- Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

In this Python Programming Tutorial, we will be learning how to read, write, and match regular expressions with the re module. Regular expressions are extremely useful for matching common patterns of text such as email addresses, phone numbers, URLs, etc. Learning how to do this within Python wil...
In this Python Programming Tutorial, we will be learning how to read, write, and match regular expressions with the re module. Regular expressions are extremely useful for matching common patterns of text such as email addresses, phone numbers, URLs, etc. Learning how to do this within Python will allow us to quickly parse files and text for the information we need. Let's get started...

The code from this video can be found at:
https://github.com/CoreyMSchafer/code_snippets/tree/master/Python-Regular-Expressions

Python String Slicing Tutorial:
https://youtu.be/ajrtAuDg3yw

Python Files Tutorial:
https://youtu.be/Uh2ebFW8OYM


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#Python

#python #regular expressions #regex #re module #python re #python regular expressions #python re module #python regex #python regex module #regular expression #re #python tutorial #python 3.6 #corey schafer #programming tutorials #python programming #match patterns #software engineering
- What are regular expressions. - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- What are regular expressions.

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:00:00 - 00:00:52
- Re module and example intro. - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- Re module and example intro.

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:00:52 - 00:01:28
- Raw string in Python - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- Raw string in Python

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:01:28 - 00:02:29
- Simple examples using re.compile and finditer - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- Simple examples using re.compile and finditer

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:02:29 - 00:05:29
- Escaping special characters with a backslash - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- Escaping special characters with a backslash

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:05:29 - 00:07:04
Early on in the video he states that adding the “r” before the string makes it so that the backslashes are taken literally as backslashes, but near  he says the period needs to be escaped with a backslash. But the r is still there!?! Meaning the back slash should be taken literally as a backslash and not representing something else? - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

Early on in the video he states that adding the “r” before the string makes it so that the backslashes are taken literally as backslashes, but near he says the period needs to be escaped with a backslash. But the r is still there!?! Meaning the back slash should be taken literally as a backslash and not representing something else?

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:06:30 - 00:53:18
@ looks like in version (3.6) we no need to escape special characters if you are searching it as part of a string. - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

@ looks like in version (3.6) we no need to escape special characters if you are searching it as part of a string.

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:06:42 - 00:53:18
- Use regex to search for patterns. Use of meta-characters. - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- Use regex to search for patterns. Use of meta-characters.

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:07:04 - 00:10:21
- Metacharacters continued. Anchors. Word boundaries. - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- Metacharacters continued. Anchors. Word boundaries.

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:10:21 - 00:12:04
- Anchors continued. ^ and $ - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- Anchors continued. ^ and $

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:12:04 - 00:13:55
- Practical examples. Matching phone numbers of patterns 123-456-7890 or 123.456.7890 - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- Practical examples. Matching phone numbers of patterns 123-456-7890 or 123.456.7890

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:13:55 - 00:18:58
I have watched till  and its perfect.Thanks - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

I have watched till and its perfect.Thanks

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:14:16 - 00:53:18
helpful from - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

helpful from

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:14:30 - 00:29:02
} powerful." - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

} powerful."

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:16:16 - 00:53:18
How did you comment multiple lines in a row at around ? Great vid, man.. keep it up! - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

How did you comment multiple lines in a row at around ? Great vid, man.. keep it up!

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:17:35 - 00:53:18
\d we can use below expression. which I learnt from you, at  . thankspattern = re.compile(r'\d{3}[-.]\d{3}[-.](\d)+') - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

\d we can use below expression. which I learnt from you, at . thankspattern = re.compile(r'\d{3}[-.]\d{3}[-.](\d)+')

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:18:00 - 00:53:18
- Precise matching using character-set - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- Precise matching using character-set

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:18:58 - 00:20:24
- Character-set only matches one character in the set - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- Character-set only matches one character in the set

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:20:24 - 00:21:27
- Match specific numbers. (800,900 phone numbers) - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- Match specific numbers. (800,900 phone numbers)

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:21:27 - 00:22:57
- Role of dash(-) within a character set. Used to specify ranges. - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- Role of dash(-) within a character set. Used to specify ranges.

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:22:57 - 00:24:16
- Role of carets (^) within a character set. Used to negate. - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- Role of carets (^) within a character set. Used to negate.

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:24:16 - 00:25:50
that bat is going to take his revenge for not getting matched with others.   ;) - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

that bat is going to take his revenge for not getting matched with others. ;)

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:25:42 - 00:53:18
- Use of quantifiers to match more than one character at once. - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- Use of quantifiers to match more than one character at once.

