numpy基本函式與操作——一篇就夠了！
阿新 • • 發佈：2019-02-06
對numpy基本函式操作進行了整理，掌握這些便可以算是入了個門，基本全部敲一遍就掌握差不多了！開發環境為jupyter notebook 基本是一個輸入一個輸出
需要原始碼的可以去我的github下載
import numpy as np
# 讀檔案的操作 分隔符為逗號 型別是str
world_alcohol = np.genfromtxt('world_alcohol.txt', delimiter = ',', dtype = str )print(type(world_alcohol))<class 'numpy.ndarray'>
world_alcohol
array([['Year', 'WHO region', 'Country', 'Beverage Types', 'Display Value'], ['1986', 'Western Pacific', 'Viet Nam', 'Wine', '0'], ['1986', 'Americas', 'Uruguay', 'Other', '0.5'], ..., ['1987', 'Africa', 'Malawi', 'Other', '0.75'], ['1989', 'Americas', 'Bahamas', 'Wine', '1.5'], ['1985', 'Africa', 'Malawi', 'Spirits', '0.31']], dtype='<U52')
# 呼叫help函式 檢視api詳情
print(help(np.genfromtxt))Help on function genfromtxt in module numpy.lib.npyio: genfromtxt(fname, dtype=<class 'float'>, comments='#', delimiter=None, skip_header=0, skip_footer=0, converters=None, missing_values=None, filling_values=None, usecols=None, names=None, excludelist=None, deletechars=None, replace_space='_', autostrip=False, case_sensitive=True, defaultfmt='f%i', unpack=None, usemask=False, loose=True, invalid_raise=True, max_rows=None) Load data from a text file, with missing values handled as specified. Each line past the first `skip_header` lines is split at the `delimiter` character, and characters following the `comments` character are discarded. Parameters ---------- fname : file, str, pathlib.Path, list of str, generator File, filename, list, or generator to read. If the filename extension is `.gz` or `.bz2`, the file is first decompressed. Note that generators must return byte strings in Python 3k. The strings in a list or produced by a generator are treated as lines. dtype : dtype, optional Data type of the resulting array. If None, the dtypes will be determined by the contents of each column, individually. comments : str, optional The character used to indicate the start of a comment. All the characters occurring on a line after a comment are discarded delimiter : str, int, or sequence, optional The string used to separate values. By default, any consecutive whitespaces act as delimiter. An integer or sequence of integers can also be provided as width(s) of each field. skiprows : int, optional `skiprows` was removed in numpy 1.10. Please use `skip_header` instead. skip_header : int, optional The number of lines to skip at the beginning of the file. skip_footer : int, optional The number of lines to skip at the end of the file. converters : variable, optional The set of functions that convert the data of a column to a value. The converters can also be used to provide a default value for missing data: ``converters = {3: lambda s: float(s or 0)}``. missing : variable, optional `missing` was removed in numpy 1.10. Please use `missing_values` instead. missing_values : variable, optional The set of strings corresponding to missing data. filling_values : variable, optional The set of values to be used as default when the data are missing. usecols : sequence, optional Which columns to read, with 0 being the first. For example, ``usecols = (1, 4, 5)`` will extract the 2nd, 5th and 6th columns. names : {None, True, str, sequence}, optional If `names` is True, the field names are read from the first valid line after the first `skip_header` lines. If `names` is a sequence or a single-string of comma-separated names, the names will be used to define the field names in a structured dtype. If `names` is None, the names of the dtype fields will be used, if any. excludelist : sequence, optional A list of names to exclude. This list is appended to the default list ['return','file','print']. Excluded names are appended an underscore: