'pandasdataframe.com4','pandasdataframe.com5'],'other_column':['other1','other2','other3','other4','other5']},index=['row1','row2','pandasdataframe.com_row','row4','row5'])# 使用filter方法选择行
Filter by Column Value:To select rows based on a specific column value, use the index chain method. For example, to filter rows where sales are over 300: Pythongreater_than = df[df['Sales'] > 300] This will return rows with sales greater than 300.Filter by Multiple Conditions:...
df.query('`country of birth` == "UK"') Source dataframe: one of the column names has spaces in it Selected rows where country of originequals'UK' Is null To filter the dataframe where a column value isNULL, use.isnull() importpandasaspdimportnumpyasnp df = pd.DataFrame({'name':['...
1. How to Filter Rows by Column Value Often, you want to find instances of a specific value in your DataFrame. You can easily filter rows based on whether they contain a value or not using the .loc indexing method. For this example, you have a simple DataFrame of random integers arrayed...
这个函数需要自己实现，函数的传入参数根据axis来定，比如axis = 1，就会把一行数据作为Series的数据 结构...
使用布尔索引是最基础的数据筛选方法。你可以通过比较数据帧(DataFrame)中的每一行或每一列的值与一个特定值,来创建一个布尔型数组,然后使用这个布尔型数组来索引数据帧。使用布尔值来筛选数据。例如,df[df['column_name'] > value] 会返回 column_name 列中大于 value 的所有行的数据。
isin([]):基于列表过滤数据。df (df (column_name”).isin ([value1, ' value2 '])) 复制 # Using isinforfiltering rows df[df['Customer Country'].isin(['United States','Puerto Rico'])] 1. 2. 复制 # Filter rows based on valuesina list and select spesific columns ...
isin([]):基于列表过滤数据。df (df (column_name”).isin ([value1, ' value2 '])) #Usingisinforfilteringrowsdf[df['Customer Country'].isin(['United States','Puerto Rico'])] #Filterrowsbasedonvaluesina listandselectspesificcolumnsdf[["Customer Id", "Order Region"]][df['Order Region'...
loc:通过标签索引进行切片,例如df.loc[df['column_name'] == value]。 iloc:通过位置索引进行切片,例如df.iloc[df['column_name'] == value]。 query:使用类似 SQL 的表达式进行查询,例如df.query('column_name == value')。 isin:判断某一列的值是否包含在给定的列表中,例如df[df['column_name'].isi...
explode(column[, ignore_index]) 将类似列表的每个元素转换为一行,复制索引值。ffill([axis, inplace, limit, downcast]) DataFrame.fillna()的同义词,方法='ffill'。fillna([value, method, axis, inplace, …]) 使用指定的方法填充NA / NaN值。filter([items, like, regex, axis]) 根据指定的索引标签...