- Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

Data is everywhere! Enhance your career and acquire new skills by taking a course on DataCamp! Click here to take the first chapter of any course for FREE: https://bit.ly/36lKg44 (you’ll be supporting my channel too!)

In this video we scrape Wikipedia pages to create a dataset on Disney movies. ...
Data is everywhere! Enhance your career and acquire new skills by taking a course on DataCamp! Click here to take the first chapter of any course for FREE: https://bit.ly/36lKg44 (you’ll be supporting my channel too!)

In this video we scrape Wikipedia pages to create a dataset on Disney movies.

The video is formatted with tasks for you to try to solve on your own throughout. For the best learning experience, at each task you should pause the video, try the task on your own, and then resume when you want to see how I would solve it.

We cover a wide range of Python & data science topics in this video. They include:
- Web scraping with BeautifulSoup
- Cleaning data
- Testing code with Pytest
- Pattern matching with regular expressions (Re library)
- Working with dates (datetime library)
- Saving & loading data with Pickle library
- Accessing data from an API using Requests library

Link to code & datasets: https://github.com/KeithGalli/disney-data-science-tasks
Previous tutorial on Beautiful Soup: https://youtu.be/GjKQ6V_ViQE

If you enjoyed this video, make sure to like & subscribe :)

This video was sponsored by DataCamp

---------------------
Video timeline!
0:00 - Video overview
1:58 - Check out DataCamp! (sponsored)
3:12 - Setup

Task #1: Scrape the infobox from Toy Story 3 wiki page (save in python dictionary) (4:24)
Link: https://en.wikipedia.org/wiki/Toy_Story_3

Task #2: Scrape infobox for all movies in List of Disney Films (save as list of dictionaries) (28:52)
Link: https://en.wikipedia.org/wiki/List_of_Walt_Disney_Pictures_films
30:30 - Robots.txt (Are you allowed to scrape a site?)
32:52 - Task #2: Scrape infobox for all movies in List of Disney Films (save as list of dictionaries)
57:27 - Save & Load dataset checkpoint (JSON file)

Task #3: Clean our data! (1:02:04)
1:09:28 - Task #3.1: Strip out all references ([1],[2],etc) from HTML
1:16:39 - Task #3.2: Split up the long strings
1:25:02 - Task #3.3: Examine errors we are getting
1:30:27 - Task #3.4: Convert “Running time” field to an integer
1:44:57 - Task #3.5: Convert “Budget” & “Box office” fields to floats
2:33:53 - Task #3.6: Convert dates into datetime objects
2:47:36 - Saving our data again (using Pickle)

Task #4: Attach IMDB, Metascore, and Rotten Tomatoes scores to dataset (working with APIs) (2:53:18)

Task #5: Save final dataset as a JSON file and as a CSV file (3:13:48)

---------------------
Extra resources!
Setup Jupyter notebook: https://jupyter.readthedocs.io/en/latest/install/notebook-classic.html
Google Colab (cloud-based notebook): https://colab.research.google.com/
Learn regular expressions: https://youtu.be/K8L6KVGG-7o

Practice your Python Pandas data science skills with problems on StrataScratch!
https://stratascratch.com/?via=keith

Join the Python Army to get access to perks!
YouTube - https://www.youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/join
Patreon - https://www.patreon.com/keithgalli

---------------------
Follow me on social media!
Instagram | https://www.instagram.com/keithgalli/
Twitter |

If you are curious to learn how I make my tutorials, check out this video: https://youtu.be/LEO4igyXbLs

*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.

#KGMIT #Keith Galli #MIT #Beautifulsoup #beautiful soup #data science #data visualization #data analysis #real world data science #data science project #regular expressions #python #python 3 #python programming #pandas library #pytest #web scraping #selenium #bs4 #bs #datetime #re #dataset #dataset creation #data cleaning #data exploration #data scientist #machine learning #ai #eda #exploratory data analysis #data engineering #engineering #python project #programming #programming project
- Video overview - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

- Video overview

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:00:00 - 00:01:58
Well do a manual inspection. When you print out movies[] is it still movies that we are looking for in our table? That's what is most important is that it is coming from the right place. The order of the specific list items shouldn't matter though. - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

Well do a manual inspection. When you print out movies[] is it still movies that we are looking for in our table? That's what is most important is that it is coming from the right place. The order of the specific list items shouldn't matter though.

