根据日期列筛选和创建列

Question

我有一个示例数据如下：

date         Deadline
2018-08-01   
2018-08-11
2018-09-18
2018-12-08
2018-12-18

我想在deadline一栏填写代码中描述的条件为“1 DL”、“2 DL”、“3 DL”等。

正在根据 python 中的日期列创建新列。

报错：

('The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().', 'occurred at index 0')

我试过如下：

df['date'] = pd.to_datetime(df['date'], format = "%y-%m-%d").dt.date


def dead_line(df5):

    if((df5['date'] >= datetime.date(2018, 8, 1)) & (df['date'] <= datetime.date(2018, 9, 14))):

        return "1 DL"

    elif ((df5['date'] >= datetime.date(2018, 9, 15)) & (df5['date'] <= datetime.date(2018, 10, 17))):

        return "2 DL"

    elif ((df5['date'] >= datetime.date(2018, 10, 18)) & (df5['date'] <= datetime.date(2018, 12, 5))):

        return "3 DL"

    elif ((df5['date'] >= datetime.date(2018, 12, 6)) & (df5['date'] <= datetime.date(2019, 2, 1))):

        return "4 DL & EDL 2"


df['Deadline'] = df.apply(dead_line, axis = 1)

预期输出：

date         Deadline
2018-08-01   1 DL
2018-09-16   2 DL
2018-12-07   3 DL

等等。

Answer 1

使用 `pd.cut` 分类分类

核心问题是您正在尝试按列操作 apply 以及 axis=1。然而 apply 这里需要行操作。

也就是说，对于 Pandas，您最好使用向量化的逐列运算。所以不要使用 apply，而是使用向量化的 pd.cut。另请注意，无需求助于 Python datetime.

# convert series to datetime
df['date'] = pd.to_datetime(df['date'])

# remember to include arbitrary lower and upper boundaries
L = ['01-01-2000', '08-01-2018', '09-14-2018', '10-17-2018',
     '12-05-2018', '02-01-2019', '01-01-2100']

# convert boundaries to datetime
dates = pd.to_datetime(L).values

# define labels for boundary ranges
labels = ['Error Lower', '1 DL', '2 DL', '3 DL', '4 DL & EDL 2', 'Error Upper']

# apply categorical binning
df['Deadline'] = pd.cut(df['date'], bins=dates, labels=labels, right=False)

print(df)

#         date      Deadline
# 0 2018-08-01          1 DL
# 1 2018-08-11          1 DL
# 2 2018-09-18          2 DL
# 3 2018-12-08  4 DL & EDL 2
# 4 2018-12-18  4 DL & EDL 2

Answer 2

与上述解决方案不同的解决方案。不要将您的 datetime 转换为 datetime 对象进行比较，而是将其保留为 datetime64，然后将您的过滤函数应用于其他 datetime64 范围：

df['date'] = pd.to_datetime(df['date'], format = "%Y-%m-%d") # leaves as datetime64[ns]

print(df['date'].dtype) #datetime64[ns]


def dead_line(x):

    if (x >= pd.to_datetime('2018-08-01')) & (x <= pd.to_datetime('2018-09-14')):
        return "1 DL"
    elif (x >= pd.to_datetime('2018-09-15')) & (x <=pd.to_datetime('2018-10-17')):
        return "2 DL"
    elif (x >= pd.to_datetime('2018-10-18')) & (x <= pd.to_datetime('2018-12-05')):
        return "3 DL"
    elif (x >=pd.to_datetime('2018-12-06')) & (x <= pd.to_datetime('2019-02-01')):
        return "4 DL & EDL 2"

df['Deadline'] = df['date'].apply(dead_line) # apply your function to column, not whole df
print(df)

输出：

        date      Deadline
0 2018-08-01          1 DL
1 2018-08-11          1 DL
2 2018-09-18          2 DL
3 2018-12-08  4 DL & EDL 2
4 2018-12-18  4 DL & EDL 2

根据日期列筛选和创建列

Filtering and creating a column based on the date column

python

python-3.x

pandas

categorical-data

使用 `pd.cut` 分类分类

根据日期列筛选和创建列

Filtering and creating a column based on the date column

python

python-3.x

pandas

categorical-data

使用 pd.cut 分类分类

使用 `pd.cut` 分类分类