In  instead of doing comprehension workaround, you could simply use results.index.(00:50:32 - 01:26:07) - Solving real world data science tasks with Python Pandas!

In instead of doing comprehension workaround, you could simply use results.index.(00:50:32 - 01:26:07)
Solving real world data science tasks with Python Pandas!

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

In this video we use Python Pandas & Python Matplotlib to analyze and answer business questions about 12 months worth of sales data. The data contains hundreds of thousands of...
Practice your Python Pandas data science skills with problems on StrataScratch!
https://stratascratch.com/?via=keith

In this video we use Python Pandas & Python Matplotlib to analyze and answer business questions about 12 months worth of sales data. The data contains hundreds of thousands of electronics store purchases broken down by month, product type, cost, purchase address, etc.

Setup!
Github source code & data: https://github.com/KeithGalli/Pandas-Data-Science-Tasks
Installing Jupyter Notebook: https://jupyter.readthedocs.io/en/latest/install.html
Installing Pandas library: https://pandas.pydata.org/pandas-docs/stable/install.html

Check out the first video I did on Pandas:
https://youtu.be/vmEHCJofslg

Check out the videos I did on Matplotlib:
https://youtu.be/DAQNHzOcO5A
https://youtu.be/0P7QnIQDBJY

Detailed video description! (timeline can be found in comments)

We start by cleaning our data. Tasks during this section include:
- Drop NaN values from DataFrame
- Removing rows based on a condition
- Change the type of columns (to_numeric, to_datetime, astype)

Once we have cleaned up our data a bit, we move the data exploration section. In this section we explore 5 high level business questions related to our data:
- What was the best month for sales? How much was earned that month?
- What city sold the most product?
- What time should we display advertisemens to maximize the likelihood of customer’s buying product?
- What products are most often sold together?
- What product sold the most? Why do you think it sold the most?

To answer these questions we walk through many different pandas & matplotlib methods. They include:
- Concatenating multiple csvs together to create a new DataFrame (pd.concat)
- Adding columns
- Parsing cells as strings to make new columns (.str)
- Using the .apply() method
- Using groupby to perform aggregate analysis
- Plotting bar charts and lines graphs to visualize our results
- Labeling our graphs

If you enjoy this video, make sure to leave it a like and subscribe to not miss any future similar tutorials :).

Check out the new "solving real world data science tasks" video I posted!
https://youtu.be/Ewgy-G9cmbg

---------------------------------------------

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Instagram | https://www.instagram.com/keithgalli/
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---------------------------------------------

Video Timeline!
0:00 - Intro
1:22 - Downloading the Data
2:57 - Getting started with the code (Jupyter Notebook)

Task #1: Merging 12 csvs into a single dataframe (3:35)
4:25 - Read single CSV file
5:44 - List all files in a directory
7:06 - Concatenating files
11:00 - Reading in Updated dataframe

Task #2: Add a Month column (12:48)
14:12 - Parse string in Pandas cell (.str)

Cleaning our data!
17:31 - Drop NaN values from df
21:25 - Remove rows based on condition

Task #3: Add a sales column (24:58)
25:58 - Another way to convert a column to numeric (ints & floats)

Question #1: What was the best month for sales? (29:20)
30:35 - Visualizing our results with bar chart in matplotlib

Question #2: What city sold the most product? (34:17)
35:32 - Add a city column
36:10 - Using the .apply() method (super useful!!)
40:35 - Why do we use the lambda x ?
40:57 - Dropping a column
46:45 - Answering the question (using groupby)
47:34 - Plotting our results

Question #3: What time should we display advertisements to maximize the likelihood of purchases? (52:13)
53:16 - Using to_datetime() method
56:01 - Creating hour & minute columns
58:17 - Matplotlib line graph to plot our results
1:00:15 - Interpreting our results

Question #4: What products are most often sold together? (1:02:17)
1:03:31 - Finding duplicate values in our DataFrame
1:05:43 - Use transform() method to join values from two rows into a single row
1:08:00 - Dropping rows with duplicate values
1:09:39 - Counting pairs of products (itertools, collections)

Question #5: What product sold the most? Why do you think it did? (1:14:04)
1:15:28 - Graphing data
1:18:41 - Overlaying a second Y-axis on existing chart
1:23:41 - Interpreting our results

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

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#KGMIT #Keith Galli #MIT #python #python 3 #python programming #data science #data analysis #pandas #python pandas #python matplotlib #matplotlib #mathplotlib #groupby #csv python #tutorial #real world #apply method in pandas #data exploration #data cleaning #anaconda #jupyter notebook #jupyter notebook tutorial #spreadsheets python #excel python #plotting #graphing #coding #programming #data scientist #machine learning #AI #artificial intelligence #csv #panda
- Intro - Solving real world data science tasks with Python Pandas!