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:00:10 - 03:24:18
movies[] - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

movies[]

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:00:10 - 03:24:18
- Check out DataCamp! (sponsored) - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

- Check out DataCamp! (sponsored)

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:01:58 - 00:03:12
- Setup - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

- Setup

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:03:12 - 00:04:24
wiki page (save in python dictionary) ()Link: https://en.wikipedia.org/wiki/Toy_Story_3 - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

wiki page (save in python dictionary) ()Link: https://en.wikipedia.org/wiki/Toy_Story_3

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:04:24 - 00:28:52
import BeautifulSoup and requests - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

import BeautifulSoup and requests

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:06:30 - 00:07:10
requests.get() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

requests.get()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:07:10 - 00:07:30
BeautifulSoup() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

BeautifulSoup()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:07:30 - 00:08:20
prettify() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

prettify()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:08:20 - 00:10:00
class_ (argument) - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

class_ (argument)

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:10:00 - 00:12:10
find() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

find()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:17:00 - 00:20:20
why i get the error 'NoneType' object has no attribute 'get_text'  when doing this print(row.find('td').get_text()) - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

why i get the error 'NoneType' object has no attribute 'get_text' when doing this print(row.find('td').get_text())

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:17:43 - 03:24:18
get_text() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

get_text()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:20:20 - 00:20:30
find_all() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

find_all()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:20:30 - 00:24:30
,  please. - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

, please.

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:22:36 - 03:24:18
string.replace() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

string.replace()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:24:30 - 00:27:30
:  movie_info = {}for index, row in enumerate(info_row):if index==0:movie_info['title']=row.find('th').get_text()elif index==1:continueelse:key_movie = row.find('th').get_text()value_movie = row.find('td').get_text().replace('\n', ' ').replace('\xa0',' ').replace('[1]','').strip()movie_info[key_movie]=value_moviemovie_info - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

: movie_info = {}for index, row in enumerate(info_row):if index==0:movie_info['title']=row.find('th').get_text()elif index==1:continueelse:key_movie = row.find('th').get_text()value_movie = row.find('td').get_text().replace('\n', ' ').replace('\xa0',' ').replace('[1]','').strip()movie_info[key_movie]=value_moviemovie_info

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:24:34 - 03:24:18
get_text()TASK 2 - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

get_text()TASK 2

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:27:30 - 00:31:00
: Scrape infobox for all movies in List of Disney Films (save as list of dictionaries) ()Link: https://en.wikipedia.org/wiki/List_of_Walt_Disney_Pictures_films - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

: Scrape infobox for all movies in List of Disney Films (save as list of dictionaries) ()Link: https://en.wikipedia.org/wiki/List_of_Walt_Disney_Pictures_films

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:28:52 - 00:30:30
- Robots.txt (Are you allowed to scrape a site?) - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

- Robots.txt (Are you allowed to scrape a site?)

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:30:30 - 00:32:52
robots.txt - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

robots.txt

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:31:00 - 00:35:20
- Task #2: Scrape infobox for all movies in List of Disney Films (save as list of dictionaries) - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

- Task #2: Scrape infobox for all movies in List of Disney Films (save as list of dictionaries)

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:32:52 - 00:57:27
final_all() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

final_all()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:35:20 - 00:36:00
select() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

select()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:36:00 - 00:45:50
Haven't seen anyone make this comment already, but the issue at  with trying to find_all tables with class_=xyz is resolved by just searching class_="wikitable". I think all those other terms are just populated in the inspector by the JavaScript but aren't present in the source HTML. - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

Haven't seen anyone make this comment already, but the issue at with trying to find_all tables with class_=xyz is resolved by just searching class_="wikitable". I think all those other terms are just populated in the inspector by the JavaScript but aren't present in the source HTML.

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:36:15 - 03:24:18
at   why do i get a different list when i usemovies = content.find_all(name ='table',attrs={'class' : 'wikitable sortable i'})movies = content.find_all('i') - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

at why do i get a different list when i usemovies = content.find_all(name ='table',attrs={'class' : 'wikitable sortable i'})movies = content.find_all('i')

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:40:22 - 00:00:10
enumerate() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

enumerate()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:45:50 - 00:46:00
.a (tag) - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

.a (tag)

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:46:00 - 00:48:00
printout exception - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

printout exception

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:48:00 - 00:53:00
Love the printing the Exception () nice trick!Why use a list of dictionaries and not a dataframe? - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

Love the printing the Exception () nice trick!Why use a list of dictionaries and not a dataframe?

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:48:41 - 03:24:18
append() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

append()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:53:00 - 00:58:00
Where did "get_info_box" at  come from? - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

Where did "get_info_box" at come from?

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:53:03 - 03:24:18
Keith im getting a     Invalid URL 'url': No schema supplied. Perhaps you meant http://url?    error at ......is there any way I could fix this? - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

Keith im getting a Invalid URL 'url': No schema supplied. Perhaps you meant http://url? error at ......is there any way I could fix this?

