如何使用 numpy 和 scipy 更改异常值（numpy改变数据类型）

25-04-21 5

对于如何使用numpy和scipy更改异常值感兴趣的读者，本文将会是一篇不错的选择，我们将详细介绍numpy改变数据类型，并为您提供关于centosinstallscipy问题：File"scipy/

对于如何使用 numpy 和 scipy 更改异常值感兴趣的读者，本文将会是一篇不错的选择，我们将详细介绍numpy改变数据类型，并为您提供关于centos install scipy 问题：File "scipy/linalg/setup.py", line 20, in configuration raise NotFoundE、Centos7安装python3、numpy、scipy、matplotlib、pandas等、matplotlib numpy scipy 的安装、Matplotlib-Scipy / Sklearn交互-scipy.linalg._flapack的有用信息。

本文目录一览：

如何使用 numpy 和 scipy 更改异常值（numpy改变数据类型）
centos install scipy 问题：File "scipy/linalg/setup.py", line 20, in configuration raise NotFoundE
Centos7安装python3、numpy、scipy、matplotlib、pandas等
matplotlib numpy scipy 的安装
Matplotlib-Scipy / Sklearn交互-scipy.linalg._flapack

如何使用 numpy 和 scipy 更改异常值（numpy改变数据类型）

如何解决如何使用 numpy 和 scipy 更改异常值

我正在尝试使用 numpy（不使用 Pandas）删除异常值。我创建了一个如下所示的数组：

[[-9.00681170e-01  1.01900435e+00 -1.34022653e+00 -1.31544430e+00]
[-1.14301691e+00 -1.31979479e-01 -1.34022653e+00 -1.31544430e+00]
[-1.38535265e+00  3.28414053e-01 -1.39706395e+00 -1.31544430e+00]
[-1.50652052e+00  9.82172869e-02 -1.28338910e+00 -1.31544430e+00]
[-1.02184904e+00  1.24920112e+00 -1.34022653e+00 -1.31544430e+00]
[-5.37177559e-01  1.93979142e+00 -1.16971425e+00 -1.05217993e+00]
[-1.50652052e+00  7.88807586e-01 -1.34022653e+00 -1.18381211e+00]
[-1.02184904e+00  7.88807586e-01 -1.28338910e+00 -1.31544430e+00]]

我想创建一个函数来检查该数组，如果它找到任何数字： x>=3 它将用 2.9 替换它如果它找到一个 x

def ignoreOutlieres(array):
for i in array:
  for x in i:
      x = float(format(x,".2f"))
      if x >= 3:
          x = 2.99
      elif x <= -3:
          x = -2.99
return array

但是我遇到了这个类型错误：

TypeError: ''float'' 对象不能被解释为整数

然后我尝试使用 numpt 和 z 测试：

def ignoreOutlieres(num_array):
for i in num_array:
    i = np.all(stats.zscore(i)>=3,axis = 2.9)
    return num_array

但我认为我并没有真正理解它背后的想法，而且我没有正确使用它。不适用于任何形式的帮助或指导。我想最终得到的输出看起来像这样：

[[-0.90068117,1.01900435,-1.34022653,-1.3154443 ],[-1.14301691,-0.13197948,[-1.38535265,0.32841405,-1.39706395,[-1.50652052,0.09821729,-1.2833891,[-1.02184904,1.24920112,[-0.53717756,1.93979142,-1.16971425,-1.05217993],0.78880759,-1.18381211],[-1.74885626,-0.36217625,-1.44707648],1.47939788,[-1.26418478,-1.22655167,[-1.87002413,-1.51073881,[-0.05250608,2.16998818,-1.45390138,[-0.17367395,2.9,[-0.90068117,1.70959465,-1.18381211]])

解决方法

在使用 numpy 时不应该使用循环。您需要 np.where，它是 numpy 的 for 和 if 的组合等价物：

patched = np.where(array <= -3,-2.99,np.where(array >= 3,2.99,array))

您可以使用 numpy 的索引来更改这些值。 numpy_array >= 3 返回一个布尔数组，因此 numpy_array[numpy_array >= 3] = 2.99 替换 numpy_array >= 3 计算结果为 True 到 2.99 的每个位置。

def change_outliers(numpy_array):
    numpy_array[numpy_array >= 3] = 2.99
    numpy_array[numpy_array <= -3] = -2.99
    return numpy_array

centos install scipy 问题：File

centos install scipy 问题：File "scipy/linalg/setup.py", line 20, in configuration raise NotFoundE