- Intro

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:00:00 - 00:01:22
: 'Or' "    ,  as my understanding this is type of data issue (expect is int , but the current is str] , right ? And in the tutorial you solved it by "all_data = all_data[all_data['Order Date'].str[] != 'Or']" , can you please help explain this ? - Solving real world data science tasks with Python Pandas!

: 'Or' " , as my understanding this is type of data issue (expect is int , but the current is str] , right ? And in the tutorial you solved it by "all_data = all_data[all_data['Order Date'].str[] != 'Or']" , can you please help explain this ?

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:00:02 - 01:26:07
_df["Month"] = c_df["Order Date"].str[]<ipython-input-56-07e71ed795ed>:2: 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 - Solving real world data science tasks with Python Pandas!

_df["Month"] = c_df["Order Date"].str[]<ipython-input-56-07e71ed795ed>:2: 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

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:00:02 - 01:26:07
:  i was able to avoid the warning with df = df.groupby('Order ID').agg({'Product': ', '.join}).reset_index(). I look forward  to more of practical projects.Thanks - Solving real world data science tasks with Python Pandas!

: i was able to avoid the warning with df = df.groupby('Order ID').agg({'Product': ', '.join}).reset_index(). I look forward to more of practical projects.Thanks

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:01:07 - 01:26:07
The error can be avoided if you declare df to be a copy of all_data using .copy() - Solving real world data science tasks with Python Pandas!

The error can be avoided if you declare df to be a copy of all_data using .copy()

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:01:07 - 01:26:07
The groupby function sorts by months I think so that will be [], same as the new month variable - Solving real world data science tasks with Python Pandas!

The groupby function sorts by months I think so that will be [], same as the new month variable

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:01:13 - 01:26:07
- Downloading the Data - Solving real world data science tasks with Python Pandas!

- Downloading the Data

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:01:22 - 00:02:57
- Getting started with the code (Jupyter Notebook) - Solving real world data science tasks with Python Pandas!

- Getting started with the code (Jupyter Notebook)

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:02:57 - 00:03:35
csvs into a single dataframe () - Solving real world data science tasks with Python Pandas!

csvs into a single dataframe ()

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:03:35 - 00:04:25
- Read single CSV file - Solving real world data science tasks with Python Pandas!

- Read single CSV file

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:04:25 - 00:05:44
At  when using os.listdir("'./"), this returns a list alread. So using [file for file in os.listdir(...)] is redundant. - Solving real world data science tasks with Python Pandas!

At when using os.listdir("'./"), this returns a list alread. So using [file for file in os.listdir(...)] is redundant.

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:04:25 - 00:40:50
- List all files in a directory - Solving real world data science tasks with Python Pandas!

- List all files in a directory

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:05:44 - 00:07:06
How would I do the code at around  in the video without list using list comprehension? - Solving real world data science tasks with Python Pandas!

How would I do the code at around in the video without list using list comprehension?

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:06:50 - 01:26:07
- Concatenating files - Solving real world data science tasks with Python Pandas!

- Concatenating files

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:07:06 - 00:11:00
'' instead of the rows containing 'or' error @ 21:46.  Also, when i merged the data my merged data has blank rows in between . Anybody else having the same issue? - Solving real world data science tasks with Python Pandas!

'' instead of the rows containing 'or' error @ 21:46. Also, when i merged the data my merged data has blank rows in between . Anybody else having the same issue?

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:08:46 - 01:26:07
If you are working with your own data and watching this video and if you do not have a date, consider adding the below. I needed to break down my data by week, so I did: - Solving real world data science tasks with Python Pandas!

If you are working with your own data and watching this video and if you do not have a date, consider adding the below. I needed to break down my data by week, so I did:

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:08:46 - 01:26:07
my approach was quite  not elegant: cities  = sorted(str(all_data["City"].unique()).split(" ' "))[] hahaha - Solving real world data science tasks with Python Pandas!

my approach was quite not elegant: cities = sorted(str(all_data["City"].unique()).split(" ' "))[] hahaha

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:09:19 - 01:26:07
nd line appears to me on all columns NaN, instead of "Bose SoundSport...". My doubt is about minute  of the video. Does someone have the same problem? - Solving real world data science tasks with Python Pandas!

nd line appears to me on all columns NaN, instead of "Bose SoundSport...". My doubt is about minute of the video. Does someone have the same problem?

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:09:35 - 01:26:07
- Reading in Updated dataframe - Solving real world data science tasks with Python Pandas!

- Reading in Updated dataframe

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:11:00 - 00:12:48
: Add a Month column () - Solving real world data science tasks with Python Pandas!

: Add a Month column ()

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:12:48 - 00:14:12
- Parse string in Pandas cell (.str) - Solving real world data science tasks with Python Pandas!

- Parse string in Pandas cell (.str)

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:14:12 - 00:17:31
, u can solve that problem with pd.to_datime(column), actually without format because it can be produced to the date time, and then u can actually find all month that u need, good luck! - Solving real world data science tasks with Python Pandas!

