Question

我有一个数据框，想将日期汇总为3列，并在末尾添加一些计算出的列。

数据框列：

cols = ["region_2",
        "trade_flag",
        "trade_target",
        "broker",
        "trade_shares",
        "total_value",
        "commission_in_gbp",
        "IS/Order Start PTA - Realized Cost/Sh",
        "IS/Order Start PTA - Realized Net Cost/Sh",
        "IS/Order Start PTA - Base Bench Price",
        "IS/Order Start PTA - P/L"]

示例输入：

    region_2    trade_flag  trade_target    broker  trade_shares    total_value commission_in_gbp   IS/Order Start PTA - Realized Cost/Sh   IS/Order Start PTA - Realized Net Cost/Sh   IS/Order Start PTA - Base Bench Price   IS/Order Start PTA - P/L    count
0   EMEA    flag1   target1 broker1 3900    39532   0.00406 -0.067  -0.067  10.2037 -261.91 1
1   APAC    flag2   target2 broker2 1700    17232   0.00406 -0.067  -0.067  10.2037 -114.17 1
2   AMER    flag1   target1 broker3 1400    14191   0.00406 -0.067  -0.067  10.2037 -94.02  1
3   EMEA    flag2   target2 broker2 2000    20273   0.00406 -0.067  -0.067  10.2037 -134.31 1

所需的输出：

region_2 | trade_flag | broker | count | total_value | perf | net perf

最后的perf列是加权平均值计算。

我跟随另一个不起作用的示例的代码（KeyError）

df['count'] = 1
df['perf'] = ""
df['net perf'] = ""

wm = lambda x: x['IS/Order Start PTA - Realized Cost/Sh'] * x['trade_shares'] * 10000 / x['IS/Order Start PTA - Base Bench Price'] * x['trade_shares']
wm2 = lambda x: x['IS/Order Start PTA - Realized Net Cost/Sh'] * x['trade_shares'] * 10000 / x['IS/Order Start PTA - Base Bench Price'] * x['trade_shares']

f = {'trade_shares': ['sum'],
     'total_value': ['sum'],
     'count': ['sum'],
     'perf': {'weighted mean' : wm},
     'net perf': {'weighted mean' : wm2}}

df = df.groupby(['region_2', 'trade_flag', 'broker']).agg(f)

df = df[['region_2', 'trade_flag', 'broker', 'count', 'total_value', 'actual', 'net']]

Answer 1

您可以使用pivot_table代替groupby

pivot = pd.pivot_table(
        df,
        index=[
            'region_2',
            'trade_flag',
            'broker',
        ],
        values=[
            'trade_shares',
            'total_value',
            'count',
            'perf',
            'net perf'
        ],
        aggfunc={
            'trade_shares': np.sum,
            'total_value': np.sum,
            'count': np.sum,
            'perf': wm,
            'net perf': wm2
        }
    )

尽管这将有助于查看实际的错误消息和示例输入以查看是否是实际的问题。

Answer 2

您需要GroupBy.apply，因为GroupBy.agg是分别与每一列一起工作的，所以KeyError：

def f(x):
    a =  x['trade_shares'].sum()
    b =  x['total_value'].sum()
    c =  len(x)
    #x['perf'] = x['IS/Order Start PTA - Realized Cost/Sh'] * x['trade_shares'] * 10000 / x['IS/Order Start PTA - Base Bench Price'] * x['trade_shares']
    #x['net perf'] = x['IS/Order Start PTA - Realized Net Cost/Sh'] * x['trade_shares'] * 10000 / x['IS/Order Start PTA - Base Bench Price'] * x['trade_shares']
    return pd.Series([a,b,c], index=['trade_shares','total_value','count'])

df = df.groupby(['region_2', 'trade_flag', 'broker']).apply(f).reset_index()

Python-如何将计算函数与pandas groupby一起使用？

2 个答案: