diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml deleted file mode 100644 index efead2d0..00000000 --- a/.github/workflows/main.yml +++ /dev/null @@ -1,57 +0,0 @@ -name: Build Documentation using MkDocs - -# Controls when the action will run. Triggers the workflow on push or pull request -# events but only for the master branch -on: - push: - branches: [master] - pull_request: - branches: [master] - -env: - GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}} - -permissions: - contents: 'write' - id-token: 'write' - pull-requests: 'write' -jobs: - build: - name: Build and Deploy Documentation - runs-on: ubuntu-latest - - steps: - - name: Checkout Branch - uses: actions/checkout@v3 - if: github.event_name == 'pull_request' - with: - fetch-depth: 0 - ref: ${{ github.event.pull_request.head.ref }} - persist-credentials: false - - - name: Checkout Master - uses: actions/checkout@v3 - - if: github.event_name == 'push' - with: - fetch-depth: 0 - persist-credentials: false - - - name: Set up Python 3.7 - uses: actions/setup-python@v2 - with: - python-version: '3.x' - - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install mkdocs-material - - - name: Check - run: | - pwd - ls -la - - name: Deploy - run: | - git pull - mkdocs gh-deploy diff --git a/.gitignore b/.gitignore deleted file mode 100644 index b7fb052b..00000000 --- a/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -site/ -.DS_Store -~$* diff --git a/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/.ipynb_checkpoints/Untitled-checkpoint.ipynb deleted file mode 100644 index 7fec5150..00000000 --- a/.ipynb_checkpoints/Untitled-checkpoint.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/.ipynb_checkpoints/Untitled1-checkpoint.ipynb b/.ipynb_checkpoints/Untitled1-checkpoint.ipynb deleted file mode 100644 index 7fec5150..00000000 --- a/.ipynb_checkpoints/Untitled1-checkpoint.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/.ipynb_checkpoints/Untitled2-checkpoint.ipynb b/.ipynb_checkpoints/Untitled2-checkpoint.ipynb deleted file mode 100644 index 7fec5150..00000000 --- a/.ipynb_checkpoints/Untitled2-checkpoint.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/ops/01-intro.md b/.nojekyll similarity index 100% rename from src/ops/01-intro.md rename to .nojekyll diff --git a/.vscode/settings.json b/.vscode/settings.json deleted file mode 100644 index 9a49861c..00000000 --- a/.vscode/settings.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "cSpell.words": [ - "Mojhave", - "Trinkit", - "coderdojo", - "jupyter", - "randint" - ] -} \ No newline at end of file diff --git a/404.html b/404.html new file mode 100644 index 00000000..90935e13 --- /dev/null +++ b/404.html @@ -0,0 +1,1864 @@ + + + + + + + + + + + + + + + + + + + + + + + CoderDojo TC Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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- + + + + + Debugging + + + + +
- + + + + + Plotting sine + + + + +
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- + + + + + Installing Python + + + + +
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+ + + + + + + + + + \ No newline at end of file diff --git a/Jenkinsfile b/Jenkinsfile deleted file mode 100644 index e5fe164b..00000000 --- a/Jenkinsfile +++ /dev/null @@ -1,30 +0,0 @@ -pipeline { - agent { label 'docker-kitchensink-slave' } // docker-kitchensink-slave is available on jenkins central, update this for other jenkins instances accordingly - environment { - ORG = 'CoderDojoTC' - REPO = 'python' - CREDENTIALS_ID = '{{JenkinsCredentialId}}' // This needs to be updated with your jenkins credential, having access to the this github repository - - // do not change any parameters below - MKDOCS_VERSION = '1.0.4' - LC_ALL='en_US.utf-8' - LANG='en_US.utf-8' - } - stages { - stage('Build & Deploy Docs') { - when { branch 'master' } - steps { - withCredentials([usernamePassword(credentialsId: "$env.CREDENTIALS_ID", usernameVariable: 'USERNAME', passwordVariable: 'PASSWORD')]) { - sh """ - . /etc/profile.d/jenkins.sh - git config --global user.name "${USERNAME}" - git remote set-url origin https://${USERNAME}:${PASSWORD}@github.com/${env.ORG}/${env.REPO}.git - export GIT_USER=${USERNAME}:${PASSWORD} - export GITHUB_HOST=github.com - mkdocs gh-deploy --force - """ - } - } - } - } -} diff --git a/README.md b/README.md deleted file mode 100644 index f3e332dd..00000000 --- a/README.md +++ /dev/null @@ -1,8 +0,0 @@ -# python -Tools, content to and tutorials help teach python to CoderDojo students. - -Documentation:[https://coderdojotc.github.io/python/](https://coderdojotc.github.io/python/) - -All content is licensed as free to use by all educational institutions according to the Creative Commons Share Alike Attribution 4.2 License: - -https://creativecommons.org/licenses/by-sa/4.0/legalcode#s3b diff --git a/advanced-python/01-intro/index.html b/advanced-python/01-intro/index.html new file mode 100644 index 00000000..9d3dee02 --- /dev/null +++ b/advanced-python/01-intro/index.html @@ -0,0 +1,1912 @@ + + + + + + + + + + + + + + + + + + + + + + + Advanced Python - CoderDojo TC Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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+ +