for example, `file` would become `file_`. deletechars : str, optional A string combining invalid characters that must be deleted from the names. defaultfmt : str, optional A format used to define default field names, such as "f%i" or "f_%02i". autostrip : bool, optional Whether to automatically strip white spaces from the variables. replace_space : char, optional Character(s) used in replacement of white spaces in the variables names. By default, use a '_'. case_sensitive : {True, False, 'upper', 'lower'}, optional If True, field names are case sensitive. If False or 'upper', field names are converted to upper case. If 'lower', field names are converted to lower case. unpack : bool, optional If True, the returned array is transposed, so that arguments may be unpacked using ``x, y, z = loadtxt(...)`` usemask : bool, optional If True, return a masked array. If False, return a regular array. loose : bool, optional If True, do not raise errors for invalid values. invalid_raise : bool, optional If True, an exception is raised if an inconsistency is detected in the number of columns. If False, a warning is emitted and the offending lines are skipped. max_rows : int, optional The maximum number of rows to read. Must not be used with skip_footer at the same time. If given, the value must be at least 1. Default is to read the entire file. .. versionadded:: 1.10.0 Returns ------- out : ndarray Data read from the text file. If `usemask` is True, this is a masked array. See Also -------- numpy.loadtxt : equivalent function when no data is missing. Notes ----- * When spaces are used as delimiters, or when no delimiter has been given as input, there should not be any missing data between two fields. * When the variables are named (either by a flexible dtype or with `names`, there must not be any header in the file (else a ValueError exception is raised). * Individual values are not stripped of spaces by default. When using a custom converter, make sure the function does remove spaces. References ---------- .. [1] NumPy User Guide, section `I/O with NumPy <http://docs.scipy.org/doc/numpy/user/basics.io.genfromtxt.html>`_. Examples --------- >>> from io import StringIO >>> import numpy as np Comma delimited file with mixed dtype >>> s = StringIO("1,1.3,abcde") >>> data = np.genfromtxt(s, dtype=[('myint','i8'),('myfloat','f8'), ... ('mystring','S5')], delimiter=",") >>> data array((1, 1.3, 'abcde'), dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')]) Using dtype = None >>> s.seek(0) # needed for StringIO example only >>> data = np.genfromtxt(s, dtype=None, ... names = ['myint','myfloat','mystring'], delimiter=",") >>> data array((1, 1.3, 'abcde'), dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')]) Specifying dtype and names >>> s.seek(0) >>> data = np.genfromtxt(s, dtype="i8,f8,S5", ... names=['myint','myfloat','mystring'], delimiter=",") >>> data array((1, 1.3, 'abcde'), dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')]) An example with fixed-width columns >>> s = StringIO("11.3abcde") >>> data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'], ... delimiter=[1,3,5]) >>> data array((1, 1.3, 'abcde'), dtype=[('intvar', '<i8'), ('fltvar', '<f8'), ('strvar', '|S5')]) None
import numpy as np
# 構造一維向量
np.array(np.arange(3))array([0, 1, 2])
# 構造二維矩陣
s = np.array([[1,2,3], [4,5,6]])sarray([[1, 2, 3],
[4, 5, 6]])
s.dtypedtype('int32')
# 索引
s[0,0]1
# 矩陣中型別必須一致
import numpy as np
numbers = np.array([1,2,3,4.])
print(numbers)
print(numbers.dtype)[ 1. 2. 3. 4.]
float64
# 值的判定 返回布林陣列
numbers == 3array([False, False, True, False], dtype=bool)
# 可用布林陣列作為索引 查出具體的值
equal_to_3 = (numbers == 3)
numbers[equal_to_3]array([ 3.])
