- Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

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

Data & code used in this Tutorial: https://github.com/KeithGalli/pandas
Python Pandas Documentation: http://pandas.pydata.org/pandas-docs/stable/

Let me know if you have any ...
Practice your Python Pandas data science skills with problems on StrataScratch!
https://stratascratch.com/?via=keith

Data & code used in this Tutorial: https://github.com/KeithGalli/pandas
Python Pandas Documentation: http://pandas.pydata.org/pandas-docs/stable/

Let me know if you have any questions!

In this video we walk through many of the fundamental concepts to use the Python Pandas Data Science Library. We start off by installing pandas and loading in an example csv. We then look at different ways to read the data. Read a column, rows, specific cell, etc. Also ways to read data based on conditioning. We then move into some more advanced ways to sort & filter data. We look at making conditional changes to our data. We also start doing aggregate stats using the groupby function. We finished the video talking about how you would work with a very large dataset (many gigabytes)

I realized as I upload this video there are some additional things I want to talk about in a later video. The first thing that comes to mind immediately is using the apply() function on a dataframe to alter the data using a custom or lambda function. If you have questions on this or anything else before I get around to making a part 2, feel free to write me a note in the comments.

If you enjoyed this video, be sure to throw it a like and make sure to subscribe to not miss any future videos!

Thanks for watching friends! Happy coding! :)

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Link to original source of data from Kaggle: https://www.kaggle.com/abcsds/pokemon

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Video Outline!
0:00 - Why Pandas?
1:46 - Installing Pandas
2:03 - Getting the data used in this video
3:50 - Loading the data into Pandas (CSVs, Excel, TXTs, etc.)
8:49 - Reading Data (Getting Rows, Columns, Cells, Headers, etc.)
13:10 - Iterate through each Row
14:11 - Getting rows based on a specific condition
15:47 - High Level description of your data (min, max, mean, std dev, etc.)
16:24 - Sorting Values (Alphabetically, Numerically)
18:19 - Making Changes to the DataFrame
18:56 - Adding a column
21:22 - Deleting a column
22:14 - Summing Multiple Columns to Create new Column.
24:14 - Rearranging columns
28:06 - Saving our Data (CSV, Excel, TXT, etc.)
31:47 - Filtering Data (based on multiple conditions)
35:40 - Reset Index
37:41 - Regex Filtering (filter based on textual patterns)
43:08 - Conditional Changes
47:57 - Aggregate Statistics using Groupby (Sum, Mean, Counting)
54:53 - Working with large amounts of data (setting chunksize)

-------------------------
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.

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- Why Pandas? - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

- Why Pandas?

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:00:45 - 00:01:46
- Installing Pandas - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

- Installing Pandas

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:01:46 - 00:02:03
- Getting the data used in this video - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

- Getting the data used in this video

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:02:03 - 00:03:50
- Loading the data into Pandas (CSVs, Excel, TXTs, etc.) - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

- Loading the data into Pandas (CSVs, Excel, TXTs, etc.)

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:03:50 - 00:08:49
#####--- adding  (10 not included) columns to new column Total ---####li_columns = df.columns.tolist()df['Total'] = 0 - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

#####--- adding (10 not included) columns to new column Total ---####li_columns = df.columns.tolist()df['Total'] = 0

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:04:10 - 00:04:10
for element in li_columns[]:
 #From HP to Speeddf['Total'] = df['Total'] + df[element]print(df.head(5)) #Print first 5 rows - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

for element in li_columns[]: #From HP to Speeddf['Total'] = df['Total'] + df[element]print(df.head(5)) #Print first 5 rows

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:04:10 - 01:00:27
Just wanna make sure that this one doesn’t go ignored haha @ - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

Just wanna make sure that this one doesn’t go ignored haha @

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:06:30 - 01:00:27
- Reading Data (Getting Rows, Columns, Cells, Headers, etc.) - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

- Reading Data (Getting Rows, Columns, Cells, Headers, etc.)