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:54:19 - 03:24:18
- Save & Load dataset checkpoint (JSON file) - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

- Save & Load dataset checkpoint (JSON file)

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:57:27 - 01:02:04
import json - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

import json

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:58:00 - 00:59:00
open()TASK 3 - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

open()TASK 3

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
00:59:00 - 01:13:00
: Clean our data! () - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

: Clean our data! ()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
01:02:04 - 01:09:28
- Task #3.1: Strip out all references ([1],[2],etc) from HTML - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

- Task #3.1: Strip out all references ([1],[2],etc) from HTML

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
01:09:28 - 01:16:39
(around ), 'clean_tag' function (using tag.decompose) is called with the original 'soup' object AFTER 'info_rows' has been defined (from 'info_box', which is itself defined from the original 'soup' object), not before; how come, then, that the references are still stripped out from 'info_rows' ? - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

(around ), 'clean_tag' function (using tag.decompose) is called with the original 'soup' object AFTER 'info_rows' has been defined (from 'info_box', which is itself defined from the original 'soup' object), not before; how come, then, that the references are still stripped out from 'info_rows' ?

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
01:13:00 - 03:24:18
decompose() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

decompose()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
01:13:00 - 01:20:30
- Task #3.2: Split up the long strings - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

- Task #3.2: Split up the long strings

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
01:16:39 - 01:25:02
Elfis :D Very funny - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

Elfis :D Very funny

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
01:18:56 - 03:24:18
.stripped_strings (generator) - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

.stripped_strings (generator)

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
01:20:30 - 01:35:00
- Task #3.3: Examine errors we are getting - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

- Task #3.3: Examine errors we are getting

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
01:25:02 - 01:30:27
isinstance() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

isinstance()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
01:29:40 - 01:52:00
- Task #3.4: Convert “Running time” field to an integer - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

- Task #3.4: Convert “Running time” field to an integer

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
01:30:27 - 01:44:57
get() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

get()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
01:35:00 - 01:38:00
split() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

split()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
01:38:00 - 01:29:40
- Task #3.5: Convert “Budget” & “Box office” fields to floats - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

- Task #3.5: Convert “Budget” & “Box office” fields to floats

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
01:44:57 - 02:33:53
import re - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

import re

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
01:52:00 - 01:54:30
search() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

search()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
01:54:30 - 01:55:20
group() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

group()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
01:55:20 - 02:00:10
replace() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

replace()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
02:00:10 - 02:26:00
search(flags=re.I) - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

search(flags=re.I)

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
02:26:00 - 02:26:10
lower() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

lower()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
02:26:10 - 02:36:30
- Task #3.6: Convert dates into datetime objects - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

- Task #3.6: Convert dates into datetime objects

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
02:33:53 - 02:47:36
import datetime - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

import datetime

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
02:36:30 - 02:42:00
strptime() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

strptime()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
02:42:00 - 02:50:00
= Many 'NONE' results in datetime is because in Wikipedia, some movie pages show 'Release date' while others 'Release dateS'.My solution:for dic in movie_info_list:if 'Release date' in dic:dic['Release dates']=dic.pop('Release date')print(movie_info_list) - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

= Many 'NONE' results in datetime is because in Wikipedia, some movie pages show 'Release date' while others 'Release dateS'.My solution:for dic in movie_info_list:if 'Release date' in dic:dic['Release dates']=dic.pop('Release date')print(movie_info_list)

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
02:45:14 - 03:24:18
- Saving our data again (using Pickle) - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

- Saving our data again (using Pickle)

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
02:47:36 - 02:53:18
pickle.dump() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

pickle.dump()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
02:49:40 - 02:49:40
pickle.load()TASK 4 - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

pickle.load()TASK 4

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
02:49:40 - 02:59:58
import pickle - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

import pickle

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
02:50:00 - 02:49:40
: Attach IMDB, Metascore, and Rotten Tomatoes scores to dataset (working with APIs) () - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

: Attach IMDB, Metascore, and Rotten Tomatoes scores to dataset (working with APIs) ()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
02:53:18 - 03:13:48
import requests - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

import requests

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
02:59:58 - 03:02:20
os.environ - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

os.environ

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
03:02:20 - 03:03:50
import urllib - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

import urllib

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
03:03:50 - 03:04:15
urlencode() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

urlencode()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
03:04:15 - 03:05:00
requests.get()TASK 5 - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

requests.get()TASK 5

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
03:05:00 - 03:17:00
: Save final dataset as a JSON file and as a CSV file () - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

: Save final dataset as a JSON file and as a CSV file ()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
03:13:48 - 03:24:18
strftime() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

strftime()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
03:17:00 - 03:19:10
import pandas - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

import pandas

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
03:19:10 - 03:20:00
pd.DataFrame() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

pd.DataFrame()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
03:20:00 - 03:20:20
head() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

head()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
03:20:20 - 03:21:40
sort_values() - Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)

sort_values()

Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation)
2020年10月02日 
03:21:40 - 03:24:18

Keith Galli

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- Video Overview & Reference Material - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

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Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日 
00:00:00 - 00:03:05
-  Data & Code Setup - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Data & Code Setup

