print(df.groupby(['key1','key2'])[['data1']].mean()) # data1 结果为DataFrame形式 # key1 key2 # a one 0.285455 # two -0.331077 # b one 1.145625 # two 0.050510 print(df.groupby(['key1','key2'])['data1'].mean()) # key1 key2 结果为Sereis形式 # a one 0.285455 # two ...
for name,group in df.groupby(df.dtypes, axis=1): print(name) print(group, end='\n\n') 1. 2. 3. 1.5 使用字典或Serise进行分组 people = pd.DataFrame(np.random.randn(5,5), columns = ['a','b','c','d','e'], index = ['joe','steve','wes','jim','travis']) # 把一些...
Example 1 shows how to group the values in a pandas DataFrame based on two group columns. To accomplish this, we can use thegroupby functionas shown in the following Python codes. The syntax below returns themean values by groupusing the variables group1 and group2 as group indicators. ...
people=pd.DataFrame(np.random.randn(5,5),columns=['a','b','c','d','e'],index=['Joe','Steve','Wes','Jim','Travies'])people.iloc[2:3,[1,2]]=np.nan people 现在,假设已知列的分组关系,并希望根据分组计算列的和: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 mapping={'a'...
dfm=df.groupby(['班级']).transform(np.mean).rename(columns={'语文':'chinese mean','math':'math mean'}) pd.concat([df,dfm],axis=1) 也可以通过mean聚合+merge连接实现: dfm=df.groupby(['班级']).mean().rename(columns={'语文':'chinese mean','math':'math mean'}) ...
columns = ['a','b','c','d'], index = ['abc','bcd','aa','b'] )print(df,'\n')# 按照字母长度分组,分组后求和print(df.groupby(len).sum()) 输出结果: 6.分组后常用数值函数 importpandasaspd s = pd.Series([1,2,3,10,20,30], index = [1,2,3,1,2,3]) ...
axis:{0 or ‘index’, 1 or ‘columns’}, default 0 Split along rows (0) or columns (1). level:int, level name, or sequence of such, default None If the axis is a MultiIndex (hierarchical), group by a particular level or levels. ...
Groupby multiple columns&Sum-使用添加的If条件创建新列 错误原因 选择多个列df['column1', 'column2']的语法错误。这应该是df[['column1', 'column2']] 即使使用df[['column1', 'column2']]表示groupby,pandas也会引发另一个错误,抱怨石斑鱼应该是one dimensional。这是因为df[['column1', 'column2']...
[2]: class order max_speed falcon bird Falconiformes 389.0 parrot bird Psittaciformes 24.0 lion mammal Carnivora 80.2 monkey mammal Primates NaN leopard mammal Carnivora 58.0 # default is axis=0 In [3]: grouped = df.groupby("class") In [4]: grouped = df.groupby("order", axis="columns"...
columns表示将函数应用到每一行,该参数的默认值为0。 3.3 对每一列数据应用同一个函数 通过agg()方法进行聚合,最简单的方式就是给该方法的func参数传入一个函数,这个函数既可以是内置的,也可以自定义的。 def range_data_group(arr): return arr.max()-arr.min() # 使用自定义函数聚合分组数据 data_group....