├── IDLEXPortable.exe ├── IPythonPortable.exe ├── JupyterPortable.exe ├── SpyderPortable.exe ├── App ├── AppInfo │ ├── appicon.ico │ ├── appicon1.ico │ ├── appicon2.ico │ ├── appicon3.ico │ ├── appicon4.ico │ ├── appicon5.ico │ ├── appicon6.ico │ ├── appicon7.ico │ ├── appicon8.ico │ ├── appicon9.ico │ ├── appicon10.ico │ ├── appicon_16.png │ ├── appicon_32.png │ ├── appicon10_16.png │ ├── appicon10_32.png │ ├── appicon1_16.png │ ├── appicon1_32.png │ ├── appicon2_16.png │ ├── appicon2_32.png │ ├── appicon3_16.png │ ├── appicon3_32.png │ ├── appicon4_16.png │ ├── appicon4_32.png │ ├── appicon5_16.png │ ├── appicon5_32.png │ ├── appicon6_16.png │ ├── appicon6_32.png │ ├── appicon7_16.png │ ├── appicon7_32.png │ ├── appicon8_16.png │ ├── appicon8_32.png │ ├── appicon9_16.png │ ├── appicon9_32.png │ ├── Launcher │ │ ├── Splash.jpg │ │ ├── QtLinguistPortable.ini │ │ ├── JupyterPortable.ini │ │ ├── IDLEXPortable.ini │ │ ├── IPythonPortable.ini │ │ ├── WinPythonCommandPortable.ini │ │ ├── WinPythonInterpreterPortable.ini │ │ ├── SpyderPortable.ini │ │ ├── WinPythonControlPanelPortable.ini │ │ ├── WinPythonPowershellPortable.ini │ │ └── QtDesignerPortable.ini │ ├── appinfo1.ini │ ├── appinfo2.ini │ ├── appinfo3.ini │ ├── appinfo6.ini │ ├── appinfo4.ini │ ├── appinfo5.ini │ ├── appinfo7.ini │ ├── appinfo10.ini │ ├── appinfo9.ini │ ├── appinfo8.ini │ └── appinfo.ini ├── DefaultData │ └── notebooks │ │ └── docs │ │ ├── test.mdb │ │ ├── test.xls │ │ ├── test.accdb │ │ ├── Qt_libraries_demo.ipynb │ │ ├── test_data_access.py │ │ ├── Beginner's FAQ.ipynb │ │ ├── seaborn_demo_from_jakevdp.ipynb │ │ ├── Winpython_checker.ipynb │ │ └── dplyr_pandas.ipynb └── Readme.txt ├── QtDesignerPortable.exe ├── QtLinguistPortable.exe ├── Other ├── Help │ └── Images │ │ ├── Favicon.ico │ │ ├── Help_Logo_Top.png │ │ ├── Donation_Button.png │ │ ├── Help_Background_Footer.png │ │ └── Help_Background_Header.png └── Source │ ├── AppNamePortable.ini │ ├── Readme.txt │ └── LauncherLicense.txt ├── WinPythonCommandPortable.exe ├── WinPythonPowershellPortable.exe ├── WinPythonControlPanelPortable.exe ├── WinPythonInterpreterPortable.exe ├── .gitattributes ├── .gitignore └── help.html /IDLEXPortable.exe: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/2wayne/WinPythonPortable/HEAD/IDLEXPortable.exe -------------------------------------------------------------------------------- /IPythonPortable.exe: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/2wayne/WinPythonPortable/HEAD/IPythonPortable.exe -------------------------------------------------------------------------------- /JupyterPortable.exe: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/2wayne/WinPythonPortable/HEAD/JupyterPortable.exe -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /App/AppInfo/appinfo2.ini: -------------------------------------------------------------------------------- 1 | [Format] 2 | Type=PortableApps.comFormat 3 | Version=3.4 4 | 5 | [Details] 6 | Name=IPython Portable 7 | AppID=IPythonPortable 8 | -------------------------------------------------------------------------------- /App/AppInfo/appinfo3.ini: -------------------------------------------------------------------------------- 1 | [Format] 2 | Type=PortableApps.comFormat 3 | Version=3.4 4 | 5 | [Details] 6 | Name=Jupyter Portable 7 | AppID=JupyterPortable 8 | -------------------------------------------------------------------------------- /App/AppInfo/appinfo6.ini: -------------------------------------------------------------------------------- 1 | [Format] 2 | Type=PortableApps.comFormat 3 | Version=3.4 4 | 5 | [Details] 6 | Name=Spyder Portable 7 | AppID=SpyderPortable 8 | -------------------------------------------------------------------------------- /App/AppInfo/appinfo4.ini: -------------------------------------------------------------------------------- 1 | [Format] 2 | Type=PortableApps.comFormat 3 | Version=3.4 4 | 5 | [Details] 6 | Name=Qt Designer Portable 7 | AppID=QtDesignerPortable 8 | -------------------------------------------------------------------------------- /App/AppInfo/appinfo5.ini: -------------------------------------------------------------------------------- 1 | [Format] 2 | Type=PortableApps.comFormat 3 | Version=3.4 4 | 5 | [Details] 6 | Name=Qt Linguist Portable 7 | AppID=QtLinguistPortable 8 | -------------------------------------------------------------------------------- /App/AppInfo/appinfo7.ini: -------------------------------------------------------------------------------- 1 | [Format] 2 | Type=PortableApps.comFormat 3 | Version=3.4 4 | 5 | [Details] 6 | Name=WinPython Command Portable 7 | AppID=WinPythonCommandPortable 8 | -------------------------------------------------------------------------------- /App/AppInfo/Launcher/QtLinguistPortable.ini: -------------------------------------------------------------------------------- 1 | [Launch] 2 | ProgramExecutable=WinPython\Qt Linguist.exe 3 | ProgramExecutable64=WinPython64\Qt Linguist.exe 4 | DirectoryMoveOK=yes 5 | -------------------------------------------------------------------------------- /App/AppInfo/appinfo10.ini: -------------------------------------------------------------------------------- 1 | [Format] 2 | Type=PortableApps.comFormat 3 | Version=3.4 4 | 5 | [Details] 6 | Name=WinPython Powershell Portable 7 | AppID=WinPythonPowershellPortable 8 | -------------------------------------------------------------------------------- /App/AppInfo/appinfo9.ini: -------------------------------------------------------------------------------- 1 | [Format] 2 | Type=PortableApps.comFormat 3 | Version=3.4 4 | 5 | [Details] 6 | Name=WinPython Interpreter Portable 7 | AppID=WinPythonInterpreterPortable 8 | -------------------------------------------------------------------------------- /App/AppInfo/Launcher/JupyterPortable.ini: -------------------------------------------------------------------------------- 1 | [Launch] 2 | ProgramExecutable=WinPython\Jupyter Notebook.exe 3 | ProgramExecutable64=WinPython64\Jupyter Notebook.exe 4 | DirectoryMoveOK=yes 5 | -------------------------------------------------------------------------------- /App/AppInfo/appinfo8.ini: -------------------------------------------------------------------------------- 1 | [Format] 2 | Type=PortableApps.comFormat 3 | Version=3.4 4 | 5 | [Details] 6 | Name=WinPython Control Panel Portable 7 | AppID=WinPythonControlPanelPortable 8 | -------------------------------------------------------------------------------- /App/AppInfo/Launcher/IDLEXPortable.ini: -------------------------------------------------------------------------------- 1 | [Launch] 2 | ProgramExecutable=WinPython\IDLEX (Python GUI).exe 3 | ProgramExecutable64=WinPython64\IDLEX (Python GUI).exe 4 | DirectoryMoveOK=yes 5 | -------------------------------------------------------------------------------- /App/AppInfo/Launcher/IPythonPortable.ini: -------------------------------------------------------------------------------- 1 | [Launch] 2 | ProgramExecutable=WinPython\IPython Qt Console.exe 3 | ProgramExecutable64=WinPython64\IPython Qt Console.exe 4 | DirectoryMoveOK=yes 5 | -------------------------------------------------------------------------------- /App/Readme.txt: -------------------------------------------------------------------------------- 1 | The files in this directory are necessary for the portable application to 2 | function. There is normally no need to directly access or alter any of the 3 | files within these directories. 4 | -------------------------------------------------------------------------------- /App/AppInfo/Launcher/WinPythonCommandPortable.ini: -------------------------------------------------------------------------------- 1 | [Launch] 2 | ProgramExecutable=WinPython\WinPython Command Prompt.exe 3 | ProgramExecutable64=WinPython64\WinPython Command Prompt.exe 4 | DirectoryMoveOK=yes 5 | -------------------------------------------------------------------------------- /App/AppInfo/Launcher/WinPythonInterpreterPortable.ini: -------------------------------------------------------------------------------- 1 | [Launch] 2 | ProgramExecutable=WinPython\WinPython Interpreter.exe 3 | ProgramExecutable64=WinPython64\WinPython Interpreter.exe 4 | DirectoryMoveOK=yes 5 | -------------------------------------------------------------------------------- /App/AppInfo/Launcher/SpyderPortable.ini: -------------------------------------------------------------------------------- 1 | [Launch] 2 | ProgramExecutable=WinPython\Spyder.exe 3 | ProgramExecutable64=WinPython64\Spyder.exe 4 | DirectoryMoveOK=yes 5 | 6 | [DirectoriesCleanupForce] 7 | 1=%APPDATA%\Jedi -------------------------------------------------------------------------------- /App/AppInfo/Launcher/WinPythonControlPanelPortable.ini: -------------------------------------------------------------------------------- 1 | [Launch] 2 | ProgramExecutable=WinPython\WinPython Control Panel.exe 3 | ProgramExecutable64=WinPython64\WinPython Control Panel.exe 4 | DirectoryMoveOK=yes 5 | -------------------------------------------------------------------------------- /App/AppInfo/Launcher/WinPythonPowershellPortable.ini: -------------------------------------------------------------------------------- 1 | [Launch] 2 | ProgramExecutable=WinPython\WinPython Powershell Prompt.exe 3 | ProgramExecutable64=WinPython64\WinPython Powershell Prompt.exe 4 | DirectoryMoveOK=yes 5 | -------------------------------------------------------------------------------- /App/AppInfo/Launcher/QtDesignerPortable.ini: -------------------------------------------------------------------------------- 1 | [Launch] 2 | ProgramExecutable=WinPython\Qt Designer.exe 3 | ProgramExecutable64=WinPython64\Qt Designer.exe 4 | DirectoryMoveOK=yes 5 | 6 | [DirectoriesMove] 7 | .designer=%USERPROFILE%\.designer 8 | -------------------------------------------------------------------------------- /Other/Source/AppNamePortable.ini: -------------------------------------------------------------------------------- 1 | AdditionalParameters= 2 | DisableSplashScreen=false 3 | RunLocally=false 4 | 5 | # The above options are explained in the included readme.txt 6 | # This INI file is an example only and is not used unless it is placed as described in the included readme.txt 7 | -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | 4 | # Custom for Visual Studio 5 | *.cs diff=csharp 6 | 7 | # Standard to msysgit 8 | *.doc diff=astextplain 9 | *.DOC diff=astextplain 10 | *.docx diff=astextplain 11 | *.DOCX diff=astextplain 12 | *.dot diff=astextplain 13 | *.DOT diff=astextplain 14 | *.pdf diff=astextplain 15 | *.PDF diff=astextplain 16 | *.rtf diff=astextplain 17 | *.RTF diff=astextplain 18 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # PA.c 2 | App/WinPython 3 | App/WinPython64 4 | 5 | # Windows image file caches 6 | Thumbs.db 7 | ehthumbs.db 8 | 9 | # Folder config file 10 | Desktop.ini 11 | 12 | # Recycle Bin used on file shares 13 | $RECYCLE.BIN/ 14 | 15 | # Windows Installer files 16 | *.cab 17 | *.msi 18 | *.msm 19 | *.msp 20 | 21 | # Windows shortcuts 22 | *.lnk 23 | 24 | # ========================= 25 | # Operating System Files 26 | # ========================= 27 | 28 | # OSX 29 | # ========================= 30 | 31 | .DS_Store 32 | .AppleDouble 33 | .LSOverride 34 | 35 | # Thumbnails 36 | ._* 37 | 38 | # Files that might appear in the root of a volume 39 | .DocumentRevisions-V100 40 | .fseventsd 41 | .Spotlight-V100 42 | .TemporaryItems 43 | .Trashes 44 | .VolumeIcon.icns 45 | 46 | # Directories potentially created on remote AFP share 47 | .AppleDB 48 | .AppleDesktop 49 | Network Trash Folder 50 | Temporary Items 51 | .apdisk 52 | -------------------------------------------------------------------------------- /App/AppInfo/appinfo.ini: -------------------------------------------------------------------------------- 1 | [Format] 2 | Type=PortableApps.comFormat 3 | Version=3.4 4 | 5 | [Details] 6 | Name=WinPython Portable 7 | AppID=WinPythonPortable 8 | Publisher=The Winpython Development Team & PortableApps.com 9 | Homepage=PortableApps.com/node/56106 10 | Category=Development 11 | Description=Python-distribution for Windows 12 | Language=English 13 | 14 | [License] 15 | Shareable=true 16 | OpenSource=true 17 | Freeware=true 18 | CommercialUse=true 19 | 20 | [Version] 21 | PackageVersion=3.4.3.1 22 | DisplayVersion=3.5.3.1 Dev Test 1 23 | 24 | [Control] 25 | Icons=9 26 | Start=IDLEXPortable.exe 27 | Start1=IDLEXPortable.exe 28 | Name1=IDLEX Portable 29 | Start2=IPythonPortable.exe 30 | Name2=IPython QtConsole Portable 31 | Start3=JupyterPortable.exe 32 | Name3=Jupyter Portable 33 | Start4=QtDesignerPortable.exe 34 | Name4=Qt Designer Portable 35 | Start5=QtLinguistPortable.exe 36 | Name5=Qt Linguist Portable 37 | Start6=SpyderPortable.exe 38 | Name6=Spyder Portable 39 | Start7=WinPythonCommandPortable.exe 40 | Name7=WinPython Command Portable 41 | Start8=WinPythonControlPanelPortable.exe 42 | Name8=WinPython Control Panel Portable 43 | Start9=WinPythonInterpreterPortable.exe 44 | Name9=WinPython Interpreter Portable 45 | Start10=WinPythonPowershell.exe 46 | Name10=WinPython Powershell Portable 47 | -------------------------------------------------------------------------------- /Other/Source/Readme.txt: -------------------------------------------------------------------------------- 1 | The base application's source code is available from the portable app's 2 | homepage listed in the help.html file (if applicable). 