1.Numpy basic introduction
- NumPy is a powerful Python library, mainly used to calculate multidimensional array to perform right.
- NumPy is numerical and python Jianpin.
2. Applications
- Machine learning models: mainly the various numerical matrix.
- Image processing and computer graphics:
- Mathematical tasks: integration, differentiation, interpolation, extrapolation.
3. Install
pip install numpy
Everything is ready, let us practice up!
4, NumPy Ndarray objects
- The most important feature is the N-dimensional array NumPy objects ndarray, which is a collection of different types of data, labeled 0 to index the start elements in a set.
- ndarray Each element has the same size storage area in memory.
- Internal ndarray consists of the following:
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A pointer to data (data memory or a memory-mapped file) pointer.
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Data type or dtype, describes a fixed grid size value in the array.
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An array of a shape (Shape) tuples, each tuple represents the size of the dimension.
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A span tuple (stride), wherein the forward integer refers to the number of bytes required element to a "cross" in the current dimension.
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Span can be negative, it would be after the memory array to the mobile, slice obj [:: - 1] or obj [:, :: - 1] is true.
Create a ndarray simply call NumPy array of functions to:
numpy.array(object, dtype = None, copy = True, order = None, subok = False, ndmin = 0)
Example:
# dtype 参数 import numpy as np a = np.array([1, 2, 3], dtype = complex) print (a)
result:
[+ 0.j 1., 2. + 0.j 3. 0.j +]
NOTE: ndarray dimensional object consists of a continuous portion of the computer memory of the composition, and the binding mode index, mapping each element into a position in the memory block. In line sequential memory blocks (C-style), or the order of columns (or the FORTRAN MatLab style, i.e. the pattern's F) to save the elements.
5, the data type
numpy supported data types than Python lot more built-in type, and may be substantially C-language data types, some of which correspond to the type of the built-in type Python. The following table lists the common NumPy basic types.
| name | description |
| bool_ | Boolean data type (True or False) |
| int_ | The default integer type (similar to the C language long, int32 or Int64) |
| intc | The same type C and int, int is generally 64 or int32 |
| intp | For the integer type of the index (an ssize_t C-like, generally remains int32 or Int64) |
| int8 | Character clause (-128 to 127) |
| int16 | Integer (-32768 to 32767) |
| int32 | Integer (-2147483648 to 2147483647) |
| int64 | Integer (-9223372036854775808 to 9223372036854775807) |
| uint8 | Unsigned integer (0 to 255) |
| uint16 | Unsigned integer (0 to 65535) |
| uint32 | Unsigned integer (0 to 4294967295) |
| uint64 | Unsigned integer (0 to 18446744073709551615) |
| float_ | float64 type of shorthand |
| float16 | Half-precision floating-point format, comprising: a sign bit, five-bit exponent, 10 mantissa bits |
| float32 | Single-precision floating point number, comprising: a sign bit, eight exponent, 23 mantissa bits |
| float64 | Double precision floating point, comprising: a sign bit, 11 exponent bits, 52 bits mantissa |
| complex_ | complex128 shorthand type, i.e., a plurality of 128-bit |
| complex64 | Complex, 32-bit floating-point number represents bis (real number part and imaginary number part) |
| complex128 | Complex, 64-bit floating-point number represents bis (real number part and imaginary number part) |
Data type object (dtype) (downright oh !!!)
dtype objects are constructed using the following syntax:
numpy.dtype(object, align, copy)
Example:
Import numpy NP AS # int8, Int16, Int32, Int64 four data types can use string 'i1', 'i2', 'i4', 'i8' replaced dt = np.dtype ( ' I4 ' ) Print (dt)
Each type has a unique built-define its character code, as follows:
| character | Corresponding type |
|---|---|
| b | Boolean |
| i | (Signed) integer |
| in | Unsigned int integer |
| f | Float |
| c | Plural float |
| m | timedelta (time interval) |
| M | datetime (date and time) |
| O | (Python) objects |
| S, a | (Byte-) string |
| The | Unicode |
| V | Raw data (void) |
6, array property
NumPy dimension of the array are called rank (Rank), the rank of a one-dimensional array, a two-dimensional array of rank 2, and so on.
In NumPy, each linear array is called a shaft (Axis), that is, the dimension (dimensions). For example, the equivalent of a two-dimensional array is a two-dimensional array, where the first one-dimensional array, each element is a one-dimensional array. So is the one-dimensional array in NumPy axis (Axis), a first shaft corresponds to the underlying array, a second axis is the underlying array to array. The number of axes - rank is the dimension of the array.
| Attributes | Explanation |
|---|---|
| ndarray.ndim | Rank number, i.e. the number or dimensions of shaft |
| ndarray.shape | Dimension of an array, for the matrix, n-rows and m columns |
| ndarray.size | The total number of array elements, corresponding to the n * m values of .shape |
| ndarray.dtype | Object element type ndarray |
| ndarray.itemsize | Ndarray size of each element in the object, in bytes |
| ndarray.flags | ndarray object memory information |
| ndarray.real | The real portion of the element ndarray |
| ndarray.imag | The imaginary part of the element ndarray |
| ndarray.data | Buffer contains the actual array elements, since the element is generally provided by the array index is generally not required to use this property. |
ndarray.flags return ndarray object memory information, comprising the following properties:
| Attributes | description |
|---|---|
| C_CONTIGUOUS (C) | The data is in a single continuous segment C-style |
| F_CONTIGUOUS (F) | The data is in a single continuous segment of the Fortran-style |
| OWNDATA (O) | Has an array of memory it uses or borrow it from another object |
| WRITEABLE (W) | May be written to the data area, and the value is set to False, then the data is read-only |
| ALIGNED (A) | And the data elements are all properly aligned to the hardware |
| UPDATEIFCOPY (U) | This array is a copy of the other array, when the array is released, the original contents of the array will be updated |
7, create an array
In addition to using the underlying array ndarray ndarray configured to create, but also can be created in several ways.