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:25:50 - 00:31:20
search string for mr|ms{name} - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

search string for mr|ms{name}

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:29:02 - 00:33:21
- Groups. Match several different patterns - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- Groups. Match several different patterns

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:31:20 - 00:33:08
- Recap of previous concepts with examples. Matching email addresses - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- Recap of previous concepts with examples. Matching email addresses

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:33:08 - 00:36:56
summary of first half - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

summary of first half

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:33:21 - 00:53:18
I got one question in () while talking about emails, when you used (com|net|edu) it worked. But I wanna know why we didn't use [a-z.]. (and it only gives the first character instead of giving everything) - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

I got one question in () while talking about emails, when you used (com|net|edu) it worked. But I wanna know why we didn't use [a-z.]. (and it only gives the first character instead of giving everything)

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:36:46 - 00:53:18
- Deciphering existing regular expressions. - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- Deciphering existing regular expressions.

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:36:56 - 00:39:05
Hi  Corey, pattern.findall(emails) at  doesn't work, and only shows [com, edu, net]. However, patten.finditer does work. - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

Hi Corey, pattern.findall(emails) at doesn't work, and only shows [com, edu, net]. However, patten.finditer does work.

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:37:20 - 00:53:18
Just to add a detail on the email regex you were discussing at around , the part before the '@' is pretty self-explanatory. After the '@' they have allowed for:a) numbers that you often get on subdomain servers andb) extra '.' after the first '.', such as you might get for a british uni for example @ox.ac.uk - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

Just to add a detail on the email regex you were discussing at around , the part before the '@' is pretty self-explanatory. After the '@' they have allowed for:a) numbers that you often get on subdomain servers andb) extra '.' after the first '.', such as you might get for a british uni for example @ox.ac.uk

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:39:00 - 00:53:18
- Capturing information from groups. The group method in the match object - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- Capturing information from groups. The group method in the match object

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:39:05 - 00:44:07
does it matter if the + in the second group is inside or outside parentheses ? - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

does it matter if the + in the second group is inside or outside parentheses ?

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:42:49 - 00:53:18
- Start from here to see how groups are implemented in Python - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- Start from here to see how groups are implemented in Python

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:43:00 - 00:53:18
minutes on start and skip to - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

minutes on start and skip to

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:43:00 - 00:53:18
- Back reference to reference the captured group. Sub method to perform a substitution - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- Back reference to reference the captured group. Sub method to perform a substitution

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:44:07 - 00:46:35
- Back reference to reference the captured group. Sub method to perform substitution - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- Back reference to reference the captured group. Sub method to perform substitution

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:44:07 - 00:46:35
- if you're coming from the previous Regexp video, start from here to skip all the things already explained in the previous video. - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- if you're coming from the previous Regexp video, start from here to skip all the things already explained in the previous video.

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:46:16 - 00:43:00
- findall method. Just returns the matches as a list of strings - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- findall method. Just returns the matches as a list of strings

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:46:35 - 00:47:58
, so if I use findall with pattern having groups, all other stuff outside groups in the pattern will be just ignored? can anyone tell me how do I get a list of matches to an entire pattern including groups and non-groups then? - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

, so if I use findall with pattern having groups, all other stuff outside groups in the pattern will be just ignored? can anyone tell me how do I get a list of matches to an entire pattern including groups and non-groups then?

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:46:50 - 00:53:18
- match method. Matches at the beginning of string. - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- match method. Matches at the beginning of string.

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:47:58 - 00:49:30
- match method. Matches at the beginning of the string. - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- match method. Matches at the beginning of the string.

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:47:58 - 00:49:30
- search method. Search the entire string. - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- search method. Search the entire string.

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:49:30 - 00:50:08
- flags . Examples: ignorecase, multiline, verbose - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- flags . Examples: ignorecase, multiline, verbose

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:50:08 - 00:53:18
- flags. Examples: ignorecase, multiline, verbose - Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)

- flags. Examples: ignorecase, multiline, verbose

Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
2017年10月25日
00:50:08 - 00:53:18

Corey Schafer

🎉 1,000,000 人達成!  📈 予測:200万人まであと686日(2024年10月23日) 

Timetable

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動画数:143件

A OAuth Django video would be awesome!(Applicable to all sorts of OAuth situations, not just the YouTube API.) - Python YouTube API Tutorial: Using OAuth to Access User Accounts

A OAuth Django video would be awesome!(Applicable to all sorts of OAuth situations, not just the YouTube API.)

