Python

Downloading YouTube Videos and converting to MP3

A simple guide to download videos from YouTube using python

  1. Objectives:
      1. Download YouTube Videos
      2. Saving as subclip (saving a portion of the video)
      3. Converting to MP3
      4.  
  2. Required Tools:
      1. PyTube— primarily for downloading youtube videos.
      2. MoviePy — for video editing and also convert to mp3.
      3.  
  3. Steps:
    1. pip install pytube and moviepy

Basic Usage

from pytube import YouTube
from moviepy.editor import *

# download a file from youtube
youtube_link = 'https://www.youtube.com/watch?v=yourtubevideos'
w = YouTube(youtube_link).streams.first()
w.download(output_path="/your/target/directory")

# download a file with only audio, to save space
# if the final goal is to convert to mp3
youtube_link = 'https://www.youtube.com/watch?v=targetyoutubevideos'
y = YouTube(youtube_link)
t = y.streams.filter(only_audio=True).all()
t[0].download(output_path="/your/target/directory")

Downloading videos from a YouTube playlist

import requests
import re
from bs4 import BeautifulSoup

website = 'https://www.youtube.com/playlist?list=yourfavouriteplaylist'
r= requests.get(website)
soup = BeautifulSoup(r.text)

tgt_list = [a['href'] for a in soup.find_all('a', href=True)]
tgt_list = [n for n in tgt_list if re.search('watch',n)]

unique_list= []
for n in tgt_list:
    if n not in unique_list:
        unique_list.append(n)

# all the videos link in a playlist
unique_list = ['https://www.youtube.com' + n for n in unique_list]

for link in unique_list:
    print(link)
    y = YouTube(link)
    t = y.streams.all()
    t[0].download(output_path="/your/target/directory")

Converting from MP4 to MP3 (from a folder with mp4 files)

import moviepy.editor as mp
import re
tgt_folder = "/folder/contains/your/mp4"

for file in [n for n in os.listdir(tgt_folder) if re.search('mp4',n)]:
full_path = os.path.join(tgt_folder, file)
output_path = os.path.join(tgt_folder, os.path.splitext(file)[0] + '.mp3')
clip = mp.AudioFileClip(full_path).subclip(10,) # disable if do not want any clipping
clip.write_audiofile(output_path)
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Custom Contour Plots with Labelled points

Creating Customized Contour Plots with Labelled Points

I was asked to create a customized contour plot based on a chart (Fig 1 ) found in IEEE Transactions on Magnetics journal with some variant in requirements. The chart shows the areal density capacity (ADC) demo of certain samples on a bit density (BPI) by track density (TPI) chart. The two different contours shown in the plot are made up of ADC (BPI * TPI) and bit aspect ratio BAR (BPI/TPI).

A way to create the plot might be to generate the contours based on Excel and manually added in the different points. This proves to be too much work. Therefore, a simpler way is needed. Further requirements include having additional points (with labels) to be added in fairly easily and charts with different sets of data can be recreated rapidly.

Creating the Contours

The idea will be to use the regression plots for both the ADC and the BAR contours while the points and labels can be automatically added to the plots after reading from an Excel table (or csv file). The regression plots are based on seaborn lmplot and the points with labels are annotated on the chart based on the individual x, and y values.

Besides the seaborn, pandas, matplotlib and numpy,  additional module adjustText is used to prevent overlapping of the text labels in the plot

import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from adjustText import adjust_text

## Create GridLines for the ADC GBPSI
ADC_tgt = range(650,2150,50)
BPI_tgt = list(range(800,2700,20))*3
data_list = [ [ADC, BPI, ADC*1000/BPI] for BPI in BPI_tgt for ADC in ADC_tgt]
ADC_df = pd.DataFrame(data_list, columns=['Contour','X','Y']) #['ADC','TPI','BPI']
ADC_df['Contour'] = ADC_df['Contour'].astype('category')

## Create GridLines for the BAR
BAR_tgt =[1.0,1.5,2.0, 2.5,3.0,3.5,4.0,4.5,5.0,5.5,6.0,6.5]
BPI_tgt = list(range(800,2700,20))*3
data_list = [ [BAR, BPI, BPI/BAR] for BPI in BPI_tgt for BAR in BAR_tgt]
BAR_df = pd.DataFrame(data_list, columns=['Contour','X','Y']) #['BAR','TPI','BPI']
BAR_df['Contour'] = BAR_df['Contour'].astype('category')

combined_df = pd.concat([ADC_df,BAR_df])