7.1 numpy.empty
numpy.empty(shape, dtype = float, order = 'C')
| 参数 | 描述 |
|---|---|
| shape | 数组形状 |
| dtype | 数据类型，可选 |
| order | 有"C"和"F"两个选项,分别代表，行优先和列优先，在计算机内存中的存储元素的顺序。 |
示例：
import numpy as np
x = np.empty([3,2], dtype = int)
print ("x =" ,x)
结果：
注意 − 数组元素为随机值，因为它们未初始化。
7.2 numpy.zeros
numpy.zeros(shape, dtype = float, order = 'C')
| 参数 | 描述 |
|---|---|
| shape | 数组形状 |
| dtype | 数据类型，可选 |
| order | 'C' 用于 C 的行数组，或者 'F' 用于 FORTRAN 的列数组 |
import numpy as np # 默认为浮点数 x = np.zeros(5) print(x) # 设置类型为整数 y = np.zeros((5,), dtype = np.int) print(y)
7.3 numpy.ones
numpy.ones(shape, dtype = None, order = 'C')
| 参数 | 描述 |
|---|---|
| shape | 数组形状 |
| dtype | 数据类型，可选 |
| order | 'C' 用于 C 的行数组，或者 'F' 用于 FORTRAN 的列数组 |
import numpy as np # 默认为浮点数 x = np.ones(5) print(x) # 自定义类型 x = np.ones([2,2], dtype = int) print(x)
8、NumPy 从已有的数组创建数组
8.1 numpy.asarray
numpy.asarray(a, dtype = None, order = None)
| 参数 | 描述 |
|---|---|
| a | 任意形式的输入参数，可以是，列表, 列表的元组, 元组, 元组的元组, 元组的列表，多维数组 |
| dtype | 数据类型，可选 |
| order | 可选，有"C"和"F"两个选项,分别代表，行优先和列优先，在计算机内存中的存储元素的顺序。 |
8.2 numpy.frombuffer
numpy.frombuffer 用于实现动态数组。
numpy.frombuffer 接受 buffer 输入参数，以流的形式读入转化成 ndarray 对象。
numpy.frombuffer(buffer, dtype = float, count = -1, offset = 0)
注意：buffer 是字符串的时候，Python3 默认 str 是 Unicode 类型，所以要转成 bytestring 在原 str 前加上 b。
| 参数 | 描述 |
|---|---|
| buffer | 可以是任意对象，会以流的形式读入。 |
| dtype | 返回数组的数据类型，可选 |
| count | 读取的数据数量，默认为-1，读取所有数据。 |
| offset | 读取的起始位置，默认为0。 |
pytnon 3.x:
import numpy as np s = b'Hello World' a = np.frombuffer(s, dtype = 'S1') print (a)
结果：
8.3 numpy.fromiter
numpy.fromiter 方法从可迭代对象中建立 ndarray 对象，返回一维数组。
numpy.fromiter(iterable, dtype, count=-1)
| 参数 | 描述 |
|---|---|
| iterable | 可迭代对象 |
| dtype | 返回数组的数据类型 |
| count | 读取的数据数量，默认为-1，读取所有数据 |
import numpy as np # 使用 range 函数创建列表对象 list=range(5) it=iter(list) # 使用迭代器创建 ndarray x=np.fromiter(it, dtype=float) print(x)
结果：
9、NumPy 从数值范围创建数组
9.1 numpy.arange
numpy.arange(start, stop, step, dtype)
| 参数 | 描述 |
|---|---|
start |
起始值，默认为0 |
stop |
终止值（不包含） |
step |
步长，默认为1 |
dtype |
返回ndarray的数据类型，如果没有提供，则会使用输入数据的类型。 |
import numpy as np # 设置了 dtype x = np.arange(5, dtype = float) print (x)
结果：
9.2 numpy.linspace
numpy.linspace 函数用于创建一个一维数组，数组是一个等差数列构成的;
np.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None)
| 参数 | 描述 |
|---|---|
start |
序列的起始值 |
stop |
序列的终止值，如果endpoint为true，该值包含于数列中 |
num |
要生成的等步长的样本数量，默认为50 |
endpoint |
该值为 ture 时，数列中中包含stop值，反之不包含，默认是True。 |
retstep |
如果为 True 时，生成的数组中会显示间距，反之不显示。 |
dtype |
ndarray 的数据类型 |
import numpy as np a =np.linspace(1,10,10,retstep= True) #显示步长 print(a) # 拓展例子 b =np.linspace(1,10,10).reshape([10,1]) print(b)
结果：
9.3 numpy.logspace
numpy.logspace 函数用于创建一个于等比数列。
np.logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None)
| 参数 | 描述 |
|---|---|
start |
序列的起始值为：base ** start |
stop |
序列的终止值为：base ** stop。如果endpoint为true，该值包含于数列中 |
num |
要生成的等步长的样本数量，默认为50 |
endpoint |
该值为 ture 时，数列中中包含stop值，反之不包含，默认是True。 |
base |
对数 log 的底数。(默认是10） |
dtype |
ndarray 的数据类型 |
import numpy as np a = np.logspace(0,9,10,base=2) print (a)
结果：
2的0次方；2的1次方；2的2次方；2的3次方；。。。；2的9次方。