df["group_col"] = df[conditions].astype(str).apply(lambda x: '/'.join(x), axis=1) df = df.groupby("group_col").agg({ target_col: aggregation }) df=df.reset_index() col_name=["group_col"]+aggregation df.columns=col_name return df 统一API 要聚合的列 关键在构建agg_dict,condi...
默认情况下,Pandas 在计算平均值时会忽略缺失值: importpandasaspdimportnumpyasnp# 创建包含缺失值的示例数据data={'group':['A','A','B','B','C'],'value1':[10,np.nan,20,25,30],'value2':[100,150,np.nan,250,300]}df=pd.DataFrame(data)# 计算平均值result=df.groupby('group').mean()...
19# group by name with maths_marks count 20print(dataframe.groupby('name')['Maths_marks'].count())
df.pivot_table(index='key1',columns='key2', margins=True)#[Out]# data1 data2#[Out]# key2 one two All one two All#[Out]# key1#[Out]# a 1.304883 -1.388267 0.407166 0.828788 -0.603653 0.351307#[Out]# b -0.514400 -1.487224 -1.000812 -0.826736 -0.192404 -0.509570#[Out]# All 0.69845...
过滤在分组中是对于组的过滤,而索引是对于行的过滤,返回值无论是布尔列表还是元素列表或者位置列表,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。...题目:请创建一个两列的DataFrame数据,自定义一个lambda函数用来两列之和,并将最终的结果添加到新的列'sum_columns'当中 import...
1.462816 -0.441652 0.075531 0.592714 1.109898 1.627081 [6 rows x 16 columns] 通用聚合方法 下面是通用的聚合方法: 函数 描述 mean() 平均值 sum() 求和 size() 计算size count() group的统计 std() 标准差 var() 方差 sem() 均值的标准误 describe() 统计信息描述 first() 第一个group值 last() 最...
As you've already seen, aggregating a Series or all of the columns of a DataFrame is a matter of using aggregate with the desired function or calling a method likemean or std. However, you may want to aggregate using a different function depending o the column, or multiple functions at ...
Yields below output. When you apply count on the entire DataFrame, pretty much all columns will have the same values. So when you want togroup by countjustselect a column, you can even select from your group columns. # Group by multiple columns and get ...
columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False, sort=True, ) -> 'DataFrame' 让我们看一个例子: importpandasaspd # 创建一个示例DataFrame data={'Category': ['A','B','A','B','A','B'], ...
read_parquet(path[, engine, columns]) 从文件路径加载parquet对象,返回DataFrame。 SAS read_sas(filepath_or_buffer[, format, …]) 读取存储为XPORT或SAS7BDAT格式文件的SAS文件。 SQL read_sql_table(table_name, con[, schema, …]) 将SQL数据库表读入DataFrame。 read_sql_query(sql, con[, index_...