# numpy | fundamental package for scientific computing | Data Manipulation library

## kandi X-RAY | numpy Summary

## kandi X-RAY | numpy Summary

NumPy is the fundamental package for scientific computing with Python.

### Support

### Quality

### Security

### License

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### Top functions reviewed by kandi - BETA

- Create a configuration object .
- Create a row from a text file .
- Analyze the group .
- Einsum operator .
- Analyze block .
- Pad an array with a given padding .
- Compute the gradient of a function .
- Calculate the percentile of an array .
- Computes the einsum path .
- Read data from a file .

## numpy Key Features

## numpy Examples and Code Snippets

```
Integer array indexing allows selection of arbitrary items in the array
based on their *N*-dimensional index. Each integer array represents a number
of indices into that dimension.
Negative values are permitted in the index arrays and work as they
```

```
The :ref:`array interface protocol ` defines a way for
array-like objects to re-use each other's data buffers. Its implementation
relies on the existence of the following attributes or methods:
- ``__array_interface__``: a Python dictionary contai
```

```
And I do not intend to export the build to other users or target a
different CPU than what the host has.
Set `native` for baseline, or manually specify the CPU features in case of option
`native` isn't supported by your platform::
python setup
```

```
import os
import re
import sys
import importlib
# Minimum version, enforced by sphinx
needs_sphinx = '4.3'
# This is a nasty hack to use platform-agnostic names for types in the
# documentation.
# must be kept alive to hold the patched names
_nam
```

```
"""
Generate CPU features tables from CCompilerOpt
"""
from os import sys, path
from numpy.distutils.ccompiler_opt import CCompilerOpt
class FakeCCompilerOpt(CCompilerOpt):
# disable caching no need for it
conf_nocache = True
def __init
```

```
"""
Scan the directory of nep files and extract their metadata. The
metadata is passed to Jinja for filling out the toctrees for various NEP
categories.
"""
import os
import jinja2
import glob
import re
def render(tpl_path, context):
path, fi
```

```
df.at[2, 'QTY'] = float('nan')
```

```
df.at[2, 'QTY'] = np.nan
```

```
import netCDF4 as nc
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import pandas as pd
def road_map():
# Open the file for highway metadata to read csv data
highway_metadata = pd.read_csv('miles
```

```
import numpy as np
d = {'listings':listings, 'scripting':scripting, 'medical':medical}
for k,v in d.items():
df[k] = df['input'].str.contains('|'.join(v))
arr = df[list(d)].to_numpy()
tmp = np.zeros(arr.shape, dtype='int8')
tmp[np.ara
```

```
y = np.array([1,2,1,3])
```

```
array([True, False, True, False])
```

```
x = np.array([[1,2],[3,4],[5,6],[7,8]])
x
Out[10]:
array([[1, 2],
[3, 4],
[5, 6],
[7, 8]])
<
```

## Community Discussions

Trending Discussions on numpy

QUESTION

I saw a video about speed of loops in python, where it was explained that doing `sum(range(N))`

is much faster than manually looping through `range`

and adding the variables together, since the former runs in C due to built-in functions being used, while in the latter the summation is done in (slow) python. I was curious what happens when adding `numpy`

to the mix. As I expected `np.sum(np.arange(N))`

is the fastest, but `sum(np.arange(N))`

and `np.sum(range(N))`

are even slower than doing the naive for loop.

Why is this?

Here's the script I used to test, some comments about the supposed cause of slowing done where I know (taken mostly from the video) and the results I got on my machine (python 3.10.0, numpy 1.21.2):

**updated script:**

ANSWER

Answered 2021-Oct-16 at 17:42From the cpython source code for `sum`

sum initially seems to attempt a fast path that assumes all inputs are the same type. If that fails it will just iterate:

QUESTION

The installation on the m1 chip for the following packages: Numpy 1.21.1, pandas 1.3.0, torch 1.9.0 and a few other ones works fine for me. They also seem to work properly while testing them. However when I try to install scipy or scikit-learn via pip this error appears:

**ERROR: Failed building wheel for numpy**

**Failed to build numpy**

**ERROR: Could not build wheels for numpy which use PEP 517 and cannot be installed directly**

Why should Numpy be build again when I have the latest version from pip already installed?

Every previous installation was done using `python3.9 -m pip install ...`

on Mac OS 11.3.1 with the apple m1 chip.

Maybe somebody knows how to deal with this error or if its just a matter of time.

