Python Pandas：分组依据和数据透视表的区别
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Pandas: group by and Pivot table difference
提问by user4943236
I just started learning Pandas and was wondering if there is any difference between pandas groupbyand pandas pivot_tablefunctions. Can anyone help me understand the difference between them.
Help would be appreciated.
我刚开始学习 Pandas，想知道pandas groupby和pandas pivot_table函数之间是否有任何区别。谁能帮我理解它们之间的区别。帮助将不胜感激。
采纳答案by David Maust
Both pivot_tableand groupbyare used to aggregate your dataframe. The difference is only with regard to the shape of the result.
双方pivot_table并groupby用于收集您的数据帧。区别仅在于结果的形状。
Using pd.pivot_table(df, index=["a"], columns=["b"], values=["c"], aggfunc=np.sum)a table is created where ais on the row axis, bis on the column axis, and the values are the sum of c.
使用pd.pivot_table(df, index=["a"], columns=["b"], values=["c"], aggfunc=np.sum)表格创建，其中a位于行轴上，b位于列轴上，并且值是 的总和c。
Example:
例子：
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
Using groupby, the dimensions given are placed into columns, and rows are created for each combination of those dimensions.
使用groupby，将给定的维度放入列中，并为这些维度的每个组合创建行。
In this example, we create a series of the sum of values c, grouped by all unique combinations of aand b.
在这个例子中，我们创建了一系列的值的总和c，由所有唯一组合进行分组a和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
A similar usage of groupbyis 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 aand b.
的类似用法groupby是如果我们省略['c']. 在这种情况下，它会创建的唯一值进行分组所有剩余列的求和的数据框（不是一个系列）a和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
回答by kyramichel
It's more appropriate to use .pivot_table()instead of .groupby()when you need to show aggregates with both rows and column labels.
它更适合使用.pivot_table()，而不是.groupby()当你需要表现出与行和列标签聚集。
.pivot_table()makes it easy to create row and column labels at the same time and is preferable, even though you can get similar results using .groupby()with few extra steps.
.pivot_table()使同时创建行和列标签变得容易并且更可取，即使您可以通过.groupby()很少的额外步骤获得类似的结果。
