nan

问题 Is there a way to use ffill method on values that are not NaN ? I have NaN in my dataframe, but I have added these NaN using addNan = sample['colA'].replace(['A'], 'NaN') So this is what my DataFrame, df looks like ColA ColB ColC ColD B A A C NaN B A A C D D A NaN A A B And I'm trying to fill these NaN using ffill , so they are populated by the last known value. fill = df.fillna(method='ffill', inplace = True) This doesn't make a difference, also tried Na instead of NaN 回答1: I think you need

问题 Is there a way to use ffill method on values that are not NaN ? I have NaN in my dataframe, but I have added these NaN using addNan = sample['colA'].replace(['A'], 'NaN') So this is what my DataFrame, df looks like ColA ColB ColC ColD B A A C NaN B A A C D D A NaN A A B And I'm trying to fill these NaN using ffill , so they are populated by the last known value. fill = df.fillna(method='ffill', inplace = True) This doesn't make a difference, also tried Na instead of NaN 回答1: I think you need

问题 I have a list of List of Tuples (string, float) with float('nan') . How can i get the tuple with the smallest number? If I use min I always get the nan . [('GroundBasedMechWT', nan), ('GroundBasedCTL', nan), ('GroundBasedManualWT', nan), ('GroundBasedManualLog', nan), ('CableManualWTLog', 60.77), ('CableManualWT', 58.52), ('CableManualLog', 68.17), ('CableManualCTL', nan), ('HelicopterManualWT', 96.82), ('HelicopterManualCTL', nan)] 回答1: You could also try this: min(filter(lambda t: not math

问题 I have a list of List of Tuples (string, float) with float('nan') . How can i get the tuple with the smallest number? If I use min I always get the nan . [('GroundBasedMechWT', nan), ('GroundBasedCTL', nan), ('GroundBasedManualWT', nan), ('GroundBasedManualLog', nan), ('CableManualWTLog', 60.77), ('CableManualWT', 58.52), ('CableManualLog', 68.17), ('CableManualCTL', nan), ('HelicopterManualWT', 96.82), ('HelicopterManualCTL', nan)] 回答1: You could also try this: min(filter(lambda t: not math

问题 If I run the following code: dft1 = pd.DataFrame({'a':[1, np.nan, np.nan]}) dft2 = pd.DataFrame({'a':[1, 1, np.nan]}) dft1.a==dft2.a The result is 0 True 1 False 2 False Name: a, dtype: bool How can I make the result to be 0 True 1 False 2 True Name: a, dtype: bool I.e., np.nan == np.nan evaluates to True. I thought this is basic functionality and I must be asking a duplicate question, but I spent a lot of time search in SO or in Google and couldn't find it. 回答1: Can't think of a function

问题 I would like to make different tables from different strings. The strings have different lengths, and thus the tables will have different amount of rows. I would like to combine these tables(at the end), and therefore need the tables I have, to have the same amount of rows. My plan is to use NaNs to do this, but yet without success. I have my code attempt here, with where I'm struggling at, marked as "Problem location." Code: String = ["Random info in middle one, "+ ... "Random info still

问题 I would like to make different tables from different strings. The strings have different lengths, and thus the tables will have different amount of rows. I would like to combine these tables(at the end), and therefore need the tables I have, to have the same amount of rows. My plan is to use NaNs to do this, but yet without success. I have my code attempt here, with where I'm struggling at, marked as "Problem location." Code: String = ["Random info in middle one, "+ ... "Random info still

问题 In Python Pandas and Numpy, why is the comparison result different? from pandas import Series from numpy import NaN NaN is not equal to NaN >>> NaN == NaN False but NaN inside a list or tuple is >>> [NaN] == [NaN], (NaN,) == (NaN,) (True, True) While Series with NaN are not equal again: >>> Series([NaN]) == Series([NaN]) 0 False dtype: bool And None : >>> None == None, [None] == [None] (True, True) While >>> Series([None]) == Series([None]) 0 False dtype: bool This answer explains the reasons

问题 In Python Pandas and Numpy, why is the comparison result different? from pandas import Series from numpy import NaN NaN is not equal to NaN >>> NaN == NaN False but NaN inside a list or tuple is >>> [NaN] == [NaN], (NaN,) == (NaN,) (True, True) While Series with NaN are not equal again: >>> Series([NaN]) == Series([NaN]) 0 False dtype: bool And None : >>> None == None, [None] == [None] (True, True) While >>> Series([None]) == Series([None]) 0 False dtype: bool This answer explains the reasons

问题 In Python Pandas and Numpy, why is the comparison result different? from pandas import Series from numpy import NaN NaN is not equal to NaN >>> NaN == NaN False but NaN inside a list or tuple is >>> [NaN] == [NaN], (NaN,) == (NaN,) (True, True) While Series with NaN are not equal again: >>> Series([NaN]) == Series([NaN]) 0 False dtype: bool And None : >>> None == None, [None] == [None] (True, True) While >>> Series([None]) == Series([None]) 0 False dtype: bool This answer explains the reasons