flatten = lambda l: [i for row in l for i in row]http://rightfootin.blogspot.com/2006/09/more-on-python-flatten.html
Serve static files in this directory:
$ python -m http.server 8000pip3 install virtualenv$ virtualenv venv
$ source venv/bin/activate
$ deactivateOnly install after setting up virtualenv to avoid polluting the global namespace.
pip3 install numpy
$ python3 main.pynp.ones((3, 4))np.zeros((2,3,4), dtype=np.int16)# 2 is the upper boundary, so values between 0 and 2 will be generated
# 10 is the array len
np.random.randint(2, size=10)
# array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])
# Generate an array of zeros
np.random.randint(1, size=10)
# array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
# Generate an 2 x 4 array of values from 0 to 4
np.random.randint(5, size=(2, 4))
array([[4, 0, 2, 1],
[3, 2, 2, 0]])np.random.random((2,2))np.empty((3,2))# 7 is a constant value
np.full((2,2), 7)# Create an array that starts at 10 with steps of 5
np.arange(10, 25, 5)# Creates an array of 9 values that lies between 0 and 2
np.linspace(0, 2, 9)# This is your data in the text file
# Value1 Value2 Value3
# 0.2536 0.1008 0.3857
# 0.4839 0.4536 0.3561
# 0.1292 0.6875 0.5929
# 0.1781 0.3049 0.8928
# 0.6253 0.3486 0.8791
# Import your data
x, y, z = np.loadtxt('data.txt',
skiprows=1,
unpack=True)
# Unpack returns the columns as separate arrays# Your data in the text file
# Value1 Value2 Value3
# 0.4839 0.4536 0.3561
# 0.1292 0.6875 MISSING
# 0.1781 0.3049 0.8928
# MISSING 0.5801 0.2038
# 0.5993 0.4357 0.7410
my_array2 = np.genfromtxt('data2.txt',
skip_header=1,
filling_values=-999)import numpy as np
x = np.arange(0.0, 5.0, 1.0)
np.savetxt('test.out', x, delimiter=',')# x.shape and y.shape must be equal
np.add(x, y)
np.substract(x, y)
np.multiply(x, y)
np.divide(x, y)
np.remainder(x, y)a.sum()
a.min()
b.max(axis=0) # Maximum value of an array row
b.cumsum(axis=1) # Cumulative sum of elements
a.mean()
b.median()
a.corrcoef() # Correlation coefficient
np.std(b) # Standard deviation# a AND b
np.logical_and(a, b)
# a OR b
np.logical_or(a, b)
# a NOT b
np.logical_not(a, b)a[start:end] # items start through the end (but the end is not included!)
a[start:] # items start through the rest of the array
a[:end] # items from the beginning through the end (but the end is not included!)
# Select items at index 0 and 1
print(my_array[0:2])
# Select items at row 0 and 1, column 1
print(my_2d_array[0:2,1])
# Select items at row 1
# This is the same as saying `my_3d_array[1,:,:]
print(my_3d_array[1,...])x = np.array([[1,2], [3,4]])
print(x[x > 1]) # array([2, 3, 4])
y = (x >= 1)
# array([[False, True],
[ True, True]], dtype=bool)
print(x[y])
# array([2, 3, 4])
# Select element at (0, 0) and (1, 1)
print(x[[0, 1], [0, 1])
# array([1, 4])
# Select a subset of the rows and columns
x[[0, 1]][:,[1]]
# array([[2],
# [4]])
# `:,` means keep the rows, and you can repeat the columns as much as you want
x[[0,1]][:,[0, 1, 0, 1]]
# array([[1, 2, 1, 2],
# [3, 4, 3, 4]])# use Transpose
np.transpose(my_2d_array)
# Or use T
my_2d_array.TIf you pass your original array together with the new dimensions, and if that new array is larger than the one that you originally had, the new array will be filled with copies of the original array that are repeated as many times as is needed.
However, if you just apply np.resize() to the array and you pass the new shape to it, the new array will be filled with zeros.
print(x.shape)
# Resize x to ((6, 4))
np.resize((6,4))
# Alternative
x.resize((6,4))Besides resizing, you can also reshape your array. This means that you give a new shape to an array without changing its data
# Print the size to see what's possible
print(x.size)
# Reshape x to (2, 6)
print(x.reshape((2,6)))
# Flatten x
z = x.ravel()
print(x, x.shape, x.size)
# array([[1, 2],
[3, 4]])
# shape = (2,2)
# size = 4
np.append(x, [[1,2], [3,4]])
# array([1, 2, 3, 4, 1, 2, 3, 4])
# shape (8,)
# size 8
np.append(x, [[1,2], [3,4]], axis=0)
# array([[1, 2],
# [3, 4],
# [1, 2],
# [3, 4]])
# shape = (4, 2)
# size = 8
np.append(x, [[1,2], [3,4]], axis=1)
# array([[1, 2, 1, 2],
# [3, 4, 3, 4]])
# shape = (2, 4)
# size = 8# Insert 4 at index 1
np.insert(my_array, 1, 4)
# Delete the value at index 1
np.delete(my_array, [1])# Concatentate `my_array` and `x`
print(np.concatenate((my_array,x)))
# Stack arrays row-wise
print(np.vstack((my_array, my_2d_array)))
# Stack arrays row-wise
print(np.r_[my_resized_array, my_2d_array])
# Stack arrays horizontally
print(np.hstack((my_resized_array, my_2d_array)))
# Stack arrays column-wise
print(np.column_stack((my_resized_array, my_2d_array)))
# Stack arrays column-wise
print(np.c_[my_resized_array, my_2d_array])# Split `my_stacked_array` horizontally at the 2nd index
print(np.hsplit(my_stacked_array, 2))
# Split `my_stacked_array` vertically at the 2nd index
print(np.vsplit(my_stacked_array, 2))# Initialize your array
my_3d_array = np.array([[[1,2,3,4], [5,6,7,8]], [[1,2,3,4], [9,10,11,12]]], dtype=np.int64)
# Pass the array to `np.histogram()`
print(np.histogram(my_3d_array))
# Specify the number of bins
print(np.histogram(my_3d_array, bins=range(0,13)))# Import numpy and matplotlib
import numpy as np
import matplotlib.pyplot as plt
# Construct the histogram with a flattened 3d array and a range of bins
plt.hist(my_3d_array.ravel(), bins=range(0,13))
# Add a title to the plot
plt.title('Frequency of My 3D Array Elements')
# Show the plot
plt.show()- http://cs231n.github.io/python-numpy-tutorial/
- https://www.datacamp.com/community/tutorials/python-numpy-tutorial
$ python3 -m pip install mypyfrom typing import Dict, List, Tuple
numbers: List[int] = ["hello", "world"]
print(numbers)$ mypy main.py
main.py:4: error: List item 0 has incompatible type "str"; expected "int"
main.py:4: error: List item 1 has incompatible type "str"; expected "int"from typing import Dict, List, Tuple
# List example
numbers: List[str] = ["hello", "world"]
print(numbers)
# Dict example
names: Dict[str, str] = {
"first_name": "John",
"last_name": "Doe"
}
# Tuple example
data: Tuple[str, int, float] = ("Adam", 10, 3.4)