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日 
00:03:05 - 00:05:04
- Task #0: Configure LLM to use with Python (OpenAI API) - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Task #0: Configure LLM to use with Python (OpenAI API)

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日 
00:05:04 - 00:20:10
- Task #0 (continued): LLM Configuration with Open-Source Model (LLama 2 via Ollama) - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Task #0 (continued): LLM Configuration with Open-Source Model (LLama 2 via Ollama)

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日 
00:20:10 - 00:27:39
- Task #1: Use LLM to Parse Simple Sentence Examples - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Task #1: Use LLM to Parse Simple Sentence Examples

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日 
00:27:39 - 00:41:22
- Sub-task #1: Convert string to Python Object - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Sub-task #1: Convert string to Python Object

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日 
00:41:22 - 00:44:29
- Task #1 (continued): Use Open-Source LLM to Parse Sentence Examples w/ LangChain - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Task #1 (continued): Use Open-Source LLM to Parse Sentence Examples w/ LangChain

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日 
00:44:29 - 00:56:24
- Quick note on a benefit of using LangChain (easily switching between models) - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Quick note on a benefit of using LangChain (easily switching between models)

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日 
00:56:24 - 00:58:06
- Task #2 (warmup): Grab Apprenticeship Agreement rows from Dataframe - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Task #2 (warmup): Grab Apprenticeship Agreement rows from Dataframe

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日 
00:58:06 - 01:06:22
- Task #2: Connect Pages that Belong to the Same Documents - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Task #2: Connect Pages that Belong to the Same Documents

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日 
01:06:22 - 01:56:36
Fantastic real world problem as a lot of your other videos. I've got to say that all models on Ollama absolutely stink in comparison to OpenAI. However I have been using a preprocessing text function I created for using in a news article project I'm working on using Spacy. I have been able to pass the transcription_text's through my function with some minor tweaking and have been able to recreate what the LLM's are doing just through code, by using the doc.ents functionality. Only  through the video at the moment and perhaps you use something similar later on, but  Spacy has been a bit of a godsend if you don't/can't pay for OpenAI - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

Fantastic real world problem as a lot of your other videos. I've got to say that all models on Ollama absolutely stink in comparison to OpenAI. However I have been using a preprocessing text function I created for using in a news article project I'm working on using Spacy. I have been able to pass the transcription_text's through my function with some minor tweaking and have been able to recreate what the LLM's are doing just through code, by using the doc.ents functionality. Only through the video at the moment and perhaps you use something similar later on, but Spacy has been a bit of a godsend if you don't/can't pay for OpenAI

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日  @MaxwellSmi41483 様 
01:27:00 - 02:39:33
- Task #3: Parse out values from merged documents - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Task #3: Parse out values from merged documents

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日 
01:56:36 - 02:12:44
- Task #4 (setup): Analyze Results - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Task #4 (setup): Analyze Results

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日 
02:12:44 - 02:17:52
- Fixing up our results from task #3 quickly - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Fixing up our results from task #3 quickly

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日 
02:17:52 - 02:20:41
- Task #4: Find the average age of apprentices in our merged contract documents - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Task #4: Find the average age of apprentices in our merged contract documents

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日 
02:20:41 - 02:30:59
- Other analysis, wlho had the most apprentices? - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Other analysis, wlho had the most apprentices?

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日 
02:30:59 - 02:39:33
- Introduction - How to make your GitHub more impressive to Employers! (5 simple tips)

- Introduction

How to make your GitHub more impressive to Employers! (5 simple tips)
2024年02月27日 
00:00:00 - 00:01:50
- Tip 1: Show Private Repository Activity - How to make your GitHub more impressive to Employers! (5 simple tips)

- Tip 1: Show Private Repository Activity

How to make your GitHub more impressive to Employers! (5 simple tips)
2024年02月27日 
00:01:50 - 00:02:57
- Tip 2: Highlight best work using pins - How to make your GitHub more impressive to Employers! (5 simple tips)

- Tip 2: Highlight best work using pins

How to make your GitHub more impressive to Employers! (5 simple tips)
2024年02月27日 
00:02:57 - 00:04:13
- Tip 3: Create a Profile README - How to make your GitHub more impressive to Employers! (5 simple tips)

- Tip 3: Create a Profile README

How to make your GitHub more impressive to Employers! (5 simple tips)
2024年02月27日 
00:04:13 - 00:11:32
- Tip 4: Fill in all Profile Details - How to make your GitHub more impressive to Employers! (5 simple tips)

- Tip 4: Fill in all Profile Details

How to make your GitHub more impressive to Employers! (5 simple tips)
2024年02月27日 
00:11:32 - 00:13:45
- Tip 5: Fill in READMEs on highlighted repos - How to make your GitHub more impressive to Employers! (5 simple tips)