3 | 4 | Details of most other things are available there as well. 5 | 6 | LICENSE 7 | ======= 8 | 9 | This package's installer and launcher are released under the GPL. The launcher 10 | is the PortableApps.com Launcher, available with full source and documentation 11 | from http://portableapps.com/development. We request that developers using the 12 | PortableApps.com Launcher please leave this directory intact and unchanged. 13 | 14 | USER CONFIGURATION 15 | ================== 16 | 17 | Some configuration in the PortableApps.com Launcher can be overridden by the 18 | user in an INI file next to WinPythonCommandPortable.exe called WinPythonCommandPortable.ini. 19 | If you are happy with the default options, it is not necessary, though. There 20 | is an example INI included with this package to get you started. To use it, 21 | copy AppNamePortable.ini from this directory to WinPythonCommandPortable.ini next to 22 | WinPythonCommandPortable.exe. The options in the INI file are as follows: 23 | 24 | AdditionalParameters= 25 | DisableSplashScreen=false 26 | RunLocally=false 27 | 28 | (There is no need for an INI header in this file; if you have one, though, it 29 | won't damage anything.) 30 | 31 | The AdditionalParameters entry allows you to pass additional command-line 32 | parameters to the application. 33 | 34 | The DisableSplashScreen entry allows you to run the launcher without the splash 35 | screen showing up. The default is false. 36 | 37 | The RunLocally entry allows you to run the portable application from a read- 38 | only medium. This is known as Live mode. It copies what it needs to to a 39 | temporary directory on the host computer, runs the application, and then 40 | deletes it afterwards, leaving nothing behind. This can be useful for running 41 | the application from a CD or if you work on a computer that may have spyware or 42 | viruses and you'd like to keep your device set to read-only. As a consequence 43 | of this technique, any changes you make during the Live mode session aren't 44 | saved back to your device. The default is false. 45 | 46 | There may be other values also permitted in the user configuration file by the 47 | portable application; refer to help.html for any details of them. 48 | -------------------------------------------------------------------------------- /App/DefaultData/notebooks/docs/Qt_libraries_demo.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Qt Demo\n", 8 | "\n", 9 | "This will launch various Qt compatible packages" 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "## Qt4 & Qt5 Dedicated Graphic libraries: PyQtgraph, guidata, guiqwt" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": null, 22 | "metadata": { 23 | "collapsed": true 24 | }, 25 | "outputs": [], 26 | "source": [ 27 | "# PyQtgraph (Scientific Graphics and GUI Library for Python)\n", 28 | "import pyqtgraph.examples; pyqtgraph.examples.run()" 29 | ] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "execution_count": null, 34 | "metadata": { 35 | "collapsed": false 36 | }, 37 | "outputs": [], 38 | "source": [ 39 | "# Guidata (Python library generating graphical user interfaces for easy dataset editing and display)\n", 40 | "from guidata import tests; tests.run()" 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": null, 46 | "metadata": { 47 | "collapsed": true 48 | }, 49 | "outputs": [], 50 | "source": [ 51 | "# Guiqwt (Efficient 2D plotting Python library based on PythonQwt)\n", 52 | "from guiqwt import tests; tests.run()" 53 | ] 54 | }, 55 | { 56 | "cell_type": "markdown", 57 | "metadata": {}, 58 | "source": [ 59 | "## Reactive programing: rx" 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": null, 65 | "metadata": { 66 | "collapsed": false 67 | }, 68 | "outputs": [], 69 | "source": [ 70 | "# from https://github.com/ReactiveX/RxPY/blob/master/examples/timeflie\n", 71 | "from rx.subjects import Subject\n", 72 | "from rx.concurrency import QtScheduler\n", 73 | "import sys\n", 74 | "\n", 75 | "try:\n", 76 | " from PyQt4 import QtCore\n", 77 | " from PyQt4.QtGui import QWidget, QLabel\n", 78 | " from PyQt4.QtGui import QApplication\n", 79 | "except ImportError:\n", 80 | " try:\n", 81 | " from PyQt5 import QtCore\n", 82 | " from PyQt5.QtWidgets import QApplication, QWidget, QLabel\n", 83 | " except ImportError:\n", 84 | " from PySide import QtCore\n", 85 | " from PySide.QtGui import QWidget, QLabel\n", 86 | " from PySide.QtGui import QApplication\n", 87 | "\n", 88 | "\n", 89 | "class Window(QWidget):\n", 90 | "\n", 91 | " def __init__(self):\n", 92 | " super(QWidget, self).__init__()\n", 93 | " self.setWindowTitle(\"Rx for Python rocks\")\n", 94 | " self.resize(600, 600)\n", 95 | " self.setMouseTracking(True)\n", 96 | "\n", 97 | " # This Subject is used to transmit mouse moves to labels\n", 98 | " self.mousemove = Subject()\n", 99 | "\n", 100 | " def mouseMoveEvent(self, event):\n", 101 | " self.mousemove.on_next((event.x(), event.y()))\n", 102 | "\n", 103 | "\n", 104 | "def main():\n", 105 | " app = QApplication(sys.argv)\n", 106 | " scheduler = QtScheduler(QtCore)\n", 107 | "\n", 108 | " window = Window()\n", 109 | " window.show()\n", 110 | "\n", 111 | " text = 'TIME FLIES LIKE AN ARROW'\n", 112 | " labels = [QLabel(char, window) for char in text]\n", 113 | "\n", 114 | " def handle_label(i, label):\n", 115 | "\n", 116 | " def on_next(pos):\n", 117 | " x, y = pos\n", 118 | " label.move(x + i*12 + 15, y)\n", 119 | " label.show()\n", 120 | "\n", 121 | " window.mousemove.delay(i*100, scheduler=scheduler).subscribe(on_next)\n", 122 | "\n", 123 | " for i, label in enumerate(labels):\n", 124 | " handle_label(i, label)\n", 125 | "\n", 126 | " sys.exit(app.exec_())\n", 127 | "\n", 128 | "if __name__ == '__main__':\n", 129 | " main()\n" 130 | ] 131 | }, 132 | { 133 | "cell_type": "code", 134 | "execution_count": null, 135 | "metadata": { 136 | "collapsed": true 137 | }, 138 | "outputs": [], 139 | "source": [] 140 | } 141 | ], 142 | "metadata": { 143 | "kernelspec": { 144 | "display_name": "Python 3", 145 | "language": "python", 146 | "name": "python3" 147 | }, 148 | "language_info": { 149 | "codemirror_mode": { 150 | "name": "ipython", 151 | "version": 3 152 | }, 153 | "file_extension": ".py", 154 | "mimetype": "text/x-python", 155 | "name": "python", 156 | "nbconvert_exporter": "python", 157 | "pygments_lexer": "ipython3", 158 | "version": "3.4.4" 159 | } 160 | }, 161 | "nbformat": 4, 162 | "nbformat_minor": 0 163 | } 164 | -------------------------------------------------------------------------------- /help.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | WinPython Portable Help 6 | 7 | 8 | 123 | 124 | 125 | 126 | 127 |
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WinPython Portable Help

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Python-distribution for Windows

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WinPython Portable is the WinPython Python-distribution packaged with a PortableApps.com launcher as a portable app, so you can use Python on your iPod, USB flash drive, portable hard drive, etc. It has all the same features as WinPython, plus, it leaves no personal information behind on the machine you run it on, so you can take it with you wherever you go. Learn more about WinPython...

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149 | 150 | 151 | -------------------------------------------------------------------------------- /App/DefaultData/notebooks/docs/test_data_access.py: -------------------------------------------------------------------------------- 1 | # pyodbc 2 | import pyodbc 3 | 4 | # look for pyodbc providers 5 | sources = pyodbc.dataSources() 6 | dsns = list(sources.keys()) 7 | sl = [' %s [%s]' % (dsn, sources[dsn]) for dsn in dsns] 8 | print("pyodbc Providers: (beware 32/64 bit driver and python version must match)\n", '\n'.join(sl)) 9 | 10 | # odbc to EXCEL .xls via pyodbc (beware 32/64 bit driver and pytho version must match) 11 | import pyodbc, os 12 | filename = os.path.join(os.getcwd(), 'test.xls') 13 | todo = "select * from [Sheet1$]" 14 | print("\nusing pyodbc to read an Excel .xls file:\n\t", filename) 15 | if os.path.exists(filename): 16 | CNXNSTRING = 'Driver={Microsoft Excel Driver (*.xls, *.xlsx, *.xlsm, *.xlsb)};DBQ=%s;READONLY=FALSE' % filename 17 | try: 18 | cnxn = pyodbc.connect(CNXNSTRING, autocommit=True) 19 | cursor = cnxn.cursor() 20 | rows = cursor.execute(todo).fetchall() 21 | print([column[0] for column in cursor.description]) 22 | print(rows) 23 | cursor.close() 24 | cnxn.close() 25 | except: 26 | print("\n *** failed ***\n") 27 | # odbc to ACCESS .mdb via pyodbc (beware 32/64 bit driver and python version must match) 28 | import pyodbc, os 29 | filename = os.path.join(os.getcwd(), 'test.mdb') 30 | print("\nusing pyodbc to read an ACCESS .mdb file:\n\t", filename) 31 | if os.path.exists(filename): 32 | CNXNSTRING = 'Driver={Microsoft Access Driver (*.mdb, *.accdb)};DBQ=%s;READONLY=FALSE' % filename 33 | try: 34 | cnxn = pyodbc.connect(CNXNSTRING, autocommit=False) 35 | cursor = cnxn.cursor() 36 | rows = cursor.execute("select * from users").fetchall() 37 | print([column[0] for column in cursor.description]) 38 | print(rows) 39 | cursor.close() 40 | cnxn.close() 41 | except: 42 | print("\n *** failed ***\n") 43 | 44 | # pythonnet 45 | import clr 46 | clr.AddReference("System.Data") 47 | import System.Data.OleDb as ADONET 48 | import System.Data.Odbc as ODBCNET 49 | import System.Data.Common as DATACOM 50 | 51 | table = DATACOM.DbProviderFactories.GetFactoryClasses() 52 | print("\n .NET Providers: (beware 32/64 bit driver and pytho version must match)") 53 | for row in table.Rows: 54 | print(" %s" % row[table.Columns[0]]) 55 | print(" ",[row[column] for column in table.Columns if column != table.Columns[0]]) 56 | 57 | 58 | # odbc to EXCEL .xls via pythonnet 59 | import clr, os 60 | clr.AddReference("System.Data") 61 | import System.Data.OleDb as ADONET 62 | import System.Data.Odbc as ODBCNET 63 | import System.Data.Common as DATACOM 64 | 65 | filename = os.path.join(os.getcwd(), 'test.xls') 66 | todo = "select * from [Sheet1$]" 67 | print("\nusing pythonnet to read an excel .xls file:\n\t", filename , "\n\t", todo) 68 | if os.path.exists(filename): 69 | CNXNSTRING = 'Driver={Microsoft Excel Driver (*.xls, *.xlsx, *.xlsm, *.xlsb)};DBQ=%s;READONLY=FALSE' % filename 70 | cnxn = ODBCNET.OdbcConnection(CNXNSTRING) 71 | try: 72 | cnxn.Open() 73 | command = cnxn.CreateCommand() 74 | command.CommandText = "select * from [Sheet1$]" 75 | rows = command.ExecuteReader() 76 | print ([rows.GetName(i) for i in range(rows.FieldCount)]) 77 | for row in rows: 78 | print([row[i] for i in range(rows.FieldCount)]) 79 | command.Dispose() 80 | cnxn.Close() 81 | except: 82 | print("\n *** failed ***\n") 83 | 84 | 85 | # odbc to ACCESS .mdb via pythonnet 86 | import clr, os 87 | clr.AddReference("System.Data") 88 | import System.Data.OleDb as ADONET 89 | import System.Data.Odbc as ODBCNET 90 | import System.Data.Common as DATACOM 91 | 92 | filename = os.path.join(os.getcwd(), 'test.mdb') 93 | todo = "select * from users" 94 | print("\nusing odbc via pythonnet to read an ACCESS .mdb file:\n\t", filename , "\n\t", todo) 95 | 96 | if os.path.exists(filename): 97 | CNXNSTRING = 'Driver={Microsoft Access Driver (*.mdb, *.accdb)};DBQ=%s;READONLY=FALSE' % filename 98 | cnxn = ODBCNET.OdbcConnection(CNXNSTRING) 99 | try: 100 | cnxn.Open() 101 | command = cnxn.CreateCommand() 102 | command.CommandText = "select * from users" 103 | rows = command.ExecuteReader() 104 | print ([rows.GetName(i) for i in range(rows.FieldCount)]) 105 | for row in rows: 106 | print([row[i] for i in range(rows.FieldCount)]) 107 | command.Dispose() 108 | cnxn.Close() 109 | except: 110 | print("\n *** failed ***\n") 111 | 112 | # DAO via pythonnet: works ONLY if you have the 32 (or 64 bit) driver. 113 | import clr, os 114 | clr.AddReference("System.Data") 115 | import System.Data.OleDb as ADONET 116 | import System.Data.Odbc as ODBCNET 117 | import System.Data.Common as DATACOM 118 | 119 | filename = os.path.join(os.getcwd(), 'test.accdb') 120 | todo = "select * from users" 121 | print("\nusing DAO via pythonnet to read an ACCESS .mdb file:\n\t", filename , "\n\t", todo) 122 | if os.path.exists(filename): 123 | # needs a driver in 32 or 64 bit like your running python 124 | # https://www.microsoft.com/download/details.aspx?id=13255 125 | CNXNSTRING = 'Provider=Microsoft.ACE.OLEDB.12.0; Data Source=%s;READONLY=FALSE' % filename 126 | cnxn = ADONET.OleDbConnection(CNXNSTRING) 127 | try: 128 | cnxn.Open() 129 | command = cnxn.CreateCommand() 130 | command.CommandText = todo 131 | # command.CommandText = 'select id, name from people where group_id = @group_id' 132 | # command.Parameters.Add(SqlParameter('group_id', 23)) 133 | rows = command.ExecuteReader() 134 | print ([rows.GetName(i) for i in range(rows.FieldCount)]) 135 | for row in rows: 136 | print([row[i] for i in range(rows.FieldCount)]) 137 | command.Dispose() 138 | cnxn.Close() 139 | except: 140 | print("\n *** failed ***\n") 141 | -------------------------------------------------------------------------------- /App/DefaultData/notebooks/docs/Beginner's FAQ.