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:08:49 - 00:13:10
, why the double third bracket in [[ 'Name' , 'Type 1' , 'HP']] ? Why not just [ 'Name' , 'Type 1' , 'HP'] ? - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

, why the double third bracket in [[ 'Name' , 'Type 1' , 'HP']] ? Why not just [ 'Name' , 'Type 1' , 'HP'] ?

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:11:15 - 01:00:27
- Iterate through each Row - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

- Iterate through each Row

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:13:10 - 00:14:11
For , refer https://stackoverflow.com/questions/16476924/how-to-iterate-over-rows-in-a-dataframe-in-pandas - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

For , refer https://stackoverflow.com/questions/16476924/how-to-iterate-over-rows-in-a-dataframe-in-pandas

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:13:16 - 01:00:27
usefor index, row in df.iterrows():print(index, row['Name']) - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

usefor index, row in df.iterrows():print(index, row['Name'])

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:14:10 - 01:00:27
- Getting rows based on a specific condition - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

- Getting rows based on a specific condition

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:14:11 - 00:15:47
In , what if i want to select both 'Grass' and 'Fire', could you show me how can I do that? - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

In , what if i want to select both 'Grass' and 'Fire', could you show me how can I do that?

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:15:26 - 01:00:27
- High Level description of your data (min, max, mean, std dev, etc.) - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

- High Level description of your data (min, max, mean, std dev, etc.)

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:15:47 - 00:16:24
- my bookmark so I can return with the stats function - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

- my bookmark so I can return with the stats function

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:15:53 - 01:00:27
- Sorting Values (Alphabetically, Numerically) - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

- Sorting Values (Alphabetically, Numerically)

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:16:24 - 00:18:19
- Making Changes to the DataFrame - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

- Making Changes to the DataFrame

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:18:19 - 00:18:56
- Adding a column - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

- Adding a column

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:18:56 - 00:21:22
If You have too many columns to add, as he has at  and even more, use this: - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

If You have too many columns to add, as he has at and even more, use this:

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:19:33 - 00:04:10
- Deleting a column - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

- Deleting a column

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:21:22 - 00:22:14
at  you could do like this :df.drop(columns=['Total'], inplace=True) - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

at you could do like this :df.drop(columns=['Total'], inplace=True)

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:22:00 - 01:00:27
- Summing Multiple Columns to Create new Column. - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

- Summing Multiple Columns to Create new Column.

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:22:14 - 00:24:14
Hi Keith,Amazing work. thanks for the Tutorial,however I got an error after i execute the .iloc method at  in the video.Can you pls explain how to get rid of the error "Indexing error:Too many Indexers".Thanks in advance - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

Hi Keith,Amazing work. thanks for the Tutorial,however I got an error after i execute the .iloc method at in the video.Can you pls explain how to get rid of the error "Indexing error:Too many Indexers".Thanks in advance

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:23:24 - 01:00:27
at  when i put pokemon['Total'] = df.iloc[:, 4:10].sum(axis=1),  bulbasaur's total is 591. but when i put pokemon['Total'] = df.iloc[:, 4:5].sum(axis=1), the total is correct at 318. What happened here? - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

at when i put pokemon['Total'] = df.iloc[:, 4:10].sum(axis=1), bulbasaur's total is 591. but when i put pokemon['Total'] = df.iloc[:, 4:5].sum(axis=1), the total is correct at 318. What happened here?

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:23:50 - 01:00:27
- Rearranging columns - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

- Rearranging columns

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:24:14 - 00:28:06
Best Pandas tutorial on YouTube, especially - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

Best Pandas tutorial on YouTube, especially

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:24:25 - 01:00:27
XD *SPAM IT* - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

XD *SPAM IT*

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:24:28 - 01:00:27
to 24:33 is essential. The other stuff is good too. - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

to 24:33 is essential. The other stuff is good too.