, u can solve that problem with pd.to_datime(column), actually without format because it can be produced to the date time, and then u can actually find all month that u need, good luck!

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:14:18 - 01:26:07
At  when I am doing : all_data.Month = all_data['Order Date'].str[:2]. I get a warning and month column does not get added up to the  dataframe. Warning I get is as follows : UserWarning: Pandas doesn't allow columns to be created via a new attribute name.Can someone please suggest how do I add my column. - Solving real world data science tasks with Python Pandas!

At when I am doing : all_data.Month = all_data['Order Date'].str[:2]. I get a warning and month column does not get added up to the dataframe. Warning I get is as follows : UserWarning: Pandas doesn't allow columns to be created via a new attribute name.Can someone please suggest how do I add my column.

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:15:19 - 01:26:07
- Drop NaN values from df - Solving real world data science tasks with Python Pandas!

- Drop NaN values from df

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:17:31 - 00:21:25
:  how the products command is working. [product for product, df in product_group] can you please explain the same. Or anyone can let me understand please. - Solving real world data science tasks with Python Pandas!

: how the products command is working. [product for product, df in product_group] can you please explain the same. Or anyone can let me understand please.

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:17:43 - 01:26:07
- Remove rows based on condition - Solving real world data science tasks with Python Pandas!

- Remove rows based on condition

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:21:25 - 00:24:58
I think is more reliable to parse column of dates as datetime type to avoid all these problems - Solving real world data science tasks with Python Pandas!

I think is more reliable to parse column of dates as datetime type to avoid all these problems

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:22:00 - 01:26:07
that duplication was because of the header rows in each of the files. I've dealt with this a lot. You would have had had to have excluded those header rows on each file before you concatenated all of them together to resolve this. - Solving real world data science tasks with Python Pandas!

that duplication was because of the header rows in each of the files. I've dealt with this a lot. You would have had had to have excluded those header rows on each file before you concatenated all of them together to resolve this.

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:23:39 - 01:26:07
AtI am getting a following error: - Solving real world data science tasks with Python Pandas!

AtI am getting a following error:

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:24:28 - 01:26:07
: Add a sales column () - Solving real world data science tasks with Python Pandas!

: Add a sales column ()

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:24:58 - 00:25:58
- Another way to convert a column to numeric (ints & floats) - Solving real world data science tasks with Python Pandas!

- Another way to convert a column to numeric (ints & floats)

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:25:58 - 00:29:20
-Even after applying pd.to_numeric type of Price Each remains object.How do I fix this issue? - Solving real world data science tasks with Python Pandas!

-Even after applying pd.to_numeric type of Price Each remains object.How do I fix this issue?

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:28:33 - 01:26:07
: What was the best month for sales? () - Solving real world data science tasks with Python Pandas!

: What was the best month for sales? ()

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:29:20 - 00:30:35
At , sales also have values from January 2020  dates too. So, the values for Jan should be lesser than what's shown. - Solving real world data science tasks with Python Pandas!

At , sales also have values from January 2020 dates too. So, the values for Jan should be lesser than what's shown.

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:30:20 - 01:26:07
- Visualizing our results with bar chart in matplotlib - Solving real world data science tasks with Python Pandas!

- Visualizing our results with bar chart in matplotlib

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:30:35 - 00:34:17
For this task: ​ - Visualizing our results with bar chart in matplotlibInstead of using the datetime library I used the calendar library. This code helps me change the month number to month name: - Solving real world data science tasks with Python Pandas!

For this task: ​ - Visualizing our results with bar chart in matplotlibInstead of using the datetime library I used the calendar library. This code helps me change the month number to month name:

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:30:35 - 01:26:07
-- Sales amount for Dec month should be 1.289987e+11, when [quantity order * price each]If multiply 28114 * 4.588415e+06 = 1.289987e+11Why Sales column is not generating the correct result or I am mistaken somewhere?Can anyone suggest here? - Solving real world data science tasks with Python Pandas!

-- Sales amount for Dec month should be 1.289987e+11, when [quantity order * price each]If multiply 28114 * 4.588415e+06 = 1.289987e+11Why Sales column is not generating the correct result or I am mistaken somewhere?Can anyone suggest here?

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:31:00 - 01:26:07
i was wondering during  the result of my Y axis is in E notation. how can i convert it into real numbers? would really appreciate if anybody would help to solve this problem! - Solving real world data science tasks with Python Pandas!

i was wondering during the result of my Y axis is in E notation. how can i convert it into real numbers? would really appreciate if anybody would help to solve this problem!