matrix = np.array(
[[11,26,38],
[32,65,96],
[21,78,84],
]
)matrixarray([[11, 26, 38],
[32, 65, 96],
[21, 78, 84]])
# 返回第二列中是否等於78的布林陣列
column_equalto65 = matrix[:,1] == 78# 由列變為行
column_equalto65array([False, False, True], dtype=bool)
# 返回第二行中等於78的一行資料
matrix[column_equalto65]array([[21, 78, 84]])
# 這樣的索引表示取第二列 :代表所有的行 返回結果為行向量
matrix[:,1]array([26, 65, 78])
# 求和操作 指定維度（axis）為1 則表示每一行求和
matrix.sum(axis = 1)array([ 75, 193, 183])
# 求和操作 指定維度（axis）為0 則表示每一列求和
matrix.sum(axis = 0)array([ 64, 169, 218])
# 通過reshape方法 指定3行5列 重構矩陣
import numpy as np
a = np.array([np.arange(15)])
print(a)
a = a.reshape((3,5))
print(a)
b = np.arange(16).reshape(2,8)
print(b)[[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14]]
[[ 0 1 2 3 4]
[ 5 6 7 8 9]
[10 11 12 13 14]]
[[ 0 1 2 3 4 5 6 7]
[ 8 9 10 11 12 13 14 15]]
# 輸出維度
a.ndim2
# 輸出型別
a.dtypedtype('int32')
# 輸出型別名
a.dtype.name'int32'
# 隨機模組的應用 指定3行4列矩陣
np.random.random((3,4))array([[ 0.19375842, 0.36607602, 0.2676583 , 0.45307936],
[ 0.43905375, 0.28215774, 0.89246178, 0.2877808 ],
[ 0.52287865, 0.3748371 , 0.85626729, 0.37688939]])
# arange的應用 從1開始每次加5 直到小於15
np.arange(1, 15, 5)array([ 1, 6, 11])
# linspace的應用 起點值為0 終點為2pi 平均取100個值
from numpy import pi
s = np.linspace(0, 2*pi, 100)
print(s)[ 0. 0.06346652 0.12693304 0.19039955 0.25386607 0.31733259
0.38079911 0.44426563 0.50773215 0.57119866 0.63466518 0.6981317
0.76159822 0.82506474 0.88853126 0.95199777 1.01546429 1.07893081
1.14239733 1.20586385 1.26933037 1.33279688 1.3962634 1.45972992
1.52319644 1.58666296 1.65012947 1.71359599 1.77706251 1.84052903
1.90399555 1.96746207 2.03092858 2.0943951 2.15786162 2.22132814
2.28479466 2.34826118 2.41172769 2.47519421 2.53866073 2.60212725
2.66559377 2.72906028 2.7925268 2.85599332 2.91945984 2.98292636
3.04639288 3.10985939 3.17332591 3.23679243 3.30025895 3.36372547
3.42719199 3.4906585 3.55412502 3.61759154 3.68105806 3.74452458
3.8079911 3.87145761 3.93492413 3.99839065 4.06185717 4.12532369
4.1887902 4.25225672 4.31572324 4.37918976 4.44265628 4.5061228
4.56958931 4.63305583 4.69652235 4.75998887 4.82345539 4.88692191
4.95038842 5.01385494 5.07732146 5.14078798 5.2042545 5.26772102
5.33118753 5.39465405 5.45812057 5.52158709 5.58505361 5.64852012
5.71198664 5.77545316 5.83891968 5.9023862 5.96585272 6.02931923
6.09278575 6.15625227 6.21971879 6.28318531]
# 取三角函式值
np.sin(np.linspace(0, 2*pi, 100))array([ 0.00000000e+00, 6.34239197e-02, 1.26592454e-01,
1.89251244e-01, 2.51147987e-01, 3.12033446e-01,
3.71662456e-01, 4.29794912e-01, 4.86196736e-01,
5.40640817e-01, 5.92907929e-01, 6.42787610e-01,
6.90079011e-01, 7.34591709e-01, 7.76146464e-01,
8.14575952e-01, 8.49725430e-01, 8.81453363e-01,
9.09631995e-01, 9.34147860e-01, 9.54902241e-01,
9.71811568e-01, 9.84807753e-01, 9.93838464e-01,
9.98867339e-01, 9.99874128e-01, 9.96854776e-01,
