通过reset_index()函数可以将groupby()的分组结果转换成DataFrame对象,这样就可保存了!! 代码举例: out_xlsx=in_f_name+'-group.xlsx' df_group=df.groupby(['推广计划','推广组']).describe().reset_index() df_group.to_excel(out_xlsx, sheet_name='Sheet1',index=False)
frame = pd.DataFrame(np.arange(9).reshape((3,3)),index=['a','c','d'], columns=['Ohio','Texas','California']) frame2 = frame.reindex(['c','b','a','d']) # 重命名索引,若有原索引,则修改顺序 print(frame2) data = frame2.drop('Ohio',axis=1) # 删除Ohio列 print(data) ...
默认情况下,pandas groupby multiple columns不对值进行排序 pandas groupby Pandas: groupby 页面内容是否对你有帮助? 有帮助 没帮助 Pandas高级教程之:GroupBy用法 简介pandas中的DF数据类型可以像数据库表格一样进行groupby操作。通常来说groupby操作可以分为三部分:分割数据,应用变换和和合并数据。...本文将会详细讲解...
Hierarchical indexing is an important featuer of pandas that enables you to have multiple(two or more) indexlevels on an axis. Somewhat abstractly, it provides a way for you to to work with higher dimensional data in a lower dimensional form.(通过多层索引的方式去从低维看待高维数据). Let's...
df.groupby('product', as_index=False)'quantity'.sum() df.groupby('product')'quantity'.sum().reset_index() 但是,尽管外观不寻常,Series的行为就像DataFrames一样,所以可能对pdi.patch_series_repr()进行“整容”就足够了。 显然,不同的列在分组时表现不同。例如,对数量求和完全没问题,但对价格求和就没...
In [1]: dates = pd.date_range('1/1/2000', periods=8) In [2]: df = pd.DataFrame(np.random.randn(8, 4), ...: index=dates, columns=['A', 'B', 'C', 'D']) ...: In [3]: df Out[3]: A B C D 2000-01-01 0.469112 -0.282863 -1.509059 -1.135632 2000-01-02 1.212112...
RangeIndex(start=0, stop=1000000, step=1) >>> s.index.memory_usage# in bytes 128# the same as for Series([0.]) 现在,如果我们删除一个元素,索引隐式地转换为类似于dict的结构,如下所示: >>>s.drop(1,inplace=True) >>>s.index
grouped=df.groupby('key1') grouped['data1'].quantile(0.9)# 0.9分位数 1. 2. 3. key1 a 1.037985 b 0.995878 Name: data1, dtype: float64 1. 2. 3. 4. To use your own aggregation functions, pass any function that aggregates an array to theaggregateoraggmethod ...
In [74]: df.groupby(["A","B"]).sum().reset_index() Out[74]: A B C D 0 bar one0.254161 1.511763 1 bar three 0.215897 -0.990582 2 bar two -0.077118 1.211526 3 foo one -0.983776 1.614581 4 foo three -0.862495 0.024580 5 foo two 0.049851 1.185429 ...
titanic_df.groupby(['sex', 'class'])['survived'].aggregate('mean').unstack() classFirstSecondThird sex female 0.968085 0.921053 0.500000 male 0.368852 0.157407 0.135447 Using pivot_table titanic_df.pivot_table(values='survived', index='sex', columns='class') classFirstSecondThird sex female ...