pandas 如何使用熊猫按周对数据透视表结果进行分组？
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How to use pandas to group pivot table results by week?
提问by jxn
Below is a snippet of my pivot table output in .csv format after using pandas pivot_table function:
以下是使用 Pandas pivot_table 函数后我的 .csv 格式数据透视表输出的片段：
Sub-Product 11/1/12 11/2/12 11/3/12 11/4/12 11/5/12 11/6/12
GP Acquisitions 164 168 54 72 203 167
GP Applications 190 207 65 91 227 200
GPF Acquisitions 1124 1142 992 1053 1467 1198
GPF Applications 1391 1430 1269 1357 1855 1510
The only thing I need to do now is to use groupby in pandas to sum up the values by week for each Sub Product before I output it to a .csv file.
我现在唯一需要做的就是在将每个子产品输出到 .csv 文件之前，在 Pandas 中使用 groupby 按周汇总每个子产品的值。
Below is the output I want, but it is done in Excel. The first column might not be exactly the same but I am fine with that. The main thing I need to do is to group the days by week such that I can get sum of the data to be by week. (See how the top row has the dates grouped by every 7 days). Hoping to be able to do this using python/pandas. Is it possible?
下面是我想要的输出，但它是在 Excel 中完成的。第一列可能不完全相同，但我对此表示满意。我需要做的主要事情是按周对天进行分组，以便我可以按周获得数据的总和。（查看顶行如何将日期按每 7 天分组）。希望能够使用 python/pandas 来做到这一点。是否可以？
Row Labels 11/4/12 - 11/10/12 11/11/12 - 11/17/12
GP
Acquisitions 926 728
Applications 1092 889
GPF
Acquisitions 8206 6425
Applications 10527 8894
采纳答案by Dan Allan
The tool you need is resample, which implicitly uses groupby over a time period/frequency and applies a function like mean or sum.
您需要的工具是resample，它在一段时间/频率内隐式使用 groupby 并应用平均值或总和等函数。
Read data.
读取数据。
In [2]: df
Out[2]:
Sub-Product 11/1/12 11/2/12 11/3/12 11/4/12 11/5/12 11/6/12
GP Acquisitions 164 168 54 72 203 167
GP Applications 190 207 65 91 227 200
GPF Acquisitions 1124 1142 992 1053 1467 1198
GPF Applications 1391 1430 1269 1357 1855 1510
Set up a MultiIndex.
设置多索引。
In [4]: df = df.reset_index().set_index(['index', 'Sub-Product'])
In [5]: df
Out[5]:
11/1/12 11/2/12 11/3/12 11/4/12 11/5/12 11/6/12
index Sub-Product
GP Acquisitions 164 168 54 72 203 167
Applications 190 207 65 91 227 200
GPF Acquisitions 1124 1142 992 1053 1467 1198
Applications 1391 1430 1269 1357 1855 1510
? ?? Parse the columns as proper datetimes. (They come in as strings.)
? ?? 将列解析为正确的日期时间。（它们以字符串形式出现。）
In [6]: df.columns = pd.to_datetime(df.columns)
In [7]: df
Out[7]:
2012-11-01 2012-11-02 2012-11-03 2012-11-04 \
index Sub-Product
GP Acquisitions 164 168 54 72
Applications 190 207 65 91
GPF Acquisitions 1124 1142 992 1053
Applications 1391 1430 1269 1357
2012-11-05 2012-11-06
index Sub-Product
GP Acquisitions 203 167
Applications 227 200
GPF Acquisitions 1467 1198
Applications 1855 1510
Resample the columns (axis=1) weekly ('w'), summing by week. (how='sum'or how=np.sumare both valid options here.)
axis=1每周 ( 'w')对列 ( ) 重新采样，按周求和。（how='sum'或者how=np.sum在这里都是有效的选项。）
In [10]: df.resample('w', how='sum', axis=1)
Out[10]:
2012-11-04 2012-11-11
index Sub-Product
GP Acquisitions 458 370
Applications 553 427
GPF Acquisitions 4311 2665
Applications 5447 3365