Python YouTube API Tutorial: Using OAuth to Access User Accounts
2020年09月10日
00:07:58 - 00:43:21
At  you move the file and see some blurred miniature version of your open folders in the dock. How did you enable that? - Python YouTube API Tutorial: Using OAuth to Access User Accounts

At you move the file and see some blurred miniature version of your open folders in the dock. How did you enable that?

Python YouTube API Tutorial: Using OAuth to Access User Accounts
2020年09月10日
00:09:58 - 00:43:21
In case you are getting Error 403 ("access_denied The developer hasn’t given you access to this app") go to the OAuth consent screen and under Test Users add your email by clicking on Add Users.  You should be able to grant access after you've added User. - Python YouTube API Tutorial: Using OAuth to Access User Accounts

In case you are getting Error 403 ("access_denied The developer hasn’t given you access to this app") go to the OAuth consent screen and under Test Users add your email by clicking on Add Users. You should be able to grant access after you've added User.

Python YouTube API Tutorial: Using OAuth to Access User Accounts
2020年09月10日
00:19:36 - 00:43:21
@corey  So why did you add the join function here, when appending the video id to the list its already a list and its working for me when I pass the vid_ids directly - Python YouTube API Tutorial: Calculating the Duration of a Playlist

@corey So why did you add the join function here, when appending the video id to the list its already a list and its working for me when I pass the vid_ids directly

Python YouTube API Tutorial: Calculating the Duration of a Playlist
2020年06月11日
00:11:24 - 00:37:38
TypeError: expected string or bytes-like object, I am taking this error in , any idea? - Python YouTube API Tutorial: Calculating the Duration of a Playlist

TypeError: expected string or bytes-like object, I am taking this error in , any idea?

Python YouTube API Tutorial: Calculating the Duration of a Playlist
2020年06月11日
00:18:50 - 00:37:38
(I love that "Whoops that went too well"-moment at  :D) - Python Tutorial: Real World Example - Using Patreon API and Pillow to Automate Image Creation

(I love that "Whoops that went too well"-moment at :D)

Python Tutorial: Real World Example - Using Patreon API and Pillow to Automate Image Creation
2020年05月11日
00:35:19 - 00:52:33
Read CSV - - Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc

Read CSV -

Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc
2020年04月02日
00:00:56 - 00:03:20
Write CSV - - Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc

Write CSV -

Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc
2020年04月02日
00:03:20 - 00:04:40
Write TSV - - Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc

Write TSV -

Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc
2020年04月02日
00:04:40 - 00:06:00
Read TSV - - Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc

Read TSV -

Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc
2020年04月02日
00:06:00 - 00:06:15
Write Excel - - Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc

Write Excel -

Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc
2020年04月02日
00:06:15 - 00:10:42
Read Excel -  (Start at 6:15 to see installed packages) - Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc

Read Excel - (Start at 6:15 to see installed packages)

Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc
2020年04月02日
00:10:42 - 00:12:18
Write JSON - - Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc

Write JSON -

Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc
2020年04月02日
00:12:18 - 00:15:41
Read JSON - - Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc

Read JSON -

Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc
2020年04月02日
00:15:41 - 00:16:59
Write SQL - - Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc

Write SQL -

Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc
2020年04月02日
00:16:59 - 00:24:57
# Just in case:If you are using sqlite3 instead of sqlalchemy, you have to use SQL string not the table name: - Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc

# Just in case:If you are using sqlite3 instead of sqlalchemy, you have to use SQL string not the table name:

Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc
2020年04月02日
00:24:57 - 00:32:45
Read SQL -  (Start at 16:59 to see installed packages) - Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc

Read SQL - (Start at 16:59 to see installed packages)

Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc
2020年04月02日
00:24:57 - 00:32:45
.000 +0200 and i used format='%Y-%m-%d %H:%M:%S.%f %z'  It say format does not match. Where did I go wrong. What will the correct format be? Anyone? - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

.000 +0200 and i used format='%Y-%m-%d %H:%M:%S.%f %z' It say format does not match. Where did I go wrong. What will the correct format be? Anyone?

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:00:00 - 00:35:41
for the resampling of the whole DataFrame, say, if I want to get the open price at  for the 'open price' of the day and the close price at 23:00:00 for the 'close price' of the day, what function am I supposed to use here?Thank you ! - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

for the resampling of the whole DataFrame, say, if I want to get the open price at for the 'open price' of the day and the close price at 23:00:00 for the 'close price' of the day, what function am I supposed to use here?Thank you !

