- Solving real world data science tasks with Python Pandas!

Solving real world data science tasks with Python Pandas!

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In this video we use Python Pandas & Python Matplotlib to analyze and answer business questions about 12 mon...
Follow me https://instagram.com/keithgalli for more tech content!

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

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

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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

Keith Galli

🎉 170,000 人達成! 🎉

【予測】20万人まであと260日(2023年6月24日)

チャンネル登録 RSS
Recent MIT Graduate. I make educational videos on Computer Science, Programming, Board Games, and more!

I found online videos to be extremely helpful as I progressed through the educational system growing up so I decided to make a channel of my own. Let me know what I should make next!

-Keith :)

Timetable

動画タイムテーブル

動画数:19件

- 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
- Introduction & video overview - 5 Jupyter Notebook Tips & Tricks to Improve your Data Science Workflow!

- Introduction & video overview

5 Jupyter Notebook Tips & Tricks to Improve your Data Science Workflow!
2022年06月01日
00:00:00 - 00:00:25
- Shoutout to this video’s sponsor, Brilliant.org! - 5 Jupyter Notebook Tips & Tricks to Improve your Data Science Workflow!

- Shoutout to this video’s sponsor, Brilliant.org!

5 Jupyter Notebook Tips & Tricks to Improve your Data Science Workflow!
2022年06月01日
00:00:25 - 00:02:11
- 1. Running terminal commands such as “pip” directly in a notebook - 5 Jupyter Notebook Tips & Tricks to Improve your Data Science Workflow!

- 1. Running terminal commands such as “pip” directly in a notebook

5 Jupyter Notebook Tips & Tricks to Improve your Data Science Workflow!
2022年06月01日
00:02:11 - 00:05:17
- Magic lines in Jupyter - 5 Jupyter Notebook Tips & Tricks to Improve your Data Science Workflow!

- Magic lines in Jupyter

5 Jupyter Notebook Tips & Tricks to Improve your Data Science Workflow!
2022年06月01日
00:05:17 - 00:06:11
- 2. Shortcuts that you need to know for Jupyter! - 5 Jupyter Notebook Tips & Tricks to Improve your Data Science Workflow!

- 2. Shortcuts that you need to know for Jupyter!

5 Jupyter Notebook Tips & Tricks to Improve your Data Science Workflow!
2022年06月01日
00:06:11 - 00:09:25
Pretty unnecessary to write all these out. Just go to the “Help” menu, and select “Keyboard Shortcuts”. They’re all there—and more. - 5 Jupyter Notebook Tips & Tricks to Improve your Data Science Workflow!

Pretty unnecessary to write all these out. Just go to the “Help” menu, and select “Keyboard Shortcuts”. They’re all there—and more.

5 Jupyter Notebook Tips & Tricks to Improve your Data Science Workflow!
2022年06月01日
00:07:26 - 00:23:17
- 3. Changing default Pandas options to improve results display - 5 Jupyter Notebook Tips & Tricks to Improve your Data Science Workflow!

- 3. Changing default Pandas options to improve results display

5 Jupyter Notebook Tips & Tricks to Improve your Data Science Workflow!
2022年06月01日
00:09:25 - 00:14:00
- 4. Setting up notifications for when a cell finishes execution - 5 Jupyter Notebook Tips & Tricks to Improve your Data Science Workflow!

- 4. Setting up notifications for when a cell finishes execution

5 Jupyter Notebook Tips & Tricks to Improve your Data Science Workflow!
2022年06月01日
00:14:00 - 00:18:08
- 5. Creating slideshows from a IPython notebook! - 5 Jupyter Notebook Tips & Tricks to Improve your Data Science Workflow!

- 5. Creating slideshows from a IPython notebook!

5 Jupyter Notebook Tips & Tricks to Improve your Data Science Workflow!
2022年06月01日
00:18:08 - 00:23:00
- Conclusion (and link to bonus tip!) - 5 Jupyter Notebook Tips & Tricks to Improve your Data Science Workflow!

- Conclusion (and link to bonus tip!)