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+ +

+ + + + + CoderDojo TC + + + + +
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+ + + Beginning Python - Trinket + +
- + + + + + Introduction + + + + +
- + + + + + Setting Up Trinket + + + + +
- + + + + + Turtle Graphics + + + + +
- + + + + + Turtle Square + + + + +
- + + + + + Variables + + + + +
- + + + + + Loops + + + + +
- + + + + + Stop Sign + + + + +
- + + + + + Conditional + + + + +
- + + + + + Functions + + + + +
- + + + + + Flower + + + + +
- + + + + + Function Parameters + + + + +
- + + + + + Lists + + + + +
- + + + + + List of Turtle Shapes + + + + +
- + + + + + Random Numbers + + + + +
- + + + + + Random Stars + + + + +
- + + + + + Input + + + + +
- + + + + + Recursion + + + + +
- + + + + + Shape Module + + + + +
- + + + + + Color Picker + + + + +
- + + + + + Sine Wave + + + + +
- + + + + + Changing background + + + + +
- + + + + + Controlling mouseclicks + + + + +
- + + + + + Other examples + + + + +
+
+ +
+ + + + + + + + + + + Beginning Python - Repl.it + + + + + + +
+ + + Beginning Python - Repl.it + +
- + + + + + Square + + + + +
- + + + + + Flower + + + + +
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- + + + + + Recursion + + + + +
+
+ +
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+ + + Intermediate Python + +
- + + + + + Introduction + + + + +
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- + + + + + Data Types + + + + +
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- + + + + + Modules + + + + +
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- + + + + + Regular Expressions + + + + +
- + + + + + Debugging + + + + +
- + + + + + Plotting sine + + + + +
- + + + + + Maze Solving with BFS + + + + +
- + + + + + Maze Solving with DFS + + + + +
- + + + + + Installing Python + + + + +
+
+ +
+ + + + + + + + + + + Jupyter + + + + + + +
+ + + Jupyter + +
- + + + + + Setup Turtle on Jupyter + + + + +
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- + + + + + Drawing Figure + + + + +
- + + + + + Setup Canvas on Jupyter Lab + + + + +
- + + + + + Canvas Color Bars + + + + +
+
+ +
+ + + + + + + + + + + Pi + + + + + + +
+ + + Pi + +
- + + + + + Python on Raspberry Pi + + + + +
+
+ +
+ + + + + + + + + + + Advanced Python + + + + + + +
+ + + Advanced Python + +
- + + + + + Stock Ticker Analysis + + + + +
- + + + + + MNIST Digit Prediction + + + + +
+
+ +
+ + + + + + + + + + + Strategy + + + + + + +
+ + + Strategy + +
- + + + + + Introduction + + + + +
- + + + + + Order of Concepts + + + + +
+
+ +
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+
+ +

+ + + + + + + +

Advanced Python¶

Here are some suggested programs for doing more advanced Python programming. Here we define advanced as anything that is not covered under our Beginning Python class that usually runs between one and three hours.

Roll the Dice¶

This lab uses the random number function to generate dice rolls. It uses a while loop to continue to roll a pair of six-sided dice repeatedly. +[./02-roll-the-dice.md]

+ + + + + + + + + + + + + + + +

+ + + +

+ +

+ + + + +

+ + + + + + + + + + \ No newline at end of file diff --git a/advanced-python/02-roll-the-dice/index.html b/advanced-python/02-roll-the-dice/index.html new file mode 100644 index 00000000..197eb40e --- /dev/null +++ b/advanced-python/02-roll-the-dice/index.html @@ -0,0 +1,1910 @@ + + + + + + + + + + + + + + + + + + + + + + + Roll the Dice - CoderDojo TC Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+ + + + Skip to content + + +