依赖包： pyparsing、dateutil、scipy、numpy、libpng 1.2 (or later)、`freetype` 1.4 (or later) 安装pyparsing： # pip install pyparsing 安装numpy： # pip install numpy 安装dateutil： # pip install python-dateutil 安装scipy： # pip install scipy 待测：sudo yum -y install gcc gcc-c++ numpy python-devel scipy(不过这些包都装好了）报错： # pip install scipy Downloading/unpacking scipy Downloading scipy-0.17.1.tar.gz (12.4MB): 81kB downloaded Exception: Traceback (most recent call last): File "/usr/local/lib/python2.7/site-packages/pip-1.3.1-py2.7.egg/pip/basecommand.py",line 139,in main status = self.run(options,args) File "/usr/local/lib/python2.7/site-packages/pip-1.3.1-py2.7.egg/pip/commands/install.py",line 266,in run requirement_set.prepare_files(finder,force_root_egg_info=self.bundle,bundle=self.bundle) File "/usr/local/lib/python2.7/site-packages/pip-1.3.1-py2.7.egg/pip/req.py",line 1033,in prepare_files self.unpack_url(url,location,self.is_download) File "/usr/local/lib/python2.7/site-packages/pip-1.3.1-py2.7.egg/pip/req.py",line 1161,in unpack_url retval = unpack_http_url(link,self.download_cache,self.download_dir) File "/usr/local/lib/python2.7/site-packages/pip-1.3.1-py2.7.egg/pip/download.py",line 554,in unpack_http_url download_hash = _download_url(resp,link,temp_location) File "/usr/local/lib/python2.7/site-packages/pip-1.3.1-py2.7.egg/pip/download.py",line 458,in _download_url chunk = resp.read(4096) File "/usr/local/lib/python2.7/socket.py",line 380,in read data = self._sock.recv(left) File "/usr/local/lib/python2.7/httplib.py",line 567,in read s = self.fp.read(amt) File "/usr/local/lib/python2.7/socket.py",in read data = self._sock.recv(left) File "/usr/local/lib/python2.7/ssl.py",line 246,in recv return self.read(buflen) File "/usr/local/lib/python2.7/ssl.py",line 165,in read return self._sslobj.read(len) SSLError: The read operation timed out Storing complete log in /root/.pip/pip.log 解决方法： # pip --default-timeout=100 install django 重新安装scipy 各个安装包版本： nose-1.3.1 lapack-3.5.0 atlas-3.10.1 scipy-0.11.0 numpy-1.6.2 依赖关系：scipy的安装需要依赖于numpy、lapack、atlas（后两者都是线性代数工具包，不清楚的自行google），而numpy和sci的测试程序的运行又依赖于nose，因此，整个安装过程必须要按顺序执行的，否则是无法执行下去的。安装nose： # pip install nose 安装lapack： #yum install lapack lapack-devel blas blas-devel 更新pip # pip install --upgrade pip 安装scipy： # pip install scipy 安装matplotlib： # pip install matplotlib 报错：安装freetype：依赖freetype-devel # yum install　freetype-devel # yum install　freetype 安装matplotlib： # cd matplotlib-1.5.1 # python setup.py install 测试：参考：http://paperman825.blog.chinaunix.NET/uid-25691489-id-5602653.html

Centos7安装python3、numpy、scipy、matplotlib、pandas等

centos 7 已经自带 python 2.7.15，这里需要安装 python 3

[root@pwm]# python
Python 2.7.15 |Anaconda, Inc.| (default, Dec 14 2018, 19:04:19)
[GCC 7.3.0] on linux2

第一步：安装依赖包

yum -y groupinstall "Development tools"

yum -y install zlib-devel bzip2-devel openssl-devel ncurses-devel sqlite-devel readline-devel tk-devel gdbm-devel db4-devel libpcap-devel xz-devel

第二步：下载python 3，这里下载 3.8.5

https://www.python.org/downloads/source/

wget https://www.python.org/ftp/python/3.8.5/Python-3.8.5.tar.xz

第三步：指定存放目录(/usr/local/python3就是python3的安装目录)

mkdir /usr/local/python3

第四步：解压压缩包，进入该目录

tar -xvJf Python-3.8.5.tar.xz
cd Python-3.8.5
./configure --prefix=/usr/local/python3

(注：./configure --prefix=/usr/local/python3这句的作用就是将程序安装在/usr/local/python3这个目录下)

第五步：安装python3

make && make install

(注：make是编译，make install是安装)

第六步：创建软链接(将 /usr/bin/python3指向/usr/local/python3/bin/python3)

ln -s /usr/local/python3/bin/python3 /usr/bin/python3

ln -s /usr/local/python3/bin/pip3 /usr/bin/pip3

升级pip(如果需要)

按照提示升级pip，命令：pip3 install --upgrade pip

使用：

/usr/local/python3/bin/python3

2.安装其它(直接输入命令)

pip3 install numpy

pip3 install scipy

pip3 install matplotlib

pip3 install pandas

pip3 install statsmodels

pip3 install scikit-learn

pip3 install gensim

pip3 install theano

pip3 install keras

pip3 install tensorflow

REF

https://www.cnblogs.com/zhangtingzu/p/8650664.html

centos 7 已经自带 python 2.7.15，这里需要安装 python 3

[root@pwm]# python
Python 2.7.15 |Anaconda, Inc.| (default, Dec 14 2018, 19:04:19)
[GCC 7.3.0] on linux2

