## 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.