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        "author": "Mike Levin SEO in NYC, alum of Commodore & 360i, Creator of HitTail.com, currently with J2/Ziff-Davis/IGN/Mashable.",
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        "description": ":Author: `Mike Levin SEO in NYC, Commodore & 360i alum, HitTail creator, J2 Global employee. <http://mikelev.in>`_\n\nPipulate Free & Open Source SEO Software (Automate GSheets for Dashboards)\n##########################################################################\n\n.. image:: https://raw.githubusercontent.com/miklevin/Pipulate/master/pipulate-logo.svg?sanitize=true\n\n- Do you use Google Spreadsheets to make Data Dashboards for Business?\n- Are you using expensive plug-ins or products to keep these Reports Updated?\n- Would you rather not have to pay and have even more creativity and control?\n\nThen It's Time To Learn a Little Python!\n========================================\n\nPipulate is designed to make playing with data in Jupyter Notebook easy, and\nset the stage for you to move your code over to cheap generic Linux servers for\nautomation. The easiest way to install Pipulate from Jupyter is to execute\nthese commands from within a Notebook::\n\n    import sys\n    !{sys.executable} -m pip install --upgrade --no-cache-dir pipulate\n\nIf you're not using Anaconda, then you will have to install Pandas first. But\nyou should really be using Anaconda. The idea is that you experiment in Jupyter\nNotebook, which is a great learning environment and move your scripts as they\nmature over to a more automated generic Linux server.\n\nThe first time you import Pipulate, you will receive a Google OAuth prompt to\nallow access to the Google Spreadsheet you want to edit. Click the big link\nand paste the resulting token back into the field shown in Jupyter Notebook or\nyour command-line. Once this is done, the basic use is::\n\n    import pipulate\n    pipulate.sheet('119mnC8Day78KexU_yv7J_wfA3p7iZeXa0YEtmg1Igu4')  # replace with yours\n    cl, df = pipulate.pull(tab=0, rows='A1:J10')\n    df.loc[:,:] = 'foo'\n    pipulate.push(0, cl, df)\n\nThis loads the rectangular region you defined with the rows and columns into\nmemory in a way where you can treat it a lot like a tab in Microsoft Excel or\ntable in SQL. You can manipulate the pandas \"DataFrame\" (abbreviated as df),\nand then push the changes back out to the Google Sheet. This is very\nopen-ended, designed to make automation of traditional tedious tasks in SEO\nmuch simpler. Once you're happy with your script, you can copy/paste it into a\n.py file and schedule it with a standard Linux scheduler, which is another part\nof this project I'll be expanding considerably (generic task-scheduling under\nLinux).\n\nThere is some flexibility in the API and ability to assign column labels from\nrow 1, allowing you to adapt to your style. Here are some variations::\n\n    cl, df = pipulate.pull('Sheet1', rows='A1:J5')                # Most common usage\n    cl, df = pipulate.pull('Sheet1', rows='A1:J5', columns=True)  # Turn row 1 into column labels\n    cl, df = pipulate.pull(tab=0, rows='A1:J5')                   # Address tab by index\n    cl, df = pipulate.pull(0, rows='A1:J5')                       # Address tab by index, no label\n    cl, df = pipulate.pull(0, rows='A1:J')                        # Automatically figure out number of rows\n    cl, df = pipulate.pull(0, rows=(1,5), cols=('A','J'))         # Use the rows=, cols= interface\n    cl, df = pipulate.pull(0, (1,5), ('A','J'))                   # Use row, col interface, no labels\n    cl, df = pipulate.pull(0, (1,5), (1,10))                      # Use row, col with numeric column index\n    cl, df = pipulate.pull(2, rows='A1:J5')                       # Work on the 3rd tab.\n    cl, df = pipulate.pull(wksht, rows='A1:J5')                   # Use GSpread Worksheet object\n\nUsing argument labels is optional. The exact string-name, 0-based numerical\nindex or a GSpread Worksheet object-type must be in the first position. The 2nd\nposition is the \"rows\" value, which may either be an Excel-like range or a\nrow-range. If a row-range then you must also have a col-range in position 3 (or\nlabel the argument \"cols\"). The details of how you do it will vary with your\nproject. It is often useful to name your tabs so you can do different\nmanipulations to different tabs without worrying about changing their order in\nthe spreadsheet Web user interface.