Pandas xs where 选项列表中的级别答案

【问题标题】：Pandas xs where level in list of optionsPandas xs where 选项列表中的级别
【发布时间】：2022-08-15 02:13:23
【问题描述】：

如果我有一个看起来像这样的 pd.DataFrame：

new_df = []
for i in range(10):
    df_example = pd.DataFrame(np.random.normal(size=[10,1]))
    cols = [round(np.random.uniform(low=0,high=10)),round(np.random.uniform(low=0,high=10)),
        round(np.random.uniform(low=0,high=10)),round(np.random.uniform(low=0,high=10))]
    keys = [\'A\',\'B\',\'C\',\'D\']
    new_ix = pd.MultiIndex.from_tuples([cols],names=keys)
    df_example.columns = new_ix
    new_df.append(df_example)
new_df = pd.concat(new_df,axis=1)

这可能会产生类似的东西：

现在，如果我想在哪里 C=4 和 A=1 我可以这样做：

df.xs(axis=1,level=[\'A\',\'C\'],key=[1,4])

如果我愿意，我该如何表达：

C in [4,2] 和 A in [5,2]
C in [4,2] 或 A in [5,2]

标签： pandas multi-index

【解决方案1】：

据我所知，xs 中的关键参数只能使用元组，因此无法进行此类查询。

下一个最好的事情是为此目的定义辅助函数，例如：

def xs_or(df: pd.DataFrame, params: dict[str, list[int]]) -> pd.DataFrame:
    """Helper function.

    Args:
        df: input dataframe.
        params: columns/values to query.

    Returns:
        Filtered dataframe.

    """
    df = pd.concat(
        [
            df.xs(axis=1, level=[level], key=(key,))
            for level, keys in params.items()
            for key in keys
        ],
        axis=1,
    )
    for level in params.keys():
        try:
            df = df.droplevel([level], axis=1)
        except KeyError:
            pass
    return df


def xs_and(df: pd.DataFrame, params: dict[str, list[int]]) -> pd.DataFrame:
    """Helper function.

    Args:
        df: input dataframe.
        params: columns/values to query.

    Returns:
        Filtered dataframe.

    """
    for level, keys in params.items():
        df = xs_or(df, {level: keys})
    return df

因此，使用以下名为 df 的数据框：

A         4         7         3         1         7         9         4         0         3         9
B         6         7         4         6         7         5         8         0         8         0
C        2         10        5         2         9         9         4         3         4         5 
D        0         1         7         3         8         3         6         7         9         10
0 -0.199458  1.155345  1.298027  0.575606  0.785291 -1.126484  0.019082  1.765094  0.034631 -0.243635
1  1.173873  0.523277 -0.709546  1.378983  0.266661  1.626118  1.647584 -0.228162 -1.708271  0.111583
2  0.321156  0.049470 -0.611111 -1.238887  1.092369  0.019503 -0.473618  1.804474 -0.850320 -0.217921
3  0.339307 -0.758909  0.072159  1.636119 -0.541920 -0.160791 -1.131100  1.081766 -0.530082 -0.546489
4 -1.523110 -0.662232 -0.434115  1.698073  0.568690  0.836359 -0.833581  0.230585  0.166119  1.085600
5  0.020645 -1.379587 -0.608083 -1.455928  1.855402  1.714663 -0.739409  1.270043  1.650138 -0.718430
6  1.280583 -1.317288  0.899278 -0.032213 -0.347234  2.543415  0.272228 -0.664116 -1.404851 -0.517939
7 -1.201619  0.724669 -0.705984  0.533725  0.820124  0.651339  0.363214  0.727381 -0.282170  0.651201
8  1.829209  0.049628  0.655277 -0.237327 -0.007662  1.849530  0.095479  0.295623 -0.856162 -0.350407
9 -0.690613  1.419008 -0.791556  0.180751 -0.648182  0.240589 -0.247574 -1.947492 -1.010009  1.549234

您可以像这样过滤：

# C in [10, 2] or A in [1, 0]
print(xs_or(df, {"C": [10, 2], "A": [1, 0]}))
# Output
B         7         6                   2         3
D         1         0         3         3         7
0  1.155345 -0.199458  0.575606  0.575606  1.765094
1  0.523277  1.173873  1.378983  1.378983 -0.228162
2  0.049470  0.321156 -1.238887 -1.238887  1.804474
3 -0.758909  0.339307  1.636119  1.636119  1.081766
4 -0.662232 -1.523110  1.698073  1.698073  0.230585
5 -1.379587  0.020645 -1.455928 -1.455928  1.270043
6 -1.317288  1.280583 -0.032213 -0.032213 -0.664116
7  0.724669 -1.201619  0.533725  0.533725  0.727381
8  0.049628  1.829209 -0.237327 -0.237327  0.295623
9  1.419008 -0.690613  0.180751  0.180751 -1.947492

# C in [10, 2] and A in [1, 7]
print(xs_and(df, {"C": [10, 2], "A": [1, 7]}))
# Output
B         6         7
D         3         1
0  0.575606  1.155345
1  1.378983  0.523277
2 -1.238887  0.049470
3  1.636119 -0.758909
4  1.698073 -0.662232
5 -1.455928 -1.379587
6 -0.032213 -1.317288
7  0.533725  0.724669
8 -0.237327  0.049628
9  0.180751  1.419008

【讨论】：