- Tip 5: Fill in READMEs on highlighted repos

How to make your GitHub more impressive to Employers! (5 simple tips)
2024年02月27日 
00:13:45 - 00:19:01
- Overview & Getting Started - Can You Solve These 3 Data Analysis Puzzles? (AnalystBuilder & Python Pandas)

- Overview & Getting Started

Can You Solve These 3 Data Analysis Puzzles? (AnalystBuilder & Python Pandas)
2023年12月27日 
00:00:00 - 00:00:50
- 1. Predicting Heart Attack Risk (Easy Problem) - Can You Solve These 3 Data Analysis Puzzles? (AnalystBuilder & Python Pandas)

- 1. Predicting Heart Attack Risk (Easy Problem)

Can You Solve These 3 Data Analysis Puzzles? (AnalystBuilder & Python Pandas)
2023年12月27日 
00:00:50 - 00:06:44
- 2. Data Anonymization (Medium Problem) - Can You Solve These 3 Data Analysis Puzzles? (AnalystBuilder & Python Pandas)

- 2. Data Anonymization (Medium Problem)

Can You Solve These 3 Data Analysis Puzzles? (AnalystBuilder & Python Pandas)
2023年12月27日 
00:06:44 - 00:11:53
- 3.  Dessert Duel (Hard Problem) - Can You Solve These 3 Data Analysis Puzzles? (AnalystBuilder & Python Pandas)

- 3. Dessert Duel (Hard Problem)

Can You Solve These 3 Data Analysis Puzzles? (AnalystBuilder & Python Pandas)
2023年12月27日 
00:11:53 - 00:29:59
- Overview - Python Project: Implement a REST API with Flask & Flasgger Libraries!

- Overview

Python Project: Implement a REST API with Flask & Flasgger Libraries!
2023年12月04日 
00:00:00 - 00:00:41
- Getting started on the Book Review API - Python Project: Implement a REST API with Flask & Flasgger Libraries!

- Getting started on the Book Review API

Python Project: Implement a REST API with Flask & Flasgger Libraries!
2023年12月04日 
00:00:41 - 00:02:20
- Set up Airtable as our database & connect to it with Python - Python Project: Implement a REST API with Flask & Flasgger Libraries!

- Set up Airtable as our database & connect to it with Python

Python Project: Implement a REST API with Flask & Flasgger Libraries!
2023年12月04日 
00:02:20 - 00:10:44
- Implement code to add reviews and view all reviews - Python Project: Implement a REST API with Flask & Flasgger Libraries!

- Implement code to add reviews and view all reviews

Python Project: Implement a REST API with Flask & Flasgger Libraries!
2023年12月04日 
00:10:44 - 00:31:40
- Adding a POST request to our API - Python Project: Implement a REST API with Flask & Flasgger Libraries!

- Adding a POST request to our API

Python Project: Implement a REST API with Flask & Flasgger Libraries!
2023年12月04日 
00:31:40 - 00:36:40
- Trying out our new endpoints (using documentation & requests library of Python) - Python Project: Implement a REST API with Flask & Flasgger Libraries!

- Trying out our new endpoints (using documentation & requests library of Python)

Python Project: Implement a REST API with Flask & Flasgger Libraries!
2023年12月04日 
00:36:40 - 00:41:32
- Commit code to Github & deploy live to Render.com - Python Project: Implement a REST API with Flask & Flasgger Libraries!

- Commit code to Github & deploy live to Render.com

Python Project: Implement a REST API with Flask & Flasgger Libraries!
2023年12月04日 
00:41:32 - 00:50:46
- Video overview - How to create & deploy an API in Python! (with interactive documentation)

- Video overview

How to create & deploy an API in Python! (with interactive documentation)
2023年11月26日 
00:00:00 - 00:01:18
- What we're building - How to create & deploy an API in Python! (with interactive documentation)

- What we're building

How to create & deploy an API in Python! (with interactive documentation)
2023年11月26日 
00:01:18 - 00:03:20
- How to get setup with Github template code - How to create & deploy an API in Python! (with interactive documentation)

- How to get setup with Github template code

How to create & deploy an API in Python! (with interactive documentation)
2023年11月26日 
00:03:20 - 00:07:00
- Taking a look at the Flask, Flasgger Python3 code - How to create & deploy an API in Python! (with interactive documentation)

- Taking a look at the Flask, Flasgger Python3 code

How to create & deploy an API in Python! (with interactive documentation)
2023年11月26日 
00:07:00 - 00:08:38
- Testing some API requests (GET) locally - How to create & deploy an API in Python! (with interactive documentation)

- Testing some API requests (GET) locally

How to create & deploy an API in Python! (with interactive documentation)
2023年11月26日 
00:08:38 - 00:13:09
- Building another GET request endpoint (with multiple parameters) - How to create & deploy an API in Python! (with interactive documentation)

