创建具有多个可变条件的新数据框并提取失败的原因-pandas

Question

我有以下代码。

考虑到以下限制，我想创建如下输出：

A > 5, B > 4, C > 3

如果不满足条件，我想读取数据框中的以下行，存储数据，并创建一个名为“失败原因”的新列，其中列出 A、B 或 C 是​​否失败。

然后我希望脚本还报告传递的数据帧行的“X”、“Y”和“Z”的相应值。

此后，脚本应按“组”分组并显示每组的最大“Hs”。

我真的很努力在我的数据框中使用多个变量来完成这项工作......任何帮助将不胜感激

期望输出

   Group   Hs Fail Reason    X    Y     Z
0      1  1.0      [A, B]  0.9  1.9  0.54
1      2  0.5   [A, B, C]  0.8  2.7  0.43

主要代码- 我目前的尝试

import pandas as pd

data = [[1,0.5,8,8,8,0.85,1.64,0.5],
        [1,1,8,8,8,0.9,1.9,0.54],
        [1,1.5,0,0,10,1.1,2.0,0.74],
        [2,0.5,6,5,4,0.8,2.7,0.43],
        [2,1,1,1,1,0.9,2.9,0.45],
        [2,1.5,1,2,1,1.1,3.1,0.47]]

columns = ['Group', 'Hs', 'A', 'B', 'C', 'X', 'Y', 'Z']

df = pd.DataFrame(data=data, columns=columns)

Limit_A = 5
Limit_B = 4
Limit_C = 3

# Opens an empty dataframe for appending
df_new = pd.DataFrame(columns=['Group', 'Hs'])

groups = df['Group'].unique()

# for g in groups
for g in groups:
    # Create new temp dataframe
    df_1 = df[df['Group'] == g]
    # Input conditions, checks the columns one by one are NOT EQUAL TO ZERO. Outputs boolean values.
    pass_criteria = (df_1['A'] > Limit_A) & (df_1['B'] > Limit_B) & (df_1['C'] > Limit_C)

    # PASSES DATAFRAME. Locates rows where the conditions of mask_1 are SATISFIED and creates another temp dataframe.
    df_passes = df_1.loc[pass_criteria]

    # Find the max value in the dataframe e.g. the greatest operational wave height
    max_num = df_passes['Hs'].max()

    # Does the opposite of mask_1
    fail_criteria = (df_1['A'] < Limit_A) & (df_1['B'] < Limit_B) &(df_1['C'] < Limit_C)

    # FAILED DATAFRAME. Locates rows where the conditions of pass_criteria are SATISFIED and creates another temp dataframe.
    df_fails = df_1.loc[fail_criteria]

    # Uses the dataframe with FAIL and mkes the value_vars rows in the melted dataframe
    melted = pd.melt(df_fails, value_vars=['A', 'B', 'C'])

    # Pulls out the reason for fails, i.e. when the condition of the df_fail is not met. Set creates a list of unique values.
    fails = list(set(melted[melted['value'] > Limit_A]['variable']))

    # Input columns of desired outputs.
    df_e = pd.DataFrame(columns=['Group', 'Hs', 'Fail Reason'])

    # Inputs the lists as defined above.
    df_e.loc[0] = [g, max_num, fails]

    # Appends to the dataframe in a loop
    df_new = df_new.append(df_e)

print(df_new)

Answer 1

IIUC 首先将 A、B、C 列与您的限制进行比较，然后agg ，最后将结果map回来：

res = df[["A","B","C"]]>[5,4,3]

s = (pd.concat([df, (~res[~res.all(1)]).agg(lambda x: res.columns[x].tolist(),
                                              axis=1).rename("Fail reason")], axis=1)
       .dropna().drop_duplicates("Group").set_index("Group")["Fail reason"])

print (df.assign(failed_reason=df["Group"].map(s))
         .loc[res.all(1)].sort_values(["Group", "Hs"])
         .drop_duplicates("Group", keep="last"))

   Group   Hs  A  B  C    X    Y     Z failed_reason
1      1  1.0  8  8  8  0.9  1.9  0.54        [A, B]
3      2  0.5  6  5  4  0.8  2.7  0.43     [A, B, C]