9.89821442e-01, 9.78802446e-01, 9.63842159e-01,
9.45000819e-01, 9.22354294e-01, 8.95993774e-01,
8.66025404e-01, 8.32569855e-01, 7.95761841e-01,
7.55749574e-01, 7.12694171e-01, 6.66769001e-01,
6.18158986e-01, 5.67059864e-01, 5.13677392e-01,
4.58226522e-01, 4.00930535e-01, 3.42020143e-01,
2.81732557e-01, 2.20310533e-01, 1.58001396e-01,
9.50560433e-02, 3.17279335e-02, -3.17279335e-02,
-9.50560433e-02, -1.58001396e-01, -2.20310533e-01,
-2.81732557e-01, -3.42020143e-01, -4.00930535e-01,
-4.58226522e-01, -5.13677392e-01, -5.67059864e-01,
-6.18158986e-01, -6.66769001e-01, -7.12694171e-01,
-7.55749574e-01, -7.95761841e-01, -8.32569855e-01,
-8.66025404e-01, -8.95993774e-01, -9.22354294e-01,
-9.45000819e-01, -9.63842159e-01, -9.78802446e-01,
-9.89821442e-01, -9.96854776e-01, -9.99874128e-01,
-9.98867339e-01, -9.93838464e-01, -9.84807753e-01,
-9.71811568e-01, -9.54902241e-01, -9.34147860e-01,
-9.09631995e-01, -8.81453363e-01, -8.49725430e-01,
-8.14575952e-01, -7.76146464e-01, -7.34591709e-01,
-6.90079011e-01, -6.42787610e-01, -5.92907929e-01,
-5.40640817e-01, -4.86196736e-01, -4.29794912e-01,
-3.71662456e-01, -3.12033446e-01, -2.51147987e-01,
-1.89251244e-01, -1.26592454e-01, -6.34239197e-02,
-2.44929360e-16])
# 做數學運算
a = np.array([12, 45, 16, 56])
b = np.arange(4)
print(a)
print(b)
c = a - b
print(c)
c = c - 1
print(c)
b = b ** 2
print(b)
# 返回布林陣列
print(a > 16)[12 45 16 56]
[0 1 2 3]
[12 44 14 53]
[11 43 13 52]
[0 1 4 9]
[False True False True]
# 矩陣乘法
a = np.array([
[1,2],
[3,4]
])
b = np.array([
[3,4],
[1,2]
])
# 對應項相乘
print(a * b)
print('-'*10)
# 矩陣乘法
print(a.dot(b))
print('-'*10)
print(np.dot(a, b))
print('-'*10)
[[3 8]
[3 8]]
----------
[[ 5 8]
[13 20]]
----------
[[ 5 8]
[13 20]]
----------
# e次冪、開根號的計算
B = np.arange(4)
print(np.exp(B))
print(np.sqrt(B))[ 1. 2.71828183 7.3890561 20.08553692]
[ 0. 1. 1.41421356 1.73205081]
# floor 表示向下取整
a = np.floor(10*np.random.random((3, 4)))
print(a)
print('-'*20)
# 利用ravel()方法將矩陣拉成向量
print(a.ravel())
print('-'*20)
a.shape = (6, 2)
print(a)
print('-'*20)
# 求轉置
print(a.T)
# -1 代表預設讓系統自己計算列數
print(a.reshape(3, -1))[[ 4. 8. 1. 7.]
[ 2. 6. 8. 9.]
[ 8. 9. 5. 6.]]
--------------------
[ 4. 8. 1. 7. 2. 6. 8. 9. 8. 9. 5. 6.]
--------------------
[[ 4. 8.]
[ 1. 7.]
[ 2. 6.]
[ 8. 9.]
[ 8. 9.]
[ 5. 6.]]
--------------------
[[ 4. 1. 2. 8. 8. 5.]
[ 8. 7. 6. 9. 9. 6.]]
[[ 4. 8. 1. 7.]
[ 2. 6. 8. 9.]
[ 8. 9. 5. 6.]]
# 資料拼接
a = np.floor(10*np.random.random((2, 2)))
b = np.floor(10*np.random.random((2, 2)))
print(a)
print('-'*20)
print(b)
print('-'*20)
# 按行拼 增加樣本特徵
print(np.hstack((a, b)))
print('-'*20)
# 按列拼 增加樣本數
print(np.vstack((a, b)))
print('-'*20)[[ 5. 6.]
[ 8. 0.]]
--------------------
[[ 9. 9.]
[ 9. 8.]]
--------------------
[[ 5. 6. 9. 9.]
[ 8. 0. 9. 8.]]
--------------------
[[ 5. 6.]
[ 8. 0.]
[ 9. 9.]
[ 9. 8.]]
