官方文件

pandas.pivot_table(data, values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All')

data : 建立透視表的dataframe

values : 要聚合的值, optional

index : 要聚合的index

columns : 要聚合的columns

aggfunc :聚合的方式, default numpy.mean

fill_value : 用來替換透視表的缺失值scalar, default None

margins : 新增所有行，列，例如在後面加個“總和”boolean, default False

dropna : 不要包含條目都是NaN的列boolean, default True

margins_name : 在邊距為True時將包含總計的行/列的名稱。string, default ‘All’

例子：

1、各種index，column寫與不寫

 df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
...                          "bar", "bar", "bar", "bar"
 
],
...                    "B": ["one", "one", "one", "two", "two",
...                          "one", "one", "two", "two"],
...                    "C": ["small", "large", "large", "small",
...                          "small", "large", "small", "small",
...                          "large"],
...                    "D"
 
: [1, 2, 2, 3, 3, 4, 5, 6, 7]})
>>> df

table1 = pd.pivot_table(df, values='D', index=['A', 'B'],columns=['C'],
...                     aggfunc=np.sum)
table1

table2 = pd.pivot_table(df, values='D', index=['A', 'B'],
...                     aggfunc=np.sum)
table2

table3 = pd.pivot_table(df, values='D',columns=['C'],
...                     aggfunc=np.sum)
table3

2、其他引數

margins

table4 = pd.pivot_table(df, values='D', index=['A', 'B'],columns=['C'],
...                     aggfunc=np.sum,margins=True)
table4

margins_name

table5 = pd.pivot_table(df, values='D', index=['A', 'B'],columns=['C'],
...                     aggfunc=np.sum,margins=True,margins_name='total')
table5

fill_value

 df['D'][2:6] = None

table6 = pd.pivot_table(df, values='D', index=['A', 'B'],columns=['C'],
...                     aggfunc=np.sum)
table6

table6 = pd.pivot_table(df, values='D', index=['A', 'B'],columns=['C'],
...                     aggfunc=np.sum,fill_value="x")
table6