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:24:28 - 01:00:27
Hi Keith, this is a very helpful introductory course for pandas and thanks a lot! I see a very interesting edit to your video though - at  there is `cols = list(df.columns.values)` but a second later at 26:40 it became `cols = list(df.columns)`. What happened here? Why did you remove the `.values`? - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

Hi Keith, this is a very helpful introductory course for pandas and thanks a lot! I see a very interesting edit to your video though - at there is `cols = list(df.columns.values)` but a second later at 26:40 it became `cols = list(df.columns)`. What happened here? Why did you remove the `.values`?

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:26:39 - 01:00:27
The correct or a better approach for mistake in . Will be to use cols[-1:] - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

The correct or a better approach for mistake in . Will be to use cols[-1:]

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:26:49 - 01:00:27
Hey, thank You very much for this tutorial, but I'm wondering what happens with "Total" column on  ? Numbers became more than twice bigger. - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

Hey, thank You very much for this tutorial, but I'm wondering what happens with "Total" column on ? Numbers became more than twice bigger.

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:27:09 - 01:00:27
why the total changes? the first row was 318, but in this moment change to 864 - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

why the total changes? the first row was 318, but in this moment change to 864

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:27:12 - 01:00:27
It seems that the dataframe got scrambled up a bit there, most likely from having the cell running multiple times. Even when there was an error message, it appears that either the Total or the Legendary column was moved to the left of HP. Upon running the cell again (with the corrected version?) it calculated a new Total adding the previous values and generating corrupted results. - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

It seems that the dataframe got scrambled up a bit there, most likely from having the cell running multiple times. Even when there was an error message, it appears that either the Total or the Legendary column was moved to the left of HP. Upon running the cell again (with the corrected version?) it calculated a new Total adding the previous values and generating corrupted results.

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:27:15 - 01:00:27
I really appreciate your work, thank you so much :) - bookmark - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

I really appreciate your work, thank you so much :) - bookmark

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:27:21 - 01:00:27
Very nice tutorial. Might be worth it to demo how to reorder columns by using indices as you allude to starting around .  That would be something a newbie might like to see illustrated - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

Very nice tutorial. Might be worth it to demo how to reorder columns by using indices as you allude to starting around . That would be something a newbie might like to see illustrated

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:27:50 - 01:00:27
- Saving our Data (CSV, Excel, TXT, etc.) - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

- Saving our Data (CSV, Excel, TXT, etc.)

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:28:06 - 00:31:47
the totals are all messed up and way too high though - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

the totals are all messed up and way too high though

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:28:10 - 01:00:27
there was a bug, each time you run your code the columns will be in different indexes and that because you set the rearrange line to the same variable which is "df", I solved this problem by setting a new variable to the rearrange line then save it to that new variable, each time I run the code now the result is the same. - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

there was a bug, each time you run your code the columns will be in different indexes and that because you set the rearrange line to the same variable which is "df", I solved this problem by setting a new variable to the rearrange line then save it to that new variable, each time I run the code now the result is the same.

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:29:00 - 01:00:27
is where he's getting texts from actual pandas - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

is where he's getting texts from actual pandas

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:29:19 - 01:00:27
@Kevin - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

@Kevin

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:29:30 - 01:00:27
- Filtering Data (based on multiple conditions) - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

- Filtering Data (based on multiple conditions)

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:31:47 - 00:35:40
You don't have to use 'loc' for filtering - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

You don't have to use 'loc' for filtering

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:32:07 - 01:00:27
When I try to do the operation at  I get a warning "future warning:element wise comparison failed, returning scalar instead, but in the future will perform element wise comparison" - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

When I try to do the operation at I get a warning "future warning:element wise comparison failed, returning scalar instead, but in the future will perform element wise comparison"

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:32:58 - 01:00:27
Hi Keith! thanks for this helpful video. How do you do the filtering data with different conditions (i.e. at  min in the video) for a data frame without headers and just based on position. I tried your example with iloc and it doesn't work. I searched for the solution but was not successful. I hope you see my comment and answer it pls! Thanks in advance for your help. - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

Hi Keith! thanks for this helpful video. How do you do the filtering data with different conditions (i.e. at min in the video) for a data frame without headers and just based on position. I tried your example with iloc and it doesn't work. I searched for the solution but was not successful. I hope you see my comment and answer it pls! Thanks in advance for your help.

