用于...
# 获取数据框中的所有列名 all_columns = df.columns.tolist() # 使用列表推导式筛选出不匹配的列名 non_matching_columns = [column for column in all_columns if not column.startswith('A')] # 通过filter方法获取仅包含不匹配列的数据框 filtered_df = df.filter(items=non_matching_columns) # 打印...
It takes DataFrame as a parameter. Secondly, it takes the column name and the list of elements that needs to be excluded. Let us understand with the help of an example. Python Program to Use 'NOT IN' Filter in Pandas # Importing pandas packageimportpandasaspd# Creating a dictionary of stu...
Use Pandas NOT IN filter (~isin) to retrieve rows not matching values in a list; apply to columns like strings, numbers, datetimes, and more.
# Filter rows based on valuesina list and select spesific columns df[["Customer Id","Order Region"]][df['Order Region'].isin(['Central America','Caribbean'])] 代码语言:javascript 复制 # UsingNOTisinforfiltering rows df[~df['Customer Country'].isin(['United States'])] ...
Apply the NOT IN filter on a single column to exclude rows based on a list of values. Use the NOT IN filter across multiple columns by combining conditions with logical operators. Utilize boolean indexing to create a mask that filters out unwanted rows from the DataFrame. ...
(most recent call last) Cell In[27], line 1 ---> 1 df.apply(f, axis="columns") File ~/work/pandas/pandas/pandas/core/frame.py:10374, in DataFrame.apply(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs) 10360 from pandas.core.apply import fr...