pandas 用另一个系列的值覆盖（更新）一个熊猫系列？
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Overwriting (updating) a pandas Series with values from another Series?
提问by Dun Peal
I have two pandas Series: serand ovr.
我有两个Pandas系列：ser和ovr。
sercontains objects, and ovris a sparse Seriesof objects and None's. serand ovrshare the same index, and I'd like to overwrite every value of serwith its corresponding value of ovr, unless that corresponding value is None.
ser包含对象，并且ovr是稀疏Series的对象 和None。ser并ovr共享相同的索引，我想ser用其对应的 值覆盖 的每个值ovr，除非对应的值是None。
What's an efficient way to accomplish that?
实现这一目标的有效方法是什么？
回答by Andy Hayden
I recommend using NaN for missing data rather than None(Note: this technique also works with None).
我建议对缺失数据使用 NaN 而不是 None（注意：此技术也适用于 None）。
In [1]: s1 = pd.Series([1, np.nan, 3, 4, 5, np.nan])
In [2]: s2 = pd.Series([7, 2, 3, np.nan, np.nan])
First see that s2 values which are not NaN (or None), these are those which you want to update s1 with:
首先看到不是 NaN（或无）的 s2 值，这些是您想要更新 s1 的值：
In [3]: s2[s2.notnull()]
Out[3]:
0 7
1 2
2 3
dtype: float64
And then you can update the values of s1 with these:
然后你可以用这些更新 s1 的值：
In [4]: s1.update(s2[s2.notnull()])
In [5]: s1
Out[5]:
0 7
1 2
2 3
3 4
4 5
5 NaN
dtype: float64