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:00:00 - 00:35:41
, can you please tell me how to convert this format to standard date and time format ?   I try this df['Datetime'] = pd.to_datetime(df['Datetime'], format='%m %d %Y %H:%M'), but it didn't work....  please help. - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

, can you please tell me how to convert this format to standard date and time format ? I try this df['Datetime'] = pd.to_datetime(df['Datetime'], format='%m %d %Y %H:%M'), but it didn't work.... please help.

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:00:02 - 00:35:41
convert to datetime using to_datetime - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

convert to datetime using to_datetime

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:03:50 - 00:07:45
Firstly at  , we converted all the values of the Series to datetime type from String type using:df[‘Date’] = pd.to_datetime(df[‘Date’], format = ‘%Y-%m-%d %I-%p’) - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

Firstly at , we converted all the values of the Series to datetime type from String type using:df[‘Date’] = pd.to_datetime(df[‘Date’], format = ‘%Y-%m-%d %I-%p’)

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:06:36 - 00:10:24
parse dates while loading data from csv - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

parse dates while loading data from csv

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:07:45 - 00:11:20
too complicated - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

too complicated

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:08:50 - 00:35:41
but when doing the same thing while loading csv ,we used a lengthier approach of using a lambda and assigning it to date_parser argument.So my doubt is whether while following the later approach, do we basically apply a function(lambda in this case) to a column('Date') so that the function is applied to each value in that column and it converts each value(String) by calling strptime() ?If so, can we assume to_datetime() method converts a whole Series to datetime type while the later approach converts each value of a Series to datetime object?Is there any way we can replicate the former way while loading the csv file? That look minimal and easy.Thanks in advence - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

but when doing the same thing while loading csv ,we used a lengthier approach of using a lambda and assigning it to date_parser argument.So my doubt is whether while following the later approach, do we basically apply a function(lambda in this case) to a column('Date') so that the function is applied to each value in that column and it converts each value(String) by calling strptime() ?If so, can we assume to_datetime() method converts a whole Series to datetime type while the later approach converts each value of a Series to datetime object?Is there any way we can replicate the former way while loading the csv file? That look minimal and easy.Thanks in advence

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:10:24 - 00:35:41
using day_name on whole series using dt class - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

using day_name on whole series using dt class

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:11:20 - 00:12:20
create column of dayname - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

create column of dayname

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:12:20 - 00:13:20
min and max methods on datetime series - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

min and max methods on datetime series

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:13:20 - 00:15:00
filtering by dates as string - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

filtering by dates as string

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:15:00 - 00:17:20
filter by to_datetime - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

filter by to_datetime

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:17:20 - 00:18:40
At  time, where you explain filtering the data on basis of start and end date, I tried doing that without changing the date string to datetime format of pandas and it still worked. What you suggested is this "filt=(df['Date']>=pandas.to_datetime('2019-01-01')) & (df['Date']<pandas.to_datetime('2020-01-01'))df.loc[filt]" what I did is filt=(df['Date']>='2019-01-01') & (df['Date']<'2020-01-01')df.loc[filt]. Could you please suggest if the way I did is wrong or will give me the correct results always or not? - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

At time, where you explain filtering the data on basis of start and end date, I tried doing that without changing the date string to datetime format of pandas and it still worked. What you suggested is this "filt=(df['Date']>=pandas.to_datetime('2019-01-01')) & (df['Date']<pandas.to_datetime('2020-01-01'))df.loc[filt]" what I did is filt=(df['Date']>='2019-01-01') & (df['Date']<'2020-01-01')df.loc[filt]. Could you please suggest if the way I did is wrong or will give me the correct results always or not?

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:18:20 - 00:35:41
set date as index - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

set date as index

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:18:40 - 00:19:21
filter data by just passing the date in brackets - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

filter data by just passing the date in brackets

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:19:21 - 00:20:05
Your tutorials are the bests on the web. Before I can do the index filter ( in the video), I had to sort the dataframe by index(df = df.sort_index()) I don't know why but it was given me an error (AssertionError: <class 'numpy.ndarray'>).Thanks for sharing. - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

Your tutorials are the bests on the web. Before I can do the index filter ( in the video), I had to sort the dataframe by index(df = df.sort_index()) I don't know why but it was given me an error (AssertionError: <class 'numpy.ndarray'>).Thanks for sharing.

