# Numpy argsort ascending

**numpy.argsort** () function is used to perform an indirect sort along the given axis using the algorithm specified by the kind keyword. It returns an array of indices of the same shape as arr that would sort the array.

**argsort** function is a pre-built function present in the **Numpy** which works in a way that it returns the indices that would be responsible for sorting an array. The array which is returned is arranged in a specified order. The **NumPy** **argsort** function is also used to do a sort which is indirect in nature along the specifies axis (at time the. New code examples in category Python. That’s basically what **NumPy** sort does it sorts **NumPy** arrays. Let me give you a quick example. Imagine that you have a 1-dimensional **NumPy** array with five values that are in random order: You can use **NumPy** sort to sort those values in **ascending** order. Essentially, **numpy**.sort will take an input array, and output a new array in sorted order.

The default value of the axis is 0. print (np. **argsort** (array_2d,axis= 0 )) print (np. **argsort** (array_2d, axis= 1 )). The array which is returned is arranged in a specified order. The **NumPy** **argsort** () function is also used to do a sort which is indirect in nature along the specifies axis (at time the when axis is not specified the .... As of **NumPy** 1.4.0 `**argsort**` works with real/complex arrays containing: nan values. The enhanced sort order is documented in `sort`. Examples----- ... **ascending** order, otherwise `sorter` must be an array of indices: that sort it. v : array_like: Values to insert into `a`.

Sorting a list of lists by multiple keys also supports **ascending** and descending order. And it works for all the keys sorting. >>> my_lists = [[1, ... How to sort arrays by column in **NumPy**. Use **argsort**() method. import **numpy** my_lists = **numpy**. array(. Python answers, examples, and documentation. We can use the following code to sort the rows of the **NumPy** array in **ascending** order based on the values in the second column: #define new matrix with rows sorted in **ascending** order by values in second column x_sorted_asc = x [x [:, 1]. **argsort** ()] #view sorted matrix print(x_sorted_asc) [ [10 5 7] [11 9 2] [14 12 8]] Notice that the rows are now..

Answered By: Anonymous. So most sorting algorithms sort in **ascending** order if nothing else is specified. You can always just reverse the output yourself to get the sorting in descending order. import **numpy** as np x = np.array ( [ 3, 1, 2 ]) **ascending** = np.**argsort** (x) descending = **ascending** [::- 1 ] For more information on sorting direction of np. **numpy**.matrix.**argsort** #. method. matrix.**argsort**(axis=- 1, kind=None, order=None) #. Returns the indices that would sort this array. Refer to **numpy**.**argsort** for full documentation. Jun 25, 2021 · To get the index we can easily use the function **numpy**.**argsort**(). The **numpy** **argsort**() function is used to return the indices that can be used to sort an array. The returned array contains the indices along the given axis in sorted order. This function returns two indices that would start an array to perform indirect sort along the given axis ....

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We can use the following code to sort the rows of the **NumPy** array in **ascending** order based on the values in the second column: #define new matrix with rows sorted in **ascending** order by values in second column x_sorted_asc = x [x [:, 1].argsort()] #view sorted matrix print(x_sorted_asc) [ [10 5 7] [11 9 2] [14 12 8]] Notice that the rows are now.

I use **numpy.argsort** all the time for 1D data, but it seems to behaving differently in 2D. For example, let's say I want to **argsort** this array along axis 1 so the items in each row are in **ascending** order. The array which is returned is arranged in a specified order. The **NumPy** **argsort** () function is also used to do a sort which is indirect in nature along the specifies axis (at time the when axis is not specified the default is executed) using a set of algorithms. This algorithm is stipulated by a keyword i.e., 'kind'.

Natural log of four minus the natural log of two forward (out) # Calculate cross-entropy loss and accuracy To do the same, in one line, in

numpywe would have to do: np A mixture model can be regarded as a type of unsupervised. To get the indices of N miniumum values inNumPyin an optimal way, use the argpartition(~) method. ... we often want the indices of smallest values that are sorted inascendingorder. We can do this like so: sorted_min_indices = min_indices[np.argsort(min_values)] sorted_min_indices. array([5, 1, 2, 6]) Here, we are first usingNumPy's.NumPyargsort() Apart from the sort() method, we also haveargsort() function that is used as a sorting techniques inNumPywhich returns an array of indices of the sorted elements. From those sorted index values, we can get the sorted array elements inascendingorder. Thus, withargsort() function, we can sort the array values and get the.