Adding the demo points with text from Excel

The various points are updated in the excel sheet (or csv) , shown in fig 2, and read using pandas. Two data frames are produced, pts_df and text_df which is the dataframe from the points and the associated text. These, together with the contour data frame from above, are then feed into the seaborn lmplot. Note the points shown in the Excel and plots are randomly generated.

class ADC_DataPts():

    def __init__(self, xls_fname, header_psn = 0):
        self.xls_fname = xls_fname
        self.header_psn = header_psn
        self.data_df = pd.read_excel(self.xls_fname, header = self.header_psn)

    def generate_pts_text_df(self):
        pts_df = self.data_df['X Y Color'.split()]
        text_df = self.data_df['X_TxtPsn Y_TxtPsn TextContent'.split()]
        return pts_df, text_df

data_excel = r"yourexcelpath.xls"
adc_data = ADC_DataPts(data_excel, header_psn =1)
pts_df, text_df = adc_data.generate_pts_text_df()

Seaborn lmplot

The seaborn lmplot is used for the contours while the points are individually annotated on the graph

def generate_contour_plots_with_points(xlabel, ylabel, title):

    # overall settings for plots
    sns.set_context("talk")
    sns.set_style("whitegrid", \
                  {'grid.linestyle': ':', 'xtick.bottom': True, 'xtick.direction': 'out',\
                    'xtick.color': '.15','axes.grid' : False}
                 )

    # Generate the different "contour"
    g = sns.lmplot("X", "Y", data=combined_df, hue='Contour', order =2, \
               height =7, aspect =1.5, ci =False, line_kws={'color':'0.9', 'linestyle':':'}, \
                scatter=False, legend_out =False)

    # Bold the key contour lines
    for n in [1.0,2.0,3.0]:
        sub_bar = BAR_df[BAR_df['Contour']==n]
        #generate the bar contour
        g.map(sns.regplot, x= "X", y="Y", data=sub_bar ,scatter= False, ci =False, \
              line_kws={'color':'0.9', 'linestyle':'-', 'alpha':0.05, 'linewidth':'3'})

    for n in [1000,1500,2000]:
        sub_adc = ADC_df[ADC_df['Contour']==n]
        #generate the bar contour
        g.map(sns.regplot, x= "X", y="Y", data=sub_adc ,scatter= False, ci =False, order =2, \
              line_kws={'color':'0.9', 'linestyle':'-', 'alpha':0.05, 'linewidth':'3'})#'color':'0.7', 'linestyle':'-', 'alpha':0.05, 'linewidth':'2'

    # Generate the different points
    for index, rows in pts_df.iterrows():
        g = g.map_dataframe(plt.plot, rows['X'], rows['Y'], 'o',  color = rows['Color'])# generate plot with differnt color or use annotation?

    ax = g.axes.flat[0]    

    # text annotation on points
    style = dict(size=12, color='black', verticalalignment='top')
    txt_grp = []
    for index, rows in text_df.iterrows():
        txt_grp.append(ax.text( rows['X_TxtPsn'], rows['Y_TxtPsn'], rows['TextContent'], **style) )#how to find space, separate data base

    style2 = dict(size=12, color='grey', verticalalignment='top')
    style3 = dict(size=12, color='grey', verticalalignment='top', rotation=30, alpha= 0.7)

    # Label the key contours
    ax.text( 2400, 430, '1000 Gfpsi', **style2)
    ax.text( 2400, 640, '1500 Gfpsi', **style2)
    ax.text( 2400, 840, '2000 Gfpsi', **style2) 

    ax.text( 1100, 570, 'BAR 2.0', **style3)
    ax.text( 1300, 460, 'BAR 3.0', **style3) 

    # Set x y limit
    ax.set_ylim(400,1000)
    ax.set_xlim(1000,2600)

    # Set general plot attributes
    g.set_xlabels(xlabel)
    g.set_ylabels(ylabel)
    plt.title(title)

    adjust_text(txt_grp, x = pts_df.X.tolist() , y = pts_df.Y.tolist() , autoalign = True, expand_points=(1.4, 1.4))

generate_contour_plots_with_points('kBPI', 'kTPI', "DEMO Areal Density Capability\n")

Untitled

Fig 1: Sample plot from Heat-Assisted Interlaced Magnetic Recording IEEE Vol 54 No2

Untitled

Fig2: Excel tables with associated demo points, the respective color and the text labels

Untitled

Fig 3: Generated chart with the ADC and BAR contours and demo pts with labels

Radix Sort in Python

Background

  1. Non comparison integer sorting by grouping numbers based on individual digits or radix (base)
  2. Perform iteratively from least significant digit (LSD) to most significant digit (MSD) or recusively from MSD to LSD.
  3. At each iteration, sorting of target digit is based usually on Counting sort as subroutine.
  4. Complexity: O(d*n+b)) where b is the base for representing numbers eg 10. d is the number of digits. Close to Linear time if d is constant amount

Counting Sort as subroutine

  • Recap on the counting sort. See Counting Sort in Python for more info
  • Taking “get_sortkey ” function that generate the keys based on objects characteristics.
  • Modified the get_sortkey function to perform radix sort.
import random, math

def get_sortkey(n):
    """ Define the method to retrieve the key """
    return n

def counting_sort(tlist, k, get_sortkey):
    """ Counting sort algo with sort in place.
        Args:
            tlist: target list to sort
            k: max value assume known before hand
            get_sortkey: function to retrieve the key that is apply to elements of tlist to be used in the count list index.
            map info to index of the count list.
        Adv:
            The count (after cum sum) will hold the actual position of the element in sorted order
            Using the above, 

    """

    # Create a count list and using the index to map to the integer in tlist.
    count_list = [0]*(k)

    # iterate the tgt_list to put into count list
    for n in tlist:
        count_list[get_sortkey(n)] = count_list[get_sortkey(n)] + 1  

    # Modify count list such that each index of count list is the combined sum of the previous counts
    # each index indicate the actual position (or sequence) in the output sequence.
    for i in range(k):
        if i ==0:
            count_list[i] = count_list[i]
        else:
            count_list[i] += count_list[i-1]

    output = [None]*len(tlist)
    for i in range(len(tlist)-1, -1, -1):
        sortkey = get_sortkey(tlist[i])
        output[count_list[sortkey]-1] = tlist[i]
        count_list[sortkey] -=1

    return output

Radix sort with up to 3-digits numbers

  • Replace the get_sortkey with the get_sortkey2 which extract the integer based on the digit place and uses the counting sort at each iteration
# radix sort
from functools import partial

def get_sortkey2(n, digit_place=2):
    """ Define the method to retrieve the key
        return the key based on the digit place. Current set base to 10
    """
    return (n//10**digit_place)%10

## Create random list for demo counting sort.
random.seed(1)
tgt_list = [random.randint(20,400) for n in range(10)]
print("Unsorted List")
print(tgt_list)

## Perform the counting sort.
print("\nSorted list using counting sort")

output = tgt_list
for n in range(3):
    output = counting_sort(output, 30, partial(get_sortkey2, digit_place=n))
    print(output)

## output
# Unsorted List
# [88, 311, 52, 150, 80, 273, 250, 261, 353, 214]

# Sorted list using counting sort
# [150, 80, 250, 311, 261, 52, 273, 353, 214, 88]
# [311, 214, 150, 250, 52, 353, 261, 273, 80, 88]
# [52, 80, 88, 150, 214, 250, 261, 273, 311, 353]

See also:

Resources:

  1. Getting To The Root Of Sorting With Radix Sort

Convert PDF pages to text with python

A simple guide to text from PDF. This is an extension of the Convert PDF pages to JPEG with python post

  1. Objectives:
      1. Extract text from PDF
  2. Required Tools:
      1. Poppler for windows— Poppler is a PDF rendering library . Include the pdftoppm utility
      2. Poppler for Mac — If HomeBrew already installed, can use brew install Poppler
      3. pdftotext— Python module. Wraps the poppler pdftotext utility to convert PDF to text.
  3. Steps:
      1. Install Poppler. For windows, Add “xxx/bin/” to env path
      2. pip install pdftotext

Usage (sample code from pdftotext github)

import pdftotext

# Load your PDF
with open("Target.pdf", "rb") as f:
    pdf = pdftotext.PDF(f)

# Save all text to a txt file.
with open('output.txt', 'w') as f:
    f.write("\n\n".join(pdf))

Further notes 

See also:

Convert PDF pages to JPEG with python

A simple guide to extract images (jpeg, png) from PDF.