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:19:35 - 00:35:41
@ df['2019'] does not work for me. Can anyone help me with this issue? ok nevermind i got it figured out. df.index = pd.to_datetime(df.index) then sort it by index if anyone run into the same issueGreat video btw! - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

@ df['2019'] does not work for me. Can anyone help me with this issue? ok nevermind i got it figured out. df.index = pd.to_datetime(df.index) then sort it by index if anyone run into the same issueGreat video btw!

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:20:00 - 00:35:41
using a slice to get specific dates data - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

using a slice to get specific dates data

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:20:05 - 00:21:00
Hi all, In minute  when filtering df['2020-01':'2020-02] I get an empty dataframe as result. However, if I change the order of the filter df['2020-02':'2020-01] it works. Is there something wrong with this? I didn't sort in any way different than Corey's video but it seems that I need to filter following the pattern of the index to get the correct result. Any comments? Thanks! (Ricardo from Argentina) - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

Hi all, In minute when filtering df['2020-01':'2020-02] I get an empty dataframe as result. However, if I change the order of the filter df['2020-02':'2020-01] it works. Is there something wrong with this? I didn't sort in any way different than Corey's video but it seems that I need to filter following the pattern of the index to get the correct result. Any comments? Thanks! (Ricardo from Argentina)

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:20:30 - 00:35:41
calculating average of a slice(timeframe) - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

calculating average of a slice(timeframe)

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:21:00 - 00:22:05
getting max value of a column on a given day - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

getting max value of a column on a given day

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:22:05 - 00:23:50
df['only_time'] = ''how can i do it without external loop code, because loop taking long time to apply.Thanks for your help. - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

df['only_time'] = ''how can i do it without external loop code, because loop taking long time to apply.Thanks for your help.

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:23:00 - 00:35:41
resampling(breaking down by days) a whole column into a new variable - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

resampling(breaking down by days) a whole column into a new variable

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:23:50 - 00:27:00
plotting with matplotlib in pandas - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

plotting with matplotlib in pandas

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:27:00 - 00:28:28
resampling df with multiple columns - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

resampling df with multiple columns

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:28:28 - 00:30:55
using agg to apply different function on different columns while resampling - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

using agg to apply different function on different columns while resampling

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:30:55 - 00:35:41
You could have also used method 'first' for open price within agg functionThanks a lot for these videos. - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

You could have also used method 'first' for open price within agg functionThanks a lot for these videos.

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:32:28 - 00:35:41
I got this error 'DataFrame' object has no attribute 'resamlpe' - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

I got this error 'DataFrame' object has no attribute 'resamlpe'

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:32:35 - 00:35:41
did the plotting series of tutorials ever come? - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

did the plotting series of tutorials ever come?

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:32:58 - 00:35:41
the date is same but the time is changing. when i am plotting the graph with this column on x axis it shows 17.07 00..... 17.07 04 i know that at end 00, 04 is the time but i wnt on x axis only time and the dstype of this column is datetime64ns. Can any one help me - Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data

the date is same but the time is changing. when i am plotting the graph with this column on x axis it shows 17.07 00..... 17.07 04 i know that at end 00, 04 is the time but i wnt on x axis only time and the dstype of this column is datetime64ns. Can any one help me

Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data
2020年03月18日
00:35:20 - 00:35:41
when she talks with me xdd - Python Pandas Tutorial (Part 9): Cleaning Data - Casting Datatypes and Handling Missing Values

when she talks with me xdd

Python Pandas Tutorial (Part 9): Cleaning Data - Casting Datatypes and Handling Missing Values
2020年02月24日
00:07:20 - 00:31:54
we can use    df.replace(['NA', 'Missing'], np.nan, inplace=True)     instead - Python Pandas Tutorial (Part 9): Cleaning Data - Casting Datatypes and Handling Missing Values

we can use df.replace(['NA', 'Missing'], np.nan, inplace=True) instead

Python Pandas Tutorial (Part 9): Cleaning Data - Casting Datatypes and Handling Missing Values
2020年02月24日
00:11:36 - 00:31:54
watch from  time stamp, Thank me later XD - Python Pandas Tutorial (Part 9): Cleaning Data - Casting Datatypes and Handling Missing Values

watch from time stamp, Thank me later XD

Python Pandas Tutorial (Part 9): Cleaning Data - Casting Datatypes and Handling Missing Values
2020年02月24日
00:17:00 - 00:31:54
Question @.So he's saying you can't convert a Nonetype to an int but you can convert a Nonetype to a float? I'm confused here. Can someone please clear this up for me? I didn't really understand Corey's explanation. - Python Pandas Tutorial (Part 9): Cleaning Data - Casting Datatypes and Handling Missing Values

Question @.So he's saying you can't convert a Nonetype to an int but you can convert a Nonetype to a float? I'm confused here. Can someone please clear this up for me? I didn't really understand Corey's explanation.

