In [6]: ser.isnull() Out[6]: 0 False 1 False 2 True 3 False dtype: bool 在DataFrames 上测试: In [7]: df = pd.DataFrame({'A': [1, np.nan, 3], 'B': [np.nan, 5, 6]}) In [8]: print(df) Out[8]: A B 0 1.0 NaN 1 NaN 5.0 2 3.0 6.0 In [9]: df.isnull(...
The dataframe has some faulty entries, where each and every article_name that belongs to one order is empty. What I'm trying to do is filter out all rows which have an order_id for which every row with the same order_id has an empty string in place of the article_...
You can load this in Python as a Pandas DataFrame or something (see here for an example of using pd.read_csv() on this), and then you can create a new column, raw_ratio, as follows: import math # You may need to filter out samples with a NaN or null log-ratio first sample_...
Inferring the type of a column in pandas in general seems complex to impossible. E.g. unstack-ing an int64 dtype that introduces NaNs (missing data) will convert the int64 to float64. We can't really prove anything other than the return value being DataFrame. Even with mypy plugins this...