## numpy.take range of array elements Python

I have an array of integers.

data = [10,20,30,40,50,60,70,80,90,100]

I want to extract a range of integers from the array and get a smaller array.

data_extracted = [20,30,40]

I tried `numpy.take`

.

data = [10,20,30,40,50,60,70,80,90,100] start = 1 # index of starting data entry (20) end = 3 # index of ending data entry (40) data_extracted = np.take(data,[start:end])

I get a syntax error pointing to the `:`

in numpy.take.

Is there a better way to use `numpy.take`

to store part of an array in a separate array?

You can directly slice the list.

import numpy as np data = [10,20,30,40,50,60,70,80,90,100] data_extracted = np.array(data[1:4])

Also, you do not need to use `numpy.array`

, you could just store the data in another list:

data_extracted = data[1:4]

If you want to use `numpy.take`

, you have to pass it a list of the desired indices as second argument:

import numpy as np data = [10,20,30,40,50,60,70,80,90,100] data_extracted = np.take(data, [1, 2, 3])

I do not think `numpy.take`

is needed for this application though.

**np.take - Numpy and Scipy,** Take elements from an array along an axis. When axis A call such as np.take(arr, indices, axis=3) is equivalent to arr[:,:,:,indices,] . 'clip' – clip to the range. numpy.arange( [start, ]stop, [step, ], dtype=None) -> numpy.ndarray. The first three parameters determine the range of the values, while the fourth specifies the type of the elements: start is the number (integer or decimal) that defines the first value in the array. stop is the number that defines the end of the array and isn’t included in

You ought to just use a slice to get a range of indices, there is no need for `numpy.take`

, which is intended as a shortcut for fancy indexing.

data_extracted = data[1:4]

**numpy.take,** NumPy arange() is one of the array creation routines based on numerical ranges. It creates an instance of ndarray with evenly spaced values Size: The total number of elements in an array; Shape: The shape of an array; Dimension: The dimension or rank of an array; Dtype: Data type of an array; Itemsize: Size of each element of an array in bytes; Nbytes: Total size of an array in bytes; Example of NumPy Arrays. Now, we will take the help of an example to understand different

As others have mentioned, you can use fancy indexing in this case. However, if you need to use np.take because e.g. the axis you're slicing over is variable, you might try:

axis=0 data.take(range(1,4), axis=axis)

Note: this might be slower than:
`data_extracted = data[1:4]`

**NumPy arange(): How to Use np.arange() – Real Python,** We use cookies to ensure you have the best browsing experience on our website. Given numpy array, the task is to find elements within some specific range. python code to demonstrate. # finding elements in range. # in numpy array. To select an element from Numpy Array , we can use [] operator i.e. ndarray [index] It will return the element at given index only. Let’s use this to select an element at index 2 from Numpy Array we created above i.e. npArray, # Select an element at index 2 (Index starts from 0) elem = npArray [2] print ('Element at 2nd index : ' , elem)

**Python,** Let's use this to select an element at index 2 from Numpy Array we created above i.e. Select a sub array from Numpy Array by index range. In other words, just as numpy.array([1,2,3,4,5]) < 5 will return array([True, True, True, True, False]), I was wondering if it was possible to do something akin to this: 1 < numpy.array([1,2,3,4,5]) < 5

**Python Numpy : Select an element or sub array by index from a ,** In a previous chapter that introduced Python lists, you learned that Python indexing begins with [0] You can also access elements (i.e. values) in numpy arrays using indexing. On this page, you will use indexing to select elements within Slice a Range of Values from One-dimensional Numpy Arrays. Pure python: x[:n] = [0] * n with numpy: y = numpy.array(x) y[:n] = 0 also note that x[:n] = 0 does not work if x is a python list (instead of a numpy array).. It is also a bad idea to use [{some object here}] * n for anything mutable, because the list will not contain n different objects but n references to the same object:

**Slice (or Select) Data From Numpy Arrays,** extension package to Python for multi-dimensional arrays; closer to hardware (efficiency); designed for scientific computation In [1]: L = range(1000) Use the functions len() , numpy.shape() on these arrays. You may have noticed that, in some instances, array elements are displayed with a trailing dot (e.g. 2. vs 2 ). If you want to use numpy.take, you have to pass it a list of the desired indices as second argument: import numpy as np data = [10,20,30,40,50,60,70,80,90,100] data_extracted = np.take(data, [1, 2, 3])