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Commit5686095

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updated examples
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‎README.md

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#Python Snippets
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These__Python Snippets__ are tested with Python 3.5.x. All snippets are
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These__Python Snippets__ are tested with Python 3.6.x. All snippets are
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available as Jupyter notebooks (http://jupyter.org/).
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##Get started...
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To get started create a virtual environment and install the required packages.
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###Virtualenv
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The following example shows how to create an environment with
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_"virtualenv"_ (https://virtualenv.pypa.io/)
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and Python 3.5 with the required packages.
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- Juypter Notebook
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- pandas
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- geojson
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- beautifulsoup4
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- feedparser
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- scikit-image
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- matplotlib
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```bash
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virtualenv --python=/usr/bin/python3.5 py35-ps
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source py35-ps/bin/activate
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pip install -r requirements.txt
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```
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###Conda
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The following example shows how to create an environment with_"conda"_
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(http://conda.pydata.org/) and Python 3.5 with
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(http://conda.pydata.org/) and Python 3.6 with
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the required packages.
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```bash
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conda create -npy35-ps python=3.5
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conda create -npy36-ps python=3.6
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source activatepy35-ps
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source activatepy36-ps
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pip install -r requirements.txt
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```
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##The Python Snippets
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-[Extract the text from a HTML document with Beautiful Soup](http/beautifulsoup4.ipynb)
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__Image__
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-tbd
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-[Image diff with scikit-image](image/image_diff.ipynb)

‎geojson/geojson_stations.ipynb

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},
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{
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"cell_type":"code",
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"execution_count":1,
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"metadata": {
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"collapsed":false
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},
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"execution_count":2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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"4 22.0 7.589551 47.547405 277.0 Basel SBB"
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]
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},
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"execution_count":1,
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"execution_count":2,
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"metadata": {},
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"output_type":"execute_result"
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}
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},
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{
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"cell_type":"code",
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"execution_count":2,
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"execution_count":3,
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"metadata": {
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"collapsed":false,
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"scrolled":true
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},
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"outputs": [],
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},
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{
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"cell_type":"code",
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"execution_count":3,
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"metadata": {
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"collapsed":false
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},
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"execution_count":4,
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"metadata": {},
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"outputs": [
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{
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"name":"stdout",
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},
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{
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"cell_type":"code",
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"execution_count":4,
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"metadata": {
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"collapsed":false
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},
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"execution_count":5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"\"\\nwith open('stations.geojson', 'w') as file:\\n file.write(dump)\\n\""
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]
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},
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"execution_count":4,
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"execution_count":5,
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"metadata": {},
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"output_type":"execute_result"
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}
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{
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"cell_type":"code",
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"execution_count":null,
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"metadata": {
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"collapsed":true
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},
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"metadata": {},
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"outputs": [],
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"source": []
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}
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"name":"python",
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"nbconvert_exporter":"python",
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"pygments_lexer":"ipython3",
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"version":"3.5.2"
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"version":"3.6.7"
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}
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},
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"nbformat":4,

‎http/beautifulsoup4.ipynb

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},
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{
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"cell_type":"code",
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"metadata": {
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"collapsed":true
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},
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"execution_count":1,
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"metadata": {},
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"outputs": [],
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"source": [
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"html_doc =\"\"\"\n",
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},
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{
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"cell_type":"code",
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"execution_count":null,
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"metadata": {
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"collapsed":false
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},
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"execution_count":2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from bs4 import BeautifulSoup\n",
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},
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{
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"cell_type":"code",
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"metadata": {
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"collapsed":false
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},
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"execution_count":3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'My new page'"
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]
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},
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"execution_count":13,
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"execution_count":3,
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"metadata": {},
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"output_type":"execute_result"
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}
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},
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"metadata": {
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"collapsed":false
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},
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"metadata": {},
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"outputs": [
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{
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},
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{
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"metadata": {
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"collapsed":false
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},
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'\\nMy new page\\n\\nCool my new page\\nI have written the following articles:\\nA1,\\nA2\\nA3;\\n\\n...\\n'"
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]
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},
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"metadata": {},
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"output_type":"execute_result"
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}
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"name":"python",
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"nbconvert_exporter":"python",
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"pygments_lexer":"ipython3",
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"version":"3.5.2"
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"version":"3.6.7"
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}
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},
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"nbformat":4,

‎http/urlib.ipynb

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},
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"metadata": {},
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"source": [
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"import urllib.request"
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"metadata": {},
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"source": [
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"url = 'https://medium.com/tag/machine-learning'\n",
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},
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"metadata": {},
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"metadata": {},
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"'text/html; charset=utf-8'"
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},
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"metadata": {},
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"outputs": [
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{
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"text/plain": [
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"b'<!DOCTYPE html><html xmlns:cc=\"http://creativecommons.org/ns#\"><head prefix=\"og: http://ogp.me/ns# fb: http://ogp.me/ns/fb# medium-com: http://ogp.me/ns/fb/medium-com#\"><meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\"><meta name=\"viewport\" content=\"width=device-width, initial-scale=1\"><title>Machine Learning\\xe2\\x80\\x93 Medium</title><link rel=\"canonical\" href=\"https://medium.com/tag/machine-learning\"><link id=\"feedLink\" rel=\"alternate\" type=\"application/rss+xml\" title=\"RSS\" href=\"/fee'"
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"b'<!DOCTYPE html><html xmlns:cc=\"http://creativecommons.org/ns#\"><head prefix=\"og: http://ogp.me/ns# fb: http://ogp.me/ns/fb# medium-com: http://ogp.me/ns/fb/medium-com#\"><meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\"><meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0, viewport-fit=contain\"><title>The most insightful stories aboutMachine Learning\\xe2\\x80\\x93 Medium</title><link rel=\"canonical\" href=\"https://medium.com/tag/machine-learning\"><link id=\"feedLink\" rel=\"al'"
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]
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},
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"text = con.read()\n",
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"text[:500]"
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]
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},
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{
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"cell_type":"code",
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"source": []
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"metadata": {
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"name":"python",
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"nbconvert_exporter":"python",
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"pygments_lexer":"ipython3",
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"version":"3.5.2"
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"version":"3.6.7"
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}
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},
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"nbformat":4,

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