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:32:15 - 01:26:07
you have created months list to pass it to plt.bar() out of thin air, in current scenario as our data is coming in sorted way by month so no issue is coming else it would have plotted Sales against wrong month. Instead I tried this, please let me know if I'm wrong about it?all_data.groupby('Month')['Daily Sale'].sum().plot(kind='bar')plt.show() - Solving real world data science tasks with Python Pandas!

you have created months list to pass it to plt.bar() out of thin air, in current scenario as our data is coming in sorted way by month so no issue is coming else it would have plotted Sales against wrong month. Instead I tried this, please let me know if I'm wrong about it?all_data.groupby('Month')['Daily Sale'].sum().plot(kind='bar')plt.show()

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:32:15 - 01:26:07
: What city sold the most product? () - Solving real world data science tasks with Python Pandas!

: What city sold the most product? ()

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:34:17 - 00:35:32
Pro tip: go to command mode (press Esc) and press 'b' to make cells below current cell or 'a' to make cells above - Solving real world data science tasks with Python Pandas!

Pro tip: go to command mode (press Esc) and press 'b' to make cells below current cell or 'a' to make cells above

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:34:34 - 01:26:07
- Add a city column - Solving real world data science tasks with Python Pandas!

- Add a city column

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:35:32 - 00:36:10
For  you can also use the following:df['Purchase Address'].str.split( ',' , n=2, expand=True) - Solving real world data science tasks with Python Pandas!

For you can also use the following:df['Purchase Address'].str.split( ',' , n=2, expand=True)

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:35:32 - 01:26:07
Instead of using lambda function you can also you can also use split function directly on the series like this: - Solving real world data science tasks with Python Pandas!

Instead of using lambda function you can also you can also use split function directly on the series like this:

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:35:32 - 01:26:07
I can already anticipate issues with cities in different states having the same name and being grouped together.  Really need to combine city+state to compare different cities. - Solving real world data science tasks with Python Pandas!

I can already anticipate issues with cities in different states having the same name and being grouped together. Really need to combine city+state to compare different cities.

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:35:45 - 00:42:00
- Using the .apply() method (super useful!!) - Solving real world data science tasks with Python Pandas!

- Using the .apply() method (super useful!!)

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:36:10 - 00:40:35
, wouldn't it be easier if the "City' column is obtained through the zipcodes of the 'Purchase Address'? - Solving real world data science tasks with Python Pandas!

, wouldn't it be easier if the "City' column is obtained through the zipcodes of the 'Purchase Address'?

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:37:58 - 01:26:07
Hey Keith, thank you for this amazing video. I just wanted to raise a query on an error I'm encountering when we are trying to split the 'Purchase Address' column to identify the City at !I'm following the tutorial step by step and the piece of apply() code that works for you in throwing an error for me -Code: all_data['City'] = all_data['Purchase Address'].apply(lambda x: x.split(',')[1])IndexError: list index out of range - Solving real world data science tasks with Python Pandas!

Hey Keith, thank you for this amazing video. I just wanted to raise a query on an error I'm encountering when we are trying to split the 'Purchase Address' column to identify the City at !I'm following the tutorial step by step and the piece of apply() code that works for you in throwing an error for me -Code: all_data['City'] = all_data['Purchase Address'].apply(lambda x: x.split(',')[1])IndexError: list index out of range

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:38:39 - 01:26:07
For anyone wondering, at , you can also just do .apply(get_city) without adding the () so that the function doesn't get called immediately, and the pandas apply method will know to pass in each value of the series to the function, so you can also avoid using the lambda to call the get_city function! - Solving real world data science tasks with Python Pandas!

For anyone wondering, at , you can also just do .apply(get_city) without adding the () so that the function doesn't get called immediately, and the pandas apply method will know to pass in each value of the series to the function, so you can also avoid using the lambda to call the get_city function!

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:39:39 - 01:26:07
I believe you can also just refer to the function by name instead of using the lambda. - Solving real world data science tasks with Python Pandas!

I believe you can also just refer to the function by name instead of using the lambda.

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:39:44 - 01:26:07
Great video Keith, thanks! At  you use .apply(lambda x:get_city(x)) and I wonder, since you have already defined the get_city function separately, couldn't we just use .apply(get_city)? Cheers - Solving real world data science tasks with Python Pandas!

Great video Keith, thanks! At you use .apply(lambda x:get_city(x)) and I wonder, since you have already defined the get_city function separately, couldn't we just use .apply(get_city)? Cheers

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:39:45 - 01:26:07
- Why do we use the lambda x ? - Solving real world data science tasks with Python Pandas!

- Why do we use the lambda x ?

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:40:35 - 00:40:57
At , you don't need to use the lambda function when you use the apply method.df['City'] = df['Purchase Address'].apply(get_city) - Solving real world data science tasks with Python Pandas!

At , you don't need to use the lambda function when you use the apply method.df['City'] = df['Purchase Address'].apply(get_city)

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:40:37 - 01:26:07
Just a tip, you actually don't need to use lambda to grab a cells contents like you said at around .  Lambda is just a tool to allow you to use functions like apply(and others) on one line, without having to define a custom function beforehand to pass in.  If you have already defined a function like get_city(address), then you can just pass that function itself into apply.  So the full line would look like this:    all_data['Column'] = all_data['Purchase Address'].apply(get_city) - Solving real world data science tasks with Python Pandas!