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Experimenting your Winpython installation\n", 8 | "\n", 9 | " . [Winpython_checker test, to see various packages](Winpython_checker.ipynb) \n", 10 | " \n", 11 | " . [Seaborn visualization Example](seaborn_demo_from_jakevdp.ipynb)\n", 12 | " \n", 13 | " . [QT libraries Example](Qt_libraries_demo.ipynb)\n", 14 | "\n", 15 | " . [Pandas Data-science example](dplyr_pandas.ipynb)" 16 | ] 17 | }, 18 | { 19 | "cell_type": "markdown", 20 | "metadata": {}, 21 | "source": [ 22 | "# Tutorials and Demonstrations on Internet\n", 23 | "\n", 24 | "\n", 25 | "## Introduction to DataScience\n", 26 | " . [Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/README.md)\n" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "metadata": {}, 32 | "source": [ 33 | "## Ipython Notebook Documentation\n", 34 | " \n", 35 | " . [IPython notebook-based online documentation](https://nbviewer.ipython.org/github/ipython/ipython/blob/master/examples/Index.ipynb)\n", 36 | " \n", 37 | " . [Galery of Interesting Notebooks](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks)\n", 38 | " \n", 39 | " . Videos of Conferences and Trainings: [Python Various Conferences](https://pyvideo.org/), [Pydata Conferences](https://www.youtube.com/user/PyDataTV) , [Scipy Conferences](https://www.youtube.com/user/EnthoughtMedia)\n", 40 | " " 41 | ] 42 | }, 43 | { 44 | "cell_type": "markdown", 45 | "metadata": {}, 46 | "source": [ 47 | "## Pandas\n", 48 | "\n", 49 | ". Beginners Training Video: [\"Brandon Rhodes - Pandas From The Ground Up - PyCon 2015 \"](https://www.youtube.com/watch?v=5JnMutdy6Fw)\n", 50 | "\n", 51 | ". Pandas [API reference](https://pandas.pydata.org/pandas-docs/stable/api.html)\n" 52 | ] 53 | }, 54 | { 55 | "cell_type": "markdown", 56 | "metadata": {}, 57 | "source": [ 58 | "## Graphics :\n", 59 | "\n", 60 | " . Matplotlib : [Beginner's guide](https://matplotlib.org/users/beginner.html) , [Gallery](https://matplotlib.org/gallery.html) , [General Content](https://matplotlib.org/contents.html) \n", 61 | " \n", 62 | " . seaborn : [Tutorial](https://stanford.edu/~mwaskom/software/seaborn/tutorial.html) , [Gallery](https://stanford.edu/~mwaskom/software/seaborn/examples/index.html)\n", 63 | " \n", 64 | " . scikit-image : [Gallery](https://scikit-image.org/docs/dev/auto_examples/), [User Guide](https://scikit-image.org/docs/dev/user_guide.html)\n", 65 | " \n", 66 | " . holoviews : [Introduction](https://ioam.github.io/holoviews) , [Tutorials](https://ioam.github.io/holoviews/Tutorials/index.html)\n", 67 | " \n", 68 | " . bqplot: [Introduction](https://bqplot.readthedocs.io/en/stable/introduction.html)\n", 69 | " \n", 70 | " . Altair: [Introduction]](https://altair-viz.github.io/)\n", 71 | " \n", 72 | " . mpld3 : [Gallery](https://mpld3.github.io/examples/index.html#example-gallery) \n", 73 | "\n", 74 | " " 75 | ] 76 | }, 77 | { 78 | "cell_type": "markdown", 79 | "metadata": {}, 80 | "source": [ 81 | "## SQL\n", 82 | " . IPython-SQL : [Tutorial](https://nbviewer.ipython.org/gist/catherinedevlin/6588378)\n", 83 | " \n", 84 | " . db.py : [Tutorial](https://nbviewer.ipython.org/github/yhat/db.py/blob/master/examples/db-example.ipynb)\n", 85 | " \n", 86 | " . baresql : [Tutorial](https://pypi.python.org/pypi/baresql)\n" 87 | ] 88 | }, 89 | { 90 | "cell_type": "markdown", 91 | "metadata": {}, 92 | "source": [ 93 | "\n", 94 | "\n", 95 | "## Machine learning / Deep Learning\n", 96 | " . scikit-learn : [Tutorial](https://scikit-learn.org/stable/tutorial/index.html) , [Gallery](https://scikit-learn.org/stable/auto_examples/index.html)\n", 97 | " \n", 98 | " . Theano: [Tutorial](https://deeplearning.net/software/theano/tutorial/), [Related Projects](https://github.com/Theano/Theano/wiki/Related-projects)\n", 99 | " \n", 100 | " . Keras: [Introduction]](https://keras.io/)\n", 101 | "\n", 102 | " . Tensorflow: [Tutorial](https://github.com/Hvass-Labs/TensorFlow-Tutorials) with [videos](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" 103 | ] 104 | }, 105 | { 106 | "cell_type": "markdown", 107 | "metadata": {}, 108 | "source": [ 109 | "\n", 110 | "\n", 111 | "## Qt User Interface Development :\n", 112 | "\n", 113 | " . PyQt4 tutorial: https://zetcode.com/gui/pyqt4/firstprograms/\n", 114 | " \n", 115 | " . PyQt5 tutorial: https://zetcode.com/gui/pyqt5/firstprograms/\n", 116 | " \n", 117 | " . guiqwt tutorial: https://pythonhosted.org/guiqwt/examples.html .\n", 118 | " \n", 119 | " . switching from guiqwt 2 to 3: https://github.com/PierreRaybaut/guiqwt/blob/master/doc/migrating_from_v2_to_v3.rst)\n", 120 | " \n", 121 | " . guidata: https://pythonhosted.org/guidata/examples.html\n", 122 | " \n", 123 | " " 124 | ] 125 | }, 126 | { 127 | "cell_type": "markdown", 128 | "metadata": { 129 | "collapsed": false 130 | }, 131 | "source": [ 132 | "\n", 133 | "## Winpython\n", 134 | "\n", 135 | ". [Winpython Discussion Group](https://groups.google.com/forum/#!forum/winpython)\n", 136 | " \n", 137 | ". [Other Winpython examples](http://nbviewer.ipython.org/github/winpython/winpython_afterdoc/tree/master/)\n" 138 | ] 139 | }, 140 | { 141 | "cell_type": "code", 142 | "execution_count": null, 143 | "metadata": { 144 | "collapsed": true 145 | }, 146 | "outputs": [], 147 | "source": [] 148 | } 149 | ], 150 | "metadata": { 151 | "kernelspec": { 152 | "display_name": "Python 3", 153 | "language": "python", 154 | "name": "python3" 155 | }, 156 | "language_info": { 157 | "codemirror_mode": { 158 | "name": "ipython", 159 | "version": 3 160 | }, 161 | "file_extension": ".py", 162 | "mimetype": "text/x-python", 163 | "name": "python", 164 | "nbconvert_exporter": "python", 165 | "pygments_lexer": "ipython3", 166 | "version": "3.6.0" 167 | } 168 | }, 169 | "nbformat": 4, 170 | "nbformat_minor": 0 171 | } 172 | -------------------------------------------------------------------------------- /App/DefaultData/notebooks/docs/seaborn_demo_from_jakevdp.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Seaborn demo per Jake VanderPlas below" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": null, 13 | "metadata": { 14 | "collapsed": true 15 | }, 16 | "outputs": [], 17 | "source": [ 18 | "from __future__ import print_function, division\n", 19 | "\n", 20 | "%matplotlib inline\n", 21 | "import matplotlib.pyplot as plt\n", 22 | "import numpy as np\n", 23 | "import pandas as pd" 24 | ] 25 | }, 26 | { 27 | "cell_type": "code", 28 | "execution_count": null, 29 | "metadata": { 30 | "collapsed": false 31 | }, 32 | "outputs": [], 33 | "source": [ 34 | "plt.style.use('ggplot')\n", 35 | "x = np.linspace(0, 10, 1000)\n", 36 | "plt.plot(x, np.sin(x), x, np.cos(x));" 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": null, 42 | "metadata": { 43 | "collapsed": false 44 | }, 45 | "outputs": [], 46 | "source": [ 47 | "import seaborn as sns\n", 48 | "sns.set()\n", 49 | "plt.plot(x, np.sin(x), x, np.cos(x));" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": null, 55 | "metadata": { 56 | "collapsed": false 57 | }, 58 | "outputs": [], 59 | "source": [ 60 | "data = np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000)\n", 61 | "data = pd.DataFrame(data, columns=['x', 'y'])\n", 62 | "\n", 63 | "for col in 'xy':\n", 64 | " plt.hist(data[col], normed=True, alpha=0.5)" 65 | ] 66 | }, 67 | { 68 | "cell_type": "code", 69 | "execution_count": null, 70 | "metadata": { 71 | "collapsed": false 72 | }, 73 | "outputs": [], 74 | "source": [ 75 | "for col in 'xy':\n", 76 | " sns.kdeplot(data[col], shade=True)" 77 | ] 78 | }, 79 | { 80 | "cell_type": "code", 81 | "execution_count": null, 82 | "metadata": { 83 | "collapsed": false 84 | }, 85 | "outputs": [], 86 | "source": [ 87 | "sns.distplot(data['x']);" 88 | ] 89 | }, 90 | { 91 | "cell_type": "code", 92 | "execution_count": null, 93 | "metadata": { 94 | "collapsed": false 95 | }, 96 | "outputs": [], 97 | "source": [ 98 | "sns.kdeplot(data);" 99 | ] 100 | }, 101 | { 102 | "cell_type": "code", 103 | "execution_count": null, 104 | "metadata": { 105 | "collapsed": false 106 | }, 107 | "outputs": [], 108 | "source": [ 109 | "with sns.axes_style('white'):\n", 110 | " sns.jointplot(\"x\", \"y\", data, kind='kde');" 111 | ] 112 | }, 113 | { 114 | "cell_type": "code", 115 | "execution_count": null, 116 | "metadata": { 117 | "collapsed": false 118 | }, 119 | "outputs": [], 120 | "source": [ 121 | "with sns.axes_style('white'):\n", 122 | " sns.jointplot(\"x\", \"y\", data, kind='hex')" 123 | ] 124 | }, 125 | { 126 | "cell_type": "code", 127 | "execution_count": null, 128 | "metadata": { 129 | "collapsed": false 130 | }, 131 | "outputs": [], 132 | "source": [ 133 | "iris = sns.load_dataset(\"iris\")\n", 134 | "iris.head()" 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": null, 140 | "metadata": { 141 | "collapsed": false 142 | }, 143 | "outputs": [], 144 | "source": [ 145 | "tips = sns.load_dataset('tips')\n", 146 | "tips.head()" 147 | ] 148 | }, 149 | { 150 | "cell_type": "code", 151 | "execution_count": null, 152 | "metadata": { 153 | "collapsed": false 154 | }, 155 | "outputs": [], 156 | "source": [ 157 | "tips['tip_pct'] = 100 * tips['tip'] / tips['total_bill']\n", 158 | "\n", 159 | "grid = sns.FacetGrid(tips, row=\"sex\", col=\"time\", margin_titles=True)\n", 160 | "grid.map(plt.hist, \"tip_pct\", bins=np.linspace(0, 40, 15));" 161 | ] 162 | }, 163 | { 164 | "cell_type": "code", 165 | "execution_count": null, 166 | "metadata": { 167 | "collapsed": false 168 | }, 169 | "outputs": [], 170 | "source": [ 171 | "with sns.axes_style(style='ticks'):\n", 172 | " g = sns.factorplot(\"day\", \"total_bill\", \"sex\", data=tips, kind=\"box\")\n", 173 | " g.set_axis_labels(\"Day\", \"Total Bill\");" 174 | ] 175 | }, 176 | { 177 | "cell_type": "code", 178 | "execution_count": null, 179 | "metadata": { 180 | "collapsed": false 181 | }, 182 | "outputs": [], 183 | "source": [ 184 | "with sns.axes_style('white'):\n", 185 | " sns.jointplot(\"total_bill\", \"tip\", data=tips, kind='hex')" 186 | ] 187 | }, 188 | { 189 | "cell_type": "code", 190 | "execution_count": null, 191 | "metadata": { 192 | "collapsed": false 193 | }, 194 | "outputs": [], 195 | "source": [ 196 | "sns.jointplot(\"total_bill\", \"tip\", data=tips, kind='reg');" 197 | ] 198 | }, 199 | { 200 | "cell_type": "code", 201 | "execution_count": null, 202 | "metadata": { 203 | "collapsed": false 204 | }, 205 | "outputs": [], 206 | "source": [ 207 | "planets = sns.load_dataset('planets')\n", 208 | "planets.head()" 209 | ] 210 | }, 211 | { 212 | "cell_type": "code", 213 | "execution_count": null, 214 | "metadata": { 215 | "collapsed": false 216 | }, 217 | "outputs": [], 218 | "source": [ 219 | "with sns.axes_style('white'):\n", 220 | " g = sns.factorplot(\"year\", data=planets, aspect=1.5)\n", 221 | " g.set_xticklabels(step=5)" 222 | ] 223 | }, 224 | { 225 | "cell_type": "code", 226 | "execution_count": null, 227 | "metadata": { 228 | "collapsed": false 229 | }, 230 | "outputs": [], 231 | "source": [ 232 | "with sns.axes_style('white'):\n", 233 | " g = sns.factorplot(\"year\", data=planets, aspect=4.0,\n", 234 | " hue='method', order=range(2001, 2015), kind=\"count\")\n", 235 | " g.set_ylabels('Number of Planets Discovered')" 236 | ] 237 | }, 238 | { 239 | "cell_type": "markdown", 240 | "metadata": {}, 241 | "source": [ 242 | "## Scikit-learn tutorial from pycon 2015 Jake VanderPlas [here](http://nbviewer.ipython.org/github/jakevdp/sklearn_pycon2015/blob/master/notebooks/Index.ipynb)" 243 | ] 244 | }, 245 | { 246 | "cell_type": "code", 247 | "execution_count": null, 248 | "metadata": { 249 | "collapsed": true 250 | }, 251 | "outputs": [], 252 | "source": [] 253 | } 254 | ], 255 | "metadata": { 256 | "kernelspec": { 257 | "display_name": "Python 3", 258 | "language": "python", 259 | "name": "python3" 260 | }, 261 | "language_info": { 262 | "codemirror_mode": { 263 | "name": "ipython", 264 | "version": 3 265 | }, 266 | "file_extension": ".py", 267 | "mimetype": "text/x-python", 268 | "name": "python", 269 | "nbconvert_exporter": "python", 270 | "pygments_lexer": "ipython3", 271 | "version": "3.4.4" 272 | } 273 | }, 274 | "nbformat": 4, 275 | "nbformat_minor": 0 276 | } 277 | -------------------------------------------------------------------------------- /Other/Source/LauncherLicense.txt: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 2, June 1991 3 | 4 | Copyright (C) 1989, 1991 Free Software Foundation, Inc., 5 | 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA 6 | Everyone is permitted to copy and distribute verbatim copies 7 | of this license document, but changing it is not allowed. 