我Pandas.DataFrame
通过以下CSV生成了一个:
Category,Brand,Product Name,Price,Expiration Date, Package ID,Quantity
Cat1,Brand1,Product1,$1000,07/14/2020,XXXXXX,34
我正在尝试在CSV后面添加一列,每行中都有一个整数,对应于到期日期有多短(4
指示大于6个月,3
指示3到6个月之间,依此类推)。
我的问题是,当尝试将Expiration Date
列转换为日期时间(使用pandas.to_datetime(df['Expiration Date'])
),然后应用我的classify_expiration()
函数时,类型要么与函数指示的内容不匹配,要么尝试将函数应用于index 0
我认为是标头的函数(并且因此与%m/%d/%Y
格式不匹配)。我试图在分类函数内以及.apply()
调用之前将其转换为datetime 。我还尝试使用timedelta
比较到期日期和今天的当前日期,但是不适用于datetime.date.today()
。
这是我尝试的第一种方式:
def classify_expiration(row):
one_week = timedelta(weeks=1, days=0, hours=0, minutes=0, seconds=0)
if ((one_week * 0) <= (date.today() - row['Expiration Date']) <= (one_week * 4)):
return 4
这种方式给我有关类型错误的错误,这些错误是index 0
不能正确使用或不能将功能应用于系列的。
这是我刚刚尝试过的,给了我一个AssertionError
:
def days_between(date1, date2):
"""Calculates the number of days between two dates
Keyword arguments:
date1 -- The first date in the subtraction.
date2 -- The second date in the subtraction.
"""
date1 = datetime.strptime(date1, '%m/%d/%Y')
date2 = datetime.strptime(date2, '%m/%d/%Y')
return abs((date2 - date1).days)
def classify_expiration(row):
"""Calculate days/weeks to expiration. Assign quartile based on value.
Keyword arguments:
row -- row in a `pandas.core.frame.DataFrame` object. e.g. `df['A']`
"""
date_today = datetime.strptime(
date.today().strftime('%m/%d/%Y'), '%m/%d/%Y')
if (days_between(row, date_today) <= 30):
return 4
if (31 <= days_between(row, date_today) <= 90):
return 3
if (91 <= days_between(row, date_today) <= 120):
return 2
if (days_between(row, date_today) >= 121):
return 1
这是我尝试应用该功能的地方:
# Convert column to `datetime` if its current type is str
pd.to_datetime(product_sales['Expiration Date'])
# Applying the `classify_expiration()` function
product_sales['Expiration Quartile'] = product_sales.apply(
lambda row: classify_expiration(row), axis=1
)
我希望该函数向DataFrame追加一个新列,该列包含每行中到期日期的生成的四分位数。我会得到其范围从错误中AssertionError
,argument 1 must be str, not Series
以及与其他各种错误index 0
。
days_between
如果分配回去product_sales['Expiration Date'] = pd.to_datetime(product_sales['Expiration Date'])
,则需要在函数中删除转换为日期时间,然后product_sales['Expiration Date'].apply(classify_expiration)
按标量用于循环:
def days_between(date1, date2):
"""Calculates the number of days between two dates
Keyword arguments:
date1 -- The first date in the subtraction.
date2 -- The second date in the subtraction.
"""
return abs((date2 - date1).days)
product_sales['Expiration Date'] = pd.to_datetime(product_sales['Expiration Date'])
product_sales['Expiration Quartile'] = (product_sales['Expiration Date']
.apply(classify_expiration))
print (product_sales)
Category Brand Product Name Price Expiration Date Package ID Quantity \
0 Cat1 Brand1 Product1 $1000 2020-07-14 XXXXXX 34
Expiration Quartile
0 1
Pandas具有binnig的特殊功能,因此可以使用cut
以下功能:
product_sales['Expiration Date'] = pd.to_datetime(product_sales['Expiration Date'])
product_sales['Expiration Quartile'] = (product_sales['Expiration Date']
.apply(classify_expiration))
s = product_sales['Expiration Date'].sub(pd.to_datetime('today').floor('d')).dt.days
product_sales['Expiration Quartile1'] = pd.cut(s,
bins=[0, 30, 90,120, np.inf],
labels=[4,3,2,1])
print (product_sales)
Category Brand Product Name Price Expiration Date Package ID Quantity \
0 Cat1 Brand1 Product1 $1000 2020-07-14 XXXXXX 34
1 Cat1 Brand1 Product1 $1000 2020-01-13 XXXXXX 34
2 Cat1 Brand1 Product1 $1000 2019-11-01 XXXXXX 34
3 Cat1 Brand1 Product1 $1000 2020-01-15 XXXXXX 34
Expiration Quartile Expiration Quartile1
0 1 1
1 3 3
2 4 4
3 2 2
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