**numpy**.argsort(a, axis=-1, kind=None, order=None) [source] ¶ Returns the indices that would sort an array. Perform an indirect sort along the given axis using the algorithm specified by the kind keyword. It returns an array of indices of the same shape as a that index data along the given axis in sorted order. Parameters aarray_like Array to sort.

The **NumPy** module provides a function **argsort** (), returns the indices which would sort an array. The **NumPy** module provides a function for performing an indirect sort along with the given axis with the help of the algorithm specified by the keyword. This function returns an array of indices of the same shape as 'a', which would sort the array.

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Answered By: Anonymous. So most sorting algorithms sort in **ascending** order if nothing else is specified. You can always just reverse the output yourself to get the sorting in descending order. import **numpy** as np x = np.array ( [ 3, 1, 2 ]) **ascending** = np.**argsort** (x) descending = **ascending** [::- 1 ] For more information on sorting direction of np.

3. The **argsort** () sorting method. **NumPy** agrsort () function performs sorting on the array elements and returns the indexes of the sorted array in an **ascending** order. It works in a similar fashion as that of sort () function with axis=None argument, but instead of returning the actual array elements, it returns the index values of those arrays. **argsort** in descending order **numpy**. python by. Answer #1. If you negate an array, the lowest elements become the highest elements and vice-versa. Therefore, the indices of the n highest elements are: (-avgDists).**argsort**() [:n] Another way to reason about this, as mentioned in the comments, is to observe that the big elements are coming last in ....

A **numpy** array is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. The number of dimensions is the rank of the array; the shape of an array is a tuple of integers giving the size of the array along each dimension. One may also ask, what is. May 13, 2022 · Python program for returning the indices that would sort the array using np.**argsort** () # Importing **numpy** as np import **numpy** as np # Creating a **numpy** array called arr arr = np.array ( [ 5, 3, 7, 8, 1, 9, 2 ]) print ( "The array is : ", arr) # Printing the shape of the array using shape function print ( "Shape of the array is : ", arr.shape) res .... That's basically what **NumPy** sort does it sorts **NumPy** arrays. Let me give you a quick example. Imagine that you have a 1-dimensional **NumPy** array with five values that are in random order: You can use **NumPy** sort to sort those values in **ascending** order. Essentially, **numpy**.sort will take an input array, and output a new array in sorted order. **argsort** in descending order **numpy**. python by. Answer #1. If you negate an array, the lowest elements become the highest elements and vice-versa. Therefore, the indices of the n highest elements are: (-avgDists).**argsort**() [:n] Another way to reason about this, as mentioned in the comments, is to observe that the big elements are coming last in .... **NumPy argsort**() Apart from the sort() method, we also have **argsort**() function that is used as a sorting techniques in **NumPy** which returns an array of indices of the sorted elements. From those sorted index values, we can get the sorted array elements in **ascending** order. Thus, with **argsort**() function, we can sort the array values and get the.

Relatedly, some **NumPy** functions often return views of arrays when possible (examples are transpose() and reshape()). JAX versions of such functions will return copies instead, although such are often optimized away by XLA when sequences of operations are compiled using jax.jit().**NumPy** is very aggressive at promoting values to float64 type..**argsort** It returns the. I use **numpy**.**argsort** all the time for 1D data, but it seems to behaving differently in 2D. For example, let’s say I want to **argsort** this array along axis 1 so the items in each row are in **ascending** order. The loop statement will elaborate its functioning until the condition get false This is the second of two guides on iterable Python tricks The sort_values() function sorts a data frame in **Ascending** or Descending order of passed Column array_of_diagonals ndarray The sorted() built-in returns a view (not a list) that is ordered The sorted() built .... **numpy** の多次元配列を普通に ソート すると、列ごと、または行ごとに個別に ソート されます。 行単位で 1 データになるようなケースでは、これだとちょっと困ります。データが散り散りになってしまいます。.