8 | 9 | Preamble 10 | 11 | The licenses for most software are designed to take away your 12 | freedom to share and change it. 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You may copy and distribute the Program (or a work based on it, 135 | under Section 2) in object code or executable form under the terms of 136 | Sections 1 and 2 above provided that you also do one of the following: 137 | 138 | a) Accompany it with the complete corresponding machine-readable 139 | source code, which must be distributed under the terms of Sections 140 | 1 and 2 above on a medium customarily used for software interchange; or, 141 | 142 | b) Accompany it with a written offer, valid for at least three 143 | years, to give any third party, for a charge no more than your 144 | cost of physically performing source distribution, a complete 145 | machine-readable copy of the corresponding source code, to be 146 | distributed under the terms of Sections 1 and 2 above on a medium 147 | customarily used for software interchange; or, 148 | 149 | c) Accompany it with the information you received as to the offer 150 | to distribute corresponding source code. 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Any attempt 174 | otherwise to copy, modify, sublicense or distribute the Program is 175 | void, and will automatically terminate your rights under this License. 176 | However, parties who have received copies, or rights, from you under 177 | this License will not have their licenses terminated so long as such 178 | parties remain in full compliance. 179 | 180 | 5. You are not required to accept this License, since you have not 181 | signed it. However, nothing else grants you permission to modify or 182 | distribute the Program or its derivative works. These actions are 183 | prohibited by law if you do not accept this License. Therefore, by 184 | modifying or distributing the Program (or any work based on the 185 | Program), you indicate your acceptance of this License to do so, and 186 | all its terms and conditions for copying, distributing or modifying 187 | the Program or works based on it. 188 | 189 | 6. 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For example, if a patent 205 | license would not permit royalty-free redistribution of the Program by 206 | all those who receive copies directly or indirectly through you, then 207 | the only way you could satisfy both it and this License would be to 208 | refrain entirely from distribution of the Program. 209 | 210 | If any portion of this section is held invalid or unenforceable under 211 | any particular circumstance, the balance of the section is intended to 212 | apply and the section as a whole is intended to apply in other 213 | circumstances. 214 | 215 | It is not the purpose of this section to induce you to infringe any 216 | patents or other property right claims or to contest validity of any 217 | such claims; this section has the sole purpose of protecting the 218 | integrity of the free software distribution system, which is 219 | implemented by public license practices. Many people have made 220 | generous contributions to the wide range of software distributed 221 | through that system in reliance on consistent application of that 222 | system; it is up to the author/donor to decide if he or she is willing 223 | to distribute software through any other system and a licensee cannot 224 | impose that choice. 225 | 226 | This section is intended to make thoroughly clear what is believed to 227 | be a consequence of the rest of this License. 228 | 229 | 8. If the distribution and/or use of the Program is restricted in 230 | certain countries either by patents or by copyrighted interfaces, the 231 | original copyright holder who places the Program under this License 232 | may add an explicit geographical distribution limitation excluding 233 | those countries, so that distribution is permitted only in or among 234 | countries not thus excluded. In such case, this License incorporates 235 | the limitation as if written in the body of this License. 236 | 237 | 9. The Free Software Foundation may publish revised and/or new versions 238 | of the General Public License from time to time. Such new versions will 239 | be similar in spirit to the present version, but may differ in detail to 240 | address new problems or concerns. 241 | 242 | Each version is given a distinguishing version number. If the Program 243 | specifies a version number of this License which applies to it and "any 244 | later version", you have the option of following the terms and conditions 245 | either of that version or of any later version published by the Free 246 | Software Foundation. If the Program does not specify a version number of 247 | this License, you may choose any version ever published by the Free Software 248 | Foundation. 249 | 250 | 10. If you wish to incorporate parts of the Program into other free 251 | programs whose distribution conditions are different, write to the author 252 | to ask for permission. For software which is copyrighted by the Free 253 | Software Foundation, write to the Free Software Foundation; we sometimes 254 | make exceptions for this. Our decision will be guided by the two goals 255 | of preserving the free status of all derivatives of our free software and 256 | of promoting the sharing and reuse of software generally. 257 | 258 | NO WARRANTY 259 | 260 | 11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY 261 | FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN 262 | OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES 263 | PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED 264 | OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF 265 | MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS 266 | TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE 267 | PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, 268 | REPAIR OR CORRECTION. 269 | 270 | 12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 271 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR 272 | REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, 273 | INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING 274 | OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED 275 | TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY 276 | YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER 277 | PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE 278 | POSSIBILITY OF SUCH DAMAGES. 279 | 280 | END OF TERMS AND CONDITIONS 281 | 282 | How to Apply These Terms to Your New Programs 283 | 284 | If you develop a new program, and you want it to be of the greatest 285 | possible use to the public, the best way to achieve this is to make it 286 | free software which everyone can redistribute and change under these terms. 287 | 288 | To do so, attach the following notices to the program. It is safest 289 | to attach them to the start of each source file to most effectively 290 | convey the exclusion of warranty; and each file should have at least 291 | the "copyright" line and a pointer to where the full notice is found. 292 | 293 | 294 | Copyright (C) 295 | 296 | This program is free software; you can redistribute it and/or modify 297 | it under the terms of the GNU General Public License as published by 298 | the Free Software Foundation; either version 2 of the License, or 299 | (at your option) any later version. 300 | 301 | This program is distributed in the hope that it will be useful, 302 | but WITHOUT ANY WARRANTY; without even the implied warranty of 303 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 304 | GNU General Public License for more details. 305 | 306 | You should have received a copy of the GNU General Public License along 307 | with this program; if not, write to the Free Software Foundation, Inc., 308 | 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. 309 | 310 | Also add information on how to contact you by electronic and paper mail. 311 | 312 | If the program is interactive, make it output a short notice like this 313 | when it starts in an interactive mode: 314 | 315 | Gnomovision version 69, Copyright (C) year name of author 316 | Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 317 | This is free software, and you are welcome to redistribute it 318 | under certain conditions; type `show c' for details. 319 | 320 | The hypothetical commands `show w' and `show c' should show the appropriate 321 | parts of the General Public License. Of course, the commands you use may 322 | be called something other than `show w' and `show c'; they could even be 323 | mouse-clicks or menu items--whatever suits your program. 324 | 325 | You should also get your employer (if you work as a programmer) or your 326 | school, if any, to sign a "copyright disclaimer" for the program, if 327 | necessary. Here is a sample; alter the names: 328 | 329 | Yoyodyne, Inc., hereby disclaims all copyright interest in the program 330 | `Gnomovision' (which makes passes at compilers) written by James Hacker. 331 | 332 | , 1 April 1989 333 | Ty Coon, President of Vice 334 | 335 | This General Public License does not permit incorporating your program into 336 | proprietary programs. If your program is a subroutine library, you may 337 | consider it more useful to permit linking proprietary applications with the 338 | library. If this is what you want to do, use the GNU Lesser General 339 | Public License instead of this License. 340 | -------------------------------------------------------------------------------- /App/DefaultData/notebooks/docs/Winpython_checker.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Winpython Default checker" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": null, 13 | "metadata": { 14 | "collapsed": true 15 | }, 16 | "outputs": [], 17 | "source": [ 18 | "import warnings\n", 19 | "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n", 20 | "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", 21 | "# warnings.filterwarnings(\"ignore\") # would silence all warnings" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": null, 27 | "metadata": { 28 | "collapsed": true 29 | }, 30 | "outputs": [], 31 | "source": [ 32 | "%matplotlib inline" 33 | ] 34 | }, 35 | { 36 | "cell_type": "markdown", 37 | "metadata": {}, 38 | "source": [ 39 | "## Compilers: Numba and Cython\n", 40 | "\n", 41 | "##### Requirement\n", 42 | "To get Cython working, Winpython 3.5 users should install \"Microsoft Visual C++ Build Tools 2015\" (visualcppbuildtools_full.exe, a 4 Go installation) at https://beta.visualstudio.com/download-visual-studio-vs/\n", 43 | "\n", 44 | "To get Numba working, not-windows10 users may have to install \"Microsoft Visual C++ 2015 Redistributable\" (vc_redist) at \n", 45 | "\n", 46 | "#### Compiler toolchains" 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "execution_count": null, 52 | "metadata": { 53 | "collapsed": true 54 | }, 55 | "outputs": [], 56 | "source": [ 57 | "# checking Numba JIT toolchain\n", 58 | "import numpy as np\n", 59 | "image = np.zeros((1024, 1536), dtype = np.uint8)\n", 60 | "\n", 61 | "from pylab import imshow, show\n", 62 | "from timeit import default_timer as timer\n", 63 | "\n", 64 | "def create_fractal(min_x, max_x, min_y, max_y, image, iters , mandelx):\n", 65 | " height = image.shape[0]\n", 66 | " width = image.shape[1]\n", 67 | " pixel_size_x = (max_x - min_x) / width\n", 68 | " pixel_size_y = (max_y - min_y) / height\n", 69 | " \n", 70 | " for x in range(width):\n", 71 | " real = min_x + x * pixel_size_x\n", 72 | " for y in range(height):\n", 73 | " imag = min_y + y * pixel_size_y\n", 74 | " color = mandelx(real, imag, iters)\n", 75 | " image[y, x] = color" 76 | ] 77 | }, 78 | { 79 | "cell_type": "markdown", 80 | "metadata": {}, 81 | "source": [ 82 | "##### Numba (a JIT Compiler)" 83 | ] 84 | }, 85 | { 86 | "cell_type": "code", 87 | "execution_count": null, 88 | "metadata": { 89 | "collapsed": false 90 | }, 91 | "outputs": [], 92 | "source": [ 93 | "from numba import autojit\n", 94 | "\n", 95 | "@autojit\n", 96 | "def mandel(x, y, max_iters):\n", 97 | " c = complex(x, y)\n", 98 | " z = 0.0j\n", 99 | " for i in range(max_iters):\n", 100 | " z = z*z + c\n", 101 | " if (z.real*z.real + z.imag*z.imag) >= 4:\n", 102 | " return i\n", 103 | " return max_iters\n", 104 | "\n", 105 | "start = timer()\n", 106 | "create_fractal(-2.0, 1.0, -1.0, 1.0, image, 20 , mandel) \n", 107 | "dt = timer() - start\n", 108 | "\n", 109 | "print (\"Mandelbrot created by numba in %f s\" % dt)\n", 110 | "imshow(image)\n", 111 | "show()" 112 | ] 113 | }, 114 | { 115 | "cell_type": "markdown", 116 | "metadata": {}, 117 | "source": [ 118 | "##### Cython (a compiler for writing C extensions for the Python language)\n", 119 | "WinPython 3.5 and 3.6 users may not have mingwpy available, and so need \"VisualStudio C++ Community Edition 2015\" https://www.visualstudio.com/downloads/download-visual-studio-vs#d-visual-c " 120 | ] 121 | }, 122 | { 123 | "cell_type": "code", 124 | "execution_count": null, 125 | "metadata": { 126 | "collapsed": false 127 | }, 128 | "outputs": [], 129 | "source": [ 130 | "# Cython + Mingwpy compiler