第一步：安装依赖包

yum -y groupinstall "Development tools"

yum -y install zlib-devel bzip2-devel openssl-devel ncurses-devel sqlite-devel readline-devel tk-devel gdbm-devel db4-devel libpcap-devel xz-devel

第二步：下载python 3，这里下载 3.8.5

https://www.python.org/downloads/source/

wget https://www.python.org/ftp/python/3.8.5/Python-3.8.5.tar.xz

第三步：指定存放目录(/usr/local/python3就是python3的安装目录)

mkdir /usr/local/python3

第四步：解压压缩包，进入该目录

tar -xvJf Python-3.8.5.tar.xz
cd Python-3.8.5
./configure --prefix=/usr/local/python3

(注：./configure --prefix=/usr/local/python3这句的作用就是将程序安装在/usr/local/python3这个目录下)

第五步：安装python3

make && make install

(注：make是编译，make install是安装)

第六步：创建软链接(将 /usr/bin/python3指向/usr/local/python3/bin/python3)

ln -s /usr/local/python3/bin/python3 /usr/bin/python3

ln -s /usr/local/python3/bin/pip3 /usr/bin/pip3

升级pip(如果需要)

按照提示升级pip，命令：pip3 install --upgrade pip

使用：

/usr/local/python3/bin/python3

2.安装其它(直接输入命令)

pip3 install numpy

pip3 install scipy

pip3 install matplotlib

pip3 install pandas

pip3 install statsmodels

pip3 install scikit-learn

pip3 install gensim

pip3 install theano

pip3 install keras

pip3 install tensorflow

REF

https://www.cnblogs.com/zhangtingzu/p/8650664.html

matplotlib numpy scipy 的安装

一：windows 端的安装

#cmd指令
python -m pip install --user numpy scipy matplotlib ipython jupyter pandas sympy nose

成功安装所需库！

二、linux端安装--基于Ubuntu的操作

第一步：下载压缩包到指定位置

jiyongjia@ubuntu:~$ cd ~/Downloads/

jiyongjia@ubuntu:~/Downloads$ wget https://downloads.sourceforge.net/project/matplotlib/matplotlib/matplotlib-1.4.3/matplotlib-1.4.3.tar.gz

第二步：解压下载的压缩包

jiyongjia@ubuntu:~/Downloads$ tar xzf matplotlib-1.4.3.tar.gz
jiyongjia@ubuntu:~/Downloads$ cd matplotlib-1.4.3/
jiyongjia@ubuntu:~/Downloads/matplotlib-1.4.3$ ls

第三步：进入文件夹进行编译

jiyongjia@ubuntu:~/Downloads/matplotlib-1.4.3$ python3 setup.py build

第四步：安装

jiyongjia@ubuntu:~/Downloads/matplotlib-1.4.3$ python3 setup.py install

Matplotlib-Scipy / Sklearn交互-scipy.linalg._flapack

如何解决Matplotlib-Scipy / Sklearn交互-scipy.linalg._flapack

我在matplotlib和scipy之间的交互方面遇到一些问题。这是我对情况的理解：

错误在于LinearRegression的{{1}}抛出sklearn
经过一些调试后，SVD not converging error引发了错误，其中scipy\\linalg\\basic.py方法返回的信息值不同于dgesld（在这种情况下为0）。在我的情况下，使用的-4是fortran flatpack lapack_func。
错误似乎取决于输入的数字和dgesld（pyplot）代码，尤其是方法matplotlib，yticks。
该错误首先发生在多线性回归问题中（xticks中的信息值等于23，在这种情况下为正值），但我编写了以下脚本来更好地概述问题

