python numpy library
python-numpy
Writing and accessing csv files
Writing csv files
CSV (Comma-Separated Value, comma separated value), is a A common file format used to store bulk data.
Write csv file
np.savetxt(frame, array, fmt='%.18e', delimiter=None) • frame : 文件、字符串或产生器，可以是.gz或.bz2的压缩文件 • array : 存入文件的数组 • fmt : 写入文件的格式，例如：%d %.2f %.18e • delimiter : 分割字符串，默认是任何空格
Example:
>>> a = np.arange(100).reshape(5,20)>>> np.savetxt('a.csv',a,fmt='%d',delimiter=',')
The obtained file is like this
0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19 20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39 40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59 60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79 80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99
Change the parameters to float Point writing
>>> a = np.arange(100).reshape(5,20)>>> np.savetxt('a.csv',a,fmt='%.1f',delimiter=',')
0.0,1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,12.0,13.0,14.0,15.0,16.0,17.0,18.0,19.0 20.0,21.0,22.0,23.0,24.0,25.0,26.0,27.0,28.0,29.0,30.0,31.0,32.0,33.0,34.0,35.0,36.0,37.0,38.0,39.0 40.0,41.0,42.0,43.0,44.0,45.0,46.0,47.0,48.0,49.0,50.0,51.0,52.0,53.0,54.0,55.0,56.0,57.0,58.0,59.0 60.0,61.0,62.0,63.0,64.0,65.0,66.0,67.0,68.0,69.0,70.0,71.0,72.0,73.0,74.0,75.0,76.0,77.0,78.0,79.0 80.0,81.0,82.0,83.0,84.0,85.0,86.0,87.0,88.0,89.0,90.0,91.0,92.0,93.0,94.0,95.0,96.0,97.0,98.0,99.0
Read csv file
Read csv file
np.loadtxt(frame, dtype=np.float, delimiter=None， unpack=False) • frame : 文件、字符串或产生器，可以是.gz或.bz2的压缩文件 • dtype : 数据类型，可选 • delimiter : 分割字符串，默认是任何空格 • unpack : 如果True，读入属性将分别写入不同变量
Example:
>>> b = np.loadtxt('a.csv',delimiter=',')>>> b
array([[ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19.],
[ 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39.],
[ 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59.],
[ 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79.],
[ 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98., 99.]])>>> b = np.loadtxt('a.csv',dtype=np.int,delimiter=',')>>> b
array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19],
[20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,37, 38, 39],
[40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,57, 58, 59],
[60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76,77, 78, 79],
[80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96,97, 98, 99]])CSV can only effectively store one-dimensional and two-dimensional arrays
np.savetxt() np.loadtxt() can only effectively access one-dimensional and two-dimensional arrays
Access to multi-dimensional data
Writing of multidimensional data
a.tofile(frame, sep='', format='%s') • frame : 文件、字符串 • sep : 数据分割字符串，如果是空串，写入文件为二进制 • format : 写入数据的格式
Example;
>>> a = np.arange(100).reshape(5,10,2)>>> a.tofile("a.dat",sep=',',format='%d')Contents of a.dat:
0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99
If the delimiter is not specified, then The resulting binary file cannot be read with a text editor.
Reading of multidimensional data
np.fromfile(frame, dtype=float, count=‐1, sep='') • frame : 文件、字符串 • dtype : 读取的数据类型 • count : 读入元素个数，‐1表示读入整个文件 • sep : 数据分割字符串，如果是空串，写入文件为二进制
numpy’s random number function
NumPy’s random sublibrary
np.random.*
| Function | Description |
|---|---|
| rand(d0,d1,...,dn) | Create a random number array based on d0-dn, floating point number, [0,1), uniform distribution |
| randn(d0,d1,...,dn) | Create a random number array based on d0-dn, standard normal distribution |
| randint(low[,high,shape]) | Create a random integer or integer array based on shape , the range is [low,high) |
| seed(s) | random number seed, s is the given seed value |
| shuffle(a) | Permute according to the first axis of array a, change array a |
| permutation(a) | According to the array The first axis of a generates a new out-of-order array without changing the array a |
| from the one-dimensional array Elements are extracted from a with probability p to form a new array of size shape. replace indicates whether it is possible to reuse elements. The default is False | |
| generated Array with uniform distribution, low starting value, high ending value, size is of shape | |
| Produces a normal distribution Array, loc is the mean, scale standard deviation, size is the shape | |
| generates an array with Poisson distribution, lam is the occurrence of random events Rate, size is the shape |
NumPy directly provides statistical function
np.*| Description | |
|---|---|
| According to the given Calculate the sum of related elements of array a with a given axis, axis integer or tuple | |
| Calculate the related elements of array a with a given axis Expectation, axis integer or tuple | ##average(a,axis=None,weights=None) |
| std(a,axis=None) | |
| var(a,axis = None) | |
| min(a) max(a) | |
| argmin(a) argmax(a) | |
| unravel_index(index,shape) | |
| ptp(a) | |
| median(a) | |
| Gradient: the rate of change between consecutive values, that is, the slope |
>>> a = np.random.randint(0,20,5)>>> np.gradient(a) array([ 9. , -0.5, -2. , -3. , -12. ])
The above is the detailed content of python numpy library. For more information, please follow other related articles on the PHP Chinese website!
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