\n\nIf you don't give any column labels, Pipulate will assign them automatically\nusing the Excel-like letter-labels for columns. If you want to name your\ncolumn, you can either use row 1 by setting your columns parameter to true or\nyou can provide your own list of column names::\n\n    cols = ['one', 'two', 'three', 'four', 'five']\n    cl, df = pipulate.pull(tab=0, rows='A1:J5', columns=cols)  # Set columns labels from list\n\nAfter you make your cl (GSpread cell_list) and df (pandas DataFrame) selection,\nyou can modify your df and push it back into Google Sheets with the symmetrical\npopulate command::\n\n    pipulate.push(0, cl, df)  # Works in most cases\n\nReal Life Example\n=================\n\nSay you have a spreadsheet with a list of URLs that were provided to you from\nsome unreliable source in column A starting from A1. You don't know whether it\nshould have a www at the beginning or if the protocol is correct::\n\n    amazon.com\n    apple.com\n    google.com\n\nAnd now let's say you write a simple function that will test what the real\nhomepage URL is. This example uses the popular \"Requests\" package for making\nhttp requests::\n\n    import requests\n\n    def cleanurl(provided):\n        guess = provided\n        if provided[:4].lower() != 'http':\n            guess = 'https://%s' % provided\n        try:\n            r = requests.head(guess, allow_redirects=True)\n            resolved = r.url\n        except:\n            resolved = \"Can't find %s\" % provided\n        return resolved\n\nTo use Pipulate and Pandas to apply this function to each line of column A, you\nwould first select columns A & B into a Pandas DataFrame::\n\n    import pipulate\n    pipulate.sheet('119mnC8Day78KexU_yv7J_wfA3p7iZeXa0YEtmg1Igu4')\n    cl, df = pipulate.pull(tab=0, rows='A1:B3')\n\nThen you apply the function to each cell in column A of your DataFrame and put\nthe results in column B and push the results back up into Google Sheets::\n\n    df['B'] = df['A'].apply(cleanurl)\n    pipulate.push(0, cl, df)\n\nAnd that's it! Column B will now contain::\n\n    https://www.apple.com/\n    https://www.google.com/\n    https://www.amazon.com/\n\nImagine the utility! I addition to working magic in Google Sheets, you're\nacquiring incredibly useful Python Pandas skills!\n\nParametrizing Your Arguments for Scheduling\n===========================================\n\nThe time has come the Walrus said to talk about automation. After you get your\nscript working in Jupyter Notebook what are you going to do? Unless you want to\nsit there and press a button every day like in Lost, then you're going to have\nto put the code in some sort of scheduling system. When you do this, it is best\nto do it in a parameterized fashion, meaning you can feed the one script\ndifferent sites, Google Analytics IDs, keywords, filters and whatnot; allowing\nthe same code to drive different dashboards. \n\nGeneric Linux script scheduling is too much to cover here, but I may as well\nshow you how to develop with parameterized arguments in Jupyter Notebook then\nhave those same arguments able to be used on the command-line when invoked from\nscheduling. So if you have filename.ipynb (A Jupyter Notebook) and you\ncopy/paste its content into filename.py, then you can change its internal\nvariable values by the way you run it form the Unix shell, thereby using it to\ndrive many different Google Sheet dashboards. You're welcome::\n\n    (py36) MikeL@LunderVand:$ filename.py --kung \"foo\"\n    kung: foo\n\nYou can alternatively use::\n\n    (py36) MikeL@LunderVand:$ filename.py -k \"foo\"\n    kung: foo\n\nTo support more arguments, just make more of the parser.add_argument() function\ncalls and access them through the args dictionary. Here's the code in\nfilename.py in the above commands. When this is run from Jupyter Notebook, the\n\"foo\" value is set in the \"if jn:\" if-block. It's fairly likely you'll have\ncode like this at the top of any Pipulate script that starts out in Jupyter\nNotebook and is destined for scheduling. Clear? Enjoy!::\n\n    name = 'kung'\n    jn = True\n    try:\n        get_ipython()\n    except NameError:\n        jn = False\n    if jn:\n        val = 'foo'\n    else:\n        import argparse\n        parser = argparse.ArgumentParser(description='Parses args when run from console.')\n        parser.add_argument('-k','--kung', default='foo', help='', required=False)\n        args = vars(parser.parse_args())\n        val = args[name]\n    print('%s: %s' % (name, val))\n\nFear Is The Mind Killer\n=======================\n\nWhy do I call Google Sheets automation components \"SEO software\"? It's because\nthis is the main missing ingredient in building all those custom reports\nnecessary to surface new actionable data every day. It's also your easy route\ninto data manipulation with Python/Pandas and the repurposing of your aging SEO\ncareer into something simultaneously more timely and timeless. SEO is dead.\nLong live SEO! Sound familiar? Well, the other shoe is finally dropping with\nall the Machine Learning tricks ol' Uncle Google is learning, and if you're a\ndinosaur then y'all better learn to fly. If you can do Excel macros, then you\ncan survive, evolve and thrive.\n\nLearning Linux, Python, vim and git (LPvg) will set you on a good course for at\nleast the next 10-years. If you're still scared of the Unix shell, that old\ntext-based command-line interface which is both the past and future of tech,\nthen stick your hand into the Gom Jabbar pain-box of Linux and cut your teeth\non my other repo here on Github `Levinux <https://github.com/miklevin/levinux>`_.\nNow repeat after me: I must not fear. Fear is the mind-killer. Fear is the\nlittle-death that brings total obliteration. I will face my fear. I will permit\nit to pass over me and through me.\n\n.. image:: https://raw.githubusercontent.com/miklevin/Pipulate/master/mike-levin-seo-nyc.png",
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