pandas :分组依据和数据透视表的区别 [英] Pandas: group by and Pivot table difference

问题描述

我刚刚开始学习Pandas，想知道pandas groupby和pandas pivot_table函数之间是否有任何区别.谁能帮助我了解他们之间的区别. 帮助将不胜感激.

I just started learning Pandas and was wondering if there is any difference between pandas groupby and pandas pivot_table functions. Can anyone help me understand the difference between them. Help would be appreciated.

推荐答案

pivot_table和groupby都用于聚合数据框.区别仅在于结果的形状.

Both pivot_table and groupby are used to aggregate your dataframe. The difference is only with regard to the shape of the result.

使用pd.pivot_table(df, index=["a"], columns=["b"], values=["c"], aggfunc=np.sum)创建一个表，其中a在行轴上，b在列轴上，并且值是c的总和.

Using pd.pivot_table(df, index=["a"], columns=["b"], values=["c"], aggfunc=np.sum) a table is created where a is on the row axis, b is on the column axis, and the values are the sum of c.

示例:

df = pd.DataFrame({"a": [1,2,3,1,2,3], "b":[1,1,1,2,2,2], "c":np.random.rand(6)})
pd.pivot_table(df, index=["a"], columns=["b"], values=["c"], aggfunc=np.sum)

b         1         2
a                    
1  0.528470  0.484766
2  0.187277  0.144326
3  0.866832  0.650100

使用groupby，将给定的维放入列中，并为这些维的每种组合创建行.

Using groupby, the dimensions given are placed into columns, and rows are created for each combination of those dimensions.

在此示例中，我们创建了一系列值c的总和，并按a和b的所有唯一组合分组.

In this example, we create a series of the sum of values c, grouped by all unique combinations of a and b.

df.groupby(['a','b'])['c'].sum()

a  b
1  1    0.528470
   2    0.484766
2  1    0.187277
   2    0.144326
3  1    0.866832
   2    0.650100
Name: c, dtype: float64

如果我们省略['c']，则与groupby相似.在这种情况下，它将创建一个数据帧(不是一系列数据)，该数据帧是按a和b的唯一值分组的所有剩余列的总和.

A similar usage of groupby is if we omit the ['c']. In this case, it creates a dataframe (not a series) of the sums of all remaining columns grouped by unique values of a and b.

print df.groupby(["a","b"]).sum()
            c
a b          
1 1  0.528470
  2  0.484766
2 1  0.187277
  2  0.144326
3 1  0.866832
  2  0.650100

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