+ +

+ + + + + + +

+ + +

+ + + + + + +

+ + CoderDojo TC Python + +

+ + + Roll the Dice + + +

+ + + + + + + + + + + + + +

+ +

+ + +

+ GitHub Repo +

+ +

+ + + + + + +

+ + + +

+ + + + +

+ + + +

+ + + CoderDojo TC Python + + +

+ +

+ + +

+ GitHub Repo +

+ +

+ + + + + CoderDojo TC + + + + +
+ + + + + + + + + + + Beginning Python - Trinket + + + + + + +
+ + + Beginning Python - Trinket + +
- + + + + + Introduction + + + + +
- + + + + + Setting Up Trinket + + + + +
- + + + + + Turtle Graphics + + + + +
- + + + + + Turtle Square + + + + +
- + + + + + Variables + + + + +
- + + + + + Loops + + + + +
- + + + + + Stop Sign + + + + +
- + + + + + Conditional + + + + +
- + + + + + Functions + + + + +
- + + + + + Flower + + + + +
- + + + + + Function Parameters + + + + +
- + + + + + Lists + + + + +
- + + + + + List of Turtle Shapes + + + + +
- + + + + + Random Numbers + + + + +
- + + + + + Random Stars + + + + +
- + + + + + Input + + + + +
- + + + + + Recursion + + + + +
- + + + + + Shape Module + + + + +
- + + + + + Color Picker + + + + +
- + + + + + Sine Wave + + + + +
- + + + + + Changing background + + + + +
- + + + + + Controlling mouseclicks + + + + +
- + + + + + Other examples + + + + +
+
+ +
+ + + + + + + + + + + Beginning Python - Repl.it + + + + + + +
+ + + Beginning Python - Repl.it + +
- + + + + + Square + + + + +
- + + + + + Flower + + + + +
- + + + + + Shape Function + + + + +
- + + + + + Recursion + + + + +
+
+ +
+ + + + + + + + + + + Intermediate Python + + + + + + +
+ + + Intermediate Python + +
- + + + + + Introduction + + + + +
- + + + + + Variables and Scope + + + + +
- + + + + + Data Types + + + + +
- + + + + + Data Types & Function Parameters + + + + +
- + + + + + Maps + + + + +
- + + + + + Files (local file system) + + + + +
- + + + + + Images (local file system) + + + + +
- + + + + + JSON + + + + +
- + + + + + Object Classes + + + + +
- + + + + + Modules + + + + +
- + + + + + Dir + + + + +
- + + + + + Regular Expressions + + + + +
- + + + + + Debugging + + + + +
- + + + + + Plotting sine + + + + +
- + + + + + Maze Solving with BFS + + + + +
- + + + + + Maze Solving with DFS + + + + +
- + + + + + Installing Python + + + + +
+
+ +
+ + + + + + + + + + + Jupyter + + + + + + +
+ + + Jupyter + +
- + + + + + Setup Turtle on Jupyter + + + + +
- + + + + + Drawing Box Labs + + + + +
- + + + + + Drawing Figure + + + + +
- + + + + + Setup Canvas on Jupyter Lab + + + + +
- + + + + + Canvas Color Bars + + + + +
+
+ +
+ + + + + + + + + + + Pi + + + + + + +
+ + + Pi + +
- + + + + + Python on Raspberry Pi + + + + +
+
+ +
+ + + + + + + + + + + Advanced Python + + + + + + +
+ + + Advanced Python + +
- + + + + + Stock Ticker Analysis + + + + +
- + + + + + MNIST Digit Prediction + + + + +
+
+ +
+ + + + + + + + + + + Strategy + + + + + + +
+ + + Strategy + +
- + + + + + Introduction + + + + +
- + + + + + Order of Concepts + + + + +
+
+ +
+ + + + + + + + + + + Contributing + + + + + + +
+ + + Contributing + +
- + + + + + How to Contribute Examples + + + + +
+
+ +
+ + + + + + + + + + + Contact + + + + + + +
+ + + Contact + +
- + + + + + Contact + + + + +
+
+ +

+ + + + + + + +

Roll the Dice¶

In this game the program generates two random numbers between 1 and 6. The program then asks the user if they want to roll the dice again. If they answer y or yes then the pair of +dice are rolled again.

import random
+min = 1
+max = 6
+
+roll_again = "yes"
+
+while roll_again == "yes" or roll_again == "y":
+    print("Rolling the dice.  The values are:")
+    print(random.randint(min, max))
+    print(random.randint(min, max))
+    roll_again = input("Roll the dices again?")
+