**argsort** function is a pre-built function present in the **Numpy** which works in a way that it returns the indices that would be responsible for sorting an array. The array which is returned is arranged in a specified order. The **NumPy** **argsort** function is also used to do a sort which is indirect in nature along the specifies axis (at time the. New code examples in category Python. Series.argsort(axis=0, kind='quicksort', order=None) [source] ¶. Return the integer indices that would sort the Series values. Override ndarray.**argsort**. **Argsorts** the value, omitting NA/null values, and places the result in the same locations as the non-NA values. Parameters. axis{0 or "index"} Has no effect but is accepted for. **NumPy** **argsort**() Apart from the sort() method, we also have **argsort**() function that is used as a sorting techniques in **NumPy** which returns an array of indices of the sorted elements. From those sorted index values, we can get the sorted array elements in **ascending** order. Thus, with **argsort**() function, we can sort the array values and get the .... Jun 22, 2021 · Answered By: Anonymous. So most sorting algorithms sort in **ascending** order if nothing else is specified. You can always just reverse the output yourself to get the sorting in descending order. import **numpy** as np x = np.array ( [ 3, 1, 2 ]) **ascending** = np.**argsort** (x) descending = **ascending** [::- 1 ] For more information on sorting direction of np ....

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You can always just reverse the output yourself to get the sorting in descending order. import **numpy** as np x = np.array ( [3, 1, 2]) **ascending** = np.**argsort** (x) descending = **ascending** [::-1] For more information on sorting direction of np.**argsort**, you can have a look at this post Is it possible to use **argsort** in descending order?. **NumPy** **argsort**() Apart from the sort() method, we also have **argsort**() function that is used as a sorting techniques in **NumPy** which returns an array of indices of the sorted elements. From those sorted index values, we can get the sorted array elements in **ascending** order. Thus, with **argsort**() function, we can sort the array values and get the.

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This is exactly where **NumPy** positions itself. It provides support for large n-dimensional arrays and has built-in support for many high-level mathematical and statistical operations. ... The values along the rows have been sorted in an **ascending** order as expected. ... Figure 1.20: First row with sorted values from **argsort**. As can be seen from. The **numpy**.**argsort** () function performs an indirect sort on input array, along the given axis and using a specified kind of sort to return the array of indices of data. This indices array is used to construct the sorted array. Example. Live Demo. import **numpy** as np x = np.array( [3, 1, 2]) print 'Our array is:' print x print ' ' print 'Applying ....

This is how to sort **numpy** array in descending in Python.. Read: Python **NumPy** Sum Python sort **NumPy** array get index. In this section, we will learn about python sort **NumPy** array get index.; To get the index we can easily use the function **numpy.argsort**().; The **numpy** **argsort**() function is used to return the indices that can be used to sort an array.; The returned array contains the indices along. New code examples in category Python. Python May 13, 2022 9:05 PM print every element in list python outside string. Python May 13, 2022 9:05 PM matplotlib legend. Python May 13, 2022 9:05 PM spacy create example object to get evaluation score. Python May 13, 2022 9:01 PM python telegram bot send image. Python May 13, 2022 9:01 PM python get.

Search: Python Sort Matrix Diagonal. You can sort **NumPy** array using the sort() method of the **NumPy** module: The sort() function takes an optional axis (an integer) which is -1 by default In this article, We will understand bubble sort in python with its working dot() in Python returns a Dot product of two arrays x and y Python has a built-in function len() for getting the total number of. Ridge and Lasso regression are some of the simple techniques to reduce model complexity and prevent over-fitting ... import math import matplotlib.pyplot as plt import pandas as pd import **numpy** as np # difference of lasso and ridge regression is that some of the coefficients can be zero i.e. some of the features are.

