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Commit 38b5300

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KyubyongKyubyong
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‎Set_routines.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Set routines"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'1.11.2'"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.__version__"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"author = 'kyubyong. longinglove@nate.com'"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Making proper sets"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q1. Get unique elements and reconstruction indices from x. And reconstruct x."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"unique elements = [1 2 3 4 6]\n",
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"reconstruction indices = [0 1 4 3 1 2 1]\n",
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"reconstructed = [1 2 6 4 2 3 2]\n"
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]
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}
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],
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"source": [
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"x = np.array([1, 2, 6, 4, 2, 3, 2])\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Boolean operations"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q2. Create a boolean array of the same shape as x. If each element of x is present in y, the result will be True, otherwise False."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[ True True False False True]\n"
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]
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}
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],
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"source": [
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"x = np.array([0, 1, 2, 5, 0])\n",
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"y = np.array([0, 1])\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q3. Find the unique intersection of x and y."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0 1]\n"
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]
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}
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],
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"source": [
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"x = np.array([0, 1, 2, 5, 0])\n",
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"y = np.array([0, 1, 4])\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q4. Find the unique elements of x that are not present in y."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[2 5]\n"
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]
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}
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],
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"source": [
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"x = np.array([0, 1, 2, 5, 0])\n",
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"y = np.array([0, 1, 4])\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q5. Find the xor elements of x and y."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[2 4 5]\n"
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]
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}
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],
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"source": [
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"x = np.array([0, 1, 2, 5, 0])\n",
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"y = np.array([0, 1, 4])\n",
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"out1 = np.setxor1d(x, y)\n",
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"out2 = np.sort(np.concatenate((np.setdiff1d(x, y), np.setdiff1d(y, x))))\n",
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"assert np.allclose(out1, out2)\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q6. Find the union of x and y."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0 1 2 4 5]\n"
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]
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}
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],
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"source": [
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"x = np.array([0, 1, 2, 5, 0])\n",
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"y = np.array([0, 1, 4])\n",
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"out1 = np.union1d(x, y)\n",
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"out2 = np.sort(np.unique(np.concatenate((x, y))))\n",
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"assert np.allclose(out1, out2)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"language": "python",
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"name": "python2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

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