toolchain test\n", 131 | "%load_ext Cython" 132 | ] 133 | }, 134 | { 135 | "cell_type": "code", 136 | "execution_count": null, 137 | "metadata": { 138 | "collapsed": false 139 | }, 140 | "outputs": [], 141 | "source": [ 142 | "%%cython -a\n", 143 | "# with %%cython -a , full C-speed lines are shown in white, slowest python-speed lines are shown in dark yellow lines \n", 144 | "# ==> put your cython rewrite effort on dark yellow lines\n", 145 | "def mandel_cython(x, y, max_iters):\n", 146 | " cdef int i \n", 147 | " cdef double cx, cy , zx, zy\n", 148 | " cx , cy = x, y \n", 149 | " zx , zy =0 ,0 \n", 150 | " for i in range(max_iters):\n", 151 | " zx , zy = zx*zx - zy*zy + cx , zx*zy*2 + cy\n", 152 | " if (zx*zx + zy*zy) >= 4:\n", 153 | " return i\n", 154 | " return max_iters" 155 | ] 156 | }, 157 | { 158 | "cell_type": "code", 159 | "execution_count": null, 160 | "metadata": { 161 | "collapsed": false 162 | }, 163 | "outputs": [], 164 | "source": [ 165 | "start = timer()\n", 166 | "create_fractal(-2.0, 1.0, -1.0, 1.0, image, 20 , mandel_cython) \n", 167 | "dt = timer() - start\n", 168 | "\n", 169 | "print (\"Mandelbrot created by cython in %f s\" % dt)\n", 170 | "imshow(image)" 171 | ] 172 | }, 173 | { 174 | "cell_type": "markdown", 175 | "metadata": {}, 176 | "source": [ 177 | "## Graphics: Matplotlib, Pandas, Seaborn, bqplot, Bokeh, Holoviews" 178 | ] 179 | }, 180 | { 181 | "cell_type": "code", 182 | "execution_count": null, 183 | "metadata": { 184 | "collapsed": false 185 | }, 186 | "outputs": [], 187 | "source": [ 188 | "# Matplotlib\n", 189 | "# for more examples, see: http://matplotlib.org/gallery.html\n", 190 | "from mpl_toolkits.mplot3d import axes3d\n", 191 | "import matplotlib.pyplot as plt\n", 192 | "from matplotlib import cm\n", 193 | "\n", 194 | "fig = plt.figure()\n", 195 | "ax = fig.gca(projection='3d')\n", 196 | "X, Y, Z = axes3d.get_test_data(0.05)\n", 197 | "ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3)\n", 198 | "cset = ax.contourf(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm)\n", 199 | "cset = ax.contourf(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm)\n", 200 | "cset = ax.contourf(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm)\n", 201 | "\n", 202 | "ax.set_xlabel('X')\n", 203 | "ax.set_xlim(-40, 40)\n", 204 | "ax.set_ylabel('Y')\n", 205 | "ax.set_ylim(-40, 40)\n", 206 | "ax.set_zlabel('Z')\n", 207 | "ax.set_zlim(-100, 100)\n", 208 | "\n", 209 | "plt.show()" 210 | ] 211 | }, 212 | { 213 | "cell_type": "code", 214 | "execution_count": null, 215 | "metadata": { 216 | "collapsed": false 217 | }, 218 | "outputs": [], 219 | "source": [ 220 | "# Seaborn\n", 221 | "# for more examples, see http://stanford.edu/~mwaskom/software/seaborn/examples/index.html\n", 222 | "import seaborn as sns\n", 223 | "sns.set()\n", 224 | "df = sns.load_dataset(\"iris\")\n", 225 | "sns.pairplot(df, hue=\"species\", size=1.5)" 226 | ] 227 | }, 228 | { 229 | "cell_type": "code", 230 | "execution_count": null, 231 | "metadata": { 232 | "collapsed": false 233 | }, 234 | "outputs": [], 235 | "source": [ 236 | "#bqplot\n", 237 | "from IPython.display import display\n", 238 | "from bqplot import (Figure, Map, Mercator, Orthographic, ColorScale, ColorAxis,\n", 239 | " AlbersUSA, topo_load, Tooltip)\n", 240 | "def_tt = Tooltip(fields=['id', 'name'])\n", 241 | "map_mark = Map(scales={'projection': Mercator()}, tooltip=def_tt)\n", 242 | "map_mark.interactions = {'click': 'select', 'hover': 'tooltip'}\n", 243 | "fig = Figure(marks=[map_mark], title='Interactions Example')\n", 244 | "display(fig)" 245 | ] 246 | }, 247 | { 248 | "cell_type": "code", 249 | "execution_count": null, 250 | "metadata": { 251 | "collapsed": false 252 | }, 253 | "outputs": [], 254 | "source": [ 255 | "# ipyleaflet (javascript library usage)\n", 256 | "from ipyleaflet import (\n", 257 | " Map, Marker, TileLayer, ImageOverlay, Polyline, Polygon,\n", 258 | " Rectangle, Circle, CircleMarker, GeoJSON, DrawControl\n", 259 | ")\n", 260 | "from traitlets import link\n", 261 | "center = [34.6252978589571, -77.34580993652344]\n", 262 | "m = Map(center=[34.6252978589571, -77.34580993652344], zoom=10)\n", 263 | "dc = DrawControl()\n", 264 | "\n", 265 | "def handle_draw(self, action, geo_json):\n", 266 | " print(action)\n", 267 | " print(geo_json)\n", 268 | "m\n", 269 | "m" 270 | ] 271 | }, 272 | { 273 | "cell_type": "code", 274 | "execution_count": null, 275 | "metadata": { 276 | "collapsed": true 277 | }, 278 | "outputs": [], 279 | "source": [ 280 | "dc.on_draw(handle_draw)\n", 281 | "m.add_control(dc)" 282 | ] 283 | }, 284 | { 285 | "cell_type": "code", 286 | "execution_count": null, 287 | "metadata": { 288 | "collapsed": false 289 | }, 290 | "outputs": [], 291 | "source": [ 292 | "# Bokeh 0.11.0\n", 293 | "# for more examples, see http://nbviewer.jupyter.org/github/bokeh/bokeh-notebooks/blob/master/index.ipynb\n", 294 | "import matplotlib.pyplot as plt\n", 295 | "import numpy as np\n", 296 | "import pandas as pd\n", 297 | "import os\n", 298 | "from bokeh import mpl\n", 299 | "from bokeh.plotting import output_notebook, show\n", 300 | "import matplotlib as mplc\n", 301 | "# Generate the pandas dataframe\n", 302 | "data = np.random.multivariate_normal([0, 0], [[1, 2], [2, 20]], size=100)\n", 303 | "data = pd.DataFrame(data, columns=[\"X\", \"Y\"])\n", 304 | "mplc.rc(\"figure\", figsize=(6, 6))\n", 305 | "\n", 306 | "# Just plot seaborn kde\n", 307 | "import seaborn as sns\n", 308 | "sns.kdeplot(data, cmap=\"BuGn_d\")\n", 309 | "\n", 310 | "plt.title(\"Seaborn kdeplot in bokeh.\")\n", 311 | "\n", 312 | "from bokeh.resources import INLINE\n", 313 | "# default solution output_notebook() relies on pydata.org (but spare 2Mo of inline jsscript in your notebook)\n", 314 | "# other method to get internal bokeh script can be\n", 315 | "# os.environ['BOKEH_RESOURCES'] = 'inline'\n", 316 | "output_notebook(resources=INLINE)\n", 317 | "\n", 318 | "show(mpl.to_bokeh())" 319 | ] 320 | }, 321 | { 322 | "cell_type": "code", 323 | "execution_count": null, 324 | "metadata": { 325 | "collapsed": false 326 | }, 327 | "outputs": [], 328 | "source": [ 329 | "# Holoviews \n", 330 | "# for more example, see http://holoviews.org/Tutorials/index.html\n", 331 | "import holoviews as hv\n", 332 | "%load_ext holoviews.ipython\n", 333 | "fractal = hv.Image(image)\n", 334 | "\n", 335 | "((fractal * hv.HLine(y=0.16)).hist() + fractal.sample(y=0.16))" 336 | ] 337 | }, 338 | { 339 | "cell_type": "markdown", 340 | "metadata": {}, 341 | "source": [ 342 | "## Ipython Notebook: Interactivity & other" 343 | ] 344 | }, 345 | { 346 | "cell_type": "code", 347 | "execution_count": null, 348 | "metadata": { 349 | "collapsed": false 350 | }, 351 | "outputs": [], 352 | "source": [ 353 | "import IPython;IPython.__version__" 354 | ] 355 | }, 356 | { 357 | "cell_type": "code", 358 | "execution_count": null, 359 | "metadata": { 360 | "collapsed": false 361 | }, 362 | "outputs": [], 363 | "source": [ 364 | "# Audio Example : https://github.com/ipython/ipywidgets/blob/master/examples/Beat%20Frequencies.ipynb\n", 365 | "%matplotlib inline\n", 366 | "import matplotlib.pyplot as plt\n", 367 | "import numpy as np\n", 368 | "from ipywidgets import interactive\n", 369 | "from IPython.display import Audio, display\n", 370 | "def beat_freq(f1=220.0, f2=224.0):\n", 371 | " max_time = 3\n", 372 | " rate = 8000\n", 373 | " times = np.linspace(0,max_time,rate*max_time)\n", 374 | " signal = np.sin(2*np.pi*f1*times) + np.sin(2*np.pi*f2*times)\n", 375 | " print(f1, f2, abs(f1-f2))\n", 376 | " display(Audio(data=signal, rate=rate))\n", 377 | " return signal\n", 378 | "v = interactive(beat_freq, f1=(200.0,300.0), f2=(200.0,300.0))\n", 379 | "display(v)" 380 | ] 381 | }, 382 | { 383 | "cell_type": "code", 384 | "execution_count": null, 385 | "metadata": { 386 | "collapsed": false 387 | }, 388 | "outputs": [], 389 | "source": [ 390 | "# Networks graph Example : https://github.com/ipython/ipywidgets/blob/master/examples/Exploring%20Graphs.ipynb\n", 391 | "%matplotlib inline\n", 392 | "from ipywidgets import interact\n", 393 | "import matplotlib.pyplot as plt\n", 394 | "import networkx as nx\n", 395 | "# wrap a few graph generation functions so they have the same signature\n", 396 | "\n", 397 | "def random_lobster(n, m, k, p):\n", 398 | " return nx.random_lobster(n, p, p / m)\n", 399 | "\n", 400 | "def powerlaw_cluster(n, m, k, p):\n", 401 | " return nx.powerlaw_cluster_graph(n, m, p)\n", 402 | "\n", 403 | "def erdos_renyi(n, m, k, p):\n", 404 | " return nx.erdos_renyi_graph(n, p)\n", 405 | "\n", 406 | "def newman_watts_strogatz(n, m, k, p):\n", 407 | " return nx.newman_watts_strogatz_graph(n, k, p)\n", 408 | "\n", 409 | "@interact(n=(2,30), m=(1,10), k=(1,10), p=(0.0, 1.0, 0.001),\n", 410 | " generator={'lobster': random_lobster,\n", 411 | " 'power law': powerlaw_cluster,\n", 412 | " 'Newman-Watts-Strogatz': newman_watts_strogatz,\n", 413 | " u'Erdős-Rényi': erdos_renyi,\n", 414 | " })\n", 415 | "def plot_random_graph(n, m, k, p, generator):\n", 416 | " g = generator(n, m, k, p)\n", 417 | " nx.draw(g)\n", 418 | " plt.title(generator.__name__)\n", 419 | " plt.show()\n", 420 | " " 421 | ] 422 | }, 423 | { 424 | "cell_type": "code", 425 | "execution_count": null, 426 | "metadata": { 427 | "collapsed": false 428 | }, 429 | "outputs": [], 430 | "source": [ 431 | "# checking nbconvert \n", 432 | "!jupyter nbconvert \"Beginner's FAQ.ipynb\" --to html" 433 | ] 434 | }, 435 | { 436 | "cell_type": "code", 437 | "execution_count": null, 438 | "metadata": { 439 | "collapsed": false 440 | }, 441 | "outputs": [], 442 | "source": [ 443 | "%%HTML\n", 444 | "" 445 | ] 446 | }, 447 | { 448 | "cell_type": "markdown", 449 | "metadata": {}, 450 | "source": [ 451 | "## Mathematical: statsmodels, lmfit, " 452 | ] 453 | }, 454 | { 455 | "cell_type": "code", 456 | "execution_count": null, 457 | "metadata": { 458 | "collapsed": false 459 | }, 460 | "outputs": [], 461 | "source": [ 462 | "# checking statsmodels\n", 463 | "import numpy as np\n", 464 | "import matplotlib.pyplot as plt\n", 465 | "plt.style.use('ggplot')\n", 466 | "import statsmodels.api as sm\n", 467 | "data = sm.datasets.anes96.load_pandas()\n", 468 | "party_ID = np.arange(7)\n", 469 | "labels = [\"Strong Democrat\", \"Weak Democrat\", \"Independent-Democrat\",\n", 470 | " \"Independent-Independent\", \"Independent-Republican\",\n", 471 | " \"Weak Republican\", \"Strong Republican\"]\n", 472 | "plt.rcParams['figure.subplot.bottom'] = 0.23 # keep labels visible\n", 473 | "plt.rcParams['figure.figsize'] = (6.0, 4.0) # make plot larger in notebook\n", 474 | "age = [data.exog['age'][data.endog == id] for id in party_ID]\n", 475 | "fig = plt.figure()\n", 476 | "ax = fig.add_subplot(111)\n", 477 | "plot_opts={'cutoff_val':5, 'cutoff_type':'abs',\n", 478 | " 'label_fontsize':'small',\n", 479 | " 'label_rotation':30}\n", 480 | "sm.graphics.beanplot(age, ax=ax, labels=labels,\n", 481 | " plot_opts=plot_opts)\n", 482 | "ax.set_xlabel(\"Party identification of respondent\")\n", 483 | "ax.set_ylabel(\"Age\")" 484 | ] 485 | }, 486 | { 487 | "cell_type": "code", 488 | "execution_count": null, 489 | "metadata": { 490 | "collapsed": false 491 | }, 492 | "outputs": [], 493 | "source": [ 494 | "# lmfit test (from http://nbviewer.ipython.org/github/lmfit/lmfit-py/blob/master/examples/lmfit-model.ipynb)\n", 495 | "import numpy as np\n", 496 | "import matplotlib.pyplot as plt\n", 497 | "def decay(t, N, tau):\n", 498 | " return N*np.exp(-t/tau)\n", 499 | "t = np.linspace(0, 5, num=1000)\n", 500 | "data = decay(t, 7, 3) + np.random.randn(*t.shape)\n", 501 | "\n", 502 | "from lmfit import Model\n", 503 | "\n", 504 | "model = Model(decay, independent_vars=['t'])\n", 505 | "result = model.fit(data, t=t, N=10, tau=1)\n", 506 | "plt.plot(t, data) # data\n", 507 | "plt.plot(t, decay(t=t, **result.values), color='orange', linewidth=5) # best-fit model" 508 | ] 509 | }, 510 | { 511 | "cell_type": "markdown", 512 | "metadata": {}, 513 | "source": [ 514 | "## DataFrames: Pandas, Dask" 515 | ] 516 | }, 517 | { 518 | "cell_type": "code", 519 | "execution_count": null, 520 | "metadata": { 521 | "collapsed": false 522 | }, 523 | "outputs": [], 524 | "source": [ 525 | "#Pandas \n", 526 | "import pandas as pd\n", 527 | "import numpy as np\n", 528 | "\n", 529 | "idx = pd.date_range('2000', '2005', freq='d', closed='left')\n", 530 | "datas = pd.DataFrame({'Color': [ 'green' if x> 1 else 'red' for x in np.random.randn(len(idx))], \n", 531 | " 'Measure': np.random.randn(len(idx)), 'Year': idx.year},\n", 532 | " index=idx.date)\n", 533 | "datas.head()" 534 | ] 535 | }, 536 | { 537 | "cell_type": "markdown", 538 | "metadata": {}, 539 | "source": [ 540 | "### Split / Apply / Combine \n", 541 | " Split your data into multiple independent groups.\n", 542 | " Apply some function to each group.\n", 543 | " Combine your groups back into a single data object.