scipy\\linalg\\basic.py

在具有以下功能的Windows 10计算机上运行时：

import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
a = [0.27236845,0.79433854,0.05986454,0.62736383,0.5732594,0.54175392,0.92359127,0.19913404,0.17357701,0.10225879,0.94727807,0.23766063,0.92438574,0.10981865,0.18669187,0.71337215,0.17843819,0.98693265,0.80787247,0.931572]
b = [1.68869178,2.20448291,1.64828788,1.95276497,1.23976119,1.61260175,1.32652345,1.94535222,1.37353248,1.47830833,1.08400723,1.91091901,1.63909271,2.37494003,1.64490261,1.90403079,1.81028796,1.66986048,1.65304452,1.60747378]
for no_plot in [True,False]:
    for i in range(len(a)-1):
        _a = a[:i + 2]
        _b = b[:i + 2]
        if not no_plot:
            bar_color = "blue"
            margin = 10
            y_label = x_label = None
            angle = 0
            title = "TestError"
            color_theme = (0 / 235,32 / 235,96 / 235)
            fig,ax = plt.subplots(figsize=(18,6.8))
            plt.bar(_a,_b,color=bar_color)
            Box = ax.get_position()
            ax.set_position([Box.x0,Box.y0 + margin * Box.height,Box.width,Box.height * (1 - margin)])
            plt.xticks(fontname="Cambria",color=color_theme,rotation=angle,fontsize=25)
            plt.yticks(fontname="Cambria",fontsize=25)
            plt.title(title,fontname="Cambria",fontsize=25)
            ax_output = plt.gca()
        try:
            reg = LinearRegression().fit(np.array(_a).reshape(-1,1),_b)
            print("Success: {},@ i={} with no_plot={}".format(reg.score(np.array(_a).reshape(-1,_b),i,no_plot))
        except Exception as e:
            print("Exception: {} @ i={} with no_plot={}".format(repr(e),no_plot))

和python version: 3.7.9 scipy version: 1.5.2 scikit-learn version: 0.23.2 numpy version: 1.19.2 matplotlib version: 3.3.2

结果如下：

_flapack.cp37-win_amd64

就堆栈跟踪而言：

Success: 1.0,@ i=0 with no_plot=True
Success: 0.9524690407545247,@ i=1 with no_plot=True
Success: 0.9248909415334777,@ i=2 with no_plot=True
Success: 0.17921330631542143,@ i=3 with no_plot=True
Success: 0.1559357435898613,@ i=4 with no_plot=True
Success: 0.001129573837944875,@ i=5 with no_plot=True
Success: 0.008667658302087822,@ i=6 with no_plot=True
Success: 0.001674117195053615,@ i=7 with no_plot=True
Success: 0.011802146118754298,@ i=8 with no_plot=True
Success: 0.024141340568111902,@ i=9 with no_plot=True
Success: 0.04144995409093344,@ i=10 with no_plot=True
Success: 0.03301917468171267,@ i=11 with no_plot=True
Success: 0.0959782634092683,@ i=12 with no_plot=True
Success: 0.08847483030078473,@ i=13 with no_plot=True
Success: 0.06428117850391502,@ i=14 with no_plot=True
Success: 0.07033033186821203,@ i=15 with no_plot=True
Success: 0.06394158828230323,@ i=16 with no_plot=True
Success: 0.0640239869160919,@ i=17 with no_plot=True
Success: 0.06734590831873866,@ i=18 with no_plot=True
Success: 1.0,@ i=0 with no_plot=False
Success: 0.9524690407545247,@ i=1 with no_plot=False
Success: 0.9248909415334777,@ i=2 with no_plot=False
Success: 0.17921330631542143,@ i=3 with no_plot=False
Success: 0.1559357435898613,@ i=4 with no_plot=False
Success: 0.001129573837944875,@ i=5 with no_plot=False
Success: 0.008667658302087822,@ i=6 with no_plot=False
Exception: ValueError(''illegal value in 4-th argument of internal None'') @ i=7 with no_plot=False
Exception: ValueError(''illegal value in 4-th argument of internal None'') @ i=8 with no_plot=False
Exception: ValueError(''illegal value in 4-th argument of internal None'') @ i=9 with no_plot=False<
Exception: ValueError(''illegal value in 4-th argument of internal None'') @ i=10 with no_plot=False
Exception: ValueError(''illegal value in 4-th argument of internal None'') @ i=11 with no_plot=False
Exception: ValueError(''illegal value in 4-th argument of internal None'') @ i=12 with no_plot=False
Exception: ValueError(''illegal value in 4-th argument of internal None'') @ i=13 with no_plot=False
Exception: ValueError(''illegal value in 4-th argument of internal None'') @ i=14 with no_plot=False
Exception: ValueError(''illegal value in 4-th argument of internal None'') @ i=15 with no_plot=False
Exception: ValueError(''illegal value in 4-th argument of internal None'') @ i=16 with no_plot=False
Exception: ValueError(''illegal value in 4-th argument of internal None'') @ i=17 with no_plot=False
Exception: ValueError(''illegal value in 4-th argument of internal None'') @ i=18 with no_plot=False

坦白说，我有点迷路了。有人对此事有想法吗？

我们今天的关于如何使用 numpy 和 scipy 更改异常值和numpy改变数据类型的分享已经告一段落，感谢您的关注，如果您想了解更多关于centos install scipy 问题：File "scipy/linalg/setup.py", line 20, in configuration raise NotFoundE、Centos7安装python3、numpy、scipy、matplotlib、pandas等、matplotlib numpy scipy 的安装、Matplotlib-Scipy / Sklearn交互-scipy.linalg._flapack的相关信息，请在本站查询。

本文标签：