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[英]Python: Returning values of matrix that are in a specific range, range is given as a tuple(from, to)
[英]How to add the values for Specific days in Python Table for a given range?
我有一個數據集(Product_ID,date_time,Sold),其產品在不同日期出售。 這些日期為9個月,每月隨機13天或更長時間。 我必須以這樣的方式分離數據:每種產品每天銷售多少產品1-3天,每天4-7天銷售,每天8-15天銷售,每天銷售> 16天。 那么如何使用pandas和其他包在python中編寫代碼呢?
PRODUCT_ID DATE_LOCATION Sold
0E4234 01-08-16 0:00 2
0E4234 02-08-16 0:00 7
0E4234 07-08-16 0:00 3
0E4234 08-08-16 0:00 1
0E4234 09-08-16 0:00 2
0E4234 10-08-16 0.00 1
.
.
.
0G2342 22-08-16 0:00 1
0G2342 23-08-16 0:00 2
0G2342 26-08-16 0:00 1
0G2342 28-08-16 0:00 1
0G2342 29-08-16 0:00 3
0G2342 30-08-16 0:00 3
.
.
.(goes for 64 products each with 9 months of data)
.
我甚至不知道如何在python中編寫代碼所需的輸出是
PRODUCT_ID Days Sold
0E4234 1-3 9 #(1,2) dates because range is 1 to 3
4-7 7 #(7,8,9,10) dates because range is 4 to 7
8-15 0
>16 0
0G2342 1-3 11 #(22,23),(26),(28,29,30) dates because range is 1 to 3
4-7 0
8-15 0
>16 0
.
.(for 64 products)
.
如果至少有人發布了從哪里開始的鏈接,那將會很高興。 我試過了
df["DATE_LOCATION"] = pd.to_datetime(df.DATE_LOCATION)
df["DAY"] = df.DATE_LOCATION.dt.day
def flag(x):
if 1<=x<=3:
return '1-3'
elif 4<=x<=7:
return '4-7'
elif 8<=x<=15:
return '8-15'
else:
return '>=16'
df["Days"] = df.DAY.apply(flag)
df.groupby(["PRODUCT_ID","Days"]).Sold.sum()
這給了我每個月這幾天之間銷售的產品數量。但我需要指定范圍內的產品總和是產品以指定的條件出售。
對與原始DataFrame
大小相同的Series
使用transform
,使用cut
和aggregate sum
合並:
df['DATE_LOCATION'] = pd.to_datetime(df['DATE_LOCATION'], format='%d-%m-%y %H:%M')
df = df.sort_values("DATE_LOCATION")
s = (df["DATE_LOCATION"].diff().dt.days > 1).cumsum()
count = s.groupby(s).transform('size')
print (count)
0 2
1 2
2 4
3 4
4 4
5 4
6 2
7 2
8 1
9 3
10 3
11 3
Name: DATE_LOCATION, dtype: int32
bins = pd.cut(count, bins=[0,3,7,15,31], labels=['1-3', '4-7','8-15', '>=16'])
df = df.groupby(['PRODUCT_ID', bins])['Sold'].sum().reset_index()
print (df)
PRODUCT_ID DATE_LOCATION Sold
0 0E4234 1-3 9
1 0E4234 4-7 7
2 0G2342 1-3 11
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