  1. Objectives:
      1. Extract Images from PDF
  2. Required Tools:
      1. Poppler for windows— Poppler is a PDF rendering library . Include the pdftoppm utility
      2. Poppler for Mac — If HomeBrew already installed, can use brew install Poppler
      3. Pdf2image— Python module. Wraps the pdftoppm utility to convert PDF to a PIL Image object.
  3. Steps:
      1. Install Poppler. For windows, Add “xxx/bin/” to env path
      2. pip install pdf2image

Usage

import os
import tempfile
from pdf2image import convert_from_path

filename = 'target.pdf'

with tempfile.TemporaryDirectory() as path:
     images_from_path = convert_from_path(filename, output_folder=path, last_page=1, first_page =0)

base_filename  =  os.path.splitext(os.path.basename(filename))[0] + '.jpg'      

save_dir = 'your_saved_dir'

for page in images_from_path:
    page.save(os.path.join(save_dir, base_filename), 'JPEG')

Further notes 

Counting Sort in Python

Background

  1. Sort a collection of objects according to integer keys. Count the number of objects belonging to a specific key value and output the sequence based on both integer key sequence + number of counts in each key.
  2. Running time linear: O(n+k) where n is the number of objects and k is the number of keys.
  3. Keys should not be significant larger than number of objects

Basic Counting Sort

  • With objects as integer key itself.
  • Limited use. Index key not able to modify for extended cases.
import random, math

def basic_counting_sort(tlist, k):
    """ Counting sort algo. Modified existing list. Only for positive integer.
        Args:
            tlist: target list to sort
            k: max value assume known before hand
        Disadv:
            It only does for positive integer and unable to handle more complex sorting (sort by str, negative integer etc)
            It straight away retrieve all data from count_list using count_list index as its ordering.
            Do not have the additional step to modify count_list to capture the actual index in output.
    """

    # Create a count list and using the index to map to the integer in tlist.
    count_list = [0]*(k)

    # loop through tlist and increment if exists
    for n in tlist:
        count_list[n] = count_list[n] + 1

    # Sort in place, copy back into original list
    i=0
    for n in range(len(count_list)):
        while count_list[n] > 0:
            tlist[i] = n
            i+=1
            count_list[n] -= 1

## Create random list for demo counting sort.
random.seed(0)
tgt_list = [random.randint(0,20) for n in range(10)]
print("Unsorted List")
print(tgt_list)

## Perform the counting sort.
print("\nSorted list using basic counting sort")
basic_counting_sort(tgt_list, max(tgt_list)+1)
print(tgt_list)

Counting sort — improved version

  • Taking “get_sortkey ” function that generate the keys based on objects characteristics.
  • Currently, function just return the object itself to work in same way as above but the function can be modified to work with other form of objects e.g. negative integers, string etc.
import random, math

def get_sortkey(n):
    """ Define the method to retrieve the key """
    return n

def counting_sort(tlist, k, get_sortkey):
    """ Counting sort algo with sort in place.
        Args:
            tlist: target list to sort
            k: max value assume known before hand
            get_sortkey: function to retrieve the key that is apply to elements of tlist to be used in the count list index.
            map info to index of the count list.
        Adv:
            The count (after cum sum) will hold the actual position of the element in sorted order
            Using the above, 

    """

    # Create a count list and using the index to map to the integer in tlist.
    count_list = [0]*(k)

    # iterate the tgt_list to put into count list
    for n in tlist:
        count_list[get_sortkey(n)] = count_list[get_sortkey(n)] + 1  

    # Modify count list such that each index of count list is the combined sum of the previous counts
    # each index indicate the actual position (or sequence) in the output sequence.
    for i in range(k):
        if i ==0:
            count_list[i] = count_list[i]
        else:
            count_list[i] += count_list[i-1]

    output = [None]*len(tlist)
    for i in range(len(tlist)-1, -1, -1):
        sortkey = get_sortkey(tlist[i])
        output[count_list[sortkey]-1] = tlist[i]
        count_list[sortkey] -=1

    return output

## Create random list for demo counting sort.
random.seed(0)
tgt_list = [random.randint(0,20) for n in range(10)]
print("Unsorted List")
print(tgt_list)

## Perform the counting sort.
print("\nSorted list using basic counting sort")
output = counting_sort(tgt_list, max(tgt_list) +1, get_sortkey) # assumption is known the max value in tgtlist  for this case.
print(output)

Simple illustration: Counting sort use for negative numbers

def get_sortkey2(n):
    """ Define the method to retrieve the key
        Shift the key such that the all keys still positive integers
        even though input may be negative
    """
    return n +5

## Create random list for demo counting sort.
random.seed(1)
tgt_list = [random.randint(-5,20) for n in range(10)]
print("Unsorted List")
print(tgt_list)

## Perform the counting sort.
print("\nSorted list using counting sort")
output = counting_sort(tgt_list, 30, get_sortkey2)
print(output)<span id="mce_SELREST_start" style="overflow:hidden;line-height:0;"></span>

Resources:

  1. https://www.geeksforgeeks.org/counting-sort/

Setup MongoDB on iOS

A simple guide to setting up MongoDB on iOS.

