Python 大熊猫相当于 np.where
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pandas equivalent of np.where
提问by max
np.wherehas the semantics of a vectorized if/else (similar to Apache Spark's when/otherwiseDataFrame method). I know that I can use np.whereon pandas Series, but pandasoften defines its own API to use instead of raw numpyfunctions, which is usually more convenient with pd.Series/pd.DataFrame.
np.where具有矢量化 if/else 的语义（类似于 Apache Spark 的when/ otherwiseDataFrame 方法）。我知道我可以np.where在 pandas 上使用Series，但pandas经常定义自己的 API 来代替原始numpy函数，这通常更方便使用pd.Series/ pd.DataFrame。
Sure enough, I found pandas.DataFrame.where. However, at first glance, it has a completely different semantics. I could not find a way to rewrite the most basic example of np.whereusing pandas where:
果然，我找到了pandas.DataFrame.where。但是，乍一看，它具有完全不同的语义。我找不到重写np.where使用 pandas 的最基本示例的方法where：
# df is pd.DataFrame
# how to write this using df.where?
df['C'] = np.where((df['A']<0) | (df['B']>0), df['A']+df['B'], df['A']/df['B'])
Am I missing something obvious? Or is pandas whereintended for a completely different use case, despite same name as np.where?
我错过了一些明显的东西吗？或者where，尽管与np.where?
采纳答案by Alex
Try:
尝试：
(df['A'] + df['B']).where((df['A'] < 0) | (df['B'] > 0), df['A'] / df['B'])
The difference between the numpywhereand DataFramewhereis that the default values are supplied by the DataFramethat the wheremethod is being called on (docs).
的之间的差numpywhere和DataFramewhere是，默认值是由所提供的DataFrame是，where正在被调用的方法上（文档）。
I.e.
IE
np.where(m, A, B)
is roughly equivalent to
大致相当于
A.where(m, B)
If you wanted a similar call signature using pandas, you could take advantage of the way method calls work in Python:
如果您想要使用 Pandas 的类似调用签名，您可以利用Python 中方法调用的工作方式：
pd.DataFrame.where(cond=(df['A'] < 0) | (df['B'] > 0), self=df['A'] + df['B'], other=df['A'] / df['B'])
or without kwargs (Note: that the positional order of arguments is different from the numpywhereargument order):
或不kwargs（注：该参数的位置顺序是从不同的numpywhere参数顺序）：
pd.DataFrame.where(df['A'] + df['B'], (df['A'] < 0) | (df['B'] > 0), df['A'] / df['B'])