Rolling the dice.  The values are:
+4
+4
+Roll the dices again?
+

+ + + + + + + + + + + + + + + +

+ + + +

+ +

+ + + + +

+ + + + + + + + + + \ No newline at end of file diff --git a/docs/advanced-python/roll-dice.py b/advanced-python/roll-dice.py similarity index 100% rename from docs/advanced-python/roll-dice.py rename to advanced-python/roll-dice.py diff --git a/advanced/dataAnalysis/stockTrader.ipynb b/advanced/dataAnalysis/stockTrader.ipynb deleted file mode 100644 index 76b9c9f0..00000000 --- a/advanced/dataAnalysis/stockTrader.ipynb +++ /dev/null @@ -1,2861 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from yahoofinancials import YahooFinancials\n", - "import pandas as pd\n", - "\n", - "stocks = [\"MMM\", \"DE\", \"MDT\", \"MSFT\", \"NVDA\", \"PRLB\", \"UNH\", \"DIS\", \"SPY\", \"SPYD\", \"TSLA\", \"AMZN\", \"GOOG\"]\n", - "stockData = {}\n", - "yahoo_financials = YahooFinancials(stocks)\n", - "data = yahoo_financials.get_historical_price_data(start_date=\"2018-01-01\", end_date=\"2020-06-27\", time_interval=\"daily\")\n", - "\n", - "metrics = [\"close\", \"volume\", \"high\", \"low\"]\n", - "for stock in stocks:\n", - " stockData[stock] = pd.DataFrame({stock + \"_\"+i: {x['formatted_date']: x[i] for x in data[stock]['prices']} for i in metrics})\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": " MMM_close MMM_volume MMM_high MMM_low DE_close \\\n2018-01-02 235.639999 2931000 237.070007 232.809998 158.009995 \n2018-01-03 235.630005 2193700 235.729996 233.289993 158.729996 \n2018-01-04 238.710007 2243100 239.440002 236.470001 159.639999 \n2018-01-05 240.570007 1835900 240.899994 237.740005 160.320007 \n2018-01-08 239.789993 1869000 240.940002 239.179993 161.110001 \n\n DE_volume DE_high DE_low MDT_close MDT_volume ... \\\n2018-01-02 1897600 158.059998 156.550003 82.209999 4783500 ... \n2018-01-03 1392300 159.100006 157.639999 83.919998 6879100 ... \n2018-01-04 1747000 160.100006 157.350006 84.120003 5943200 ... \n2018-01-05 1574400 160.470001 156.589996 85.540001 7428500 ... \n2018-01-08 2711800 161.179993 158.899994 85.510002 6117600 ... \n\n TSLA_high TSLA_low AMZN_close AMZN_volume AMZN_high \\\n2018-01-02 322.109985 311.000000 1189.010010 2694500 1190.000000 \n2018-01-03 325.250000 315.549988 1204.199951 3108800 1205.489990 \n2018-01-04 318.549988 305.679993 1209.589966 3022100 1215.869995 \n2018-01-05 317.239990 312.000000 1229.140015 3544700 1229.140015 \n2018-01-08 337.019989 315.500000 1246.869995 4279500 1253.079956 \n\n AMZN_low GOOG_close GOOG_volume GOOG_high GOOG_low \n2018-01-02 1170.510010 1065.000000 1237600 1066.939941 1045.229980 \n2018-01-03 1188.300049 1082.479980 1430200 1086.290039 1063.209961 \n2018-01-04 1204.660034 1086.400024 1004600 1093.569946 1084.001953 \n2018-01-05 1210.000000 1102.229980 1279100 1104.250000 1092.000000 \n2018-01-08 1232.030029 1106.939941 1047600 1111.270020 1101.619995 \n\n[5 rows x 52 columns]", - "text/html": "

\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

	MMM_close	MMM_volume	MMM_high	MMM_low	DE_close	DE_volume	DE_high	DE_low	MDT_close	MDT_volume	...	TSLA_high	TSLA_low	AMZN_close	AMZN_volume	AMZN_high	AMZN_low	GOOG_close	GOOG_volume	GOOG_high	GOOG_low
2018-01-02	235.639999	2931000	237.070007	232.809998	158.009995	1897600	158.059998	156.550003	82.209999	4783500	...	322.109985	311.000000	1189.010010	2694500	1190.000000	1170.510010	1065.000000	1237600	1066.939941	1045.229980
2018-01-03	235.630005	2193700	235.729996	233.289993	158.729996	1392300	159.100006	157.639999	83.919998	6879100	...	325.250000	315.549988	1204.199951	3108800	1205.489990	1188.300049	1082.479980	1430200	1086.290039	1063.209961
2018-01-04	238.710007	2243100	239.440002	236.470001	159.639999	1747000	160.100006	157.350006	84.120003	5943200	...	318.549988	305.679993	1209.589966	3022100	1215.869995	1204.660034	1086.400024	1004600	1093.569946	1084.001953
2018-01-05	240.570007	1835900	240.899994	237.740005	160.320007	1574400	160.470001	156.589996	85.540001	7428500	...	317.239990	312.000000	1229.140015	3544700	1229.140015	1210.000000	1102.229980	1279100	1104.250000	1092.000000
2018-01-08	239.789993	1869000	240.940002	239.179993	161.110001	2711800	161.179993	158.899994	85.510002	6117600	...	337.019989	315.500000	1246.869995	4279500	1253.079956	1232.030029	1106.939941	1047600	1111.270020	1101.619995