--------------------
# 資料的切割
a = np.floor(10*np.random.random((2, 12)))
b = np.floor(10*np.random.random((2, 12)))
print(a)
print('-'*20)
print(b)
print('-'*20)
# 表示按行切開
print(np.hsplit(a, 3))
print('-'*20)
# 表示從某位置切割 (3, 4) 切兩下 最左邊記為0
print(np.hsplit(a, (3, 4)))
print('-'*20)
# 表示按列切開
print(np.vsplit(b , 2))[[ 2. 2. 1. 9. 3. 9. 3. 6. 8. 1. 0. 2.]
[ 6. 3. 7. 7. 0. 0. 5. 3. 5. 8. 5. 0.]]
--------------------
[[ 0. 0. 6. 4. 3. 1. 8. 9. 7. 7. 8. 5.]
[ 8. 2. 4. 1. 5. 2. 0. 8. 2. 4. 8. 0.]]
--------------------
[array([[ 2., 2., 1., 9.],
[ 6., 3., 7., 7.]]), array([[ 3., 9., 3., 6.],
[ 0., 0., 5., 3.]]), array([[ 8., 1., 0., 2.],
[ 5., 8., 5., 0.]])]
--------------------
[array([[ 2., 2., 1.],
[ 6., 3., 7.]]), array([[ 9.],
[ 7.]]), array([[ 3., 9., 3., 6., 8., 1., 0., 2.],
[ 0., 0., 5., 3., 5., 8., 5., 0.]])]
--------------------
[array([[ 0., 0., 6., 4., 3., 1., 8., 9., 7., 7., 8., 5.]]), array([[ 8., 2., 4., 1., 5., 2., 0., 8., 2., 4., 8., 0.]])]
# 物件的複製 傳引用的方式
a = np.arange(12)
b = a
b.shape = (3, -1)
print(a.shape)
print(id(a))
print(id(b))(3, 4)
2262218295696
2262218295696
# 用view方法建立拷貝物件
# a、c指向不同的記憶體 但共用了一堆值 改變c的值 a的值也會改變
c = a.view()
c.shape = (4, -1)
print(a.shape)
print(id(a))
print(id(c))
c[1, 1] = 123456
print(c)
print(a)(3, 4)
2262218295696
2262218297216
[[ 0 1 2]
[ 3 123456 5]
[ 6 7 8]
[ 9 10 11]]
[[ 0 1 2 3]
[123456 5 6 7]
[ 8 9 10 11]]
# 用copy進行深拷貝物件 改變d的值 a不會改變
d = a.copy()
print(d is a)
d[0, 0] = 2356
print(a)False
[[ 0 1 2 3]
[123456 5 6 7]
[ 8 9 10 11]]
# 根據索引做運算
data = np.sin(np.arange(20)).reshape(5, 4)
print(data)
# 求出每一列中最大元素的索引
ind = data.argmax(axis=0)
print(ind)
# 將索引傳進去 range(data.shape[1])值為[0,1,2,3]代表四列
data_max = data[ind, range(data.shape[1])]
print(data_max)[[ 0. 0.84147098 0.90929743 0.14112001]
[-0.7568025 -0.95892427 -0.2794155 0.6569866 ]
[ 0.98935825 0.41211849 -0.54402111 -0.99999021]
[-0.53657292 0.42016704 0.99060736 0.65028784]
[-0.28790332 -0.96139749 -0.75098725 0.14987721]]
[2 0 3 1]
[ 0.98935825 0.84147098 0.99060736 0.6569866 ]
# 運用tile進行擴充套件
a = np.arange(0, 40, 10)
print(a)
b = np.tile(a, (2, 2))
print(b)[ 0 10 20 30]
[[ 0 10 20 30 0 10 20 30]
[ 0 10 20 30 0 10 20 30]]
# 排序操作
a = np.array([
[4, 3, 5],
[1, 2, 1],
])
# 按行進行排序
print(np.sort(a, axis = 1))
# 按列進行排序
print(np.sort(a, axis = 0))
b = np.array([2, 6, 1, 3])
# 對索引進行排序
c = np.argsort(b)
print(c)
# 按排序的索引進行輸出 則是從小到大輸出
print(b[c])[[3 4 5]
[1 1 2]]
[[1 2 1]
[4 3 5]]
[2 0 3 1]
[1 2 3 6]