Python Pandas Tutorial (Part 9): Cleaning Data - Casting Datatypes and Handling Missing Values
2020年02月24日
00:17:51 - 00:31:54
I mean if you're coding for more than 50 years, you probably just started as soon as computers fucking came out lol😂😂😂 - Python Pandas Tutorial (Part 9): Cleaning Data - Casting Datatypes and Handling Missing Values

I mean if you're coding for more than 50 years, you probably just started as soon as computers fucking came out lol😂😂😂

Python Pandas Tutorial (Part 9): Cleaning Data - Casting Datatypes and Handling Missing Values
2020年02月24日
00:26:03 - 00:31:54
In  of the video. For a one liner code. df['YearsCode'].replace(['Less than 1 year','More than 50 years'],[0,51]), inplace=True). Correct me if I'm wrong I'm new to Python. But great video again Corey! Hats off! - Python Pandas Tutorial (Part 9): Cleaning Data - Casting Datatypes and Handling Missing Values

In of the video. For a one liner code. df['YearsCode'].replace(['Less than 1 year','More than 50 years'],[0,51]), inplace=True). Correct me if I'm wrong I'm new to Python. But great video again Corey! Hats off!

Python Pandas Tutorial (Part 9): Cleaning Data - Casting Datatypes and Handling Missing Values
2020年02月24日
00:27:28 - 00:31:54
Intro: - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

Intro:

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:00:00 - 00:00:56
Recap: - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

Recap:

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:00:56 - 00:01:16
Basic Aggregate Functions:  (`df['col'].mean()`, `df['col'].median()`, `df['col'].mode()`, `df['col'].describe()`, `df.describe()`)- `describe` gives count (# non-NaN rows), mean, std, min, max, and 25%, 50%, and 75% quantiles - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

Basic Aggregate Functions: (`df['col'].mean()`, `df['col'].median()`, `df['col'].mode()`, `df['col'].describe()`, `df.describe()`)- `describe` gives count (# non-NaN rows), mean, std, min, max, and 25%, 50%, and 75% quantiles

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:01:16 - 00:12:00
Aggregate Column - - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

Aggregate Column -

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:02:00 - 00:03:55
median function - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

median function

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:03:10 - 00:05:00
Aggregate DataFrame - - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

Aggregate DataFrame -

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:03:55 - 00:07:51
describe function - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

describe function

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:05:00 - 00:07:20
count() - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

count()

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:07:20 - 00:08:05
Value Counts - - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

Value Counts -

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:07:51 - 00:12:30
value_counts() - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

value_counts()

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:08:05 - 00:12:51
if you have to do that to a specific country e.g. UK, how many people hobyist from UK and how many are not, how do you do that? - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

if you have to do that to a specific country e.g. UK, how many people hobyist from UK and how many are not, how do you do that?

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:09:08 - 00:49:06
Counts by Percentage:  (`df['col'].value_counts(normalize=True)`) - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

Counts by Percentage: (`df['col'].value_counts(normalize=True)`)

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:12:00 - 00:12:32
value count in % - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

value count in %

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:12:05 - 00:49:06
Grouping - - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

Grouping -

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:12:30 - 00:26:00
Intro to Groupby:- split object, apply function, combine results - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

Intro to Groupby:- split object, apply function, combine results

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:12:32 - 00:13:38
grouping the data - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

grouping the data

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:12:51 - 00:14:39
Split:df.groupby(['col'])df.get_group(['col_val']) - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

Split:df.groupby(['col'])df.get_group(['col_val'])

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:13:38 - 00:18:23
groupby() function - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

groupby() function

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:14:39 - 00:16:07
get_group(), grabbing a specific group by name - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

get_group(), grabbing a specific group by name

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:16:07 - 00:17:30
doing same by using the filters - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

doing same by using the filters

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:17:30 - 00:18:40
Apply Function & Combine:.agg(['mean', 'median']) - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

Apply Function & Combine:.agg(['mean', 'median'])

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:18:23 - 00:27:02
using value_counts on filters - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

using value_counts on filters

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:18:40 - 00:20:20
at , using .loc u got 20,00 rows and using group by u got 9,000 rows, how?? - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

at , using .loc u got 20,00 rows and using group by u got 9,000 rows, how??