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To sort **numpy** array in descending order, we have to use np.sort on the negative values in the array. import **numpy** as np x=np.array ( [5,3,2,1,4) print (abs (np.sort (-x))) #descending order. Output: [5,4,3,2,1] You can also do a similar case for sorting along columns and rows in descending order. There are various approaches to the same but I. **numpy**.sort(a, axis=- 1, kind=None, order=None) [source] #. Return a sorted copy of an array. Parameters. aarray_like. Array to be sorted. axisint or None, optional. Axis along which to sort. If None, the array is flattened before sorting. The default is -1, which sorts along the last axis.

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The **numpy** linalg package does not sort eigenvalues and eigenvectors. Sometimes it is useful to put the eigenvalues in **ascending** order. But when we do, we might also want to rearrange the eigenvectors so they still go with the eigenvalues. We do this using an indirect sort, provided by the **numpy** **argsort**() function. An indirect sort generates a.

**NumPy** **argsort**() Apart from the sort() method, we also have **argsort**() function that is used as a sorting techniques in **NumPy** which returns an array of indices of the sorted elements. From those sorted index values, we can get the sorted array elements in **ascending** order.. **numpy.argsort** **numpy.argsort** (a, axis=-1, kind='quicksort',. **numpy.argsort**¶ **numpy.argsort** (a, axis=-1, kind='quicksort', order=None) [source] ¶ Returns the indices that would sort an array. Perform an indirect sort along the given axis using the algorithm specified by the kind keyword. It returns an array of indices of the same shape as a that index data along the given axis in sorted order. **numpy.argsort** (a, axis=-1, kind=None, order=None) [source] Returns the indices that would sort an array. Perform an indirect sort along the given axis using the algorithm specified by the kind keyword. ... For example, let's say I want to **argsort** this array along axis 1 so the items in each row are in **ascending** order. 2020-8-21 · np.**argsort**():.

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. magpul rifleman loop sling install. The **NumPy** module provides a function **argsort** (), returns the indices which would sort an array. The **NumPy** module provides a function for performing an indirect sort along with the given axis with the help of the algorithm specified by the keyword. This function returns an array of indices of the same shape as 'a', which would sort the array.

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For the **ascending** case, ranking is assigning values to numbers such that each value denotes the index of its corresponding number in a would-be sorted list. ... Quoting **numpy.argsort** documentation, "[**argsort**] Returns the indices that would sort an array.". For simplicity, let us assume that our array does not have duplicates. >>> import.

**NumPy** arrays can be sorted by a single column, row, or by multiple columns or rows using the **argsort**() function. The **argsort** function returns a list of indices that will sort the values in an array in **ascending** value. The kind argument of the **argsort** function makes it possible to sort arrays on multiple rows or columns. This article will go through sorting single columns and rows and.

We use sort and sorted() Sorting variables, specified as a scalar integer, a vector of integers, a variable name, a cell array of variable names, or a logical vector Count of the diangonal elements of matrix M*N will be min(M, N.

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To get the indices of N miniumum values in **NumPy** in an optimal way, use the argpartition(~) method. ... we often want the indices of smallest values that are sorted in **ascending** order. We can do this like so: sorted_min_indices = min_indices[np. **argsort** (min_values)] sorted_min_indices. array([5, 1, 2, 6]) Here, we are first using **NumPy's**. **Numpy** **argsort** **ascending** arrays can be sorted by a single column, row, or by multiple columns or rows using the function returns a list of indices that will sort the values in an array in **ascending** best castles to stay in uk **Numpy** **argsort** **ascending** best solicitors in dumfries sjc apartments saya nagori simple health.

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Retrieving ordered top-k elements from a certain array is a common problem. However, **NumPy** does not support this operation natively. A naive solution is to carry out full sort and then take the top-k elements like this: import **numpy** as np def topk_by_sort(input, k, axis=None, ascending=True): if not **ascending**: input *= -1 ind = np.argsort(input. . In order to sort the various elements present in the array structure, **NumPy** provides us with sort () function. With sort () function, we can sort the elements and segregate them in **ascending** to descending order, respectively. Have a look at the below syntax! Syntax: **numpy**.sort (array, axis).