5 Jupyter Notebook Tips & Tricks to Improve your Data Science Workflow!
2022年06月01日
00:23:00 - 00:23:17
- Intro - Solving real world data science problems with Python! (computer vision edition)

- Intro

Solving real world data science problems with Python! (computer vision edition)
2022年05月11日
00:00:00 - 00:00:40
- Video overview (what we’ll be working on) - Solving real world data science problems with Python! (computer vision edition)

- Video overview (what we’ll be working on)

Solving real world data science problems with Python! (computer vision edition)
2022年05月11日
00:00:40 - 00:01:53
- Code setup (GitHub repo & HP challenge link) - Solving real world data science problems with Python! (computer vision edition)

- Code setup (GitHub repo & HP challenge link)

Solving real world data science problems with Python! (computer vision edition)
2022年05月11日
00:01:53 - 00:05:11
- Exploring the dataset that we’ll be using - Solving real world data science problems with Python! (computer vision edition)

- Exploring the dataset that we’ll be using

Solving real world data science problems with Python! (computer vision edition)
2022年05月11日
00:05:11 - 00:06:20
- Reviewing template code (starter-code.ipynb) - Solving real world data science problems with Python! (computer vision edition)

- Reviewing template code (starter-code.ipynb)

Solving real world data science problems with Python! (computer vision edition)
2022年05月11日
00:06:20 - 00:08:53
- Installing necessary Python libraries (opencv-python, tensorflow) - Solving real world data science problems with Python! (computer vision edition)

- Installing necessary Python libraries (opencv-python, tensorflow)

Solving real world data science problems with Python! (computer vision edition)
2022年05月11日
00:08:53 - 00:10:31
- Reviewing template code (part 2) - Solving real world data science problems with Python! (computer vision edition)

- Reviewing template code (part 2)

Solving real world data science problems with Python! (computer vision edition)
2022年05月11日
00:10:31 - 00:11:03
- How we load in the dataset (ImageDataGenerator, flow_from_directory) - Solving real world data science problems with Python! (computer vision edition)

- How we load in the dataset (ImageDataGenerator, flow_from_directory)

Solving real world data science problems with Python! (computer vision edition)
2022年05月11日
00:11:03 - 00:14:33
- Building our first classifier (convolutional neural net - CNN) - Solving real world data science problems with Python! (computer vision edition)

- Building our first classifier (convolutional neural net - CNN)

Solving real world data science problems with Python! (computer vision edition)
2022年05月11日
00:14:33 - 00:25:19
Buy more GPUs. - Solving real world data science problems with Python! (computer vision edition)

Buy more GPUs.

Solving real world data science problems with Python! (computer vision edition)
2022年05月11日
00:21:31 - 01:21:38
- Methods to improve neural network performance (MaxPooling, dropout, network architecture) - Solving real world data science problems with Python! (computer vision edition)

- Methods to improve neural network performance (MaxPooling, dropout, network architecture)

Solving real world data science problems with Python! (computer vision edition)
2022年05月11日
00:25:19 - 00:29:30
- Quick discussion about importance of precision & recall versus accuracy - Solving real world data science problems with Python! (computer vision edition)

- Quick discussion about importance of precision & recall versus accuracy

Solving real world data science problems with Python! (computer vision edition)
2022年05月11日
00:29:30 - 00:32:35
- Data augmentation & preprocessing (another way to improve performance) - Solving real world data science problems with Python! (computer vision edition)

- Data augmentation & preprocessing (another way to improve performance)

Solving real world data science problems with Python! (computer vision edition)
2022年05月11日
00:32:35 - 00:47:15
- Programmatically finding the best neural network architectures (Keras Tuner) - Solving real world data science problems with Python! (computer vision edition)

- Programmatically finding the best neural network architectures (Keras Tuner)

Solving real world data science problems with Python! (computer vision edition)
2022年05月11日
00:47:15 - 01:20:00
- Video recap & conclusion - Solving real world data science problems with Python! (computer vision edition)

- Video recap & conclusion

Solving real world data science problems with Python! (computer vision edition)
2022年05月11日
01:20:00 - 01:21:38
- Announcements! - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

- Announcements!

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
00:00:00 - 00:01:12
- Video overview & timeline - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

- Video overview & timeline

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
00:01:12 - 00:03:06
- Bag of words (BOW) overview - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

- Bag of words (BOW) overview

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
00:03:06 - 00:04:42
- Bag of words example code! (sklearn | CountVectorizer, fit_transform) - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

- Bag of words example code! (sklearn | CountVectorizer, fit_transform)

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
00:04:42 - 00:11:20
- Building a text classification model using bag-of-words (SVM) - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

- Building a text classification model using bag-of-words (SVM)

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
00:11:20 - 00:14:07
keith enum 😂👌 - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

keith enum 😂👌

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
00:11:57 - 01:37:46
- Predicting new utterances classes using our model (transform) - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

