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def hello_function():
    print "Hello World"
    
# This is comment. 
# calling function 
hello_function()

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chrome.webRequest.onHeadersReceived.addListener(
  function (details) {
    for (var i = 0; i < details.responseHeaders.length; ++i) {
      if (details.responseHeaders[i].name.toLowerCase() == 'x-frame-options') {
        details.responseHeaders.splice(i, 1);
        return {
          responseHeaders: details.responseHeaders
        };
      }
    }

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art = document.querySelectorAll("#your-select-element-id-here option");
function tag_g(tag_name, tag_value){
    clean_name = tag_name.replace('-'," ");
    clean_name = clean_name.replace('&',"AND");
    clean_name = clean_name.replace(/\s\s+/g, ' ');
    clean_name = clean_name.replace(/[^a-zA-Z ]/g, " ");
    tag_slug = clean_name.toUpperCase().split(" ").join("_");
    tag_str ="\
/** \n \

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#at bottom of my.conf

[mysqld]
character-set-client-handshake = FALSE
character-set-server = utf8mb4
collation-server = utf8mb4_unicode_ci

[mysql]
default-character-set = utf8mb4

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SQL to MongoDB Mapping Chart

SQL to MongoDB Mapping Chart¶

In addition to the charts that follow, you might want to consider the Frequently Asked Questions section for a selection of common questions about MongoDB.

Executables¶

The following table presents the MySQL/Oracle executables and the corresponding MongoDB executables.

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from pyspark.sql.types import *

# Auxiliar functions
# Pandas Types -> Sparks Types
def equivalent_type(f):
  if f == 'datetime64[ns]': return DateType()
  elif f == 'int64': return LongType()
  elif f == 'int32': return IntegerType()
  elif f == 'float64': return FloatType()
  else: return StringType()

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import os
from bs4 import BeautifulSoup
import requests
import wget
import pandas as pd
from requests.compat import urljoin
import  tqdm

url = "https://towardsdatascience.com/springer-has-released-65-machine-learning-and-data-books-for-free-961f8181f189"
folder = "D:/books/SpringerDataScience"