So by starting with an array sorted **ascending**, we can use [::-1] to reverse the sort. Sorting Using **Argsort**. ... There's this very useful **NumPy** function called np.**argsort**() that we can use. Rather than sorting the array, np.**argsort**() returns the indices that would sort the array. sma america login "**ascending** order in python **numpy**" Code Answer.**argsort** in descending order **numpy**. python by.Search: Python Sort Matrix Diagonal. simple, flexible, fun test framework sort function turns out to be much more efficient and useful for our purposes Pesquise outras perguntas com a tag python array algoritmo quicksort ou faça sua própria pergunta Minor diagonal of a matrix A is. **numpy.argsort**. #. Returns the indices that would sort an array. Perform an indirect sort along the given axis using the algorithm specified by the kind keyword. It returns an array of indices of the same shape as a that index data along the given axis in sorted order. Array to sort. Axis along which to sort. The default is -1 (the last axis)..

We can use the following code to sort the rows of the **NumPy** array in **ascending** order based on the values in the second column: #define new matrix with rows sorted in **ascending** order by values in second column x_sorted_asc = x [x [:, 1]. **argsort** ()] #view sorted matrix print(x_sorted_asc) [ [10 5 7] [11 9 2] [14 12 8]] Notice that the rows are now.. The array which is returned is arranged in a specified order. The **NumPy** **argsort** () function is also used to do a sort which is indirect in nature along the specifies axis (at time the when axis is not specified the default is executed) using a set of algorithms. This algorithm is stipulated by a keyword i.e., 'kind'. 2. **NumPy** **argsort**() Apart from the sort() method, we also have **argsort**() function that is used as a **sorting techniques in NumPy** which returns an array of indices of the sorted elements. From those sorted index values, we can get the sorted array elements in **ascending** order.. Here, ﬁmanipu- To count the occurences of a value in a **numpy** array It is the array for which the diagonals are to be obtained Or Java Program to calculate the sum of the opposite diagonal elements in a Matrix or multi-dimensional array For this reason tridiagonal matrices of dimension smaller than or equal to 3 seem meaningless For this. The standard syntax for writing this function is as follows : **numpy**.sort (a, axis=-1, kind=None, order=None) The parameters of **numpy**.sort are : a: array-like object - The input array to be sorted. axis: 0, -1 or none - The axis along which the array has to be sort. If nothing is mentioned, then the array is flattened before sorting.

**NumPy** Installation in Python. In the command line (cmd) type the following command, pip install **numpy**. 20 **NumPy** Exercises for Beginners. Importing **NumPy** and printing version number. import **numpy** as np print (np. __version__) Corresponding Output. 1.19.2. EXERCISE 1 - Element-wise addition of 2 **numpy** arrays.

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If 'Categorical.**argsort'** is called via the **'numpy'** library, the first: parameter in its signature is 'axis', which takes either an integer or 'None', so check if the **'ascending'** parameter has either integer type or is: None, since **'ascending'** itself should be a boolean """ if is_integer (**ascending**) or **ascending** is None: args = (**ascending**. .

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The **numpy**.**argsort** () function performs an indirect sort on input array, along the given axis and using a specified kind of sort to return the array of indices of data. This indices array is used to construct the sorted array. Example. Live Demo. import **numpy** as np x = np.array( [3, 1, 2]) print 'Our array is:' print x print ' ' print 'Applying .... **NumPy** arrays can be sorted by a single column, row, or by multiple columns or rows using the **argsort**() function. The **argsort** function returns a list of indices that will sort the values in an array in **ascending** value. The kind argument of the **argsort** function makes it possible to sort arrays on multiple rows or columns. This article will go.

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**numpy.argsort**. This function in **numpy** returns the indices of the sorted array instead of the array elements. In the below example we take the array, print its elements along with the index for each element. ... But the result shows 4 and 2 as the **ascending** order as the respective values in column B which are 0 and 3 are also sorted as first 0.

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**jax.numpy.argsort**# **jax.numpy. argsort** (a, axis =-1, kind = 'stable', order = None) [source] # Returns the indices that would sort an array. LAX-backend implementation of **numpy**.**argsort**().. Only kind='stable' is supported. Other kind values will produce a warning and be treated as if they were 'stable'.. Original docstring below. Perform an indirect sort along the given axis using the.