Just a tip, you actually don't need to use lambda to grab a cells contents like you said at around . Lambda is just a tool to allow you to use functions like apply(and others) on one line, without having to define a custom function beforehand to pass in. If you have already defined a function like get_city(address), then you can just pass that function itself into apply. So the full line would look like this: all_data['Column'] = all_data['Purchase Address'].apply(get_city)

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:40:40 - 01:26:07
At  you don't need to use the lambda function, even if you want to access a cell content. If you simply pass the reference to a function, by default the *args will be passed. Example: - Solving real world data science tasks with Python Pandas!

At you don't need to use the lambda function, even if you want to access a cell content. If you simply pass the reference to a function, by default the *args will be passed. Example:

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:40:50 - 01:26:07
- Dropping a column - Solving real world data science tasks with Python Pandas!

- Dropping a column

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:40:57 - 00:46:45
edit:    Ahh you noticed it too :) - Solving real world data science tasks with Python Pandas!

edit: Ahh you noticed it too :)

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:42:00 - 01:26:07
Great Video. At  you could've used this address.split(',')[2][1:3] instead of using split again. - Solving real world data science tasks with Python Pandas!

Great Video. At you could've used this address.split(',')[2][1:3] instead of using split again.

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:44:10 - 01:26:07
I keep getting an error "TypeError: 'builtin_function_or_method' object is not subscriptable" when trying to use the .apply() method at . - Solving real world data science tasks with Python Pandas!

I keep getting an error "TypeError: 'builtin_function_or_method' object is not subscriptable" when trying to use the .apply() method at .

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:45:20 - 01:26:07
- Answering the question (using groupby) - Solving real world data science tasks with Python Pandas!

- Answering the question (using groupby)

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:46:45 - 00:47:34
@ can we use data from results to plot a bar graph [city, Sales]? - Solving real world data science tasks with Python Pandas!

@ can we use data from results to plot a bar graph [city, Sales]?

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:47:23 - 01:26:07
- Plotting our results - Solving real world data science tasks with Python Pandas!

- Plotting our results

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:47:34 - 00:52:13
hey Keith, how do you remove the scientific notation and display your yticks in "millions" at ? - Solving real world data science tasks with Python Pandas!

hey Keith, how do you remove the scientific notation and display your yticks in "millions" at ?

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:47:34 - 01:26:07
At  Why do we need to find the unique value when the group by (''city") will provide the value unique itself. I simply didsales_by_city = merged_df.groupby("city").sum().reset_index() - Solving real world data science tasks with Python Pandas!

At Why do we need to find the unique value when the group by (''city") will provide the value unique itself. I simply didsales_by_city = merged_df.groupby("city").sum().reset_index()

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:48:13 - 01:26:07
Hey Galli, I am following your tutorials...  Why don't you useresults.index() orlist(results.index()) to get name of cities.... - Solving real world data science tasks with Python Pandas!

Hey Galli, I am following your tutorials... Why don't you useresults.index() orlist(results.index()) to get name of cities....

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:48:22 - 01:26:07
At  you ran into the issue where you had the names in the wrong order: - Solving real world data science tasks with Python Pandas!

At you ran into the issue where you had the names in the wrong order:

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:49:23 - 01:26:07
I have a question! At , when you go through using the list comprehension to match the cities with sales in the plot, couldn't you have just used the column from that very "results" df you created which has the unique cities and their respective sales sums? - Solving real world data science tasks with Python Pandas!

I have a question! At , when you go through using the list comprehension to match the cities with sales in the plot, couldn't you have just used the column from that very "results" df you created which has the unique cities and their respective sales sums?

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:49:34 - 01:26:07
For the issue at  I just use results.index instead of creating a unique list of cities from the all_data.plt.bar(results.index, results['Sales']) - Solving real world data science tasks with Python Pandas!

For the issue at I just use results.index instead of creating a unique list of cities from the all_data.plt.bar(results.index, results['Sales'])

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:49:57 - 01:26:07
, use result.index as x values and x ticks. - Solving real world data science tasks with Python Pandas!

, use result.index as x values and x ticks.

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:50:00 - 01:26:07
- 51:00We can also use the index of the previous dataframe which contains the city names :)cities = <name_of_the_previous_table>.index - Solving real world data science tasks with Python Pandas!

- 51:00We can also use the index of the previous dataframe which contains the city names :)cities = <name_of_the_previous_table>.index

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:50:00 - 01:26:07
At  for anyone who wants to use .unique(), when you calculate the sales for each city make sure to throw in a .reset_index() in there, it will reset the indexes and your bar is going to be alright. - Solving real world data science tasks with Python Pandas!