\n" 544 | ] 545 | }, 546 | { 547 | "cell_type": "code", 548 | "execution_count": null, 549 | "metadata": { 550 | "collapsed": false 551 | }, 552 | "outputs": [], 553 | "source": [ 554 | "datas.query('Measure > 0').groupby(['Color','Year']).size().unstack()" 555 | ] 556 | }, 557 | { 558 | "cell_type": "markdown", 559 | "metadata": {}, 560 | "source": [ 561 | "## Web Scraping: Beautifulsoup" 562 | ] 563 | }, 564 | { 565 | "cell_type": "code", 566 | "execution_count": null, 567 | "metadata": { 568 | "collapsed": false 569 | }, 570 | "outputs": [], 571 | "source": [ 572 | "# checking Web Scraping: beautifulsoup and requests \n", 573 | "import requests\n", 574 | "from bs4 import BeautifulSoup\n", 575 | "\n", 576 | "URL = 'http://en.wikipedia.org/wiki/Franklin,_Tennessee'\n", 577 | "\n", 578 | "req = requests.get(URL, headers={'User-Agent' : \"Mining the Social Web\"})\n", 579 | "soup = BeautifulSoup(req.text, \"lxml\")\n", 580 | "\n", 581 | "geoTag = soup.find(True, 'geo')\n", 582 | "\n", 583 | "if geoTag and len(geoTag) > 1:\n", 584 | " lat = geoTag.find(True, 'latitude').string\n", 585 | " lon = geoTag.find(True, 'longitude').string\n", 586 | " print ('Location is at', lat, lon)\n", 587 | "elif geoTag and len(geoTag) == 1:\n", 588 | " (lat, lon) = geoTag.string.split(';')\n", 589 | " (lat, lon) = (lat.strip(), lon.strip())\n", 590 | " print ('Location is at', lat, lon)\n", 591 | "else:\n", 592 | " print ('No location found')" 593 | ] 594 | }, 595 | { 596 | "cell_type": "markdown", 597 | "metadata": {}, 598 | "source": [ 599 | "## Operations Research: Pulp" 600 | ] 601 | }, 602 | { 603 | "cell_type": "code", 604 | "execution_count": null, 605 | "metadata": { 606 | "collapsed": false, 607 | "scrolled": true 608 | }, 609 | "outputs": [], 610 | "source": [ 611 | "# Pulp example : minimizing the weight to carry 99 pennies\n", 612 | "# (from Philip I Thomas)\n", 613 | "# see https://www.youtube.com/watch?v=UmMn-N5w-lI#t=995\n", 614 | "# Import PuLP modeler functions\n", 615 | "from pulp import *\n", 616 | "# The prob variable is created to contain the problem data \n", 617 | "prob = LpProblem(\"99 pennies Problem\",LpMinimize)\n", 618 | "\n", 619 | "# Variables represent how many of each coin we want to carry\n", 620 | "pennies = LpVariable(\"Number of pennies\",0,None,LpInteger)\n", 621 | "nickels = LpVariable(\"Number of nickels\",0,None,LpInteger)\n", 622 | "dimes = LpVariable(\"Number of dimes\",0,None,LpInteger)\n", 623 | "quarters = LpVariable(\"Number of quarters\",0,None,LpInteger)\n", 624 | "\n", 625 | "# The objective function is added to 'prob' first\n", 626 | "\n", 627 | "# we want to minimize (LpMinimize) this \n", 628 | "prob += 2.5 * pennies + 5 * nickels + 2.268 * dimes + 5.670 * quarters, \"Total coins Weight\"\n", 629 | "\n", 630 | "# We want exactly 99 cents\n", 631 | "prob += 1 * pennies + 5 * nickels + 10 * dimes + 25 * quarters == 99, \"\"\n", 632 | "\n", 633 | "# The problem data is written to an .lp file\n", 634 | "prob.writeLP(\"99cents.lp\")\n", 635 | "prob.solve()\n", 636 | "\n", 637 | "# print (\"status\",LpStatus[prob.status] )\n", 638 | "print (\"Minimal Weight to carry exactly 99 pennies is %s grams\" % value(prob.objective))\n", 639 | "# Each of the variables is printed with it's resolved optimum value\n", 640 | "for v in prob.variables():\n", 641 | " print (v.name, \"=\", v.varValue)" 642 | ] 643 | }, 644 | { 645 | "cell_type": "markdown", 646 | "metadata": {}, 647 | "source": [ 648 | "## Deep Learning: Theano" 649 | ] 650 | }, 651 | { 652 | "cell_type": "code", 653 | "execution_count": null, 654 | "metadata": { 655 | "collapsed": false 656 | }, 657 | "outputs": [], 658 | "source": [ 659 | "# Checking Theano\n", 660 | "import theano.tensor as T\n", 661 | "from theano import function\n", 662 | "x = T.dmatrix('x')\n", 663 | "y = T.dmatrix('y')\n", 664 | "z = x + y\n", 665 | "f = function([x, y], z)\n", 666 | "f([[1, 2], [3, 4]], [[10, 20], [30, 40]])" 667 | ] 668 | }, 669 | { 670 | "cell_type": "markdown", 671 | "metadata": {}, 672 | "source": [ 673 | "## Symbolic Calculation: sympy" 674 | ] 675 | }, 676 | { 677 | "cell_type": "code", 678 | "execution_count": null, 679 | "metadata": { 680 | "collapsed": false 681 | }, 682 | "outputs": [], 683 | "source": [ 684 | "# checking sympy \n", 685 | "import sympy\n", 686 | "a, b =sympy.symbols('a b')\n", 687 | "e=(a+b)**5\n", 688 | "e.expand()" 689 | ] 690 | }, 691 | { 692 | "cell_type": "markdown", 693 | "metadata": {}, 694 | "source": [ 695 | "## SQL tools: sqlite, Ipython-sql, sqlite_bro, baresql, db.py" 696 | ] 697 | }, 698 | { 699 | "cell_type": "code", 700 | "execution_count": null, 701 | "metadata": { 702 | "collapsed": false 703 | }, 704 | "outputs": [], 705 | "source": [ 706 | "# checking Ipython-sql, sqlparse, SQLalchemy\n", 707 | "%load_ext sql" 708 | ] 709 | }, 710 | { 711 | "cell_type": "code", 712 | "execution_count": null, 713 | "metadata": { 714 | "collapsed": false 715 | }, 716 | "outputs": [], 717 | "source": [ 718 | "%%sql sqlite:///.baresql.db\n", 719 | "DROP TABLE IF EXISTS writer;\n", 720 | "CREATE TABLE writer (first_name, last_name, year_of_death);\n", 721 | "INSERT INTO writer VALUES ('William', 'Shakespeare', 1616);\n", 722 | "INSERT INTO writer VALUES ('Bertold', 'Brecht', 1956);\n", 723 | "SELECT * , sqlite_version() as sqlite_version from Writer order by Year_of_death" 724 | ] 725 | }, 726 | { 727 | "cell_type": "code", 728 | "execution_count": null, 729 | "metadata": { 730 | "collapsed": false 731 | }, 732 | "outputs": [], 733 | "source": [ 734 | "# checking baresql\n", 735 | "from __future__ import print_function, unicode_literals, division # line needed only if Python2.7\n", 736 | "from baresql import baresql\n", 737 | "bsql = baresql.baresql(connection=\"sqlite:///.baresql.db\")\n", 738 | "bsqldf = lambda q: bsql.df(q, dict(globals(),**locals()))\n", 739 | "\n", 740 | "users = ['Alexander', 'Billy', 'Charles', 'Danielle', 'Esmeralda', 'Franz', 'Greg']\n", 741 | "# We use the python 'users' list like a SQL table\n", 742 | "sql = \"select 'Welcome ' || c0 || ' !' as say_hello, length(c0) as name_length from users$$ where c0 like '%a%' \"\n", 743 | "bsqldf(sql)" 744 | ] 745 | }, 746 | { 747 | "cell_type": "code", 748 | "execution_count": null, 749 | "metadata": { 750 | "collapsed": false 751 | }, 752 | "outputs": [], 753 | "source": [ 754 | "# Transfering Datas to sqlite, doing transformation in sql, going back to Pandas and Matplotlib\n", 755 | "bsqldf('''\n", 756 | "select Color, Year, count(*) as size \n", 757 | "from datas$$ \n", 758 | "where Measure > 0 \n", 759 | "group by Color, Year'''\n", 760 | " ).set_index(['Year', 'Color']).unstack().plot(kind='bar')" 761 | ] 762 | }, 763 | { 764 | "cell_type": "code", 765 | "execution_count": null, 766 | "metadata": { 767 | "collapsed": false 768 | }, 769 | "outputs": [], 770 | "source": [ 771 | "# checking db.py\n", 772 | "from db import DB\n", 773 | "db=DB(dbtype=\"sqlite\", filename=\".baresql.db\")\n", 774 | "db.query(\"select sqlite_version() as sqlite_version ;\") " 775 | ] 776 | }, 777 | { 778 | "cell_type": "code", 779 | "execution_count": null, 780 | "metadata": { 781 | "collapsed": false 782 | }, 783 | "outputs": [], 784 | "source": [ 785 | "db.tables" 786 | ] 787 | }, 788 | { 789 | "cell_type": "code", 790 | "execution_count": null, 791 | "metadata": { 792 | "collapsed": false 793 | }, 794 | "outputs": [], 795 | "source": [ 796 | "# checking sqlite_bro: this should lanch a separate non-browser window with sqlite_bro's welcome\n", 797 | "!cmd start cmd /C sqlite_bro" 798 | ] 799 | }, 800 | { 801 | "cell_type": "code", 802 | "execution_count": null, 803 | "metadata": { 804 | "collapsed": false 805 | }, 806 | "outputs": [], 807 | "source": [ 808 | "# pyodbc \n", 809 | "import pyodbc\n", 810 | "\n", 811 | "# look for pyodbc providers\n", 812 | "sources = pyodbc.dataSources()\n", 813 | "dsns = list(sources.keys())\n", 814 | "sl = [' %s [%s]' % (dsn, sources[dsn]) for dsn in dsns]\n", 815 | "print(\"pyodbc Providers: (beware 32/64 bit driver and python version must match)\\n\", '\\n'.join(sl))" 816 | ] 817 | }, 818 | { 819 | "cell_type": "code", 820 | "execution_count": null, 821 | "metadata": { 822 | "collapsed": false 823 | }, 824 | "outputs": [], 825 | "source": [ 826 | "# pythonnet\n", 827 | "import clr\n", 828 | "clr.AddReference(\"System.Data\")\n", 829 | "import System.Data.OleDb as ADONET\n", 830 | "import System.Data.Odbc as ODBCNET\n", 831 | "import System.Data.Common as DATACOM\n", 832 | "\n", 833 | "table = DATACOM.DbProviderFactories.GetFactoryClasses()\n", 834 | "print(\"\\n .NET Providers: (beware 32/64 bit driver and python version must match)\")\n", 835 | "for row in table.Rows:\n", 836 | " print(\" %s\" % row[table.Columns[0]])\n", 837 | " print(\" \",[row[column] for column in table.Columns if column != table.Columns[0]])" 838 | ] 839 | }, 840 | { 841 | "cell_type": "markdown", 842 | "metadata": {}, 843 | "source": [ 844 | "## Qt libraries Demo\n", 845 | "\n", 846 | " \n", 847 | "#### See [Dedicated Qt Libraries Demo](Qt_libraries_demo.ipynb)" 848 | ] 849 | }, 850 | { 851 | "cell_type": "markdown", 852 | "metadata": {}, 853 | "source": [ 854 | "## Wrap-up" 855 | ] 856 | }, 857 | { 858 | "cell_type": "code", 859 | "execution_count": null, 860 | "metadata": { 861 | "collapsed": false 862 | }, 863 | "outputs": [], 864 | "source": [ 865 | "# optional scipy full test (takes up to 10 minutes)\n", 866 | "#!cmd /C start cmd /k python.exe -c \"import scipy;scipy.test()\"" 867 | ] 868 | }, 869 | { 870 | "cell_type": "code", 871 | "execution_count": null, 872 | "metadata": { 873 | "collapsed": true 874 | }, 875 | "outputs": [], 876 | "source": [] 877 | } 878 | ], 879 | "metadata": { 880 | "kernelspec": { 881 | "display_name": "Python 3", 882 | "language": "python", 883 | "name": "python3" 884 | }, 885 | "language_info": { 886 | "codemirror_mode": { 887 | "name": "ipython", 888 | "version": 3 889 | }, 890 | "file_extension": ".py", 891 | "mimetype": "text/x-python", 892 | "name": "python", 893 | "nbconvert_exporter": "python", 894 | "pygments_lexer": "ipython3", 895 | "version": "3.6.0" 896 | }, 897 | "widgets": { 898 | "state": { 899 | "056d32c70f644417b86a152d3a2385bd": { 900 | "views": [ 901 | { 902 | "cell_index": 14 903 | } 904 | ] 905 | }, 906 | "2307e84bf81346d49818eef8862360ca": { 907 | "views": [ 908 | { 909 | "cell_index": 22 910 | } 911 | ] 912 | }, 913 | "4e7a6f5db8e74905a08d4636afa3b82f": { 914 | "views": [ 915 | { 916 | "cell_index": 15 917 | } 918 | ] 919 | }, 920 | "e762d7875083491eb2933958cc3331a9": { 921 | "views": [ 922 | { 923 | "cell_index": 21 924 | } 925 | ] 926 | } 927 | }, 928 | "version": "1.2.0" 929 | } 930 | }, 931 | "nbformat": 4, 932 | "nbformat_minor": 0 933 | } 934 | -------------------------------------------------------------------------------- /App/DefaultData/notebooks/docs/dplyr_pandas.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Tom Augspurger Dplyr/Pandas comparison (copy of 2016-01-01)\n", 8 | "\n", 9 | "### See result there\n", 10 | "http://nbviewer.ipython.org/urls/gist.githubusercontent.com/TomAugspurger/6e052140eaa5fdb6e8c0/raw/627b77addb4bcfc39ab6be6d85cb461e956fb3a3/dplyr_pandas.ipynb\n", 11 | "\n", 12 | "### to reproduce on your WinPython you'll need to get flights.csv in this directory" 13 | ] 14 | }, 15 | { 16 | "cell_type": "markdown", 17 | "metadata": {}, 18 | "source": [ 19 | "This notebook compares [pandas](http://pandas.pydata.org)\n", 20 | "and [dplyr](http://cran.r-project.org/web/packages/dplyr/index.html).\n", 21 | "The comparison is just on syntax (verbage), not performance. Whether you're an R user looking to switch to pandas (or the other way around), I hope this guide will help ease the transition.\n", 22 | "\n", 23 | "We'll work through the [introductory dplyr vignette](http://cran.r-project.org/web/packages/dplyr/vignettes/introduction.html) to analyze some flight data.\n", 24 | "\n", 25 | "I'm working on a better layout to show the two packages side by side.\n", 26 | "But for now I'm just putting the ``dplyr`` code in a comment above each python call.