You can use assert_frame_equals with check_names=False (so as not to check the index/columns names), which will raise if they are not equal: In [11]: from pandas.testing import assert_frame_equal In [12]: assert_frame_equal(df, expected, check_names=False) You can wrap this in a function with something like: try: assert_frame_equal(df, expected, check_names=False) return True except AssertionError: return False In more … Read more
From my investigations, d3.min is supposed to work on any kind of orderable values, not only numbers. isNaN would only work numbers. d3 was actually using == at some point. This commit introduced the x == x test: Unlike Math.min and Math.max, it doesn’t make sense to return negative or positive infinity for d3.min and … Read more
if(val1 == val2 || (isNaN(val1) && isNaN(val2))) Nothing to improve. Just add the parentheses to make it clear to everyone.
My approach is similar to @hellpanderrr’s, but instead tests for list-ness rather than using isnan: df[‘ids’] = df[‘ids’].apply(lambda d: d if isinstance(d, list) else []) I originally tried using pd.isnull (or pd.notnull) but, when given a list, that returns the null-ness of each element.
Lets define first a simple helper function in order to make it more straightforward to handle indices and logical indices of NaNs: import numpy as np def nan_helper(y): “””Helper to handle indices and logical indices of NaNs. Input: – y, 1d numpy array with possible NaNs Output: – nans, logical indices of NaNs – index, … Read more
isNaN() and Number.isNaN() both test if a value is (or, in the case of isNaN(), can be converted to a number-type value that represents) the NaN value. In other words, “NaN” does not simply mean “this value is not a number”, it specifically means “this value is a numeric Not-a-Number value according to IEEE-754″. The … Read more
Strict answer: Because the JS spec says so: If Type(x) is Number, then If x is NaN, return false. If y is NaN, return false. Useful answer: The IEEE 754 spec for floating-point numbers (which is used by all languages for floating-point) says that NaNs are never equal.
Short answer Sometimes missing values are simply not applicable. Imputing them is meaningless. In these cases you should use a model that can handle missing values. Scitkit-learn’s models cannot handle missing values. XGBoost can. More on scikit-learn and XGBoost As mentioned in this article, scikit-learn’s decision trees and KNN algorithms are not (yet) robust enough … Read more
Try this: In [107]: pd.isnull(df.iloc[1,0]) Out[107]: True UPDATE: in a newer Pandas versions use pd.isna(): In [7]: pd.isna(df.iloc[1,0]) Out[7]: True
The reason why your code doesn’t work is because Math.max is expecting each parameter to be a valid number. This is indicated in the documentation as follows: If at least one of arguments cannot be converted to a number, the result is NaN. In your instance you are only providing 1 argument, and that 1 … Read more