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:33:18 - 01:00:27
I think Pandas and NumPy (When using arrays) implement a "C" language style syntax when doing boolean and a few data structures. - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

I think Pandas and NumPy (When using arrays) implement a "C" language style syntax when doing boolean and a few data structures.

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:33:28 - 01:00:27
I tried to do it with and operator but I got an Error.I tried it with & operator but I got a KeyError: FalseI did it in the PyCharm. Therefore, should I install Anaconda and use it instead of PyCharm? - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

I tried to do it with and operator but I got an Error.I tried it with & operator but I got a KeyError: FalseI did it in the PyCharm. Therefore, should I install Anaconda and use it instead of PyCharm?

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:33:31 - 01:00:27
- Reset Index - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

- Reset Index

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:35:40 - 00:37:41
At the "Reset index" part in  , even after applying two methods -1) new_df = new_df.reset_index(drop=True)new_df2)new_df.reset_index(drop=True,inplace=True)new_dfMy old index column was still in the data set.So i just normally deleted that column by ' - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

At the "Reset index" part in , even after applying two methods -1) new_df = new_df.reset_index(drop=True)new_df2)new_df.reset_index(drop=True,inplace=True)new_dfMy old index column was still in the data set.So i just normally deleted that column by '

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:37:04 - 01:00:27
- Regex Filtering (filter based on textual patterns) - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

- Regex Filtering (filter based on textual patterns)

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:37:41 - 00:43:08
i was looking for .str.contains('*') filterthanks - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

i was looking for .str.contains('*') filterthanks

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:39:27 - 01:00:27
I have one comment though. When you run (at ) - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

I have one comment though. When you run (at )

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:40:00 - 01:00:27
At  flags = re.I parameter in str.contains function didn't work.  Looked it up, and it should read flags = re.IGNORECASE.  Possibly this is due to a version change? - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

At flags = re.I parameter in str.contains function didn't work. Looked it up, and it should read flags = re.IGNORECASE. Possibly this is due to a version change?

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:41:00 - 01:00:27
just one correction: at  'pi[a-z]*'  the star means zero or more, instead of 'one or more' as said by Keith. - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

just one correction: at 'pi[a-z]*' the star means zero or more, instead of 'one or more' as said by Keith.

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:42:18 - 01:00:27
At , the following may be simpler:  new_df = df.loc[df['Name'].str.contains('^pi', regex=True, flags=re.I)] - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

At , the following may be simpler: new_df = df.loc[df['Name'].str.contains('^pi', regex=True, flags=re.I)]

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:42:33 - 01:00:27
- Conditional Changes - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

- Conditional Changes

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:43:08 - 00:47:57
' after 'Fire' at  ? - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

' after 'Fire' at ?

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:44:00 - 01:00:27
When i do multiple filtering  get this error: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead. Any suggestion ? - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

When i do multiple filtering get this error: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead. Any suggestion ?

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:47:33 - 01:00:27
- Aggregate Statistics using Groupby (Sum, Mean, Counting) - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

- Aggregate Statistics using Groupby (Sum, Mean, Counting)

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:47:57 - 00:54:53
at  what happens with the "Type 1" column label? I tried it here with my own data set and it seems to get lost as a label that I can query for.. How do I group exactly like this but keep the column label as is? - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

at what happens with the "Type 1" column label? I tried it here with my own data set and it seems to get lost as a label that I can query for.. How do I group exactly like this but keep the column label as is?