...ANSWER

Answered 2021-Aug-02 at 14:33Please see this note of `scikit-learn`

about

**Installing on Apple Silicon M1 hardware**

The recently introduced

`macos/arm64`

platform (sometimes also known as`macos/aarch64`

) requires the open source community to upgrade the build configuation and automation to properly support it.At the time of writing (January 2021),

the only way to get a working installation of scikit-learn on this hardware is to install scikit-learn and its dependencies from the conda-forge distribution, for instance using the miniforge installers:https://github.com/conda-forge/miniforge

The following issue tracks progress on making it possible to install scikit-learn from PyPI with pip:

QUESTION

I am working on a spatial search case for spheres in which I want to find connected spheres. For this aim, I searched around each sphere for spheres that centers are in a (* maximum sphere diameter*) distance from the searching sphere’s center. At first, I tried to use scipy related methods to do so, but scipy method takes longer times comparing to equivalent numpy method. For scipy, I have determined the number of K-nearest spheres firstly and then find them by

`cKDTree.query`

, which lead to more time consumption. However, it is slower than numpy method even by omitting the first step with a constant value (it is not good to omit the first step in this case). *So, I tried to use some list-loops instead some numpy lines for speeding up using numba*

**It is contrary to my expectations about scipy spatial searching speed.**`prange`

. Numba run the code a little faster, but I believe that this code can be optimized for better performances, perhaps by vectorization, using other alternative numpy modules or using numba in another way. I have used iteration on all spheres due to prevent probable memory leaks and …, where number of spheres are high.ANSWER

Answered 2022-Feb-14 at 10:23Have you tried FLANN?

This code doesn't solve your problem completely. It simply finds the nearest 50 neighbors to each point in your 500000 point dataset:

QUESTION

version pip 21.2.4 python 3.6

The command:

...ANSWER

Answered 2021-Nov-19 at 13:30It looks like `setuptools>=58`

breaks support for `use_2to3`

:

So you should update `setuptools`

to `setuptools<58`

or avoid using packages with `use_2to3`

in the setup parameters.

I was having the same problem, `pip==19.3.1`

QUESTION

I am trying to do a regular import in Google Colab.

This import worked up until now.

If I try:

ANSWER

Answered 2021-Oct-15 at 21:11Found the problem.

I was installing `pandas_profiling`

, and this package updated `pyyaml`

to version 6.0 which is not compatible with the current way Google Colab imports packages.

So just reverting back to `pyyaml`

version 5.4.1 solved the problem.

For more information check versions of `pyyaml`

here.

See this issue and formal answers in GitHub

##################################################################

For reverting back to `pyyaml`

version 5.4.1 in your code, add the next line at the end of your packages installations:

QUESTION

I need to calculate the square root of some numbers, for example `√9 = 3`

and `√2 = 1.4142`

. How can I do it in Python?

The inputs will probably be all positive integers, and relatively small (say less than a billion), but just in case they're not, is there anything that might break?

**Related**

- Integer square root in python
- Is there a short-hand for nth root of x in Python?
- Difference between **(1/2), math.sqrt and cmath.sqrt?
- Why is math.sqrt() incorrect for large numbers?
- Python sqrt limit for very large numbers?
- Which is faster in Python: x**.5 or math.sqrt(x)?
- Why does Python give the "wrong" answer for square root? (specific to Python 2)
- calculating n-th roots using Python 3's decimal module
- How can I take the square root of -1 using python? (focused on NumPy)
- Arbitrary precision of square roots

_{Note: This is an attempt at a canonical question after a discussion on Meta about an existing question with the same title.}

ANSWER

Answered 2022-Feb-04 at 19:44`math.sqrt()`

The `math`

module from the standard library has a `sqrt`

function to calculate the square root of a number. It takes any type that can be converted to `float`

(which includes `int`

) as an argument and returns a `float`

.

QUESTION

I have a `requirements.txt`

like

ANSWER

Answered 2022-Jan-23 at 13:29A recent change in the Pip code has changed its behavior to be more strict with respect to `file:`

URI syntax. As pointed out by a PyPA member and Pip developer, the syntax `file:requirements.txt`

is not a valid URI according to the RFC8089 specification.

Instead, one must either drop the `file:`

scheme altogether:

QUESTION

I used a function in Python/Numpy to solve a problem in combinatorial game theory.

...ANSWER

Answered 2022-Jan-19 at 09:34The original code can be re-written in the following way:

QUESTION

I am trying to efficiently compute a summation of a summation in Python:

WolframAlpha is able to compute it too a high n value: sum of sum.

I have two approaches: a *for* loop method and an np.sum method. I thought the np.sum approach would be faster. However, they are the same until a large n, after which the np.sum has overflow errors and gives the wrong result.

I am trying to find the fastest way to compute this sum.

...ANSWER

Answered 2022-Jan-16 at 12:49(fastest methods, 3 and 4, are at the end)

In a fast NumPy method you need to specify `dtype=np.object`

so that NumPy does not convert Python `int`

to its own dtypes (`np.int64`

or others). It will now give you correct results (checked it up to N=100000).

QUESTION

Python 3.10 is released and when I try to install `NumPy`

it gives me this: `NumPy 1.21.2 may not yet support Python 3.10.`

. what should I do?

ANSWER

Answered 2021-Oct-06 at 12:26If on Windows, numpy has not yet released a precompiled wheel for Python 3.10. However you can try the unofficial wheels available at https://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy . Specifically look for

`numpy‑1.21.2+mkl‑cp310‑cp310‑win_amd64.whl`

or`numpy‑1.21.2+mkl‑cp310‑cp310‑win32.whl`

depending on you system architecture.

After downloading the file go to the download directory and run `pip install ".whl"`

.)

(I have personally installed `numpy‑1.21.2+mkl‑cp310‑cp310‑win_amd64.whl`

and it worked for me.)

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

## Vulnerabilities

No vulnerabilities reported

## Install numpy

You can use numpy like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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