Python 基于多列分组的数据帧分割

我有这种数据帧，我会在多个列中分割成多个具有唯一值的数据帧。 DF:

我可以基于一列代码来完成这项工作，代码是

df_list=[d for，d在df.groupby（['a']）]]

我能够通过以下方式完成我想要的操作：

for df in df_list:
    df["e"] = df.apply(lambda x: df.loc[x.name+1:,"c"].mean(),axis=1)

输出

df_list
[       a      b  c  d    e
 2  black   grey  0  0  2.0
 5  black  brown  2  8  NaN,
        a      b  c  d    e
 1  brown    red  4  5  NaN,
        a      b  c  d    e
 4  green   blue  0  3  NaN,
       a       b  c  d    e
 0    red  green  1  2  5.0
 3    red   blue  6  1  4.0
 6    red   grey  4  6  NaN]

但是如何处理多个列呢

“红色”值的预期结果：

---

您可以提取

a

和

b

列的唯一值，并将每个列用作筛选器。比如说,

import pandas as pd

df = pd.DataFrame(
    [
        ["red", "green", 1, 2],
        ["brown", "red", 4, 5],
        ["black", "grey", 0, 0],
        ["red", "blue", 6, 1],
        ["green", "blue", 0, 3],
        ["black", "brown", 2, 8],
        ["red", "grey", 4, 6],
    ],
    columns=["a", "b", "c", "d"]
)

colors = pd.unique(df[['a', 'b']].values.ravel('K'))

>>> colors
    array(['red', 'brown', 'black', 'green', 'grey', 'blue'], dtype=object)

迭代每种颜色，并在过滤后对生成的

当前_df

执行操作

df_list = []
for color in colors:
    current_df = df[(df.a == color) | (df.b == color)].copy().reset_index(drop=True)
    current_df["e"] = current_df.apply(
        lambda x: (
            current_df[(current_df.a == color)].loc[x.name + 1 :, "c"].sum()
            + current_df[(current_df.b == color)].loc[x.name + 1 :, "d"].sum()
        )
        / (current_df.shape[0] - x.name - 1),
        axis=1
    )
    df_list.append(current_df)

（current_-df.shape[0]-x.name-1）

成为添加的值的数目，因为

x.name

是行号，

current_-df.shape[0]

是当前过滤的

df

的总行数。这相当于：

df_list = []
for color in colors:
    current_df = df[(df.a == color) | (df.b == color)].copy()
    current_df["e"] = current_df.apply(
        lambda x: (
            current_df[(current_df.a == color)].loc[x.name + 1 :, "c"].sum()
            + current_df[(current_df.b == color)].loc[x.name + 1 :, "d"].sum()
        )
        / (
            current_df[(current_df.a == color)].loc[x.name + 1 :, "c"].size
            + current_df[(current_df.b == color)].loc[x.name + 1 :, "d"].size
        ),
        axis=1,
    )
    df_list.append(current_df)

红色的结果：

>>> df_list[0]
           a      b  c  d    e
    0    red  green  1  2  5.0
    1  brown    red  4  5  5.0
    3    red   blue  6  1  4.0
    6    red   grey  4  6  NaN

---

伟大的但有一个问题，它计算列“c”中的值，但列“d”中有一个“red”值，因此第一行的结果应该是5+4+6，而不是4+6+4。你是对的。让我更正我的答案。现在检查一下，@charlesalakissgreat！非常感谢你！

df_list = []
for color in colors:
    current_df = df[(df.a == color) | (df.b == color)].copy()
    current_df["e"] = current_df.apply(
        lambda x: (
            current_df[(current_df.a == color)].loc[x.name + 1 :, "c"].sum()
            + current_df[(current_df.b == color)].loc[x.name + 1 :, "d"].sum()
        )
        / (
            current_df[(current_df.a == color)].loc[x.name + 1 :, "c"].size
            + current_df[(current_df.b == color)].loc[x.name + 1 :, "d"].size
        ),
        axis=1,
    )
    df_list.append(current_df)

>>> df_list[0]
           a      b  c  d    e
    0    red  green  1  2  5.0
    1  brown    red  4  5  5.0
    3    red   blue  6  1  4.0
    6    red   grey  4  6  NaN