\n", 27 | "\n" 28 | ] 29 | }, 30 | { 31 | "cell_type": "markdown", 32 | "metadata": {}, 33 | "source": [ 34 | "### using R steps to get flights.csv\n", 35 | "\n", 36 | "un-comment the next cell unless you have installed R and want to get Flights example from the source\n", 37 | "\n", 38 | "to install R on your Winpython:\n", 39 | "[how to install R](installing_R.ipynb)\n" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": null, 45 | "metadata": { 46 | "collapsed": true 47 | }, 48 | "outputs": [], 49 | "source": [ 50 | "#%load_ext rpy2.ipython\n", 51 | "#%R install.packages(\"nycflights13\", repos='http://cran.us.r-project.org')\n", 52 | "#%R library(nycflights13)\n", 53 | "#%R write.csv(flights, \"flights.csv\")" 54 | ] 55 | }, 56 | { 57 | "cell_type": "markdown", 58 | "metadata": {}, 59 | "source": [ 60 | "### using an internet download to get flight.qcsv" 61 | ] 62 | }, 63 | { 64 | "cell_type": "code", 65 | "execution_count": null, 66 | "metadata": { 67 | "collapsed": false 68 | }, 69 | "outputs": [], 70 | "source": [ 71 | "# Downloading and unzipg a file, without R method :\n", 72 | "# source= http://stackoverflow.com/a/34863053/3140336\n", 73 | "import io\n", 74 | "from zipfile import ZipFile\n", 75 | "import requests\n", 76 | "\n", 77 | "def get_zip(file_url):\n", 78 | " url = requests.get(file_url)\n", 79 | " zipfile = ZipFile(io.BytesIO(url.content))\n", 80 | " zip_names = zipfile.namelist()\n", 81 | " if len(zip_names) == 1:\n", 82 | " file_name = zip_names.pop()\n", 83 | " extracted_file = zipfile.open(file_name)\n", 84 | " return extracted_file\n", 85 | "\n", 86 | "url=r'https://github.com/winpython/winpython_afterdoc/raw/master/examples/nycflights13_datas/flights.zip'\n", 87 | "with io.open(\"flights.csv\", 'wb') as f:\n", 88 | " f.write(get_zip(url).read())\n" 89 | ] 90 | }, 91 | { 92 | "cell_type": "code", 93 | "execution_count": null, 94 | "metadata": { 95 | "collapsed": false 96 | }, 97 | "outputs": [], 98 | "source": [ 99 | "# Some prep work to get the data from R and into pandas\n", 100 | "%matplotlib inline\n", 101 | "import matplotlib.pyplot as plt\n", 102 | "#%load_ext rpy2.ipython\n", 103 | "\n", 104 | "import pandas as pd\n", 105 | "import seaborn as sns\n", 106 | "\n", 107 | "pd.set_option(\"display.max_rows\", 5)" 108 | ] 109 | }, 110 | { 111 | "cell_type": "markdown", 112 | "metadata": {}, 113 | "source": [ 114 | "# Data: nycflights13" 115 | ] 116 | }, 117 | { 118 | "cell_type": "code", 119 | "execution_count": null, 120 | "metadata": { 121 | "collapsed": false 122 | }, 123 | "outputs": [], 124 | "source": [ 125 | "flights = pd.read_csv(\"flights.csv\", index_col=0)" 126 | ] 127 | }, 128 | { 129 | "cell_type": "code", 130 | "execution_count": null, 131 | "metadata": { 132 | "collapsed": false 133 | }, 134 | "outputs": [], 135 | "source": [ 136 | "# dim(flights) <--- The R code\n", 137 | "flights.shape # <--- The python code" 138 | ] 139 | }, 140 | { 141 | "cell_type": "code", 142 | "execution_count": null, 143 | "metadata": { 144 | "collapsed": false 145 | }, 146 | "outputs": [], 147 | "source": [ 148 | "# head(flights)\n", 149 | "flights.head()" 150 | ] 151 | }, 152 | { 153 | "cell_type": "markdown", 154 | "metadata": {}, 155 | "source": [ 156 | "# Single table verbs" 157 | ] 158 | }, 159 | { 160 | "cell_type": "markdown", 161 | "metadata": {}, 162 | "source": [ 163 | "``dplyr`` has a small set of nicely defined verbs. I've listed their closest pandas verbs.\n", 164 | "\n", 165 | "\n", 166 | "\n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | "\n", 204 | "
dplyrpandas
filter() (and slice())query() (and loc[], iloc[])
arrange()sort_values and sort_index()
select() (and rename())__getitem__ (and rename())
distinct()drop_duplicates()
mutate() (and transmute())assign
summarise()None
sample_n() and sample_frac()sample
%>%pipe
\n", 205 | "\n", 206 | "\n", 207 | "Some of the \"missing\" verbs in pandas are because there are other, different ways of achieving the same goal. For example `summarise` is spread across `mean`, `std`, etc. It's closest analog is actually the `.agg` method on a `GroupBy` object, as it reduces a DataFrame to a single row (per group). This isn't quite what `.describe` does.\n", 208 | "\n", 209 | "I've also included the `pipe` operator from R (`%>%`), the `pipe` method from pandas, even though it isn't quite a verb." 210 | ] 211 | }, 212 | { 213 | "cell_type": "markdown", 214 | "metadata": {}, 215 | "source": [ 216 | "# Filter rows with filter(), query()" 217 | ] 218 | }, 219 | { 220 | "cell_type": "code", 221 | "execution_count": null, 222 | "metadata": { 223 | "collapsed": false 224 | }, 225 | "outputs": [], 226 | "source": [ 227 | "# filter(flights, month == 1, day == 1)\n", 228 | "flights.query(\"month == 1 & day == 1\")" 229 | ] 230 | }, 231 | { 232 | "cell_type": "markdown", 233 | "metadata": {}, 234 | "source": [ 235 | "We see the first big *language* difference between R and python.\n", 236 | "Many python programmers will shun the R code as too magical.\n", 237 | "How is the programmer supposed to know that `month` and `day` are supposed to represent columns in the DataFrame?\n", 238 | "On the other hand, to emulate this *very* convenient feature of R, python has to write the expression as a string, and evaluate the string in the context of the DataFrame." 239 | ] 240 | }, 241 | { 242 | "cell_type": "markdown", 243 | "metadata": {}, 244 | "source": [ 245 | "The more verbose version:" 246 | ] 247 | }, 248 | { 249 | "cell_type": "code", 250 | "execution_count": null, 251 | "metadata": { 252 | "collapsed": false 253 | }, 254 | "outputs": [], 255 | "source": [ 256 | "# flights[flights$month == 1 & flights$day == 1, ]\n", 257 | "flights[(flights.month == 1) & (flights.day == 1)]" 258 | ] 259 | }, 260 | { 261 | "cell_type": "code", 262 | "execution_count": null, 263 | "metadata": { 264 | "collapsed": false 265 | }, 266 | "outputs": [], 267 | "source": [ 268 | "# slice(flights, 1:10)\n", 269 | "flights.iloc[:9]" 270 | ] 271 | }, 272 | { 273 | "cell_type": "markdown", 274 | "metadata": {}, 275 | "source": [ 276 | "# Arrange rows with arrange(), sort()" 277 | ] 278 | }, 279 | { 280 | "cell_type": "code", 281 | "execution_count": null, 282 | "metadata": { 283 | "collapsed": false 284 | }, 285 | "outputs": [], 286 | "source": [ 287 | "# arrange(flights, year, month, day) \n", 288 | "flights.sort_values(['year', 'month', 'day'])" 289 | ] 290 | }, 291 | { 292 | "cell_type": "code", 293 | "execution_count": null, 294 | "metadata": { 295 | "collapsed": false 296 | }, 297 | "outputs": [], 298 | "source": [ 299 | "# arrange(flights, desc(arr_delay))\n", 300 | "flights.sort_values('arr_delay', ascending=False)" 301 | ] 302 | }, 303 | { 304 | "cell_type": "markdown", 305 | "metadata": {}, 306 | "source": [ 307 | "It's worth mentioning the other common sorting method for pandas DataFrames, `sort_index`. Pandas puts much more emphasis on indicies, (or row labels) than R.\n", 308 | "This is a design decision that has positives and negatives, which we won't go into here. Suffice to say that when you need to sort a `DataFrame` by the index, use `DataFrame.sort_index`." 309 | ] 310 | }, 311 | { 312 | "cell_type": "markdown", 313 | "metadata": {}, 314 | "source": [ 315 | "# Select columns with select(), []" 316 | ] 317 | }, 318 | { 319 | "cell_type": "code", 320 | "execution_count": null, 321 | "metadata": { 322 | "collapsed": false 323 | }, 324 | "outputs": [], 325 | "source": [ 326 | "# select(flights, year, month, day) \n", 327 | "flights[['year', 'month', 'day']]" 328 | ] 329 | }, 330 | { 331 | "cell_type": "code", 332 | "execution_count": null, 333 | "metadata": { 334 | "collapsed": false 335 | }, 336 | "outputs": [], 337 | "source": [ 338 | "# select(flights, year:day) \n", 339 | "flights.loc[:, 'year':'day']" 340 | ] 341 | }, 342 | { 343 | "cell_type": "code", 344 | "execution_count": null, 345 | "metadata": { 346 | "collapsed": true 347 | }, 348 | "outputs": [], 349 | "source": [ 350 | "# select(flights, -(year:day)) \n", 351 | "\n", 352 | "# No direct equivalent here. I would typically use\n", 353 | "# flights.drop(cols_to_drop, axis=1)\n", 354 | "# or fligths[flights.columns.difference(pd.Index(cols_to_drop))]\n", 355 | "# point to dplyr!" 356 | ] 357 | }, 358 | { 359 | "cell_type": "code", 360 | "execution_count": null, 361 | "metadata": { 362 | "collapsed": false 363 | }, 364 | "outputs": [], 365 | "source": [ 366 | "# select(flights, tail_num = tailnum)\n", 367 | "flights.rename(columns={'tailnum': 'tail_num'})['tail_num']" 368 | ] 369 | }, 370 | { 371 | "cell_type": "markdown", 372 | "metadata": {}, 373 | "source": [ 374 | "But like Hadley mentions, not that useful since it only returns the one column. ``dplyr`` and ``pandas`` compare well here." 375 | ] 376 | }, 377 | { 378 | "cell_type": "code", 379 | "execution_count": null, 380 | "metadata": { 381 | "collapsed": false 382 | }, 383 | "outputs": [], 384 | "source": [ 385 | "# rename(flights, tail_num = tailnum)\n", 386 | "flights.rename(columns={'tailnum': 'tail_num'})" 387 | ] 388 | }, 389 | { 390 | "cell_type": "markdown", 391 | "metadata": {}, 392 | "source": [ 393 | "Pandas is more verbose, but the the argument to `columns` can be any mapping. So it's often used with a function to perform a common task, say `df.rename(columns=lambda x: x.replace('-', '_'))` to replace any dashes with underscores. Also, ``rename`` (the pandas version) can be applied to the Index." 394 | ] 395 | }, 396 | { 397 | "cell_type": "markdown", 398 | "metadata": {}, 399 | "source": [ 400 | "One more note on the differences here.\n", 401 | "Pandas could easily include a `.select` method.\n", 402 | "[`xray`](http://xray.readthedocs.org/en/stable/), a library that builds on top of NumPy and pandas to offer labeled N-dimensional arrays (along with many other things) does [just that](http://xray.readthedocs.org/en/stable/indexing.html#indexing-with-labeled-dimensions).\n", 403 | "Pandas chooses the `.loc` and `.iloc` accessors because *any valid selection is also a valid assignment*. This makes it easier to modify the data.\n", 404 | "\n", 405 | "```python\n", 406 | "flights.loc[:, 'year':'day'] = data\n", 407 | "```\n", 408 | "\n", 409 | "where `data` is an object that is, or can be broadcast to, the correct shape." 410 | ] 411 | }, 412 | { 413 | "cell_type": "markdown", 414 | "metadata": {}, 415 | "source": [ 416 | "# Extract distinct (unique) rows " 417 | ] 418 | }, 419 | { 420 | "cell_type": "code", 421 | "execution_count": null, 422 | "metadata": { 423 | "collapsed": false 424 | }, 425 | "outputs": [], 426 | "source": [ 427 | "# distinct(select(flights, tailnum))\n", 428 | "flights.tailnum.unique()" 429 | ] 430 | }, 431 | { 432 | "cell_type": "markdown", 433 | "metadata": {}, 434 | "source": [ 435 | "FYI this returns a numpy array instead of a Series." 436 | ] 437 | }, 438 | { 439 | "cell_type": "code", 440 | "execution_count": null, 441 | "metadata": { 442 | "collapsed": false 443 | }, 444 | "outputs": [], 445 | "source": [ 446 | "# distinct(select(flights, origin, dest))\n", 447 | "flights[['origin', 'dest']].drop_duplicates()" 448 | ] 449 | }, 450 | { 451 | "cell_type": "markdown", 452 | "metadata": {}, 453 | "source": [ 454 | "OK, so ``dplyr`` wins there from a consistency point of view. ``unique`` is only defined on Series, not DataFrames." 455 | ] 456 | }, 457 | { 458 | "cell_type": "markdown", 459 | "metadata": {}, 460 | "source": [ 461 | "# Add new columns with mutate() " 462 | ] 463 | }, 464 | { 465 | "cell_type": "markdown", 466 | "metadata": {}, 467 | "source": [ 468 | "We at pandas shamelessly stole this for [v0.16.0](http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#whatsnew-0160-enhancements-assign)." 469 | ] 470 | }, 471 | { 472 | "cell_type": "code", 473 | "execution_count": null, 474 | "metadata": { 475 | "collapsed": false 476 | }, 477 | "outputs": [], 478 | "source": [ 479 | "# mutate(flights,\n", 480 | "# gain = arr_delay - dep_delay,\n", 481 | "# speed = distance / air_time * 60)\n", 482 | "\n", 483 | "flights.assign(gain=flights.arr_delay - flights.dep_delay,\n", 484 | " speed=flights.distance / flights.air_time * 60)" 485 | ] 486 | }, 487 | { 488 | "cell_type": "code", 489 | "execution_count": null, 490 | "metadata": { 491 | "collapsed": false 492 | }, 493 | "outputs": [], 494 | "source": [ 495 | "# mutate(flights,\n", 496 | "# gain = arr_delay - dep_delay,\n", 497 | "# gain_per_hour = gain / (air_time / 60)\n", 498 | "# )\n", 499 | "\n", 500 | "(flights.assign(gain=flights.arr_delay - flights.dep_delay)\n", 501 | " .assign(gain_per_hour = lambda df: df.gain / (df.air_time / 60)))\n" 502 | ] 503 | }, 504 | { 505 | "cell_type": "markdown", 506 | "metadata": {}, 507 | "source": [ 508 | "The first example is pretty much identical (aside from the names, `mutate` vs. `assign`).\n", 509 | "\n", 510 | "The second example just comes down to language differences. In `R`, it's possible to implement a function like `mutate` where you can refer to `gain` in the line calcuating `gain_per_hour`, even though `gain` hasn't actually been calcuated yet.\n", 511 | "\n", 512 | "In Python, you can have arbitrary keyword arguments to functions (which we needed for `.assign`), but the order of the argumnets is arbitrary since `dict`s are unsorted and `**kwargs*` is a `dict`. So you can't have something like `df.assign(x=df.a / df.b, y=x **2)`, because you don't know whether `x` or `y` will come first (you'd also get an error saying `x` is undefined.\n", 513 | "\n", 514 | "To work around that with pandas, you'll need to split up the assigns, and pass in a *callable* to the second assign. The callable looks at itself to find a column named `gain`. Since the line above returns a DataFrame with the `gain` column added, the pipeline goes through just fine." 515 | ] 516 | }, 517 | { 518 | "cell_type": "code", 519 | "execution_count": null, 520 | "metadata": { 521 | "collapsed": false 522 | }, 523 | "outputs": [], 524 | "source": [ 525 | "# transmute(flights,\n", 526 | "# gain = arr_delay - dep_delay,\n", 527 | "# gain_per_hour = gain / (air_time / 60)\n", 528 | "# )\n", 529 | "(flights.assign(gain=flights.arr_delay - flights.dep_delay)\n", 530 | " .assign(gain_per_hour = lambda df: df.gain / (df.air_time / 60))\n", 531 | " [['gain', 'gain_per_hour']])\n" 532 | ] 533 | }, 534 | { 535 | "cell_type": "markdown", 536 | "metadata": {}, 537 | "source": [ 538 | "# Summarise values with summarise()" 539 | ] 540 | }, 541 | { 542 | "cell_type": "code", 543 | "execution_count": null, 544 | "metadata": { 545 | "collapsed": false 546 | }, 547 | "outputs": [], 548 | "source": [ 549 | "# summarise(flights,\n", 550 | "# delay = mean(dep_delay, na.rm = TRUE))\n", 551 | "flights.dep_delay.mean()" 552 | ] 553 | }, 554 | { 555 | "cell_type": "markdown", 556 | "metadata": {}, 557 | "source": [ 558 | "This is only roughly equivalent.