Python 在熊猫中按周分组
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group by week in pandas
提问by Ram
I'm having this data frame:
我有这个数据框：
Name Date Quantity
Apple 07/11/17 20
orange 07/14/17 20
Apple 07/14/17 70
Orange 07/25/17 40
Apple 07/20/17 30
I want to aggregate this by Name and Date to get sum of quantities Details:
我想按名称和日期汇总它以获得数量总和详细信息：
Date: Group, the result should be at the beginning of the week (or just on Monday)
日期：组，结果应该是在一周的开始（或就在星期一）
Quantity: Sum, if two or more record have same Name and Date(if falls on same interval)
数量：总和，如果两个或多个记录具有相同的名称和日期（如果属于相同的间隔）
The desired output is given below:
所需的输出如下：
Name Date Quantity
Apple 07/10/17 90
orange 07/10/17 20
Apple 07/17/17 30
orange 07/24/17 40
Thanks in advance
提前致谢
回答by jezrael
First convert column dateto_datetimeand substract one week.
首先转换列dateto_datetime并减去一周。
Then use groupbywith Grouperby W-MONand aggregate sum:
然后使用groupbywith Grouperby W-MON和聚合sum：
df['Date'] = pd.to_datetime(df['Date']) - pd.to_timedelta(7, unit='d')
df = df.groupby(['Name', pd.Grouper(key='Date', freq='W-MON')])['Quantity']
.sum()
.reset_index()
.sort_values('Date')
print (df)
Name Date Quantity
0 Apple 2017-07-10 90
3 orange 2017-07-10 20
1 Apple 2017-07-17 30
2 Orange 2017-07-24 40
回答by Scott Boston
Let's use groupby, resamplewith W-Mon, and sum:
让我们使用groupby，resample与W-Mon和sum：
df.groupby('Name').resample('W-Mon', on='Date').sum().reset_index().sort_values(by='Date')
Output:
输出：
Name Date Quantity
0 Apple 2017-07-17 90
3 orange 2017-07-17 20
1 Apple 2017-07-24 30
2 Orange 2017-07-31 40
回答by LN_P
First convert column date to_datetime. This will group by week starting with Mondays. It will output the week number (but you can change that looking up in
首先将列日期转换为_datetime。这将从星期一开始按周分组。它将输出周数（但您可以在
df.groupby(['name', df['date'].dt.strftime('%W')])['quantity'].sum()
Output:
输出：
name date
apple 28 90
29 30
orange 28 20
30 40