  1. Objectives:
      1. Install MongoDB on MacBook.
  2. Required Tools:
      1. Homebrew —  package manager for Mac
      2. MongoDB — MongoDB community version
      3. pymongo — python API for MongoDB.
  3. Steps (terminal command in blue):
      1. brew update
      2. brew install mongodb
      3. Create MongoDB Data directory (/data/db) with updated permission
        1. $ sudo mkdir -p /data/db
        2. $ sudo chown <user>/data/db
      4. Create/open bash_profile
        1. $ cd to users/<username>
        2. $ touch .bash_profile # skip if .bash_profile present
        3. $ open .bash_profile
      5. Insert command in  bash_profile for MongoDB commands to work in terminal
        1. export MONGO_PATH=/usr/local/mongodb
        2. export PATH=$PATH:$MONGO_PATH/bin
      6. Test: Run MongoDB
        1. terminal 1: mongod
        2. terminal 2: mongo.
      7. Install pymongo
        1. pip install pymongo

Further notes 

Fast Install Python Virtual Env in Windows

A simple guide to install virtual environment with different python version on Windows.

  1. Objectives:
      1. Install Virtual Environment on Windows
  2. Required Tools:
      1. Python —  Python 3 chosen in this case.
      2. VirtualEnv — Main virtualenv tool.
      3. VirtualEnvWrapper-Win — VirtualEnv Wrapper for Windows.
  3. Steps:
      1. Install python with python windows installer.
      2. Add python path to Windows PATH. Python 3 will enable this option for users. If not found, add the following two path (Python 3 sample default path )
        1. C:\Users\\AppData\Local\Programs\Python\Python36
        2. C:\Users\MyUserName\AppData\Local\Programs\Python\Python36\Scripts
      3. pip install virtualenv
      4. pip install virtualenvwrapper-win
      5. Main commands use with virtualenv wrapper in windows command prompt
        1. mkvirtualenv : create a new virtual env
        2. workon : list all the environment created
        3. workon  : Activate particular environment.
        4. deactivate: deactivate active environment
        5. rmvirtualenv : remove target environment.

Further notes 

  • Most of the guide reference from Timmy Reilly’s Blog.
  • To create virtualenv with specified python version
    • virtualenv -p <path/win dir of python version>
    • mkvirtualenv -p <path/win dir of python version>
  • Retrieve a list of python modules installed via pip and save to requirement.txt
    • pip freeze > requirement.txt
  • to install a list of required modules (from other virtual env etc)
    • pip install -r requirements.txt

Shorte.st Url Shortener API with Python: Create multiple shorteners at one go (& monetize your links)

A mini project that shortens urls with Shorte.st using python. Shorte.st only provides the “curl” command version of the API. In this post, the command is translated in the form of python requests for easy integration with rest of python scripts and enable multiple urls shortening.

Please note that I have an account with Shorte.st.

  1. Objectives:
      1. Create python function to shorten url using Shorte.st
  2. Required Tools:
      1. Requests —  for handling HTML protocol. Use pip install requests.
      2. Shorte.st account — Shorte.st account to shorten url.
  3. Steps:
      1. Retrieve the API token from Shorte.st by going to Link Tools –> Developer API and copy the API token.
      2. Use request.put with the following parameters:
        1. headers containing the API token and user-agent
        2. data which contains the target url to shorten.
      3. Get the response.text which contain the shortened url
      4. Complete! Include shortened url in target sites/twitter/social media etc.

