合并多索引数据框列表

Question

我正在尝试将多个结果数据帧从我用 tqdm process_map 调用的函数合并在一起。每个 df 有 1 列、1 个索引和 3 个子索引。

cost_values =(process_map(run_simulation_a0_b0_search, param_list, max_workers=4))

以下是 dfs 的示例：

                        0.01
0.01 Collisions        0.0073125
     Average distance    3.05586
     Minimum distance    0.86763


                           10.0
0.01 Collisions               0
     Average distance  0.423096
     Minimum distance  0.332057

                           0.01
10.0 Collisions        0.00090625
     Average distance    0.445388
     Minimum distance     0.28061

                           10.0
10.0 Collisions               0
     Average distance  0.418373
     Minimum distance   0.29708

我尝试将它们连接起来，但这不起作用，所以我正在尝试合并它们

Answer 1

从一方面来说，我建议您检查输出数据帧的格式。
它使处理变得混乱和缓慢。
根据 pandas 的经验，我总是使用平面数据集 - 1D 或 2D。

无论如何，这里有一个带有处理数据的最小示例的代码：

import pandas as pd
from tabulate import tabulate


def replicate_nested_df(df, a, b, columns):
    # add nested index
    df[''] = a
    df = df.set_index([''] + [columns])
    # add numeric named column
    df = df.rename(columns={0: b})
    return df


def flatten_nested_df(df):
    # flatten and save simulation parameter a and b from nested structure
    b = df.columns.values.tolist()[0]
    df = df.reset_index()
    a = df.iloc[0, :]['']

    # rename and drop columns
    df = df.rename(columns={"level_1": "feature"})
    df = df.rename(columns={b: "values"})
    df = df[["feature", "values"]]

    # transpose data
    df = df.set_index(["feature"])
    df = df.transpose().reset_index(drop=True)
    df.rename_axis('', axis=1)

    # add simulation parameters
    df["a"] = a
    df["b"] = b

    return df


# create mockup dataframes
columns = ["Collisions", "Average distance", "Minimum distance"]

df1 = pd.DataFrame([[0.0073125, 3.05586, 0.86763]], columns=columns).transpose()
df1 = replicate_nested_df(df1, a=0.01, b=0.01, columns=columns)

df2 = pd.DataFrame([[0.003, 3.2, 0.8]], columns=columns).transpose()
df2 = replicate_nested_df(df2, a=0.01, b=10, columns=columns)

# process each dataframe
df_processed = []
for df_i in [df1, df2]:
    df_processed.append(flatten_nested_df(df_i))

# create unique frame
df_concat = pd.concat(df_processed).reset_index(drop=True)

print("Mockup Input:")
print("df1:\n", df1)
print("df2:\n", df2)
print("Processed and merged dataset:")
print(tabulate(df_concat, headers=df_concat.columns, tablefmt='psql'))

输入：

输出：

+----+--------------+--------------------+--------------------+------+-------+
|    |   Collisions |   Average distance |   Minimum distance |    a |     b |
|----+--------------+--------------------+--------------------+------+-------|
|  0 |    0.0073125 |            3.05586 |            0.86763 | 0.01 |  0.01 |
|  1 |    0.003     |            3.2     |            0.8     | 0.01 | 10    |
+----+--------------+--------------------+--------------------+------+-------+