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:49:20 - 01:00:27
At  , how can I plot the data grouped by types , where the type with the highest total is shown from the left to right? Without losing the mapping between the type and its corresponding total value? In my barplot graph, the order doesn´t make sense when I add:  .sort_values("Total", ascending = False). - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

At , how can I plot the data grouped by types , where the type with the highest total is shown from the left to right? Without losing the mapping between the type and its corresponding total value? In my barplot graph, the order doesn´t make sense when I add: .sort_values("Total", ascending = False).

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:51:00 - 01:00:27
You can also just write the code: df.groupby(['Type 1']).count()['Name'] - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

You can also just write the code: df.groupby(['Type 1']).count()['Name']

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:52:49 - 01:00:27
you can use .size() to get the count of each Pokemon type instead of adding a new column.It would look like this:df.groupby(['Type 1']).size() - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

you can use .size() to get the count of each Pokemon type instead of adding a new column.It would look like this:df.groupby(['Type 1']).size()

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:53:30 - 01:00:27
- Working with large amounts of data (setting chunksize) - Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

- Working with large amounts of data (setting chunksize)

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
2018年10月26日 
00:54:53 - 01:00:27

Keith Galli

※本サイトに掲載されているチャンネル情報や動画情報はYouTube公式のAPIを使って取得・表示しています。

Timetable

動画タイムテーブル

動画数:87件

- Intro & Setup - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Intro & Setup

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:00:00 - 00:02:14
great video! however, regarding the usage of the terminal to create directories etc at  , can anyone recommend some youtube videos or sources to get more familiar with it? thanks a bunch! good luck getting good at pandas everybody :) - Solving 100 Python Pandas Problems! (from easy to very difficult)

great video! however, regarding the usage of the terminal to create directories etc at , can anyone recommend some youtube videos or sources to get more familiar with it? thanks a bunch! good luck getting good at pandas everybody :)

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日  @realzeejay 様 
00:00:59 - 05:20:18
- Problems (1-3) Initial pandas setup - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Problems (1-3) Initial pandas setup

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:02:14 - 00:04:42
- Problems (4-10) DataFrame operations - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Problems (4-10) DataFrame operations

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:04:42 - 00:04:52
- 4) Create a dataframe from dictionary - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 4) Create a dataframe from dictionary

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:04:52 - 00:05:24
- 5) Display dataframe summary - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 5) Display dataframe summary

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:05:24 - 00:05:41
- 6) First 3 rows of the dataframe - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 6) First 3 rows of the dataframe

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:05:41 - 00:06:02
- 7) Select ‘animal’ and ‘age’ columns - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 7) Select ‘animal’ and ‘age’ columns

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:06:02 - 00:07:42
- 8) Data in specific rows and columns - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 8) Data in specific rows and columns

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:07:42 - 00:09:06
- 9) Rows with visits greater than 3 - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 9) Rows with visits greater than 3

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:09:06 - 00:09:57
- 10) Rows with NaN in age - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 10) Rows with NaN in age

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:09:57 - 00:10:56
- 11) Cats younger than 3 years - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 11) Cats younger than 3 years

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:10:56 - 00:11:35
- 12) Age between 2 and 4 - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 12) Age between 2 and 4

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:11:35 - 00:12:45
- 13) Change age in row ‘f’ - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 13) Change age in row ‘f’

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:12:45 - 00:15:56
- 14) Sum of all visits - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 14) Sum of all visits

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:15:56 - 00:16:41
- 15) Average age by animal - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 15) Average age by animal

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:16:41 - 00:20:21
I do this a lot, by passing a dict to the agg function after grouping (it allows you to asign multiple operators to several cols at once). Eg df.groupby(“animal”).agg({“age”:”mean”}) - Solving 100 Python Pandas Problems! (from easy to very difficult)

I do this a lot, by passing a dict to the agg function after grouping (it allows you to asign multiple operators to several cols at once). Eg df.groupby(“animal”).agg({“age”:”mean”})