5 rows × 52 columns

" - }, - "metadata": {}, - "execution_count": 2 - } - ], - "source": [ - "data = stockData[list(stockData.keys())[0]]\n", - "for ticker in list(stockData.keys())[1:]:\n", - " data = data.merge(stockData[ticker], left_index=True, right_index=True, how=\"outer\")\n", - "\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "for stock in stocks:\n", - " data[\"5DayMA_\"+stock] = data[stock+\"_close\"].rolling(window=5).mean()\n", - " data[\"10DayMA_\"+stock] = data[stock+\"_close\"].rolling(window=10).mean()\n", - " data[\"20DayMA_\"+stock] = data[stock+\"_close\"].rolling(window=20).mean()\n", - " data[\"50DayMA_\"+stock] = data[stock+\"_close\"].rolling(window=50).mean()\n", - " data[\"100DayMA_\"+stock] = data[stock+\"_close\"].rolling(window=100).mean()\n", - " data[\"200DayMA_\"+stock] = data[stock+\"_close\"].rolling(window=200).mean()\n", - "\n", - "data.dropna(inplace=True)\n", - "\n", - "data[\"numericalIndex\"] = [i for i in range(0, len(data))]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "class Stock:\n", - " def __init__(self, ticker, numberOfShares, price):\n", - " self.ticker = ticker\n", - " self.numberOfShares = numberOfShares\n", - " self.price = price\n", - " \n", - " def __repr__(self):\n", - " return(self.ticker + \": \" + str(self.numberOfShares) + \" @ $\"+str(self.price))\n", - "\n", - "cash = 0 \n", - "portfolio = []" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": " index MMM_close MMM_volume MMM_high MMM_low DE_close \\\n0 2018-10-16 202.330002 1939500 202.520004 199.229996 151.960007 \n1 2018-10-17 202.929993 1824200 203.770004 200.449997 151.639999 \n2 2018-10-18 200.380005 2339400 202.520004 198.399994 147.199997 \n3 2018-10-19 199.850006 3167300 201.100006 198.369995 145.910004 \n4 2018-10-22 201.360001 3263300 203.399994 199.690002 145.440002 \n\n DE_volume DE_high DE_low MDT_close ... 50DayMA_AMZN \\\n0 2124500 152.320007 148.210007 96.550003 ... 1920.197803 \n1 1516000 152.479996 149.429993 97.290001 ... 1919.582803 \n2 2491800 151.059998 145.710007 95.919998 ... 1917.266802 \n3 1901500 149.000000 145.369995 93.940002 ... 1914.577002 \n4 1702300 146.949997 144.330002 93.620003 ... 1912.637002 \n\n 100DayMA_AMZN 200DayMA_AMZN 5DayMA_GOOG 10DayMA_GOOG 20DayMA_GOOG \\\n0 1829.428901 1648.138599 1096.829980 1130.042969 1157.325983 \n1 1831.644701 1651.352199 1103.723975 1121.316968 1154.555981 \n2 1833.223201 1654.184799 1105.453979 1113.294971 1149.610980 \n3 1834.614601 1656.957000 1102.729980 1107.205969 1146.129480 \n4 1836.211401 1659.757800 1104.511987 1102.424976 1142.518982 \n\n 50DayMA_GOOG 100DayMA_GOOG 200DayMA_GOOG numericalIndex \n0 1186.640586 1173.449993 1128.366644 0 \n1 1184.109985 1173.850292 1128.620094 1 \n2 1180.957185 1174.126792 1128.647544 2 \n3 1177.904385 1174.413391 1128.697843 3 \n4 1175.175386 1174.575092 1128.692494 4 \n\n[5 rows x 132 columns]", - "text/html": "

	index	MMM_close	MMM_volume	MMM_high	MMM_low	DE_close	DE_volume	DE_high	DE_low	MDT_close	...	50DayMA_AMZN	100DayMA_AMZN	200DayMA_AMZN	5DayMA_GOOG	10DayMA_GOOG	20DayMA_GOOG	50DayMA_GOOG	100DayMA_GOOG	200DayMA_GOOG	numericalIndex
0	2018-10-16	202.330002	1939500	202.520004	199.229996	151.960007	2124500	152.320007	148.210007	96.550003	...	1920.197803	1829.428901	1648.138599	1096.829980	1130.042969	1157.325983	1186.640586	1173.449993	1128.366644	0
1	2018-10-17	202.929993	1824200	203.770004	200.449997	151.639999	1516000	152.479996	149.429993	97.290001	...	1919.582803	1831.644701	1651.352199	1103.723975	1121.316968	1154.555981	1184.109985	1173.850292	1128.620094	1
2	2018-10-18	200.380005	2339400	202.520004	198.399994	147.199997	2491800	151.059998	145.710007	95.919998	...	1917.266802	1833.223201	1654.184799	1105.453979	1113.294971	1149.610980	1180.957185	1174.126792	1128.647544	2
3	2018-10-19	199.850006	3167300	201.100006	198.369995	145.910004	1901500	149.000000	145.369995	93.940002	...	1914.577002	1834.614601	1656.957000	1102.729980	1107.205969	1146.129480	1177.904385	1174.413391	1128.697843	3
4	2018-10-22	201.360001	3263300	203.399994	199.690002	145.440002	1702300	146.949997	144.330002	93.620003	...	1912.637002	1836.211401	1659.757800	1104.511987	1102.424976	1142.518982	1175.175386	1174.575092	1128.692494	4

