Python Pandas:获取列不为null的DataFrame的行 [英] Python Pandas: get rows of a DataFrame where a column is not null

问题描述

我正在过滤我的DataFrame，删除其中特定列的单元格值为无"的那些行.

I'm filtering my DataFrame dropping those rows in which the cell value of a specific column is None.

df = df[df['my_col'].isnull() == False]

工作正常，但是PyCharm告诉我:

Works fine, but PyCharm tells me:

PEP8:与False的比较应为如果cond为False:"或如果非cond:"

PEP8: comparison to False should be 'if cond is False:' or 'if not cond:'

但是我不知道如何将其应用于我的用例?使用"not ..."或"is False"无效.我当前的解决方案是:

But I wonder how I should apply this to my use-case? Using 'not ...' or ' is False' did not work. My current solution is:

df = df[df['my_col'].notnull()]

推荐答案

因此python具有短路逻辑运算符not，and，or.这些在python中具有非常特定的含义，不能被覆盖(not必须返回bool，而a and/or b始终返回a或b或抛出错误.

So python has the short-circuiting logic operators not, and, or. These have a very specific meaning in python and cannot be overridden (not must return a bool and a and/or b always returns either a or b or throws an error.

但是，python还具有可重载的布尔运算符~(不是)，&(和)，|(或)和^(xor).

However, python also has over-loadable boolean operators ~ (not), & (and), | (or) and ^ (xor).

您可能会认识到它们是int逐位运算符，但是Numpy(因此是熊猫)使用它们来执行数组/系列布尔运算.

You may recognise these as the int bitwise operators, but Numpy (and therefore pandas) use these to do array / series boolean operations.

例如

b = np.array([True, False, True]) & np.array([True, False, False])
# b --> [True False False]
b = ~b 
# b --> [False True True]

因此，您想要的是

df = df[~df['my_col'].isnull()]

我同意PEP8，不同意== False.

I agree with PEP8, don't do == False.

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