- Building another GET request endpoint (with multiple parameters)

How to create & deploy an API in Python! (with interactive documentation)
2023年11月26日 
00:13:09 - 00:14:34
- Using ChatGPT to help us build another endpoint - How to create & deploy an API in Python! (with interactive documentation)

- Using ChatGPT to help us build another endpoint

How to create & deploy an API in Python! (with interactive documentation)
2023年11月26日 
00:14:34 - 00:22:43
- Deploying our API to a live public URL endpoint (using render.com) - How to create & deploy an API in Python! (with interactive documentation)

- Deploying our API to a live public URL endpoint (using render.com)

How to create & deploy an API in Python! (with interactive documentation)
2023年11月26日 
00:22:43 - 00:29:27
- Video overview & topics covered - Complete Regular Expressions Tutorial! (with exercises for practice)

- Video overview & topics covered

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:00:00 - 00:01:43
- Basic regex syntax (building up an intuition) - Complete Regular Expressions Tutorial! (with exercises for practice)

- Basic regex syntax (building up an intuition)

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:01:43 - 00:04:23
- Character Sets Overview ([A-Za-z0-9]) - Complete Regular Expressions Tutorial! (with exercises for practice)

- Character Sets Overview ([A-Za-z0-9])

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:04:23 - 00:05:57
- Quantifiers Guide (*, +, ?, {3,5}) - Complete Regular Expressions Tutorial! (with exercises for practice)

- Quantifiers Guide (*, +, ?, {3,5})

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:05:57 - 00:09:30
- Guided Exercise: Find all words that don't use vowels - Complete Regular Expressions Tutorial! (with exercises for practice)

- Guided Exercise: Find all words that don't use vowels

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:09:30 - 00:11:08
Linguistically speaking, [y] can be a vowel, especially in words like "crypt". Pedantry of course, since it could just be added into the regex if needed. 🤓 - Complete Regular Expressions Tutorial! (with exercises for practice)

Linguistically speaking, [y] can be a vowel, especially in words like "crypt". Pedantry of course, since it could just be added into the regex if needed. 🤓

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日  Anon Viewer 様 
00:10:50 - 00:36:40
- Helpful cheat sheet to remember regex syntax in the real-world - Complete Regular Expressions Tutorial! (with exercises for practice)

- Helpful cheat sheet to remember regex syntax in the real-world

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:11:08 - 00:12:47
- Matching words/patterns of a specific length ({3,5}) - Complete Regular Expressions Tutorial! (with exercises for practice)

- Matching words/patterns of a specific length ({3,5})

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:12:47 - 00:14:58
- OR operator overview - Complete Regular Expressions Tutorial! (with exercises for practice)

- OR operator overview

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:14:58 - 00:17:14
- Guided Exercise: Match valid sentences (starts with capital letter, ends with period) - Complete Regular Expressions Tutorial! (with exercises for practice)

- Guided Exercise: Match valid sentences (starts with capital letter, ends with period)

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:17:14 - 00:21:18
- Character classes overview (\w, \b, \d, \s) - Complete Regular Expressions Tutorial! (with exercises for practice)

- Character classes overview (\w, \b, \d, \s)

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:21:18 - 00:23:13
- Escaping Characters - Complete Regular Expressions Tutorial! (with exercises for practice)

- Escaping Characters

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:23:13 - 00:25:02
- Practice Exercise #1: Write a regular expression to match meme text format - Complete Regular Expressions Tutorial! (with exercises for practice)

- Practice Exercise #1: Write a regular expression to match meme text format

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:25:02 - 00:30:39
- Practice Exercise #2: Write a regular expression to match a specific date format - Complete Regular Expressions Tutorial! (with exercises for practice)

- Practice Exercise #2: Write a regular expression to match a specific date format

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:30:39 - 00:39:03
Might not really be up to regex to do data validation. There are better tools for that. 🧰In fact, integrating these into data workflows would be a good follow-up video for the future. ▶ - Complete Regular Expressions Tutorial! (with exercises for practice)

Might not really be up to regex to do data validation. There are better tools for that. 🧰In fact, integrating these into data workflows would be a good follow-up video for the future. ▶

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日  Anon Viewer 様 
00:36:40 - 01:19:21
- Groups overview - Complete Regular Expressions Tutorial! (with exercises for practice)

- Groups overview

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:39:03 - 00:50:16
You could definitely get everything if you add an extra parenthesis around the thing you want to get in this case (([a-z][A-Z])+[a-z]?)@(\w+\.\w+) - Complete Regular Expressions Tutorial! (with exercises for practice)

You could definitely get everything if you add an extra parenthesis around the thing you want to get in this case (([a-z][A-Z])+[a-z]?)@(\w+\.\w+)

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日  Dendrocnide Moroides 様 
00:49:02 - 01:19:21
- Lookahead & Lookbehind Assertions - Complete Regular Expressions Tutorial! (with exercises for practice)

