pandas 如何将 DatetimeIndexResampler 转换为 DataFrame？
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How to convert DatetimeIndexResampler to DataFrame?
提问by displayname
I want to build a matrix from series but before that I have to resample those series. However, to avoid processing the whole matrix twice with replace(np.nan, 0.0)I want to append the dataframes to a collecting dataframe and then remove NaNvalues in one pass.
我想从系列构建一个矩阵，但在此之前我必须重新采样这些系列。但是，为了避免处理整个矩阵两次，replace(np.nan, 0.0)我想将数据帧附加到收集数据帧，然后NaN一次性删除值。
So instead of
所以代替
user_activities = user.groupby(["DOC_ACC_DT", "DOC_ACTV_CD"]).agg("sum")["SUM_DOC_CNT"].unstack().resample("1D").replace(np.nan, 0)
df = df.append(user_activities[activity].rename(user_id))
I want
我想要
user_activities = user.groupby(["DOC_ACC_DT", "DOC_ACTV_CD"]).agg("sum")["SUM_DOC_CNT"].unstack().resample("1D")
df = df.append(user_activities[activity].rename(user_id))
but that is not working because user_activitiesis not a dataframe after resample().
但这不起作用，因为user_activities在resample().
The error suggests that I try apply()but that method expects a parameter:
该错误表明我尝试apply()但该方法需要一个参数：
/usr/local/lib/python2.7/dist-packages/pandas/core/groupby.pyc in _make_wrapper(self, name)
507 "using the 'apply' method".format(kind, name,
508 type(self).__name__))
--> 509 raise AttributeError(msg)
510
511 # need to setup the selection
AttributeError: Cannot access callable attribute 'rename' of 'SeriesGroupBy' objects, try using the 'apply' method
How can I solve this issue?
我该如何解决这个问题？
回答by scls
The interface to .resamplehas changed in Pandas 0.18.0 to be more groupby-like and hence more flexible ie resampleno longer returns a DataFrame: it's now "lazyly evaluated" at the moment of the aggregation or interpolation.
.resamplePandas 0.18.0 中的接口已更改为更像 groupby，因此更灵活，即resample不再返回 DataFrame：它现在在聚合或插值时“惰性求值”。
I suggest reading resample API changes http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#resample-api
我建议阅读重采样 API 更改http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#resample-api
See also:
也可以看看：
http://pandas.pydata.org/pandas-docs/stable/timeseries.html#resampling
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.resample.html
http://pandas.pydata.org/pandas-docs/stable/timeseries.html#resampling
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.resample.html
for upscaling
升级
df.resample("1D").interpolate()
for downscaling
用于缩小规模
using mean
使用平均值
df.resample("1D").mean()
using OHLC
使用 OHLC
ie open high low close values or first maximal minimal last values
即打开高低关闭值或第一个最大最小最后一个值
df.resample("1D").ohlc()