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**numpy**.arrange() in Python. Python **numpy**.arrange() The arrange() function of Python **numpy** class returns an array with equally spaced elements as per the interval where the interval mentioned is half opened, i.e. [Start, Stop). Syntax. **numpy**.arange([start, ]stop, [step, ]dtype=None) Parameter. It implements the same logic as the function **argsort** of **Numpy** . ... times differ significantly only in looking up multiple elements where the simple Python solution has worse run- time complexity . unity game crash log; fitnessgram chart 2022; providence holy cross jobs; dillon animal shelter.

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A **numpy** array is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. The number of dimensions is the rank of the array; the shape of an array is a tuple of integers giving the size of the array along each dimension. One may also ask, what is. **numpy.argsort**¶ **numpy.argsort** (a, axis=-1, kind='quicksort', order=None) [source] ¶ Returns the indices that would sort an array. Perform an indirect sort along the given axis using the algorithm specified by the kind keyword. It returns an array of indices of the same shape as a that index data along the given axis in sorted order. Here, ﬁmanipu- To count the occurences of a value in a **numpy** array It is the array for which the diagonals are to be obtained Or Java Program to calculate the sum of the opposite diagonal elements in a Matrix or multi-dimensional array For this reason tridiagonal matrices of dimension smaller than or equal to 3 seem meaningless For this. **argsort** ([**ascending**, kind, na_position]) Return the indices that would sort this array. astype (dtype[, copy]) Cast to a **NumPy** array or ExtensionArray with 'dtype'. copy Return a copy of the array. dropna Return ExtensionArray without NA values. factorize ([na_sentinel]) Encode the extension array as an enumerated type. fillna ([value, method. A typical **numpy** array function for creating an array looks something like this: **numpy**. array (object, dtype =None, copy =True, order ='K', subok =False, ndmin =0) Here, all attributes other than objects are optional. So, do not worry even if you do not understand a lot about other parameters. Object: specify the object for which you want an array.

The **argsort** function returns a list of indices that will sort the values in an array in **ascending** value. 12 mai 2017 / Viewed: 30550 / Comments: 0 / Edit Some examples on how to find the nearest value and the index in array using python and **numpy**: 1d array >>> import **numpy** as np >>> value = 0.5 >>> A = np.random.random(10). common west. Count of the diangonal elements of matrix M*N will be min(M, N) The **NumPy** ndarray object has a function called sort(), that will sort a specified array indx,pd_sum = 0,0 sort() and a custom compare function, and avoid the need for a library If a is 2-D, then a 1-D array containing the diagonal and of the same type as a is returned unless a is a. **NumPy** **argsort**() Apart from the sort() method, we also have **argsort**() function that is used as a sorting techniques in **NumPy** which returns an array of indices of the sorted elements. From those sorted index values, we can get the sorted array elements in **ascending** order. Thus, with **argsort**() function, we can sort the array values and get the.

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Natural log of four minus the natural log of two forward (out) # Calculate cross-entropy loss and accuracy To do the same, in one line, in **numpy** we would have to do: np A mixture model can be regarded as a type of unsupervised.

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For the **ascending** case, ranking is assigning values to numbers such that each value denotes the index of its corresponding number in a would-be sorted list. ... Quoting **numpy.argsort** documentation, "[**argsort**] Returns the indices that would sort an array.". For simplicity, let us assume that our array does not have duplicates. >>> import. How do I sort a **NumPy** array in descending order?.

As of **NumPy** 1.4.0 `**argsort**` works with real/complex arrays containing: nan values. The enhanced sort order is documented in `sort`. Examples----- ... **ascending** order, otherwise `sorter` must be an array of indices: that sort it. v : array_like: Values to insert into `a`.

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**ascending** bool, default True. Whether the indices should result in an **ascending** or descending sort. kind {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional. Sorting algorithm. *args, **kwargs: Passed through to **numpy.argsort**(). Returns np.ndarray[np.intp] Array of indices that sort self. If NaN values are contained, NaN.