示例代码 6: 结合多个布尔序列过滤数据 importpandasaspd# 创建示例 DataFramedata={'name':['Alice','Bob','Charlie','David'],'age':[25,30,35,40],'website':['pandasdataframe.com','example.com','pandasdataframe.com','test.com']}df=
query(condition) print(filtered_df) 输出: 代码语言:txt AI代码解释 Name Age Department 2 Charlie 35 Sales 复杂条件过滤 对于更复杂的条件,可以使用 apply 方法自定义过滤逻辑。 代码语言:python 代码运行次数:0 运行 AI代码解释 def custom_filter(row): return row['Age'] > 30 and row['Department']...
首先应该先写出分组条件: con = df.weight > df.weight.mean() 然后将其传入groupby中: df.groupby(condition)['Height'].mean...,本质上都是对于行的筛选,如果符合筛选条件的则选入结果表,否则不选入。...在groupby对象中,定义了filter方法进行组的筛选,...
condition ="Age > 30 & Department == 'Sales'"filtered_df = df.query(condition)print(filtered_df) 输出: Name Age Department 2 Charlie 35 Sales 复杂条件过滤 对于更复杂的条件,可以使用apply方法自定义过滤逻辑。 defcustom_filter(row):returnrow['Age'] >30androw['Department']in['Sales','Marketi...
df.query('Order_Quantity > 3 and Customer_Fname == "Mary"') between():根据在指定范围内的值筛选行。df[df['column_name'].between(start, end)] # Filter rows based on values within a range df[df['Order Quantity'].between(3, 5)] ...
condition="Age > 30 & Department == 'Sales'"filtered_df=df.query(condition)print(filtered_df) 输出: Name Age Department2Charlie35Sales 复杂条件过滤 对于更复杂的条件,可以使用apply方法自定义过滤逻辑。 defcustom_filter(row):returnrow['Age']>30androw['Department']in['Sales','Marketing']filtered...
df.apply(lambda row: '|'.join(filter(pd.notna, [row['source_1'], row['source_2']])), axis=1)sort_values(by=multiple columns) sort_values可以不止一个column,可以多个。 df.sort_values(by=['name', 'number']) 还可以指定ascending=[False, True]比较两个dataframe是否相等 ...
# Filter rows based on values within a range df[df['Order Quantity'].between(3, 5)] 字符串方法:根据字符串匹配条件筛选行。例如str.startswith(), str.endswith(), str.contains() # Using str.startswith() for filtering rows df[df['Category Name'].str.startswith('Cardio')] ...
# Filter rows based on values within a range df[df['Order Quantity'].between(3,5)] 1. 2. 字符串方法:根据字符串匹配条件筛选行。例如str.startswith(), str.endswith(), str.contains() 复制 # Using str.startswith()forfiltering rows ...
Thewheremethod in Pandas allows you to filter DataFrame or Series based on conditions, akin to SQL’s WHERE clause. Have you ever found yourself needing to replace certain values in a DataFrame based on a specific condition, or perhaps wanting to mask data that doesn’t meet certain criteria...