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日  @AgustinGonzalez-tz3yr 様 
00:19:30 - 05:20:18
- 16) Modify and revert rows - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 16) Modify and revert rows

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:20:21 - 00:24:06
- 17) Count by animal type - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 17) Count by animal type

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:24:06 - 00:25:28
- Quick review - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Quick review

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:25:28 - 00:26:17
- 18) Sort by age and visits - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 18) Sort by age and visits

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:26:17 - 00:28:07
- 19) Convert 'priority' to boolean - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 19) Convert 'priority' to boolean

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:28:07 - 00:29:42
- 20) Replace 'snake' with 'python' - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 20) Replace 'snake' with 'python'

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:29:42 - 00:30:53
- 21) Mean age by animal and visits - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 21) Mean age by animal and visits

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:30:53 - 00:33:49
- Advanced DataFrame techniques - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Advanced DataFrame techniques

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:33:49 - 00:33:57
- 22) Filter duplicate integers - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 22) Filter duplicate integers

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:33:57 - 00:43:18
- 23) Subtract row mean - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 23) Subtract row mean

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:43:18 - 00:45:42
- 24) Column with smallest sum - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 24) Column with smallest sum

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:45:42 - 00:50:39
- 25) Count unique rows - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 25) Count unique rows

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:50:39 - 00:53:17
- 26) Column with third NaN - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 26) Column with third NaN

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
00:53:17 - 01:10:27
- Solution review for 26 - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Solution review for 26

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
01:10:27 - 01:17:13
- 27) Sum of top three values - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 27) Sum of top three values

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
01:17:13 - 01:24:01
- 28) Sum by column condition - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 28) Sum by column condition

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
01:24:01 - 01:40:11
- Recent problem review - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Recent problem review

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
01:40:11 - 01:42:53
- 29) Count differences since last zero - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 29) Count differences since last zero

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
01:42:53 - 01:56:19
- 30) Locate largest values - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 30) Locate largest values

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
01:56:19 - 02:08:38
- 31) Replace negatives with mean - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 31) Replace negatives with mean

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
02:08:38 - 02:17:43
- 32) Rolling mean over groups - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 32) Rolling mean over groups

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
02:17:43 - 02:23:10
- Series and DatetimeIndex - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Series and DatetimeIndex

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
02:23:10 - 02:23:12
- 33) DatetimeIndex for 2015 - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 33) DatetimeIndex for 2015

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
02:23:12 - 02:27:56
- 34) Sum values on Wednesdays - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 34) Sum values on Wednesdays

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
02:27:56 - 02:45:04
- 35) Monthly mean values - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 35) Monthly mean values

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
02:45:04 - 02:46:16
- 36) Best value in four-month groups - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 36) Best value in four-month groups

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
02:46:16 - 02:50:26
- 37) DatetimeIndex of third Thursdays - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 37) DatetimeIndex of third Thursdays

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
02:50:26 - 02:59:03
- Cleaning Data - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Cleaning Data

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
02:59:03 - 02:59:40
- 38) Fill missing FlightNumber - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 38) Fill missing FlightNumber

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
02:59:40 - 03:02:45
- 39) Split column by delimiter - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 39) Split column by delimiter

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
03:02:45 - 03:06:47
- 40) Fix city name capitalization - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 40) Fix city name capitalization

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
03:06:47 - 03:08:30
- 41) Reattach columns - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 41) Reattach columns

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
03:08:30 - 03:13:11
- 42) Fix airline name punctuation - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 42) Fix airline name punctuation

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
03:13:11 - 03:17:45
- 43) Expand RecentDelays into columns - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 43) Expand RecentDelays into columns

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
03:17:45 - 03:27:31
- MultiIndexes in Pandas - Solving 100 Python Pandas Problems! (from easy to very difficult)

