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stacking.py
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#!/usr/bin/python3
# -*- coding: utf-8 -*-
# Author: xurongzhong#126.com 技术支持qq群：630011153
# CreateDate: 2018-04-12
import numpy as np
# Chapter 2 Beginning with NumPy fundamentals
#
# Demonstrates array stacking.
#
# Run from the commandline with
#
# python stacking.py
print("In: a = arange(9).reshape(3,3)")
a = np.arange(9).reshape(3,3)
print("In: a")
print(a)
#Out:
#array([[0, 1, 2],
# [3, 4, 5],
# [6, 7, 8]])
print("In: b = 2 * a")
b = 2 * a
print("In: b")
print(b)
#Out:
#array([[ 0, 2, 4],
# [ 6, 8, 10],
# [12, 14, 16]])
# 水平组合
print("In: hstack((a, b))")
print(np.hstack((a, b)))
#Out:
#array([[ 0, 1, 2, 0, 2, 4],
# [ 3, 4, 5, 6, 8, 10],
# [ 6, 7, 8, 12, 14, 16]])
print("In: concatenate((a, b), axis=1)")
print(np.concatenate((a, b), axis=1))
#Out:
#array([[ 0, 1, 2, 0, 2, 4],
# [ 3, 4, 5, 6, 8, 10],
# [ 6, 7, 8, 12, 14, 16]])
# 垂直组合
print("In: vstack((a, b))")
print(np.vstack((a, b)))
#Out:
#array([[ 0, 1, 2],
# [ 3, 4, 5],
# [ 6, 7, 8],
# [ 0, 2, 4],
# [ 6, 8, 10],
# [12, 14, 16]])
#
print("In: concatenate((a, b), axis=0)")
print(np.concatenate((a, b), axis=0))
#Out:
#array([[ 0, 1, 2],
# [ 3, 4, 5],
# [ 6, 7, 8],
# [ 0, 2, 4],
# [ 6, 8, 10],
# [12, 14, 16]])
# 深度组合
print("In: dstack((a, b))")
print(np.dstack((a, b)))
#Out:
#array([[[ 0, 0],
# [ 1, 2],
# [ 2, 4]],
#
# [[ 3, 6],
# [ 4, 8],
# [ 5, 10]],
#
# [[ 6, 12],
# [ 7, 14],
# [ 8, 16]]])
# 列组合
print("In: oned = arange(2)")
oned = np.arange(2)
print("In: oned")
print(oned)
#Out: array([0, 1])
print("In: twice_oned = 2 * oned")
twice_oned = 2 * oned
print("In: twice_oned")
print(twice_oned)
#Out: array([0, 2])
print("In: column_stack((oned, twice_oned))")
print(np.column_stack((oned, twice_oned)))
#Out:
#array([[0, 0],
# [1, 2]])
print("In: column_stack((a, b))")
print(np.column_stack((a, b)))
#Out:
#array([[ 0, 1, 2, 0, 2, 4],
# [ 3, 4, 5, 6, 8, 10],
# [ 6, 7, 8, 12, 14, 16]])
print("In: column_stack((a, b)) == hstack((a, b))")
print(np.column_stack((a, b)) == np.hstack((a, b)))
#Out:
#array([[ True, True, True, True, True, True],
# [ True, True, True, True, True, True],
# [ True, True, True, True, True, True]], dtype=bool)
# 行组合
# 对于两个一维数组，将直接层叠起来组合成一个二维数组。
print("In: row_stack((oned, twice_oned))")
print(np.row_stack((oned, twice_oned)))
#Out:
#array([[0, 1],
# [0, 2]])
# 对于二维数组， row_stack 与 vstack 的效果是相同的：
print("In: row_stack((a, b))")
print(np.row_stack((a, b)))
#Out:
#array([[ 0, 1, 2],
# [ 3, 4, 5],
# [ 6, 7, 8],
# [ 0, 2, 4],
# [ 6, 8, 10],
# [12, 14, 16]])
print("In: row_stack((a,b)) == vstack((a, b))")
print(np.row_stack((a,b)) == np.vstack((a, b)))
#Out:
#array([[ True, True, True],
# [ True, True, True],
# [ True, True, True],
# [ True, True, True],
# [ True, True, True],
# [ True, True, True]], dtype=bool)