In C, NAN is declared in <math.h>. In C++, std::numeric_limits<double>::quiet_NaN() is declared in <limits>. But for checking whether a value is NaN, you can’t compare it with another NaN value. Instead use isnan() from <math.h> in C, or std::isnan() from <cmath> in C++.
Question #1: why is NaN found in a container when it’s an identical object. From the documentation: For container types such as list, tuple, set, frozenset, dict, or collections.deque, the expression x in y is equivalent to any(x is e or x == e for e in y). This is precisely what I observe with … Read more
Because that’s how floating-point is defined (more generally than just Javascript). See for example: http://en.wikipedia.org/wiki/Floating-point#Infinities http://en.wikipedia.org/wiki/NaN#Creation Crudely speaking, you could think of 1/0 as the limit of 1/x as x tends to zero (from the right). And 0/0 has no reasonable interpretation at all, hence NaN.
The problem here is that NaN is not equal to itself, as defined in the IEEE standard for floating point numbers: >>> float(“nan”) == float(“nan”) False When a dictionary looks up a key, it roughly does this: Compute the hash of the key to be looked up. For each key in the dict with the … Read more
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
Where A is your 2D array: import numpy as np A[np.isnan(A)] = 0 The function isnan produces a bool array indicating where the NaN values are. A boolean array can by used to index an array of the same shape. Think of it like a mask.
df = df.fillna(”) or just df.fillna(”, inplace=True) This will fill na’s (e.g. NaN’s) with ”. If you want to fill a single column, you can use: df.column1 = df.column1.fillna(”) One can use df[‘column1’] instead of df.column1.
Here’s a NumPy solution using justify – In [455]: df Out[455]: 0 1 2 0 0.0 1.0 2.0 1 NaN 1.0 2.0 2 NaN NaN 2.0 In [456]: pd.DataFrame(justify(df.values, invalid_val=np.nan, axis=1, side=”left”)) Out[456]: 0 1 2 0 0.0 1.0 2.0 1 1.0 2.0 NaN 2 2.0 NaN NaN If you want to save memory, assign … Read more
The IEEE-754 standard defines a NaN as a number with all ones in the exponent, and a non-zero significand. The highest-order bit in the significand specifies whether the NaN is a signaling or quiet one. The remaining bits of the significand form what is referred to as the payload of the NaN. Whenever one of … Read more
First, extract a slice of columns beginning with item – m = df.columns.str.contains(‘item’) i = df.iloc[:, m] Mask all values which meet your criteria. Use isin – j = i[~i.isin(df.makrc.tolist() + [‘not’])] Now. sort values based on NaNs and assign back – df.loc[:, m] = j.apply(sorted, key=pd.isnull, axis=1) df key sellyr brand makrc item1 item2 … Read more