8. Combine & Split an Array.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr1 = np.array([[1,2,3,4], [1,2,3,4]])\n",
    "arr2 = np.array([[5,6,7,8], [5,6,7,8]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ```np.concatenate((a, b), axis=0)```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [5 6 7 8]\n",
      " [5 6 7 8]]\n"
     ]
    }
   ],
   "source": [
    "# concat along the row\n",
    "cat = np.concatenate((arr1, arr2), axis=0)        \n",
    "print(cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3 4 5 6 7 8]\n",
      " [1 2 3 4 5 6 7 8]]\n"
     ]
    }
   ],
   "source": [
    "# concat along the column\n",
    "cat = np.concatenate((arr1, arr2), axis=1)    \n",
    "print(cat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ```np.vstack((a, b))``` \n",
    "### ```np.r_[a, b]```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [5 6 7 8]\n",
      " [5 6 7 8]]\n"
     ]
    }
   ],
   "source": [
    "# stack arrays vertically\n",
    "cat = np.vstack((arr1, arr2))\n",
    "print(cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [5 6 7 8]\n",
      " [5 6 7 8]]\n"
     ]
    }
   ],
   "source": [
    "# stack arrays vertically\n",
    "cat = np.r_[arr1, arr2]\n",
    "print(cat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ```np.hstack((a, b))```\n",
    "### ```np.c_[a, b]```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3 4 5 6 7 8]\n",
      " [1 2 3 4 5 6 7 8]]\n"
     ]
    }
   ],
   "source": [
    "# stack arrays horizontally\n",
    "cat = np.hstack((arr1, arr2))\n",
    "print(cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3 4 5 6 7 8]\n",
      " [1 2 3 4 5 6 7 8]]\n"
     ]
    }
   ],
   "source": [
    "# stack arrays horizontally\n",
    "cat = np.c_[arr1, arr2]\n",
    "print(cat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### split an array "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.random.rand(6,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[1.85084671e-01, 8.19931716e-01, 1.88785472e-01, 7.32790656e-01,\n",
      "        2.32967008e-01, 4.27668094e-01],\n",
      "       [3.36885470e-01, 1.69041396e-01, 3.00931103e-01, 3.34405185e-01,\n",
      "        9.33827335e-05, 3.62887755e-01],\n",
      "       [3.88854091e-01, 3.62926147e-02, 2.89134857e-01, 4.08424369e-02,\n",
      "        4.54621961e-01, 6.10321623e-02]]), array([[0.02005274, 0.48136698, 0.61415211, 0.72337006, 0.901569  ,\n",
      "        0.4099042 ],\n",
      "       [0.0788683 , 0.0391728 , 0.7366417 , 0.16636387, 0.81541604,\n",
      "        0.50994101],\n",
      "       [0.03721661, 0.52077208, 0.57089404, 0.30703524, 0.86827655,\n",
      "        0.03858626]])]\n"
     ]
    }
   ],
   "source": [
    "# split the array vertically into n evenly spaced chunks\n",
    "arr1 = np.vsplit(arr, 2)\n",
    "print(arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[0.18508467, 0.81993172, 0.18878547],\n",
      "       [0.33688547, 0.1690414 , 0.3009311 ],\n",
      "       [0.38885409, 0.03629261, 0.28913486],\n",
      "       [0.02005274, 0.48136698, 0.61415211],\n",
      "       [0.0788683 , 0.0391728 , 0.7366417 ],\n",
      "       [0.03721661, 0.52077208, 0.57089404]]), array([[7.32790656e-01, 2.32967008e-01, 4.27668094e-01],\n",
      "       [3.34405185e-01, 9.33827335e-05, 3.62887755e-01],\n",
      "       [4.08424369e-02, 4.54621961e-01, 6.10321623e-02],\n",
      "       [7.23370058e-01, 9.01568997e-01, 4.09904205e-01],\n",
      "       [1.66363873e-01, 8.15416042e-01, 5.09941012e-01],\n",
      "       [3.07035238e-01, 8.68276552e-01, 3.85862638e-02]])]\n"
     ]
    }
   ],
   "source": [
    "# split the array horizontally into n evenly spaced chunks\n",
    "arr2 = np.hsplit(arr, 2)\n",
    "print(arr2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}