At for anyone who wants to use .unique(), when you calculate the sales for each city make sure to throw in a .reset_index() in there, it will reset the indexes and your bar is going to be alright.

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:50:10 - 01:26:07
Really like this tutorial. Very helpful. I tried to work ahead as you instructed, and I think I found an alternative method for . - Solving real world data science tasks with Python Pandas!

Really like this tutorial. Very helpful. I tried to work ahead as you instructed, and I think I found an alternative method for .

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:50:15 - 01:26:07
In  for cities: can always use the index values from 'results' DF:cities = results.index.valuesinstead of a for loop - Solving real world data science tasks with Python Pandas!

In for cities: can always use the index values from 'results' DF:cities = results.index.valuesinstead of a for loop

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:50:26 - 01:26:07
Hey Keith, great work! Could you  please give a   "one line-ish" explanation for  the trick in ?  Keep on rockin'! cheers - Solving real world data science tasks with Python Pandas!

Hey Keith, great work! Could you please give a "one line-ish" explanation for the trick in ? Keep on rockin'! cheers

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:50:27 - 01:26:07
In  instead of doing comprehension workaround, you could simply use results.index. - Solving real world data science tasks with Python Pandas!

In instead of doing comprehension workaround, you could simply use results.index.

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:50:32 - 01:26:07
- Instead of looping, we can just pass results.index which solved my problem - Solving real world data science tasks with Python Pandas!

- Instead of looping, we can just pass results.index which solved my problem

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:50:35 - 01:26:07
What i did at  is : - Solving real world data science tasks with Python Pandas!

What i did at is :

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:50:37 - 01:26:07
On that part where you groupby cities then plot the total sales, then go through a complicated plotting procedure (  ) .That's not necessary. - Solving real world data science tasks with Python Pandas!

On that part where you groupby cities then plot the total sales, then go through a complicated plotting procedure ( ) .That's not necessary.

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:50:45 - 01:26:07
at  you can just do city=results.index to print the city in order with sales - Solving real world data science tasks with Python Pandas!

at you can just do city=results.index to print the city in order with sales

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:50:46 - 01:26:07
cities = result.Sales.keys()  works as expected.   great tutorial, tks! - Solving real world data science tasks with Python Pandas!

cities = result.Sales.keys() works as expected. great tutorial, tks!

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:50:47 - 01:26:07
I tried using cities=result.index instead of the list comprehension. Thanks for the video. - Solving real world data science tasks with Python Pandas!

I tried using cities=result.index instead of the list comprehension. Thanks for the video.

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:51:01 - 01:26:07
At  we can get the list of cities by just passing list(results.index) - Solving real world data science tasks with Python Pandas!

At we can get the list of cities by just passing list(results.index)

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:51:03 - 01:26:07
Instead of using a confusing for loop and comprehension list for sorting values of cities for X-axis. You can just sort the values of the city column beforehand like this: - Solving real world data science tasks with Python Pandas!

Instead of using a confusing for loop and comprehension list for sorting values of cities for X-axis. You can just sort the values of the city column beforehand like this:

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:51:04 - 01:26:07
At  is it possible to sort the bar chart, so that the cities are ordered from left to right in ascending order with respect to their sales? When I try to use sort_values("Total", ascending = False) the order gets mixed. - Solving real world data science tasks with Python Pandas!

At is it possible to sort the bar chart, so that the cities are ordered from left to right in ascending order with respect to their sales? When I try to use sort_values("Total", ascending = False) the order gets mixed.

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:51:11 - 01:26:07
in  instead of using that list comprehension , we can simply change the "city " index to columns in result data frame throgh this -> results.reset_index(level=["city"]) and simply  use  the city column as  x axis and sales column as y axis in barplot - Solving real world data science tasks with Python Pandas!

in instead of using that list comprehension , we can simply change the "city " index to columns in result data frame throgh this -> results.reset_index(level=["city"]) and simply use the city column as x axis and sales column as y axis in barplot

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:51:20 - 01:26:07
: What time should we display advertisements to maximize the likelihood of purchases? () - Solving real world data science tasks with Python Pandas!

: What time should we display advertisements to maximize the likelihood of purchases? ()

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:52:13 - 00:53:16
And here isdf['hour'] = df.loc[:, 'Order Date'].map(lambda x: x.hour )plt.figure(figsize=(18,10))sns.barplot(df.groupby('hour').sum().sort_values(by='sales', ascending=False).index,df.groupby('hour').sum().sort_values(by='sales', ascending=False)['sales']) - Solving real world data science tasks with Python Pandas!

And here isdf['hour'] = df.loc[:, 'Order Date'].map(lambda x: x.hour )plt.figure(figsize=(18,10))sns.barplot(df.groupby('hour').sum().sort_values(by='sales', ascending=False).index,df.groupby('hour').sum().sort_values(by='sales', ascending=False)['sales'])

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:52:13 - 01:26:07
- Using to_datetime() method - Solving real world data science tasks with Python Pandas!