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:19:00 - 00:49:06
Small note: at , the more correct syntax would be to select your column within the .loc method: df.loc[filt, 'SocialMedia'].value_counts(). - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

Small note: at , the more correct syntax would be to select your column within the .loc method: df.loc[filt, 'SocialMedia'].value_counts().

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:19:06 - 00:49:06
@ How are you able to put the two brackets side-by-side in the line: df.loc[filt]['socialmedia'].value_counts()I dont get why it works with df.loc, but when I tried to set a variable "group" to the groupby function, it doesnt work? - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

@ How are you able to put the two brackets side-by-side in the line: df.loc[filt]['socialmedia'].value_counts()I dont get why it works with df.loc, but when I tried to set a variable "group" to the groupby function, it doesnt work?

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:19:15 - 00:49:06
value_counts() for groups - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

value_counts() for groups

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:20:20 - 00:21:49
@ How would return just the top row for each country?I want it to look something like this...Country           Social MediaAfghanistan    Facebook         15Zimbabwe       Facebook          3 - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

@ How would return just the top row for each country?I want it to look something like this...Country Social MediaAfghanistan Facebook 15Zimbabwe Facebook 3

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:21:46 - 00:49:06
using loc to find for one country - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

using loc to find for one country

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:21:49 - 00:23:40
Corey, at  the groupby object forms a series with multindex. I'll try later to use pivot_table() to make index the countries and the columns representing different social apps. Although it won't be tidy data, I think it will be useful to explore it. - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

Corey, at the groupby object forms a series with multindex. I'll try later to use pivot_table() to make index the countries and the columns representing different social apps. Although it won't be tidy data, I think it will be useful to explore it.

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:21:50 - 00:49:06
percentage by using normalize - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

percentage by using normalize

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:23:40 - 00:25:00
median by country group - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

median by country group

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:25:00 - 00:26:13
Multiple Aggregates on Group - - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

Multiple Aggregates on Group -

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:26:00 - 00:27:20
agg function for multiple functions - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

agg function for multiple functions

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:26:13 - 00:27:30
Gotchas:.apply() on SeriesGroup - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

Gotchas:.apply() on SeriesGroup

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:27:02 - 00:33:36
People Who Know Python By Country - - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

People Who Know Python By Country -

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:27:20 - 00:34:20
using filtering to get python users by country - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

using filtering to get python users by country

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:27:30 - 00:30:20
At  in the video, would the following statement be more accurate as we want to know how many people know Python in Indiafilt = df['Country']=='India'df[filt]['LanguageWorkedWith'].str.contains('Python').count() - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

At in the video, would the following statement be more accurate as we want to know how many people know Python in Indiafilt = df['Country']=='India'df[filt]['LanguageWorkedWith'].str.contains('Python').count()

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:30:07 - 00:49:06
error on using same approach for groups - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

error on using same approach for groups

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:30:20 - 00:31:40
apply method to run that on group - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

apply method to run that on group

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:31:40 - 00:35:40
Hi Corey. in the group by method in , in the apply() method, the x in the lambda represent the series? in the apply method in the previous lectures isn't the apply method use in every cell/value in a series? Not in the series itself just like the 32:58? I'm new to python.  Thanks! - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

Hi Corey. in the group by method in , in the apply() method, the x in the lambda represent the series? in the apply method in the previous lectures isn't the apply method use in every cell/value in a series? Not in the series itself just like the 32:58? I'm new to python. Thanks!

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:32:58 - 00:49:06
Very interesting tutorial. Thumb up! Particularly at  !! - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

Very interesting tutorial. Thumb up! Particularly at !!

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:33:30 - 00:49:06
Real-World Problem (Calculation from Several Groups):- What percentage of people from each country know Python?country_respondents = df['Country'].value_counts()country_uses_python = country_grp['LanguageWorkedWith'].apply(lambda x: x.str.contains('Python').sum())result = pd.concat([country_respondents, country_uses_python], axis='columns')result.rename(columns={'Country': 'NumRespondents', 'LanguageWorkedWith': 'NumKnowsPython'})result['PctKnowsPython'] = country_uses_python / country_uses_python * 100result.sort_values(by='PctKnowsPython', ascending=False)result.loc['Japan'] - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

Real-World Problem (Calculation from Several Groups):- What percentage of people from each country know Python?country_respondents = df['Country'].value_counts()country_uses_python = country_grp['LanguageWorkedWith'].apply(lambda x: x.str.contains('Python').sum())result = pd.concat([country_respondents, country_uses_python], axis='columns')result.rename(columns={'Country': 'NumRespondents', 'LanguageWorkedWith': 'NumKnowsPython'})result['PctKnowsPython'] = country_uses_python / country_uses_python * 100result.sort_values(by='PctKnowsPython', ascending=False)result.loc['Japan']

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:33:36 - 00:45:23
#Ques at , what percentage of people from each country know Python? - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

#Ques at , what percentage of people from each country know Python?

