在Pandas Groupby中查找重叠的开始日期和结束日期

Question

我正在尝试找到重叠何时开始以及何时以以下DF结束。

我能够使用以下代码来确定重叠群集，现在我想找出重叠何时开始，何时结束时，

    d = [
    {'G1': 'A', 'G2': 'A1','Start_Date': '6/1/2020', 'End_Date': '5/31/2022'},
    {'G1': 'B', 'G2': 'A1','Start_Date': '12/1/2020', 'End_Date': '11/30/2021'},
    {'G1': 'B', 'G2': 'B1','Start_Date': '6/1/2020', 'End_Date': '5/31/2021'},
    {'G1': 'Y', 'G2': 'B1','Start_Date': '6/1/2021', 'End_Date': '6/1/2022'},
    {'G1': 'C', 'G2': 'C1','Start_Date': '1/1/2020', 'End_Date': '3/31/2020'},
    {'G1': 'C', 'G2': 'C1','Start_Date': '4/1/2020', 'End_Date': '5/31/2020'},
    {'G1': 'C', 'G2': 'C2','Start_Date': '6/1/2020', 'End_Date': '7/31/2020'},
    {'G1': 'I', 'G2': 'C3','Start_Date': '8/1/2020', 'End_Date': '10/31/2020'},
    {'G1': 'O', 'G2': 'C3','Start_Date': '11/1/2020', 'End_Date': '12/31/2021'},
    {'G1': 'D', 'G2': 'D1','Start_Date': '1/1/2020', 'End_Date': '2/28/2020'},
    {'G1': 'R', 'G2': 'D2','Start_Date': '3/1/2020', 'End_Date': '3/31/2020'},
    {'G1': 'F', 'G2': 'D4','Start_Date': '4/1/2020', 'End_Date': '8/31/2020'},
    {'G1': 'Y', 'G2': 'D4','Start_Date': '8/1/2020', 'End_Date': '10/31/2020'},
    {'G1': 'D', 'G2': 'D4','Start_Date': '11/1/2020', 'End_Date': '12/31/2021'},
]

df = pd.DataFrame(d)
df['Start_Date'] = pd.to_datetime(df['Start_Date'],format='%m/%d/%Y', errors='coerce')
df['End_Date'] = pd.to_datetime(df['End_Date'],format='%m/%d/%Y', errors='coerce')

df['Range'] = df.apply(lambda x: pd.date_range(start=x['Start_Date'], end=x['End_Date']), axis=1)

def determine_cluster(group):
    bucket = pd.DatetimeIndex(['1/1/1900'])

    for x in group:
        if x.isin(bucket).any():
            return [True]*len(group)

        bucket = bucket.append(x)

    return [False]*len(group)

df['Cluster'] = df.groupby(['G2'])['Range'].transform(determine_cluster)
df.drop('Range', axis=1)

它给出以下结果：

但是，我所需的输出是：

comarts：comarts agropby（群集）重叠开始和结束日期

Answer 1

尝试：

def fn(x):
    z = (
        x.apply(
            lambda y: pd.date_range(y["Start_Date"], y["End_Date"]),
            axis=1,
        )
        .explode()
        .sort_values()
    )

    y = z[z.duplicated()]
    y = y[(y.diff() != pd.Timedelta("1d")).cumsum() == 1]

    if len(y) == 0:
        x["Cluster"] = False
    else:
        x["Overlap_Start_Date"] = y.iloc[0]
        x["Overlap_End_Date"] = y.iloc[-1]
        x["Cluster"] = True

    return x


x = df.groupby("G2").apply(fn)
print(x)

打印：

   G1  G2 Start_Date   End_Date Overlap_Start_Date Overlap_End_Date  Cluster
0   A  A1 2020-06-01 2022-05-31         2020-12-01       2021-11-30     True
1   B  A1 2020-12-01 2021-11-30         2020-12-01       2021-11-30     True
2   B  B1 2020-06-01 2021-05-31                NaT              NaT    False
3   Y  B1 2021-06-01 2022-06-01                NaT              NaT    False
4   C  C1 2020-01-01 2020-03-31                NaT              NaT    False
5   C  C1 2020-04-01 2020-05-31                NaT              NaT    False
6   C  C2 2020-06-01 2020-07-31                NaT              NaT    False
7   I  C3 2020-08-01 2020-10-31                NaT              NaT    False
8   O  C3 2020-11-01 2021-12-31                NaT              NaT    False
9   D  D1 2020-01-01 2020-02-28                NaT              NaT    False
10  R  D2 2020-03-01 2020-03-31                NaT              NaT    False
11  F  D4 2020-04-01 2020-08-31         2020-08-01       2020-08-31     True
12  Y  D4 2020-08-01 2020-10-31         2020-08-01       2020-08-31     True
13  D  D4 2020-11-01 2021-12-31         2020-08-01       2020-08-31     True

Answer 2

您可以使用：

# Remove after this line:
df['Range'] = df.apply(lambda x: pd.date_range(start=x['Start_Date'], 
                                               end=x['End_Date']), axis=1)

# Replace by:
def determine_cluster_and_overlap(sr):
    x = pd.Series(np.hstack(sr)).value_counts(sort=False).loc[lambda x: x > 1]
    cluster = any(x > 1)
    start = x.index[0].strftime('%-m/%-d/%Y') if cluster else pd.NaT
    end = x.index[-1].strftime('%-m/%-d/%Y') if cluster else pd.NaT
    return pd.Series({'Cluster': cluster,
                      'Overlap_Start_Date': start,
                      'Overlap_End_Date': end})

df = df.merge(df.pop('Range').groupby(df['G2'])
                .apply(determine_cluster_and_overlap)
                .unstack(level=1), on='G2')

输出：

>>> df
   G1  G2 Start_Date   End_Date Cluster Overlap_Start_Date Overlap_End_Date
0   A  A1 2020-06-01 2022-05-31    True          12/1/2020       11/30/2021
1   B  A1 2020-12-01 2021-11-30    True          12/1/2020       11/30/2021
2   B  B1 2020-06-01 2021-05-31   False                NaT              NaT
3   Y  B1 2021-06-01 2022-06-01   False                NaT              NaT
4   C  C1 2020-01-01 2020-03-31   False                NaT              NaT
5   C  C1 2020-04-01 2020-05-31   False                NaT              NaT
6   C  C2 2020-06-01 2020-07-31   False                NaT              NaT
7   I  C3 2020-08-01 2020-10-31   False                NaT              NaT
8   O  C3 2020-11-01 2021-12-31   False                NaT              NaT
9   D  D1 2020-01-01 2020-02-28   False                NaT              NaT
10  R  D2 2020-03-01 2020-03-31   False                NaT              NaT
11  F  D4 2020-04-01 2020-08-31    True           8/1/2020        8/31/2020
12  Y  D4 2020-08-01 2020-10-31    True           8/1/2020        8/31/2020
13  D  D4 2020-11-01 2021-12-31    True           8/1/2020        8/31/2020