## numpy: linspace calculation generating nan. How to remove point pair from both arrays?

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I'm curious if there's an elegant way to solve the problem below, preferably using as few lines of code as possible and is easy to remember, possibly a built-in numpy function?

Let's say I have a function f(x) and I want to be lazy and generate a np.linspace over an x range that purposely generates values of x where f(x) is not defined, so some of the points from my linspace correspond to "nan" in the my f(x) array.

My question is how to filter (or generate two new arrays) that only saves point pairs (x, f(x)), where f(x) != nan?

%matplotlib inline import matplotlib.pyplot as plt import numpy as np x = np.linspace(-2,2,20) with np.errstate(all='ignore'): fx = np.sqrt(x**2 - 1) # here we can see "nan" print(fx) # want something here that removes # (x[i], fx[i]) pair from arrays if x[i] has 'nan' as value # <insert code> plt.plot(x,fx)

There isn't really any need to filter out the `nan`

, they are simply not plotted.

If you want to filter them out, the line gets connected.

plt.plot(x[~np.isnan(fx)],fx[~np.isnan(fx)])

**numpy.linspace — NumPy v1.20.dev0 Manual,** numpy. linspace (start, stop, num=50, endpoint=True, retstep=False, dtype=None , Returns num evenly spaced samples, calculated over the interval [start, stop]. Number of samples to generate. Default Relevant only if start or stop are array-like. Similar to geomspace , but with the end points specified as logarithms. 0 numpy: linspace calculation generating nan. How to remove point pair from both arrays? Mar 11 '19. View all questions and answers → Badges (8) Gold —

You can try this out:

X_ = [(X) for (X,FX) in zip(x,fx) if math.isnan(FX)==False] FX_ = [(FX) for FX in fx if math.isnan(FX)==False]

`plt.plot(X_,FX_)`

**how to delete nan values in python Code Example,** Get code examples like "how to delete nan values in python" instantly right from your google search results with the x = x[~numpy.isnan(x)]. Although we were able to do the calculation we wanted, the code is fairly complex, and it won’t be fun to have to do something similar every time we want to compute a quantity. Luckily, we can use NumPy to make it easier to work with our data. Numpy 2-Dimensional Arrays. With NumPy, we work with multidimensional arrays.

%matplotlib inline import matplotlib.pyplot as plt import numpy as np import pandas as pd x = np.linspace(-2,2,20) #print(x) with np.errstate(all='ignore'): fx = np.sqrt(x**2 - 1) # here we can see "nan" #print(fx) # (x[i], fx[i]) pair from arrays if x[i] has 'nan' as value # <insert code> a=pd.DataFrame({'Column1':x[:],'Column2':fx[:],}).dropna(inplace=True) print(a) plt.plot(a.Column1,a.Column2) plt.xlabel("x") plt.ylabel("fx")

**Python Examples of numpy.NaN,** The following are 40 code examples for showing how to use numpy.NaN(). same meta2 = self.meta.copy() assert (meta2 == self.meta) # different way to create meta object meta3 = pysat. def convert(x): # need to convert array([ Decimal(NaN)], dtype='object') to np. Point calculate index at input point tree : scipy.spatial. A new method numpy.lib.recfunctions.repack_fields has been introduced to help mitigate the effects of this change, which can be used to write code compatible with both numpy 1.15 and 1.16. For more information on how to update code to account for this future change see the “accessing multiple fields” section of the user guide .

%matplotlib inline import matplotlib.pyplot as plt import numpy as np x = np.linspace(-5, 5,100) with np.errstate(all='ignore'): f1 = np.sqrt(x**2 - 1) # filter nan based on f1 position f2 = f1[~np.isnan(f1)] x2 = x[~np.isnan(f1)] plt.plot(x2,f2, '.')

**(Tutorial) Python NUMPY Array TUTORIAL,** Learn how to create a NumPy ARRAY, use broadcasting, ACCESS With np. linspace() and np.arange() you can make arrays of evenly spaced values. in numeric columns to nan , you can convert these values to other ones by Besides from these two points, the easiest way to see how this all fits� Calling list(a) means you get a list of the NumPy float types (not Python float objects). When printed, the shell prints more digits of the float value. NumPy arrays by default only print up to 1 decimal place of the float. If you set the precision of NumPy arrays higher, you'll see the same values as you get in your list:

**10 Numpy functions you should know,** To generate x-axis data, we employ the linspace function, generating 111 data points from 0 to 100, both included. The numpy.digitize(x, bins, right=False) function has two arguments: (1) an input array x, and (2) an array of bins, returning the We can achieve that by using the numpy.nan constant. Intercept: We can remove the intercept using - 1 in the formula, or force the use of an intercept using + 1. Tip By default, statsmodels treats a categorical variable with K possible values as K-1 ‘dummy’ boolean variables (the last level being absorbed into the intercept term).

**python,** numpy: linspace calculation generating nan. How to remove point pair from both arrays? 发表于 2019-03-11 13:06:53. 活跃于 2019-03-11 14:11:22. 查看54 次. Array vs. Matrix Operations Introduction. MATLAB ® has two different types of arithmetic operations: array operations and matrix operations. You can use these arithmetic operations to perform numeric computations, for example, adding two numbers, raising the elements of an array to a given power, or multiplying two matrices.

**生成nan的linspace计算。如何从两个数组中删除点对?(numpy ,** 生成nan的linspace计算。如何从两个数组中删除点对?(numpy: linspace calculation generating nan. How to remove point pair from both arrays?) The mod function follows the convention that mod(a,0) returns a, whereas the rem function follows the convention that rem(a,0) returns NaN. Both variants have their uses. For example, in signal processing, the mod function is useful in the context of periodic signals because its output is periodic (with period equal to the divisor).

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- what does the ~tilde do?