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:34:20 - 00:49:06
Practice Question - - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

Practice Question -

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:34:20 - 00:37:27
Possible solutions for the question atcountry_grp['LanguageWorkedWith'].apply(lambda series: series.str.contains('Python').sum()/len(series)) - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

Possible solutions for the question atcountry_grp['LanguageWorkedWith'].apply(lambda series: series.str.contains('Python').sum()/len(series))

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:34:28 - 00:49:06
try this as the solution to question asked by respected Corey sir at  :l_countries = pd.unique(csv.dropna()['Country'])a = [(i, ((gbo.get_group(i)['LanguageWorkedWith'].str.contains('Python').sum()) / (gbo.get_group(i)['LanguageWorkedWith'].count()) * 100)) for i in l_countries] - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

try this as the solution to question asked by respected Corey sir at :l_countries = pd.unique(csv.dropna()['Country'])a = [(i, ((gbo.get_group(i)['LanguageWorkedWith'].str.contains('Python').sum()) / (gbo.get_group(i)['LanguageWorkedWith'].count()) * 100)) for i in l_countries]

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:34:35 - 00:49:06
I was just wondering if on  you could have just modified the lambda expression so it could just do all the calculations in place like so:country_grp["LanguageWorkedWith"].apply(lambda country_ser: (country_ser.str.contains("Python").sum()*/country_ser.value_counts().sum())*100)What I tried to do was to count the sum of total respondents for each country and do all the necessary calculations inside the lambda,  and, though the result that I got were similar to yours, they still were slightly different. - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

I was just wondering if on you could have just modified the lambda expression so it could just do all the calculations in place like so:country_grp["LanguageWorkedWith"].apply(lambda country_ser: (country_ser.str.contains("Python").sum()*/country_ser.value_counts().sum())*100)What I tried to do was to count the sum of total respondents for each country and do all the necessary calculations inside the lambda, and, though the result that I got were similar to yours, they still were slightly different.

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:35:38 - 00:49:06
At  since you want to get the percentage of boolean values that are 1 you can pretty much just replace .sum() with .mean(). It's sum/length. - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

At since you want to get the percentage of boolean values that are 1 you can pretty much just replace .sum() with .mean(). It's sum/length.

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:35:40 - 00:49:06
finding the percentage of people using python in each country(group) - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

finding the percentage of people using python in each country(group)

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:35:40 - 00:37:40
. I don’t have the data or code to run to see if this would work right now, but at first glance I’d run:.apply(lambda x: x.str.contains(‘Python’).mean()) - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

. I don’t have the data or code to run to see if this would work right now, but at first glance I’d run:.apply(lambda x: x.str.contains(‘Python’).mean())

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:35:55 - 00:49:06
I just wanted to share something regarding LanguagesHaveWorked grouped by Country (on ). I think the below code is rather simpler if we just wanna look at the percentages only, but I don't how reliable this method might be - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

I just wanted to share something regarding LanguagesHaveWorked grouped by Country (on ). I think the below code is rather simpler if we just wanna look at the percentages only, but I don't how reliable this method might be

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:36:00 - 00:49:06
() I added just a short method to get percentage of python users.country_grp['LanguageWorkedWith'].apply(lambda x: x.str.contains('Python').sum())->country_grp['LanguageWorkedWith'].apply(lambda x: (x.str.contains('Python').sum())/x.count()) - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

() I added just a short method to get percentage of python users.country_grp['LanguageWorkedWith'].apply(lambda x: x.str.contains('Python').sum())->country_grp['LanguageWorkedWith'].apply(lambda x: (x.str.contains('Python').sum())/x.count())

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:36:18 - 00:49:06
Concat Series - - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

Concat Series -

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:37:27 - 00:49:06
using concat for combining series in a dataframe - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

using concat for combining series in a dataframe

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:37:40 - 00:45:30
-  U S A ! U S A ! U S A ! - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

- U S A ! U S A ! U S A !

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:44:30 - 00:49:06
Outro: - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

Outro:

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:45:23 - 00:49:06
adding percentage column - Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data

adding percentage column

Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data
2020年02月14日
00:45:30 - 00:49:06