- Solving real world data science tasks with Python Pandas!

Solving real world data science tasks with Python Pandas!

Practice your Python Pandas data science skills with problems on StrataScratch!
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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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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

---------------------
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- 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: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?

Ask me anything! (data science, LLMs, landing a job, and more)
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?

Ask me anything! (data science, LLMs, landing a job, and more)
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?

Ask me anything! (data science, LLMs, landing a job, and more)
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?

Ask me anything! (data science, LLMs, landing a job, and more)
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?

Ask me anything! (data science, LLMs, landing a job, and more)
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?

Ask me anything! (data science, LLMs, landing a job, and more)
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?

Ask me anything! (data science, LLMs, landing a job, and more)
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.

Ask me anything! (data science, LLMs, landing a job, and more)
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.

Ask me anything! (data science, LLMs, landing a job, and more)
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