Question

我有一个DataFrame，我需要通过开始和结束日期获取更大的空行序列以供进一步研究。我的索引是一个DatatimeIndex对象，DataFrame看起来像这样：

                           C Instalation  N Serial Number D Register Read  \
Z Ts Read                                                                    
2016-12-25 00:00:00  PT0002000080299561BD   10101516046456              A+   
2016-12-25 00:15:00  PT0002000080299561BD   10101516046456              A+   
2016-12-25 00:30:00  PT0002000080299561BD   10101516046456              A+   
2016-12-25 00:45:00  PT0002000080299561BD   10101516046456              A+   
2016-12-25 01:00:00  PT0002000080299561BD   10101516046456              A+   

                    M Read D Read Unit  
Z Ts Read                               
2016-12-25 00:00:00  0,002         kWh  
2016-12-25 00:15:00  0,002         kWh  
2016-12-25 00:30:00  0,002         kWh  
2016-12-25 00:45:00  0,002         kWh  
2016-12-25 01:00:00  0,002         kWh

NaN值可以分散在列数据框中，没问题。但如果他们是连续的，我会介意的。在这种情况下，我想知道每行至少有一个NaN值，开始和结束index并计算两者之间的范围差异。最后，我希望获得更大的范围。

可以这样做吗？

Answer 1

不确定我理解Q 100％但也许这就是你想要的：

df = pd.DataFrame({"a": [1, 2, 3, np.nan, np.nan, np.nan, 7, 8], "b": [1, 2, 3, np.nan, 5, 6, 7, 8]}

print df

     a    b
0  1.0  1.0
1  2.0  2.0
2  3.0  3.0
3  NaN  NaN
4  NaN  5.0
5  NaN  6.0
6  7.0  7.0
7  8.0  8.0

counts = df.isnull()
counts[~counts] = np.nan
print counts

    a    b
0  NaN  NaN
1  NaN  NaN
2  NaN  NaN
3  1.0  1.0
4  1.0  NaN
5  1.0  NaN
6  NaN  NaN
7  NaN  NaN

runs = counts.cumsum()
print runs

     a    b
0  NaN  NaN
1  NaN  NaN
2  NaN  NaN
3  1.0  1.0
4  2.0  NaN
5  3.0  NaN
6  NaN  NaN
7  NaN  NaN

runs.max(axis=0)

a    3.0
b    1.0
dtype: float64

获取pandas数据帧中的最大连续空行

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