- MultiIndexes in Pandas

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
03:27:31 - 03:27:34
- 44) Construct a MultiIndex - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 44) Construct a MultiIndex

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
03:27:34 - 03:30:37
- Solution review - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Solution review

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
03:30:37 - 03:32:44
- 45) Lexicographically sorted check - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 45) Lexicographically sorted check

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
03:32:44 - 03:32:58
- 46) Select specific MultiIndex labels - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 46) Select specific MultiIndex labels

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
03:32:58 - 03:34:23
- 47) Slice Series with MultiIndex - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 47) Slice Series with MultiIndex

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
03:34:23 - 03:35:24
- 48) Sum by first level - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 48) Sum by first level

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
03:35:24 - 03:37:47
- 49) Alternative sum method - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 49) Alternative sum method

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
03:37:47 - 03:40:08
- Additional solution insights - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Additional solution insights

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
03:40:08 - 03:41:22
- 50) Swap MultiIndex levels - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 50) Swap MultiIndex levels

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
03:41:22 - 03:45:27
- Minesweeper problems - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Minesweeper problems

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
03:45:27 - 03:45:44
- 51) Generate coordinate grid - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 51) Generate coordinate grid

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
03:45:44 - 04:00:28
- 52) Add 'safe' or 'mine' column - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 52) Add 'safe' or 'mine' column

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
04:00:28 - 04:03:04
- 53) Count adjacent mines - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 53) Count adjacent mines

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
04:03:04 - 04:27:33
- Review solution to 53 - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Review solution to 53

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
04:27:33 - 04:33:02
- Skipped problems 54 & 55 - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Skipped problems 54 & 55

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
04:33:02 - 04:33:11
- Plotting - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Plotting

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
04:33:11 - 04:33:12
- 56) Scatter plot with black x markers - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 56) Scatter plot with black x markers

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
04:33:12 - 04:41:26
- 57) Plot four data types - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 57) Plot four data types

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
04:41:26 - 04:52:50
- 58) Overlay multiple graphs - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 58) Overlay multiple graphs

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
04:52:50 - 05:03:11
- 59) Hourly stock data summary - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 59) Hourly stock data summary

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
05:03:11 - 05:14:12
- 60) Candlestick plot - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 60) Candlestick plot

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日 
05:14:12 - 05:20:18
- Intro & Live Stream Overview - Ask me anything! (data science, LLMs, landing a job, and more)

- Intro & Live Stream Overview

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日 
00:00:00 - 00:04:58
- How over saturated is the data science job market and will things improve in your opinion? - Ask me anything! (data science, LLMs, landing a job, and more)

- How over saturated is the data science job market and will things improve in your opinion?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日 
00:04:58 - 00:07:26
- How much maths is needed to get a data science job? - Ask me anything! (data science, LLMs, landing a job, and more)

- How much maths is needed to get a data science job?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日 
00:07:26 - 00:09:55
- Can you share a basic roadmap to learn generative AI and LLMs? - Ask me anything! (data science, LLMs, landing a job, and more)

- Can you share a basic roadmap to learn generative AI and LLMs?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日 
00:09:55 - 00:13:08
- What future-proof tech career to should someone focus on who’s looking to change career? - Ask me anything! (data science, LLMs, landing a job, and more)

- What future-proof tech career to should someone focus on who’s looking to change career?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日 
00:13:08 - 00:16:52
- Is data structures & algorithms (dsa) necessary to get a job in data science? - Ask me anything! (data science, LLMs, landing a job, and more)

- Is data structures & algorithms (dsa) necessary to get a job in data science?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日 
00:16:52 - 00:19:17
- How to get good at data structures and algorithms? - Ask me anything! (data science, LLMs, landing a job, and more)

- How to get good at data structures and algorithms?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日 
00:19:17 - 00:22:20
- Why don’t you make videos regularly now? - Ask me anything! (data science, LLMs, landing a job, and more)