\n", 559 | "`summarise` takes a callable (e.g. `mean`, `sum`) and evaluates that on the DataFrame. In pandas these are spread across `pd.DataFrame.mean`, `pd.DataFrame.sum`. This will come up again when we look at `groupby`." 560 | ] 561 | }, 562 | { 563 | "cell_type": "markdown", 564 | "metadata": {}, 565 | "source": [ 566 | "# Randomly sample rows with sample_n() and sample_frac()" 567 | ] 568 | }, 569 | { 570 | "cell_type": "code", 571 | "execution_count": null, 572 | "metadata": { 573 | "collapsed": false 574 | }, 575 | "outputs": [], 576 | "source": [ 577 | "# sample_n(flights, 10)\n", 578 | "flights.sample(n=10)" 579 | ] 580 | }, 581 | { 582 | "cell_type": "code", 583 | "execution_count": null, 584 | "metadata": { 585 | "collapsed": false 586 | }, 587 | "outputs": [], 588 | "source": [ 589 | "# sample_frac(flights, 0.01)\n", 590 | "flights.sample(frac=.01)" 591 | ] 592 | }, 593 | { 594 | "cell_type": "markdown", 595 | "metadata": {}, 596 | "source": [ 597 | "# Grouped operations " 598 | ] 599 | }, 600 | { 601 | "cell_type": "code", 602 | "execution_count": null, 603 | "metadata": { 604 | "collapsed": false 605 | }, 606 | "outputs": [], 607 | "source": [ 608 | "# planes <- group_by(flights, tailnum)\n", 609 | "# delay <- summarise(planes,\n", 610 | "# count = n(),\n", 611 | "# dist = mean(distance, na.rm = TRUE),\n", 612 | "# delay = mean(arr_delay, na.rm = TRUE))\n", 613 | "# delay <- filter(delay, count > 20, dist < 2000)\n", 614 | "\n", 615 | "planes = flights.groupby(\"tailnum\")\n", 616 | "delay = (planes.agg({\"year\": \"count\",\n", 617 | " \"distance\": \"mean\",\n", 618 | " \"arr_delay\": \"mean\"})\n", 619 | " .rename(columns={\"distance\": \"dist\",\n", 620 | " \"arr_delay\": \"delay\",\n", 621 | " \"year\": \"count\"})\n", 622 | " .query(\"count > 20 & dist < 2000\"))\n", 623 | "delay" 624 | ] 625 | }, 626 | { 627 | "cell_type": "markdown", 628 | "metadata": {}, 629 | "source": [ 630 | "For me, dplyr's ``n()`` looked is a bit starge at first, but it's already growing on me.\n", 631 | "\n", 632 | "I think pandas is more difficult for this particular example.\n", 633 | "There isn't as natural a way to mix column-agnostic aggregations (like ``count``) with column-specific aggregations like the other two. You end up writing could like `.agg{'year': 'count'}` which reads, \"I want the count of `year`\", even though you don't care about `year` specifically. You could just as easily have said `.agg('distance': 'count')`.\n", 634 | "Additionally assigning names can't be done as cleanly in pandas; you have to just follow it up with a ``rename`` like before." 635 | ] 636 | }, 637 | { 638 | "cell_type": "markdown", 639 | "metadata": {}, 640 | "source": [ 641 | "We may as well reproduce the graph. It looks like `ggplots` `geom_smooth` is some kind of lowess smoother. We can either us [seaborn](http://stanford.edu/~mwaskom/software/seaborn/):" 642 | ] 643 | }, 644 | { 645 | "cell_type": "code", 646 | "execution_count": null, 647 | "metadata": { 648 | "collapsed": false 649 | }, 650 | "outputs": [], 651 | "source": [ 652 | "fig, ax = plt.subplots(figsize=(12, 6))\n", 653 | "\n", 654 | "sns.regplot(\"dist\", \"delay\", data=delay, lowess=True, ax=ax,\n", 655 | " scatter_kws={'color': 'k', 'alpha': .5, 's': delay['count'] / 10}, ci=90,\n", 656 | " line_kws={'linewidth': 3});" 657 | ] 658 | }, 659 | { 660 | "cell_type": "markdown", 661 | "metadata": {}, 662 | "source": [ 663 | "Or using statsmodels directly for more control over the lowess, with an extremely lazy\n", 664 | "\"confidence interval\"." 665 | ] 666 | }, 667 | { 668 | "cell_type": "code", 669 | "execution_count": null, 670 | "metadata": { 671 | "collapsed": true 672 | }, 673 | "outputs": [], 674 | "source": [ 675 | "import statsmodels.api as sm" 676 | ] 677 | }, 678 | { 679 | "cell_type": "code", 680 | "execution_count": null, 681 | "metadata": { 682 | "collapsed": false 683 | }, 684 | "outputs": [], 685 | "source": [ 686 | "smooth = sm.nonparametric.lowess(delay.delay, delay.dist, frac=1/8)\n", 687 | "ax = delay.plot(kind='scatter', x='dist', y = 'delay', figsize=(12, 6),\n", 688 | " color='k', alpha=.5, s=delay['count'] / 10)\n", 689 | "ax.plot(smooth[:, 0], smooth[:, 1], linewidth=3);\n", 690 | "std = smooth[:, 1].std()\n", 691 | "ax.fill_between(smooth[:, 0], smooth[:, 1] - std, smooth[:, 1] + std, alpha=.25);" 692 | ] 693 | }, 694 | { 695 | "cell_type": "code", 696 | "execution_count": null, 697 | "metadata": { 698 | "collapsed": false 699 | }, 700 | "outputs": [], 701 | "source": [ 702 | "# destinations <- group_by(flights, dest)\n", 703 | "# summarise(destinations,\n", 704 | "# planes = n_distinct(tailnum),\n", 705 | "# flights = n()\n", 706 | "# )\n", 707 | "\n", 708 | "destinations = flights.groupby('dest')\n", 709 | "destinations.agg({\n", 710 | " 'tailnum': lambda x: len(x.unique()),\n", 711 | " 'year': 'count'\n", 712 | " }).rename(columns={'tailnum': 'planes',\n", 713 | " 'year': 'flights'})" 714 | ] 715 | }, 716 | { 717 | "cell_type": "markdown", 718 | "metadata": {}, 719 | "source": [ 720 | "There's a little know feature to `groupby.agg`: it accepts a dict of dicts mapping\n", 721 | "columns to `{name: aggfunc}` pairs. Here's the result:" 722 | ] 723 | }, 724 | { 725 | "cell_type": "code", 726 | "execution_count": null, 727 | "metadata": { 728 | "collapsed": false 729 | }, 730 | "outputs": [], 731 | "source": [ 732 | "destinations = flights.groupby('dest')\n", 733 | "r = destinations.agg({'tailnum': {'planes': lambda x: len(x.unique())},\n", 734 | " 'year': {'flights': 'count'}})\n", 735 | "r" 736 | ] 737 | }, 738 | { 739 | "cell_type": "markdown", 740 | "metadata": {}, 741 | "source": [ 742 | "The result is a `MultiIndex` in the columns which can be a bit awkard to work with (you can drop a level with `r.columns.droplevel()`). Also the syntax going into the `.agg` may not be the clearest." 743 | ] 744 | }, 745 | { 746 | "cell_type": "markdown", 747 | "metadata": {}, 748 | "source": [ 749 | "Similar to how ``dplyr`` provides optimized C++ versions of most of the `summarise` functions, pandas uses [cython](http://cython.org) optimized versions for most of the `agg` methods." 750 | ] 751 | }, 752 | { 753 | "cell_type": "code", 754 | "execution_count": null, 755 | "metadata": { 756 | "collapsed": false 757 | }, 758 | "outputs": [], 759 | "source": [ 760 | "# daily <- group_by(flights, year, month, day)\n", 761 | "# (per_day <- summarise(daily, flights = n()))\n", 762 | "\n", 763 | "daily = flights.groupby(['year', 'month', 'day'])\n", 764 | "per_day = daily['distance'].count()\n", 765 | "per_day" 766 | ] 767 | }, 768 | { 769 | "cell_type": "code", 770 | "execution_count": null, 771 | "metadata": { 772 | "collapsed": false 773 | }, 774 | "outputs": [], 775 | "source": [ 776 | "# (per_month <- summarise(per_day, flights = sum(flights)))\n", 777 | "per_month = per_day.groupby(level=['year', 'month']).sum()\n", 778 | "per_month" 779 | ] 780 | }, 781 | { 782 | "cell_type": "code", 783 | "execution_count": null, 784 | "metadata": { 785 | "collapsed": false 786 | }, 787 | "outputs": [], 788 | "source": [ 789 | "# (per_year <- summarise(per_month, flights = sum(flights)))\n", 790 | "per_year = per_month.sum()\n", 791 | "per_year" 792 | ] 793 | }, 794 | { 795 | "cell_type": "markdown", 796 | "metadata": {}, 797 | "source": [ 798 | "I'm not sure how ``dplyr`` is handling the other columns, like `year`, in the last example. With pandas, it's clear that we're grouping by them since they're included in the groupby. For the last example, we didn't group by anything, so they aren't included in the result." 799 | ] 800 | }, 801 | { 802 | "cell_type": "markdown", 803 | "metadata": {}, 804 | "source": [ 805 | "# Chaining" 806 | ] 807 | }, 808 | { 809 | "cell_type": "markdown", 810 | "metadata": {}, 811 | "source": [ 812 | "Any follower of Hadley's [twitter account](https://twitter.com/hadleywickham/) will know how much R users *love* the ``%>%`` (pipe) operator. And for good reason!" 813 | ] 814 | }, 815 | { 816 | "cell_type": "code", 817 | "execution_count": null, 818 | "metadata": { 819 | "collapsed": false 820 | }, 821 | "outputs": [], 822 | "source": [ 823 | "# flights %>%\n", 824 | "# group_by(year, month, day) %>%\n", 825 | "# select(arr_delay, dep_delay) %>%\n", 826 | "# summarise(\n", 827 | "# arr = mean(arr_delay, na.rm = TRUE),\n", 828 | "# dep = mean(dep_delay, na.rm = TRUE)\n", 829 | "# ) %>%\n", 830 | "# filter(arr > 30 | dep > 30)\n", 831 | "(\n", 832 | "flights.groupby(['year', 'month', 'day'])\n", 833 | " [['arr_delay', 'dep_delay']]\n", 834 | " .mean()\n", 835 | " .query('arr_delay > 30 | dep_delay > 30')\n", 836 | ")" 837 | ] 838 | }, 839 | { 840 | "cell_type": "markdown", 841 | "metadata": {}, 842 | "source": [ 843 | "A bit of soapboxing here if you'll indulge me.\n", 844 | "\n", 845 | "The example above is a bit contrived since it only uses methods on `DataFrame`. But what if you have some function to work into your pipeline that pandas hasn't (or won't) implement? In that case you're required to break up your pipeline by assigning your intermediate (probably uninteresting) DataFrame to a temporary variable you don't actually care about.\n", 846 | "\n", 847 | "`R` doesn't have this problem since the `%>%` operator works with any function that takes (and maybe returns) DataFrames.\n", 848 | "The python language doesn't have any notion of right to left function application (other than special cases like `__radd__` and `__rmul__`).\n", 849 | "It only allows the usual left to right `function(arguments)`, where you can think of the `()` as the \"call this function\" operator.\n", 850 | "\n", 851 | "Pandas wanted something like `%>%` and we did it in a farily pythonic way. The `pd.DataFrame.pipe` method takes a function and optionally some arguments, and calls that function with `self` (the DataFrame) as the first argument.\n", 852 | "\n", 853 | "So\n", 854 | "\n", 855 | "```R\n", 856 | "flights >%> my_function(my_argument=10)\n", 857 | "```\n", 858 | "\n", 859 | "becomes\n", 860 | "\n", 861 | "```python\n", 862 | "flights.pipe(my_function, my_argument=10)\n", 863 | "```\n", 864 | "\n", 865 | "We initially had grander visions for `.pipe`, but the wider python community didn't seem that interested." 866 | ] 867 | }, 868 | { 869 | "cell_type": "markdown", 870 | "metadata": {}, 871 | "source": [ 872 | "# Other Data Sources" 873 | ] 874 | }, 875 | { 876 | "cell_type": "markdown", 877 | "metadata": {}, 878 | "source": [ 879 | "Pandas has tons [IO tools](http://pandas.pydata.org/pandas-docs/version/0.15.0/io.html) to help you get data in and out, including SQL databases via [SQLAlchemy](http://www.sqlalchemy.org)." 880 | ] 881 | }, 882 | { 883 | "cell_type": "markdown", 884 | "metadata": {}, 885 | "source": [ 886 | "# Summary" 887 | ] 888 | }, 889 | { 890 | "cell_type": "markdown", 891 | "metadata": {}, 892 | "source": [ 893 | "I think pandas held up pretty well, considering this was a vignette written for dplyr. I found the degree of similarity more interesting than the differences. The most difficult task was renaming of columns within an operation; they had to be followed up with a call to ``rename`` *after* the operation, which isn't that burdensome honestly.\n", 894 | "\n", 895 | "More and more it looks like we're moving towards future where being a language or package partisan just doesn't make sense. Not when you can load up a [Jupyter](http://jupyter.org) (formerly IPython) notebook to call up a library written in R, and hand those results off to python or Julia or whatever for followup, before going back to R to make a cool [shiny](http://shiny.rstudio.com) web app.\n", 896 | "\n", 897 | "There will always be a place for your \"utility belt\" package like dplyr or pandas, but it wouldn't hurt to be familiar with both.\n", 898 | "\n", 899 | "If you want to contribute to pandas, we're always looking for help at https://github.com/pydata/pandas/.\n", 900 | "You can get ahold of me directly on [twitter](https://twitter.com/tomaugspurger)." 901 | ] 902 | } 903 | ], 904 | "metadata": { 905 | "kernelspec": { 906 | "display_name": "Python 3", 907 | "language": "python", 908 | "name": "python3" 909 | }, 910 | "language_info": { 911 | "codemirror_mode": { 912 | "name": "ipython", 913 | "version": 3 914 | }, 915 | "file_extension": ".py", 916 | "mimetype": "text/x-python", 917 | "name": "python", 918 | "nbconvert_exporter": "python", 919 | "pygments_lexer": "ipython3", 920 | "version": "3.4.4" 921 | } 922 | }, 923 | "nbformat": 4, 924 | "nbformat_minor": 0 925 | } 926 | --------------------------------------------------------------------------------