Curl commands as provided by Shorte.st

curl -H "public-api-token: your_api_token" -X PUT -d "urlToShorten=target_url_to_shortened.com" https://api.shorte.st/v1/data/url

Python function to insert to part of your code or as standalone

import os, sys, re
import requests

USER_AGENT = "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36"

def shorten_url(target_url, api_token):
    """
        Function to shorten url (With your shorte.st) account.
        Args:
            target_url (str): url to shorten
            api_token (str): api token str
        Returns:
            shortened_url (str)

    """

    headers = {'user_agent':USER_AGENT, 'public-api-token':api_token}
    data = dict(urlToShorten=target_url)

    url = 'https://api.shorte.st/v1/data/url'

    r= requests.put(url, data, headers= headers)

    shortened_url = re.search('"shortenedUrl":"(.*)"',r.text).group(1)
    shortened_url = shortened_url.replace('\\','')

    return shortened_url

if __name__ == "__main__":

    api_token = 'your_api_token'

    urllist = [
                'https://simply-python.com/2018/07/20/fast-download-images-from-google-image-search-with-python-requests-grequests',
                'https://simply-python.com/2018/04/22/building-a-twitter-bot-with-python'

                ]

    for target_url in urllist:
        shortened_url = shorten_url(target_url, api_token)
        print 'shortened_url: {}'.format(shortened_url)

Results

shortened_url: http://destyy.com/wKqD2s
shortened_url: http://destyy.com/wKqD17

 

Further notes 

  1. If you have some fantastic links to share and hope to monetize your links, you can click on below banner to explore more.
  2. The above script is not meant for spamming with huge amount of urls. Shorte.st will monitor on the quality of the urls be shortened.
  3. An ads-free shortener will be with bit.ly. Please see post on using the bit.ly shortener with python if prefer an alternative.

Package your python code made simple & Fast

A mini project that create the required python packaging template folders, submit to GitHub & enable pip installation.

  1. Objectives:
      1. Upload a python project to GitHub and enable py-installable.
  2. Required Tools:
      1. Cookie Cutter–  for templating. Use pip install cookiecutter.
      2. GitHub account, Github desktop, Git shell — version control, git command line.
      3. PyPI account — for uploading to pypi so a user can just do “pip install your_project”.
  3. Steps:
      1. Cookie Cutter to set up the template directory and required folders with relevant docs and files (Readme.md, .gitignore, setup.py etc) for uploading. –> See commands section 1 below.
        • use commands in cmd prompt or Git shell  for windows (preferred Git shell if you executing additional git commands in step 2).
      2. Create a folder with same name as the directory name created in step 1 and place the relevant python codes inside.
      3. Use Git commands to upload files to GitHub. The below commands will only work if the repository is first created in your GitHub account. –> See commands section 2 below.
      4. Alternatively, you can use the GUI version for the GitHub instead of command line to submit your project to the repository.
      5. Create a .pypirc in same directory as the setup.py file. This will be used to provide the info to upload to pypi. –> See section 3
      6. Updates:
        1. Ensure setuptools and wheel are up to date and install twine
          • pip install -U setuptools wheel; pip install twine
        2. Package the code
          • python setup.py sdist bdist_wheel
        3. Upload the package
          • twine upload –repository pypi dist/*

Windows Command prompt for step 1

pip install cookiecutter
cookiecutter https://github.com/wdm0006/cookiecutter-pipproject.git
cd projectname

Git Commands for step 3

git init
git add -A
git commit -m 'first commit'
git remote add origin http://repository_url # works only if repository is created in Git. See Git commands for repository url.
git push origin master
git tag {{version}} -m 'adds the version you entered in cookiecutter as the first tag for release, change the version 0.0.1 etc'
git push --tags origin master

.pypirc contents for step 5

[distutils] # this tells distutils what package indexes you can push to
index-servers =
pypi

[pypi]
repository: https://pypi.python.org/pypi
username: {{your_username}}
password: {{your_password}}

Further notes 

  1. Most of the commands above are from Will McGinnis’ post and python packaging tutorial
  2. To create an empty file in windows for the .pypirc, use cmd echo >.pypirc
  3. Uploading to PyPI require a verfiied email address else there will be error uploading.
  4. When encounter “fatal: remote origin already exists.”. See link
  5. Basic GIT commands. See link
  6. Updates: uploading packages to pypi using twine. (link)
  7. Making changes to the code and uploading (link)

Update changes to github

git add -A
git commit -m 'whatever'
git push origin master
git tag {{version}} -m 'adds the version you entered in cookiecutter as the first tag for release, change the version 0.0.1 etc'
git push --tags origin master

Update changes to pypi

Simply upload your new code to github, create a new release, then adapt the setup.py file (new download_url — according to your new release tag, new version), then run the setup.py and the twin command again

python setup.py sdist
twine upload dist