{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": "## Basic array manipulations"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": "import numpy as np"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": "x = np.arange(1, 11)"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": "x"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": "array([[ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10]])"
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": "x_row = x.reshape((1, -1))\nx_row"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": "array([[ 1],\n       [ 2],\n       [ 3],\n       [ 4],\n       [ 5],\n       [ 6],\n       [ 7],\n       [ 8],\n       [ 9],\n       [10]])"
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": "x_col = x[:, np.newaxis]\nx_col"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": "array([[  1,   2,   3,   4,   5,   6,   7,   8,   9,  10],\n       [  2,   4,   6,   8,  10,  12,  14,  16,  18,  20],\n       [  3,   6,   9,  12,  15,  18,  21,  24,  27,  30],\n       [  4,   8,  12,  16,  20,  24,  28,  32,  36,  40],\n       [  5,  10,  15,  20,  25,  30,  35,  40,  45,  50],\n       [  6,  12,  18,  24,  30,  36,  42,  48,  54,  60],\n       [  7,  14,  21,  28,  35,  42,  49,  56,  63,  70],\n       [  8,  16,  24,  32,  40,  48,  56,  64,  72,  80],\n       [  9,  18,  27,  36,  45,  54,  63,  72,  81,  90],\n       [ 10,  20,  30,  40,  50,  60,  70,  80,  90, 100]])"
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": "np.dot(x_col, x_row)"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": "array([[  1,   4,   9,  16,  25,  36,  49,  64,  81, 100]])"
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": "x_row * x_row"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": "array([[  1,   2,   3,  ...,   9,  10],\n       [  2,   4,   6,  ...,  18,  20],\n       ...\n       [  9,  18,  27,  ...,  81,  90],\n       [ 10,  20,  30,  ...,  90, 100]])"
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": "x_row * x_col"
    }
  ],
  "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 0
}