5 rows × 132 columns

" - }, - "metadata": {}, - "execution_count": 5 - } - ], - "source": [ - "data = data.reset_index()\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def getAssetValue(cash, portfolio, index):\n", - " assets = cash\n", - " for stock in portfolio:\n", - " assets += stock.numberOfShares * data.loc[index, stock.ticker+\"_close\"]\n", - " return(assets)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": "Selling: SPYD: 3 @ $37.33299942016602 for $37.151099853515625\nSelling: SPYD: 3 @ $37.097999572753906 for $36.87899978637695\nSelling: TSLA: 3 @ $294.9790008544922 for $333.3902014160156\nSelling: DE: 7 @ $145.3280014038086 for $160.21339996337892\nSelling: SPYD: 2 @ $35.92400016784668 for $38.14839988708496\nSelling: DIS: 10 @ $112.3859992980957 for $140.51160034179688\nSelling: MSFT: 10 @ $136.5729995727539 for $136.78259948730468\nSelling: MDT: 1 @ $89.18499984741212 for $107.44320083618165\nSelling: SPYD: 3 @ $37.23299980163574 for $39.09300033569336\nSelling: MDT: 1 @ $108.025 for $116.2715997314453\nSelling: SPYD: 3 @ $39.25299987792969 for $39.20900009155273\nSelling: NVDA: 8 @ $162.36999969482423 for $256.43160125732425\n" - } - ], - "source": [ - "import random as rd\n", - "cash = 1000\n", - "spycash = 1000\n", - "portfolio = []\n", - "spyportfolio = []\n", - "data[\"portfolioValue\"] = 0\n", - "data[\"spyPortfolioValue\"] = 0\n", - "\n", - "for i, row in data.tail(len(data)-1).iterrows():\n", - " if i == 1:\n", - " purchasePrice = data.loc[i, \"SPY_close\"]\n", - " numberOfShares = int(spycash/purchasePrice)\n", - " spyportfolio.append(Stock(\"SPY\", numberOfShares, purchasePrice))\n", - " spycash -= numberOfShares * purchasePrice\n", - " data.loc[i, \"spyPortfolioValue\"] = getAssetValue(spycash, spyportfolio, i)\n", - "\n", - " rd.shuffle(stocks)\n", - " for stock in stocks:\n", - " shortTerm = \"10DayMA_\"+stock\n", - " longTerm = \"50DayMA_\" + stock\n", - " close = stock + \"_close\"\n", - " if row[shortTerm] >= row[longTerm] and data.loc[i-1, shortTerm] < data.loc[i-1, longTerm] and cash > data.loc[i, close]:\n", - " purchasePrice = data.loc[i, shortTerm]\n", - " numberOfShares = int(cash/purchasePrice)\n", - " purchased = Stock(stock, numberOfShares, purchasePrice)\n", - " portfolio.append(purchased)\n", - " cash -= purchasePrice * numberOfShares\n", - " #print(\"Purchasing: \"+ str(purchased))\n", - " elif row[shortTerm] <= row[longTerm] and data.loc[i-1, shortTerm] > data.loc[i-1, longTerm]:\n", - " sold = []\n", - " for position in portfolio:\n", - " if position.ticker == stock:\n", - " sold.append(position)\n", - " cash += data.loc[i, longTerm] * position.numberOfShares\n", - " print(\"Selling: \" + str(position) + \" for $\" + str(data.loc[i, longTerm]))\n", - " for item in sold:\n", - " portfolio.remove(item)\n", - " data.loc[i, \"portfolioValue\"] = getAssetValue(cash, portfolio, i)\n", - " data.loc[i, \"spyPortfolioValue\"] = getAssetValue(spycash, spyportfolio, i)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "0.06512714386008156" - }, - "metadata": {}, - "execution_count": 8 - } - ], - "source": [ - "cash" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "[NVDA: 8 @ $255.46599884033202,\n MSFT: 1 @ $165.625,\n SPYD: 3 @ $27.01900005340576]" - }, - "metadata": {}, - "execution_count": 9 - } - ], - "source": [ - "portfolio" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "3206.6952254867556" - }, - "metadata": {}, - "execution_count": 10 - } - ], - "source": [ - "getAssetValue(cash, portfolio, i)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "1058.7999267578125" - }, - "metadata": {}, - "execution_count": 11 - } - ], - "source": [ - "getAssetValue(spycash, spyportfolio, i)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - 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" - }, - "metadata": {} - } - ], - "source": [ - "import plotly.express as px\n", - "import plotly.graph_objects as go\n", - "\n", - "data = data.tail(len(data)-1)\n", - "\n", - "fig = px.line()\n", - "fig.add_trace(go.Scatter(x=data[\"index\"], y=data[\"portfolioValue\"], mode=\"lines\", name=\"Active Trading\"))\n", - "fig.add_trace(go.Scatter(x=data[\"index\"], y=data[\"spyPortfolioValue\"], mode=\"lines\", name=\"SPY Portfolio\"))\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9-final" - }, - "orig_nbformat": 2, - "kernelspec": { - "name": "python36964bit5e4270a542e54d6788076e0986af2669", - "display_name": "Python 3.6.9 64-bit" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/advanced/machineLearning/mnist.ipynb b/advanced/machineLearning/mnist.ipynb deleted file mode 100644 index 29c831cc..00000000 --- a/advanced/machineLearning/mnist.ipynb +++ /dev/null @@ -1,333 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Intro to Machine Learning\n", - "Parker Erickson" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# But What *Is* Machine Learnig?\n", - "\n", - "Process of determining the function that gives a plausible result **y** for a set of inputs, in form of a vector **x**. In its simplest form, this could be linear regression, where we find the line of best fit through the data.\n", - "\n", - "In more complex forms, however, this can be training a neural network to fit very non-linear data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Image of Machine Learning Algorithms](https://www.7wdata.be/wp-content/uploads/2017/04/CheatSheet.