- Using to_datetime() method

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:53:16 - 00:56:01
When parsing a column into datetime, specifying the format manually will decrease the execution time significantly:all_data['Order Date'] = pd.to_datetime(all_data['Order Date'], format='%m/%d/%y %H:%M') - Solving real world data science tasks with Python Pandas!

When parsing a column into datetime, specifying the format manually will decrease the execution time significantly:all_data['Order Date'] = pd.to_datetime(all_data['Order Date'], format='%m/%d/%y %H:%M')

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:55:00 - 01:26:07
- Creating hour & minute columns - Solving real world data science tasks with Python Pandas!

- Creating hour & minute columns

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:56:01 - 00:58:17
- Matplotlib line graph to plot our results - Solving real world data science tasks with Python Pandas!

- Matplotlib line graph to plot our results

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:58:17 - 01:00:15
Extremely helpful video Keith! So happy that I found this channel. Also, voice crack at  :) - Solving real world data science tasks with Python Pandas!

Extremely helpful video Keith! So happy that I found this channel. Also, voice crack at :)

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:58:22 - 01:26:07
I heard that - Solving real world data science tasks with Python Pandas!

I heard that

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:58:22 - 01:26:07
Hi Keith! Actually I have problems executing  where plt.plot(hours, all_data.groupby([‘Hour’]).count()). It turns out to be a InvalidIndexError. I checked the syntax and accuracy but still it does not work on my notebook. Is there anyways to tackle this problem? - Solving real world data science tasks with Python Pandas!

Hi Keith! Actually I have problems executing where plt.plot(hours, all_data.groupby([‘Hour’]).count()). It turns out to be a InvalidIndexError. I checked the syntax and accuracy but still it does not work on my notebook. Is there anyways to tackle this problem?

Solving real world data science tasks with Python Pandas!
2020年01月14日 
00:59:00 - 01:26:07
The analysis at  (advertising time) is incorrect, you need to group by order ids as well, otherwise orders with many different products in them have more "weight" than ones with single products. Also, perhaps not use count and instead use order value so they can be weighted by amount, since more expensive orders are more valuable for the business. - Solving real world data science tasks with Python Pandas!

The analysis at (advertising time) is incorrect, you need to group by order ids as well, otherwise orders with many different products in them have more "weight" than ones with single products. Also, perhaps not use count and instead use order value so they can be weighted by amount, since more expensive orders are more valuable for the business.

Solving real world data science tasks with Python Pandas!
2020年01月14日 
01:00:00 - 01:26:07
: What products are most often sold together? () - Solving real world data science tasks with Python Pandas!

: What products are most often sold together? ()

Solving real world data science tasks with Python Pandas!
2020年01月14日 
01:02:17 - 01:26:07
(Which set of items are mostly bought together) at  I thought of an alternate solutionbought_together = all_data.groupby(by='Order ID').count()   # Because this will count the number of times each order has been boughtmax_order = bought_together['Product'].idxmax()  # Gives the order ID of the product which was bought most frequently (The count after groupby is the maximum)most_bought_together = all_data[all_data['Order ID'] == max_order]['Product'] # Gives the product name where Order ID is same as the max_order - Solving real world data science tasks with Python Pandas!

(Which set of items are mostly bought together) at I thought of an alternate solutionbought_together = all_data.groupby(by='Order ID').count() # Because this will count the number of times each order has been boughtmax_order = bought_together['Product'].idxmax() # Gives the order ID of the product which was bought most frequently (The count after groupby is the maximum)most_bought_together = all_data[all_data['Order ID'] == max_order]['Product'] # Gives the product name where Order ID is same as the max_order

Solving real world data science tasks with Python Pandas!
2020年01月14日 
01:02:21 - 01:26:07
- Finding duplicate values in our DataFrame - Solving real world data science tasks with Python Pandas!

- Finding duplicate values in our DataFrame

Solving real world data science tasks with Python Pandas!
2020年01月14日 
01:03:31 - 01:05:43
- Use transform() method to join values from two rows into a single row - Solving real world data science tasks with Python Pandas!

- Use transform() method to join values from two rows into a single row

Solving real world data science tasks with Python Pandas!
2020年01月14日 
01:05:43 - 01:08:00
...I used the .apply function here .. worked pretty much the same ... I think it worked better since it removed all duplicates from the returned series unlike transform where the returned series was of the same length as the passed dataframe - Solving real world data science tasks with Python Pandas!

...I used the .apply function here .. worked pretty much the same ... I think it worked better since it removed all duplicates from the returned series unlike transform where the returned series was of the same length as the passed dataframe

Solving real world data science tasks with Python Pandas!
2020年01月14日 
01:07:24 - 01:26:07
- Dropping rows with duplicate values - Solving real world data science tasks with Python Pandas!