- Lookahead & Lookbehind Assertions

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:50:16 - 01:00:18
- Practice Exercise #3: Detect if same word pops up multiple times in a sentence - Complete Regular Expressions Tutorial! (with exercises for practice)

- Practice Exercise #3: Detect if same word pops up multiple times in a sentence

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
01:00:18 - 01:06:04
- Practice Exercise #4: Password matching with rules - Complete Regular Expressions Tutorial! (with exercises for practice)

- Practice Exercise #4: Password matching with rules

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
01:06:04 - 01:16:16
- Some final recommendations! (additional practice, chatgpt, etc.) - Complete Regular Expressions Tutorial! (with exercises for practice)

- Some final recommendations! (additional practice, chatgpt, etc.)

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
01:16:16 - 01:19:21
- Video overview & format - Full Data Science Mock Interview! (featuring Kylie Ying)

- Video overview & format

Full Data Science Mock Interview! (featuring Kylie Ying)
2023年01月09日 
00:00:00 - 00:03:38
- Introductory Behavioral questions | Data science interview - Full Data Science Mock Interview! (featuring Kylie Ying)

- Introductory Behavioral questions | Data science interview

Full Data Science Mock Interview! (featuring Kylie Ying)
2023年01月09日 
00:03:38 - 00:09:11
- Social media platform bot issue task overview | Data science interview - Full Data Science Mock Interview! (featuring Kylie Ying)

- Social media platform bot issue task overview | Data science interview

Full Data Science Mock Interview! (featuring Kylie Ying)
2023年01月09日 
00:09:11 - 00:16:51
- What are some features we should investigate regarding the bot issue? | Data science interview - Full Data Science Mock Interview! (featuring Kylie Ying)

- What are some features we should investigate regarding the bot issue? | Data science interview

Full Data Science Mock Interview! (featuring Kylie Ying)
2023年01月09日 
00:16:51 - 00:26:27
- Classification model implementation details (using feature vectors) | Data science interview - Full Data Science Mock Interview! (featuring Kylie Ying)

- Classification model implementation details (using feature vectors) | Data science interview

Full Data Science Mock Interview! (featuring Kylie Ying)
2023年01月09日 
00:26:27 - 00:43:03
- What would a dataset to train models to detect bots look like? How would you approach collecting this data? | Data science interview - Full Data Science Mock Interview! (featuring Kylie Ying)

- What would a dataset to train models to detect bots look like? How would you approach collecting this data? | Data science interview

Full Data Science Mock Interview! (featuring Kylie Ying)
2023年01月09日 
00:43:03 - 00:53:03
- Technical implementation details (python libraries, cloud services, etc) | Data science interview - Full Data Science Mock Interview! (featuring Kylie Ying)

- Technical implementation details (python libraries, cloud services, etc) | Data science interview

Full Data Science Mock Interview! (featuring Kylie Ying)
2023年01月09日 
00:53:03 - 00:57:26
- Any questions for me? | Data science interview - Full Data Science Mock Interview! (featuring Kylie Ying)

- Any questions for me? | Data science interview

Full Data Science Mock Interview! (featuring Kylie Ying)
2023年01月09日 
00:57:26 - 01:05:07
- Post-interview breakdown & analysis - Full Data Science Mock Interview! (featuring Kylie Ying)

- Post-interview breakdown & analysis

Full Data Science Mock Interview! (featuring Kylie Ying)
2023年01月09日 
01:05:07 - 01:27:34
- Video Introduction - Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.

- Video Introduction

Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.
2022年11月23日 
00:00:00 - 00:01:19
- How podcasts work (RSS feeds overview) - Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.

- How podcasts work (RSS feeds overview)

Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.
2022年11月23日 
00:01:19 - 00:05:11
- How can we utilize the XML webpages? (breakdown of RSS feed information & how we’ll use it to create a smart program) - Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.

- How can we utilize the XML webpages? (breakdown of RSS feed information & how we’ll use it to create a smart program)

Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.
2022年11月23日 
00:05:11 - 00:07:47
- Accessing this project on GitHub - Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.

- Accessing this project on GitHub

Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.
2022年11月23日 
00:07:47 - 00:09:22
-Writing Python code to download podcasts locally (requests & beautifulsoup libraries) - Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.

-Writing Python code to download podcasts locally (requests & beautifulsoup libraries)

Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.
2022年11月23日 
00:09:22 - 00:18:10
- Modify our script to be able to download many podcasts - Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.

- Modify our script to be able to download many podcasts

Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.
2022年11月23日 
00:18:10 - 00:22:51
- Building in smart search capabilities to grab podcasts we’ll find most interesting! - Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.

- Building in smart search capabilities to grab podcasts we’ll find most interesting!

Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.
2022年11月23日 
00:22:51 - 00:31:00
- Using the AssemblyAI API to transcribe the podcasts we’ve downloaded - Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.

- Using the AssemblyAI API to transcribe the podcasts we’ve downloaded

Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.
2022年11月23日 
00:31:00 - 01:06:08
- Cleaning our code with functions & classes and putting everything into Python scripts. - Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.

- Cleaning our code with functions & classes and putting everything into Python scripts.

Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.
2022年11月23日 
01:06:08 - 01:18:09
- Portfolio project extension ideas! (Spotify API, NLP semantic search) - Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.

- Portfolio project extension ideas! (Spotify API, NLP semantic search)

Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.
2022年11月23日 
01:18:09 - 01:19:56
- Smash like & subscribe pretty please :) - Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.

- Smash like & subscribe pretty please :)

Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.
2022年11月23日 
01:19:56 - 01:20:39
- Intro & Video Overview - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Intro & Video Overview

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
00:00:00 - 00:00:46
- Check out this Video’s Sponsor, Brilliant! - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Check out this Video’s Sponsor, Brilliant!

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
00:00:46 - 00:03:10
- Coding #1 (Microsoft, Easy) - Finding Updated Records - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Coding #1 (Microsoft, Easy) - Finding Updated Records

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
00:03:10 - 00:10:36
- Coding #2 (Airbnb, Easy) - Number of Bathrooms and Bedrooms - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Coding #2 (Airbnb, Easy) - Number of Bathrooms and Bedrooms

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
00:10:36 - 00:16:38
- Coding #3 (Google, Medium) - Counting Instances in Text - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Coding #3 (Google, Medium) - Counting Instances in Text

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
00:16:38 - 00:28:23
I know it's more a reference to the stock market terms, but I can't stop thinking of Fallout: New Vegas. - Solving Real-World Data Science Interview Questions! (with Python Pandas)

I know it's more a reference to the stock market terms, but I can't stop thinking of Fallout: New Vegas.

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
00:17:20 - 01:11:00
- Coding #4 (Meta/Facebook, Medium) - Customer Revenue in March - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Coding #4 (Meta/Facebook, Medium) - Customer Revenue in March

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
00:28:23 - 00:36:51
That first one and others are SQL problems converted to pandas. I suppose that's a decent way to get basic pd questions. () - Solving Real-World Data Science Interview Questions! (with Python Pandas)

That first one and others are SQL problems converted to pandas. I suppose that's a decent way to get basic pd questions. ()

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
00:28:48 - 00:17:20
- Coding #5 (Amazon, Hard) - Monthly Percentage Difference - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Coding #5 (Amazon, Hard) - Monthly Percentage Difference

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
00:36:51 - 00:56:38
AtI work for Amazon's RPA team, trying to make a career in data science. Last month I was appearing for an IJP and got the same question in SQL coding round.Thanks for making this Keith. Keep them coming. - Solving Real-World Data Science Interview Questions! (with Python Pandas)

AtI work for Amazon's RPA team, trying to make a career in data science. Last month I was appearing for an IJP and got the same question in SQL coding round.Thanks for making this Keith. Keep them coming.

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
00:37:48 - 01:47:50
- Coding #6 (Microsoft, Hard) - Premium vs Freemium - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Coding #6 (Microsoft, Hard) - Premium vs Freemium

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
00:56:38 - 01:10:28
- Non-Coding #1 (Visa, Easy) - Credit Card Activity - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Non-Coding #1 (Visa, Easy) - Credit Card Activity

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
01:10:28 - 01:13:33
If you have the locations that's just a simple matter of putting it on a map and seeing where it clusters the most. - Solving Real-World Data Science Interview Questions! (with Python Pandas)

If you have the locations that's just a simple matter of putting it on a map and seeing where it clusters the most.

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
01:11:00 - 01:28:00
- Non-Coding #2 (IBM, Easy) - Outliers Detection - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Non-Coding #2 (IBM, Easy) - Outliers Detection

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
01:13:33 - 01:16:46
- Non-Coding #3 (Google, Medium) - Probability of Having a Sister - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Non-Coding #3 (Google, Medium) - Probability of Having a Sister

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
01:16:46 - 01:27:19
- Non-Coding #4 (Uber, Medium) - Uber Black Rides - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Non-Coding #4 (Uber, Medium) - Uber Black Rides

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
01:27:19 - 01:36:57
Context, context, context. Was that the only reduction? - Solving Real-World Data Science Interview Questions! (with Python Pandas)

Context, context, context. Was that the only reduction?

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
01:28:00 - 01:47:50
- Non-Coding #5 (Capital One, Hard) - Terabyte of Data - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Non-Coding #5 (Capital One, Hard) - Terabyte of Data

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
01:36:57 - 01:46:41
- Video Conclusion & Recap - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Video Conclusion & Recap

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
01:46:41 - 01:47:50