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Neural Networks\n", - "![Types of Neural Networks](https://miro.medium.com/max/4000/1*cuTSPlTq0a_327iTPJyD-Q.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Hand Written Digit Classification\n", - "\n", - "Since we have image data, we will use a Convolutional Neural Network (CNN). A CNN works by sliding \"windows\" over the input image (represented as a matrix).\n", - "\n", - "![CNN Windowing](https://miro.medium.com/max/790/1*nYf_cUIHFEWU1JXGwnz-Ig.gif)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Packages" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import keras\n", - "from keras.datasets import mnist # Get dataset\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense, Dropout, Flatten # Need these types of layers\n", - "from keras.layers import Conv2D, MaxPooling2D # Need convolutional layers\n", - "\n", - "import matplotlib.pyplot as plt \n", - "import matplotlib.image as mpimg\n", - "\n", - "import numpy as np " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load Data \n", - "\n", - "Important to load both a training and testing set to make sure the CNN is not \"memorizing\" the set of images it will train on. This would lead to awful accuracy in the \"real world\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# input image dimensions\n", - "img_rows, img_cols = 28, 28\n", - "\n", - "# the data, split between train and test sets\n", - "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", - "\n", - "x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n", - "x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n", - "input_shape = (img_rows, img_cols, 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Preprocessing Data\n", - "\n", - "Make sure the data is in the right format and normalize the grayscale value so that it is in between 0 and 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x_train = x_train.astype('float32')\n", - "x_test = x_test.astype('float32')\n", - "x_train /= 255\n", - "x_test /= 255\n", - "print('x_train shape:', x_train.shape)\n", - "print(x_train.shape[0], 'train samples')\n", - "print(x_test.shape[0], 'test samples')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example of Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x_train[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pixels = x_train[0].reshape((28, 28))\n", - "\n", - "plt.imshow(pixels, cmap='gray')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(y_train[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup Desired Output\n", - "\n", - "Convert a single number n to a vector where the nth element is 1 and the rest are 0.\n", - "\n", - "Example: n = 5\n", - "Resulting Vector = {0, 0, 0, 0, 0, 1, 0, 0, 0, 0}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# How many types of digits there are\n", - "num_classes = 10\n", - "\n", - "y_train = keras.utils.to_categorical(y_train, num_classes)\n", - "y_test = keras.utils.to_categorical(y_test, num_classes)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define Model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = Sequential()\n", - "model.add(Conv2D(32, kernel_size=(3, 3),\n", - " activation='relu',\n", - " input_shape=input_shape))\n", - "model.add(Conv2D(64, (3, 3), activation='relu'))\n", - "model.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model.add(Dropout(0.25)) # Prevent overfitting\n", - "model.add(Flatten())\n", - "model.add(Dense(128, activation='relu'))\n", - "model.add(Dropout(0.5))\n", - "model.add(Dense(num_classes, activation='softmax'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "batch_size = 128 # How many training examples will we look at before updating the weights in the matrix\n", - "epochs = 12 # How many times we will run through the complete training set" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compile and Fit Model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.compile(loss=keras.losses.categorical_crossentropy,\n", - " optimizer=keras.optimizers.Adam(),\n", - " metrics=['accuracy'])\n", - "\n", - "model.fit(x_train, y_train,\n", - " batch_size=batch_size,\n", - " epochs=epochs,\n", - " verbose=1,\n", - " validation_data=(x_test, y_test))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test Model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "score = model.evaluate(x_test, y_test, verbose=0)\n", - "print('Test loss:', score[0])\n", - "print('Test accuracy:', score[1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "to_predict = np.array([x_test[0]])\n", - "\n", - "output = model.predict(to_predict)\n", - "\n", - "pixels = to_predict[0].reshape((28, 28))\n", - "\n", - "plt.imshow(pixels, cmap='gray')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "output" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.argmax(output)" - ] - } - ], - "metadata": { - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9-final" - }, - "orig_nbformat": 2, - "kernelspec": { - "name": "python36964bit5e4270a542e54d6788076e0986af2669", - "display_name": "Python 3.6.9 64-bit" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/advanced/mnist/index.html b/advanced/mnist/index.html new file mode 100644 index 00000000..fc921cc1 --- /dev/null +++ b/advanced/mnist/index.html @@ -0,0 +1,2314 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + MNIST Digit Prediction - CoderDojo TC Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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Handwritten Digit Classification¶

