Numpy get maximum value based on XYZ

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I'm trying to read an CSV file with some XYZ data but when gridding using Python Natgrid is causing an error: two input triples have the same x/y coordinates. Here is my array:

np.array([[41.540588, -100.348335, 0.052785],
   [41.540588, -100.348335, 0.053798],
   [42.540588, -102.348335, 0.021798],
   [42.540588, -102.348335, 0.022798],
   [43.540588, -103.348335, 0.031798]])

I want to remove XY duplicates and get the maximum Z value. Based on the example above, I want to remove any minimum values of this array:

np.array([[41.540588, -100.348335, 0.053798],
   [42.540588, -102.348335, 0.022798],
   [43.540588, -103.348335, 0.031798]])

I have tried using np.unique, but so far I haven't had any luck because it doesn't work with rows (only columns).

Here is a numpy way, sorting first by Z, then finding the first of each unique X and Y pair, and indexing:

a = np.array([[41.540588, -100.348335, 0.052785],
   [41.540588, -100.348335, 0.053798],
   [42.540588, -102.348335, 0.021798],
   [42.540588, -102.348335, 0.022798],
   [43.540588, -103.348335, 0.031798]])

# sort by Z
b = a[np.argsort(a[:,2])[::-1]]
# get first index for each unique x,y pair
u = np.unique(b[:,:2],return_index=True,axis=0)[1]
# index
c = b[u]
>>> c
array([[ 4.15405880e+01, -1.00348335e+02,  5.37980000e-02],
       [ 4.25405880e+01, -1.02348335e+02,  2.27980000e-02],
       [ 4.35405880e+01, -1.03348335e+02,  3.17980000e-02]])

numpy.amax - Numpy and Scipy, As a quick example, consider computing the sum of all values in an array. Python itself can do this using the built-in sum function: In [1]:. import numpy as np. In this article we will discuss how to get the maximum / largest value in a Numpy array and its indices using numpy.amax(). numpy.amax() Python’s numpy module provides a function to get the maximum value from a Numpy array i.e.

If you are able to use pandas, you can take advantage of groupby and max

>>> pandas.DataFrame(arr).groupby([0,1], as_index=False).max().values

array([[ 4.15405880e+01, -1.00348335e+02,  5.37980000e-02],
       [ 4.25405880e+01, -1.02348335e+02,  2.27980000e-02],
       [ 4.35405880e+01, -1.03348335e+02,  3.17980000e-02]])

Aggregations: Min, Max, and Everything In Between, Python's Pandas Library provides a member function in Dataframe to find the maximum Get maximum values in every row & column of the Dataframe Maximum value in each column : x 66.0. y 36.0. z 23.0. dtype: float64 Based on the value provided in axis it will return the index position of maximum  Python Maximum Value of Numpy Array. Given a numpy array, you can find the maximum value of all the elements in the array. To get the maximum value of a Numpy Array, you can use numpy function numpy.max() function. Syntax. The syntax of max() function as given below. max_value = numpy.max(arr)

You can use Pandas via sorting and dropping duplicates:

import pandas as pd

df = pd.DataFrame(arr)

res = df.sort_values(2, ascending=False)\
        .drop_duplicates([0, 1])\
        .sort_values(0).values

print(res)

array([[  4.15405880e+01,  -1.00348335e+02,   5.37980000e-02],
       [  4.25405880e+01,  -1.02348335e+02,   2.27980000e-02],
       [  4.35405880e+01,  -1.03348335e+02,   3.17980000e-02]])

Pandas: Find maximum values & position in columns or rows of a , Python's Pandas Library provides a member function in Dataframe to find the Get minimum values in every row & column of the Dataframe Based on the value provided in axis it will return the index position of minimum value along 3​. 4. 5. min values of columns are at row index position : x a. y a. z b. Find the index of a value in 1D Numpy array. Let’s find the numpy array element with value 19 occurs at different places let’s see all its indices.

Pandas Dataframe: Get minimum values in rows or columns & their , numpy. amax (a, axis=None, out=None, keepdims=<no value>, initial=<no value​>, where=<no value>)[source]¶. Return the maximum of an array or maximum  Create Numpy Array of different shapes & initialize with identical values using numpy.full() in Python; Python: Check if all values are same in a Numpy Array (both 1D and 2D) Sorting 2D Numpy Array by column or row in Python; 6 Ways to check if all values in Numpy Array are zero (in both 1D & 2D arrays) - Python

numpy.amax, extension package to Python for multi-dimensional arrays; closer to hardware computation (convenience); Also known as array oriented computing pixels of an image, grey-level or colour; 3-D data measured at different X-Y-Z positions, e.g. MRI scan … Try setting the seed before creating an array with random values. One of the computations you can perform is calculating the maximum value of a NumPy array. That’s where the np.max function comes in. NumPy max computes the maxiumum of the values in a NumPy array. The numpy.max() function computes the maximum value of the numeric values contained in a NumPy array. It can also compute the maximum value of the rows, columns, or other axes.

1.4.1. The NumPy array object, Each column has x, y, z value of a function z=sin(x2+y2)x2+y2. znorm is the 2) + np.power(mesh_y, 2))) z_norm = (z - z.min()) / (z.max() - z.min()) xyz  Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized. **kwargs. For other keyword-only arguments, see the ufunc docs. Returns y ndarray or scalar. The maximum of x1 and x2, element-wise.