nan

I need to fill the nan value in a dataframe by using the previous available value in a row, as the data are timeseries. Here there is an example: 1 2 3 4 b b nan c c nan d nan d nan nan c What I need is: 1 2 3 4 b b b c c c d d d d d c I know the method fillna from panda, but the methods are only based on columns. I think about doing a transposition and after the use of the fillna method, but I would like to know if there are more efficient way to do that. Use ffill along the first axis. df.ffill(axis=1) 1 2 3 4 0 b b b c 1 c c d d 2 d d d c 来源： https://stackoverflow.com/questions/47087907/how

I have a couple of textboxes, Which return javascript Number values if valid data is in the textboxes, otherwise NaN . I get this strange behavior. When I checked in firebug (both the textboxes are blank): >>> hours NaN >>> minutes NaN >>> minutes == NaN false >>> hours == NaN false >>> hours == minutes false Why is it behaving so? Ray Toal NaN is not equal to anything, not even NaN . Reference at MDN More detailed SO question and answer For the authoritative source, see the ECMAScript 5 Official Specification , sections 11.9.1 and 11.9.3 : 1. If Type(x) is the same as Type(y), then [...] c.

I'd like to winsorize several columns of data in a pandas Data Frame. Each column has some NaN, which affects the winsorization, so they need to be removed. The only way I know how to do this is to remove them for all of the data, rather than remove them only column-by-column. MWE: import numpy as np import pandas as pd from scipy.stats.mstats import winsorize # Create Dataframe N, M, P = 10**5, 4, 10**2 dates = pd.date_range('2001-01-01', periods=N//P, freq='D').repeat(P) df = pd.DataFrame(np.random.random((N, M)) , index=dates) df.index.names = ['DATE'] df.columns = ['one','two','three',

My excel sheet: A B 1 first second 2 3 4 x y 5 z j Python code: df = pd.read_excel (filename, parse_cols=1) return a correct output: first second 0 NaN NaN 1 NaN NaN 2 x y 3 z j If i want work only with second column df = pd.read_excel (filename, parse_cols=[1]) return: second 0 y 1 j I'd have information about empty excel rows (NaN in my df) even if I work only with a specific column. If output loose NaN information it's not ok, for example, for skiprows paramater, etc Thanks For me works parameter skip_blank_lines=False : df = pd.read_excel ('test.xlsx', parse_cols=1, skip_blank_lines=False)

java.lang.Math#min(double, double) : public static double min(double a, double b) { if (a != a) return a; // a is NaN if (a == 0.0d && b == 0.0d && Double.doubleToLongBits(b) == negativeZeroDoubleBits) return b; return (a <= b) ? a : b; } In which case could a != a return true ? It seems that it's when a is NaN, but I can't imagine an example. Could you please provide one? A simple example is double d = Double.NaN; // or double d = 0.0/0.0; // or double d = Double.POSITIVE_INFINITY + Double.NEGATIVE_INFINITY; if (Double.isNaN(a)) { // tests if a != a // do something BTW Double.compare() does

This question already has an answer here: How to test whether something is identically NaN? 3 answers How can I check whether the input value is NaN or not without using the isNaN function? If you can use ECMAScript 6 , you have Object.is : return Object.is(obj, NaN); Otherwise, here is one option, from the source code of underscore.js : // Is the given value `NaN`? _.isNaN = function(obj) { // `NaN` is the only value for which `===` is not reflexive. return obj !== obj; }; Also their note for that function: Note: this is not the same as the native isNaN function, which will also return true

I know what Java Double.NaN is. I have some Java code that produces NaN . // calculate errors delta = m1 + m2 - M; eta = f1 + f2 - F; for (int i = 0; i < numChildren; i++) { epsilon[i] = p[i]*m1+(1-p[i])*m2+q[i]*f1+(1-q[i])*f2-C[i]; } // use errors in gradient descent // set aside differences for the p's and q's float mDiff = m1 - m2; float fDiff = f1 - f2; // first update m's and f's m1 -= rate*delta; m2 -= rate*delta; f1 -= rate*eta; f2 -= rate*eta; for (int i = 0; i < numChildren; i++) { m1 -= rate*epsilon[i]*p[i]; m2 -= rate*epsilon[i]*(1-p[i]); f1 -= rate*epsilon[i]*q[i]; f2 -= rate