We will program and train a machine learning model to recognize handwritten numbers. The code for this lesson can be found here.

But What Is Machine Learnig?¶

Process of determining the function that gives a plausible result y for a set of inputs, in form of a vector x. In its simplest form, this could be linear regression, where we find the line of best fit through the data.

In more complex forms, however, this can be training a neural network to fit very non-linear data. We will use a type of neural network to recognize our handwritten numbers.

Image of Machine Learning Algorithms

Types of Neural Networks¶

Types of Neural Networks

As you can see, there are many different types, or architectures of neural networks. Some of the most common are Feed Forward, Recurrent, LSTM, and AutoEncoder neural networks. The other most common is the one that we will use, or a Convolutional Neural Network.

Convolutional Neural Network¶

Since we have image data, we will use a Convolutional Neural Network (CNN). A CNN works by sliding "windows" over the input image and aggregating nearby pixels together. + CNN Windowing

Importing Packages¶

For this project, we will be using a machine learning package called Keras. This makes it really easy to construct our neural network. We also import matplotlib to visualize our data, and numpy for some utilities. +

import keras
+from keras.datasets import mnist  # Get dataset
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Flatten  # Need these types of layers
+from keras.layers import Conv2D, MaxPooling2D  # Need convolutional layers
+
+import matplotlib.pyplot as plt 
+import matplotlib.image as mpimg
+
+import numpy as np 
+

Loading Data¶

Important to load both a training and testing set to make sure the CNN is not "memorizing" the set of images it will train on. This would lead to awful accuracy in the "real world" +

# input image dimensions (pixels)
+img_rows, img_cols = 28, 28
+
+# the data, split between train and test sets
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+
+# manipulate the data for the CNN
+x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
+x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
+input_shape = (img_rows, img_cols, 1)
+

Preprocessing Data¶

We want to have our CNN work with numbers between 0 and 1. Since pixel values are from 0-255, we divide all numbers by 255 in order to make them between 0 and 1. We then can see how many training and testing samples we have. +

x_train = x_train.astype('float32')
+x_test = x_test.astype('float32')
+x_train /= 255
+x_test /= 255
+print('x_train shape:', x_train.shape)
+print(x_train.shape[0], 'train samples')
+print(x_test.shape[0], 'test samples')
+

Look At Data¶

In the next few cells, we will look at an example of a digit and see what number it is in the training set.

x_train[0]
+

pixels = x_train[0].reshape((28, 28))
+
+plt.imshow(pixels, cmap='gray')
+

print(y_train[0])
+

Desired Output¶

We want to convert the desired output from a number n to a vector where the nth element is 1 and the rest are 0.

Example: n = 5 Resulting Vector = {0, 0, 0, 0, 0, 1, 0, 0, 0, 0}

num_classes = 10
+
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
+

Define Model¶

Now here is the fun part. Building the model! We will have a 5 layer neural network. Feel free to play around with some of the parameters and see what works better or worse!

model = Sequential()
+model.add(Conv2D(32, kernel_size=(3, 3),
+                 activation='relu',
+                 input_shape=input_shape))
+model.add(Conv2D(64, (3, 3), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))  # Prevent overfitting
+model.add(Flatten())
+model.add(Dense(128, activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(num_classes, activation='softmax'))
+
+model.summary()
+

Set Parameters¶

Here, we will set two parameters for the network. The first is batch size, which determines how many training examples we will look at before changing the weights in the model. We will also set the number of epochs, or iterations through the entire training set.

batch_size = 128  # How many training examples will we look at before updating the weights in the matrix
+epochs = 12 # How many times we will run through the complete training set
+

Compile and Train Model¶

Here, we compile the model and then train it. A couple things to note. We are using the Adam optimizer, as compared to others such as stochastic gradient descent. We are measuring the success of the model through the categorical crossentropy loss metric, which determines how to adjust the weights within the model.

model.compile(loss=keras.losses.categorical_crossentropy,
+              optimizer=keras.optimizers.Adam(),
+              metrics=['accuracy'])
+
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=epochs,
+          verbose=1,
+          validation_data=(x_test, y_test))
+

Test Model¶

Now here is the big finish, lets see how well the model performs! We will do this by running through our test set and having the model produce a prediction. Thankfully, there is already a function built into the model for this. +

score = model.evaluate(x_test, y_test, verbose=0)
+print('Test loss:', score[0])
+print('Test accuracy:', score[1])
+

Let's see what happens on an individual image level: +

to_predict = np.array([x_test[0]])
+
+output = model.predict(to_predict)
+
+pixels = to_predict[0].reshape((28, 28))
+
+plt.imshow(pixels, cmap='gray')
+

output
+

np.argmax(output)
+

+And there you have it! Congratulations on successfully creating, training, and testing your own neural network.

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404 - Not found

Advanced Python¶

Roll the Dice¶

Roll the Dice¶

Handwritten Digit Classification¶

But What Is Machine Learnig?¶

Types of Neural Networks¶

Convolutional Neural Network¶

Importing Packages¶

Loading Data¶

Preprocessing Data¶

Look At Data¶

Desired Output¶

Define Model¶

Set Parameters¶

Compile and Train Model¶

Test Model¶

Stock Analysis and (Virtual) Trading With Python¶