[英]how to resample without skipping nan values in pandas
我正在嘗試獲取具有 NaN 值的數據的 10 天聚合。 如果在 10 天持續時間內存在 NaN 值,則 10 天的總和應返回 nan 值。
當我應用以下代碼時,pandas 將 NaN 視為零並返回剩余天數的總和。
dateRange = pd.date_range(start_date, periods=len(data), freq='D')
# Creating a data frame so that the timeseries can handle numpy array.
df = pd.DataFrame(data)
base_Series = pd.DataFrame(list(df.values), index=dateRange)
# Converting to aggregated series
agg_series = base_Series.resample('10D', how='sum')
agg_data = agg_series.values
樣本數據:
2011-06-01 46.520536
2011-06-02 8.988311
2011-06-03 0.133823
2011-06-04 0.274521
2011-06-05 1.283360
2011-06-06 2.556313
2011-06-07 0.027461
2011-06-08 0.001584
2011-06-09 0.079193
2011-06-10 2.389549
2011-06-11 NaN
2011-06-12 0.195844
2011-06-13 0.058720
2011-06-14 6.570925
2011-06-15 0.015107
2011-06-16 0.031066
2011-06-17 0.073008
2011-06-18 0.072198
2011-06-19 0.044534
2011-06-20 0.240080
輸出:
2011-06-01 62.254651
2011-06-11 7.301481
這將使用numpy sum,如果總和中包含nan,則將返回nan
In [35]: s = Series(randn(100),index=date_range('20130101',periods=100))
In [36]: s.iloc[11] = np.nan
In [37]: s.resample('10D',how=lambda x: x.values.sum())
Out[37]:
2013-01-01 6.910729
2013-01-11 NaN
2013-01-21 -1.592541
2013-01-31 -2.013012
2013-02-10 1.129273
2013-02-20 -2.054807
2013-03-02 4.669622
2013-03-12 3.489225
2013-03-22 0.390786
2013-04-01 -0.005655
dtype: float64
為了過濾掉那些含有NaN的日子,我建議您
noNaN_days_only = s.groupby(lambda x: x.date).filter(lambda x: ~x.isnull().any()
其中s
是一個DataFrame
只需應用 agg 函數:
agg_series = base_Series.resample('10D').agg(lambda x: np.nan if np.isnan(x).all() else np.sum(x) )
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