- Why don’t you make videos regularly now?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日 
00:22:20 - 00:24:18
- How much do you need to know for entry-level roles / college internships? - Ask me anything! (data science, LLMs, landing a job, and more)

- How much do you need to know for entry-level roles / college internships?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日 
00:24:18 - 00:27:02
- How important is domain knowledge for data science? - Ask me anything! (data science, LLMs, landing a job, and more)

- How important is domain knowledge for data science?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日 
00:27:02 - 00:29:29
- Amazon’s AI-based ‘just walk out’ retail checkout tech controversy thoughts - Ask me anything! (data science, LLMs, landing a job, and more)

- Amazon’s AI-based ‘just walk out’ retail checkout tech controversy thoughts

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日 
00:29:29 - 00:32:30
- Any good data projects to increase visibility to companies? - Ask me anything! (data science, LLMs, landing a job, and more)

- Any good data projects to increase visibility to companies?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日 
00:32:30 - 00:36:05
- Do you think we should all learn vector databases? - Ask me anything! (data science, LLMs, landing a job, and more)

- Do you think we should all learn vector databases?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日 
00:36:05 - 00:39:10
- Is webscraping illegal? what can I do and not do? - Ask me anything! (data science, LLMs, landing a job, and more)

- Is webscraping illegal? what can I do and not do?

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2024年04月07日 
00:39:10 - 00:43:14
- What are you working on at the moment? - Ask me anything! (data science, LLMs, landing a job, and more)

- What are you working on at the moment?

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2024年04月07日 
00:43:14 - 00:45:25
- How can I turn a financial database I’m building into an interesting portfolio project to showcase work? - Ask me anything! (data science, LLMs, landing a job, and more)

- How can I turn a financial database I’m building into an interesting portfolio project to showcase work?

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2024年04月07日 
00:45:25 - 00:49:23
- What advice do you have for data scientists who want to get into freelance/consulting? - Ask me anything! (data science, LLMs, landing a job, and more)

- What advice do you have for data scientists who want to get into freelance/consulting?

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2024年04月07日 
00:49:23 - 00:55:15
- What are important skills for DS beyond ML & AI? - Ask me anything! (data science, LLMs, landing a job, and more)

- What are important skills for DS beyond ML & AI?

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2024年04月07日 
00:55:15 - 00:59:42
- Do I need to become a full-stack programmer to have success in this field? - Ask me anything! (data science, LLMs, landing a job, and more)

- Do I need to become a full-stack programmer to have success in this field?

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2024年04月07日 
00:59:42 - 01:02:31
- If you weren’t allowed to do programming or create content, what would you do? - Ask me anything! (data science, LLMs, landing a job, and more)

- If you weren’t allowed to do programming or create content, what would you do?

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2024年04月07日 
01:02:31 - 01:03:39
- How did you achieve your advanced height? Asking for a friend. - Ask me anything! (data science, LLMs, landing a job, and more)

- How did you achieve your advanced height? Asking for a friend.

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2024年04月07日 
01:03:39 - 01:04:23
- Final thoughts. Thanks for coming!-------------------------Follow me on social media!Instagram | https://www.instagram.com/keithgalli/Twitter | https://twitter.com/keithgalliTikTok | https://tiktok.com/@keithgalli-------------------------Practice your Python Pandas data science skills with problems on StrataScratch!https://stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - https://www.youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - https://www.patreon.com/keithgalli*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. - Ask me anything! (data science, LLMs, landing a job, and more)

- Final thoughts. Thanks for coming!-------------------------Follow me on social media!Instagram | https://www.instagram.com/keithgalli/Twitter | https://twitter.com/keithgalliTikTok | https://tiktok.com/@keithgalli-------------------------Practice your Python Pandas data science skills with problems on StrataScratch!https://stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - https://www.youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - https://www.patreon.com/keithgalli*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.

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2024年04月07日 
01:04:23 - 01:05:26
- Video Overview & Reference Material - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Video Overview & Reference Material

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