- Dropping rows with duplicate values

Solving real world data science tasks with Python Pandas!
2020年01月14日 
01:08:00 - 01:09:39
- Counting pairs of products (itertools, collections) - Solving real world data science tasks with Python Pandas!

- Counting pairs of products (itertools, collections)

Solving real world data science tasks with Python Pandas!
2020年01月14日 
01:09:39 - 01:14:04
For the section:  - Counting pairs of products (itertools, collections) - Solving real world data science tasks with Python Pandas!

For the section: - Counting pairs of products (itertools, collections)

Solving real world data science tasks with Python Pandas!
2020年01月14日 
01:09:39 - 01:26:07
Nice and useful video Keith. At around  when you want to count the pairs of products wouldn't be simpler to just do df['Grouped'].value_counts() ? This gives me same answers (order) as you but the total count is different for some reason: iPhone,Lightning Charging Cable  882 ;  Google Phone,USB-C Charging Cable 856 ;  iPhone,Wired Headphones 361.  Thanks again ! - Solving real world data science tasks with Python Pandas!

Nice and useful video Keith. At around when you want to count the pairs of products wouldn't be simpler to just do df['Grouped'].value_counts() ? This gives me same answers (order) as you but the total count is different for some reason: iPhone,Lightning Charging Cable 882 ; Google Phone,USB-C Charging Cable 856 ; iPhone,Wired Headphones 361. Thanks again !

Solving real world data science tasks with Python Pandas!
2020年01月14日 
01:09:39 - 01:26:07
Stackoverflow link used at  : https://stackoverflow.com/questions/52195887/counting-unique-pairs-of-numbers-into-a-python-dictionary - Solving real world data science tasks with Python Pandas!

Stackoverflow link used at : https://stackoverflow.com/questions/52195887/counting-unique-pairs-of-numbers-into-a-python-dictionary

Solving real world data science tasks with Python Pandas!
2020年01月14日 
01:10:23 - 01:26:07
: What product sold the most? Why do you think it did? () - Solving real world data science tasks with Python Pandas!

: What product sold the most? Why do you think it did? ()

Solving real world data science tasks with Python Pandas!
2020年01月14日 
01:14:04 - 01:15:28
- Graphing data - Solving real world data science tasks with Python Pandas!

- Graphing data

Solving real world data science tasks with Python Pandas!
2020年01月14日 
01:15:28 - 01:18:41
- Overlaying a second Y-axis on existing chart - Solving real world data science tasks with Python Pandas!

- Overlaying a second Y-axis on existing chart

Solving real world data science tasks with Python Pandas!
2020年01月14日 
01:18:41 - 01:23:41
- Interpreting our results - Solving real world data science tasks with Python Pandas!

- Interpreting our results

Solving real world data science tasks with Python Pandas!
2020年01月14日 
01:23:41 - 01:26:07

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:84件

- 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
- Introduction - How to make your GitHub more impressive to Employers! (5 simple tips)

- Introduction

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

- Tip 1: Show Private Repository Activity

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

- Tip 2: Highlight best work using pins

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

- Tip 3: Create a Profile README

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

- Tip 4: Fill in all Profile Details

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

- Tip 5: Fill in READMEs on highlighted repos

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

- Overview & Getting Started

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

- 1. Predicting Heart Attack Risk (Easy Problem)

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

- 2. Data Anonymization (Medium Problem)

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

- 3. Dessert Duel (Hard Problem)

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

- Overview

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

- Getting started on the Book Review API

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

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

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

- Implement code to add reviews and view all reviews

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

- Adding a POST request to our API

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

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

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

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

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

- Video overview

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

- What we're building

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

- How to get setup with Github template code

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

- Taking a look at the Flask, Flasgger Python3 code

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

- Testing some API requests (GET) locally

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

- Building another GET request endpoint (with multiple parameters)

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

- Using ChatGPT to help us build another endpoint

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

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

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

- Video overview & topics covered

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

- Basic regex syntax (building up an intuition)

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

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

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

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

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

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

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

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

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

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

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

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

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

- OR operator overview

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

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

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

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

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

- Escaping Characters

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

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

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

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

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

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

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

- Groups overview

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

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

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

- Lookahead & Lookbehind Assertions

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

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

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

- Practice Exercise #4: Password matching with rules

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

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

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

- Video overview & format

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

- Introductory Behavioral questions | Data science interview

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

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

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

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

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

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

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

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

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

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

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

- Any questions for me? | Data science interview

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

- Post-interview breakdown & analysis

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

- Video Introduction

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

- How podcasts work (RSS feeds overview)

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

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

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

- Accessing this project on GitHub

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

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

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

- Modify our script to be able to download many podcasts

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

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

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

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

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

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

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

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

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

- Smash like & subscribe pretty please :)

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

- Intro & Video Overview

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

- Check out this Video’s Sponsor, Brilliant!

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

- Video Conclusion & Recap

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