- Predicting new utterances classes using our model (transform)

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
00:14:07 - 00:16:02
If anyone else is getting "CLOTHING" here, try creating your vectorizer like this:vectorizer = CountVectorizer(stop_words=["the"]) - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

If anyone else is getting "CLOTHING" here, try creating your vectorizer like this:vectorizer = CountVectorizer(stop_words=["the"])

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
00:15:01 - 01:37:46
- Unigram, bigram, ngrams (using consecutive words in your model) - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

- Unigram, bigram, ngrams (using consecutive words in your model)

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
00:16:02 - 00:19:28
- Word vectors overview - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

- Word vectors overview

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
00:19:28 - 00:23:27
- Word vectors example code! (Using spaCy library) - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

- Word vectors example code! (Using spaCy library)

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
00:23:27 - 00:28:10
- Building a text classification model using word vectors - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

- Building a text classification model using word vectors

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
00:28:10 - 00:34:04
- Predicting new utterances using our model - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

- Predicting new utterances using our model

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
00:34:04 - 00:40:42
"I went to the bank and wrote a check" is an incorrect English sentence.It should have been "I went to the bank and wrote a cheque" - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

"I went to the bank and wrote a check" is an incorrect English sentence.It should have been "I went to the bank and wrote a cheque"

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
00:39:52 - 01:37:46
- Regexes (pattern matching) in Python. - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

- Regexes (pattern matching) in Python.

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
00:40:42 - 00:52:30
- Stemming/Lemmatization in Python (text normalization w/ NLTK library) - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

- Stemming/Lemmatization in Python (text normalization w/ NLTK library)

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
00:52:30 - 01:01:17
- Stopwords Removal (removing most common words from sentences) - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

- Stopwords Removal (removing most common words from sentences)

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
01:01:17 - 01:05:56
- Various other techniques (spell correction, sentiment analysis, part-of-speech tagging). - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

- Various other techniques (spell correction, sentiment analysis, part-of-speech tagging).

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
01:05:56 - 01:12:45
- Recurrent Neural Networks (RNNs) for text classification - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

- Recurrent Neural Networks (RNNs) for text classification

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
01:12:45 - 01:17:00
- Transformer architectures (attention is all you need) - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

- Transformer architectures (attention is all you need)

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
01:17:00 - 01:21:00
- Writing Python code to leverage transformers (BERT | spacy-transformers) - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

- Writing Python code to leverage transformers (BERT | spacy-transformers)

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
01:21:00 - 01:25:00
- Writing a classification model using transformers/BERT - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

- Writing a classification model using transformers/BERT

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
01:25:00 - 01:29:37
- Fine-tuning transformer models - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

- Fine-tuning transformer models

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
01:29:37 - 01:31:16
- Bring it all together and build a high performance model to classify the categories of Amazon reviews! - Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

- Bring it all together and build a high performance model to classify the categories of Amazon reviews!

Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)
2022年03月17日
01:31:16 - 01:37:46
- Introduction - Solving real-world data analysis problems with Python Pandas! (Lego dataset analysis)

- Introduction

Solving real-world data analysis problems with Python Pandas! (Lego dataset analysis)
2022年03月01日
00:00:00 - 00:01:05
- Getting started w/ Lego analysis project - Solving real-world data analysis problems with Python Pandas! (Lego dataset analysis)

- Getting started w/ Lego analysis project

Solving real-world data analysis problems with Python Pandas! (Lego dataset analysis)
2022年03月01日
00:01:05 - 00:02:33
- How to follow along if you are not a premium DataCamp subscriber (GitHub) - Solving real-world data analysis problems with Python Pandas! (Lego dataset analysis)

- How to follow along if you are not a premium DataCamp subscriber (GitHub)

Solving real-world data analysis problems with Python Pandas! (Lego dataset analysis)
2022年03月01日
00:02:33 - 00:04:01
- Project tasks overview - Solving real-world data analysis problems with Python Pandas! (Lego dataset analysis)

- Project tasks overview

Solving real-world data analysis problems with Python Pandas! (Lego dataset analysis)
2022年03月01日
00:04:01 - 00:05:40
- Basic exploration of the dataset - Solving real-world data analysis problems with Python Pandas! (Lego dataset analysis)

- Basic exploration of the dataset

Solving real-world data analysis problems with Python Pandas! (Lego dataset analysis)
2022年03月01日
00:05:40 - 00:09:45
- Task #1: What percentage of all licensed sets ever released were Star Wars Themed? - Solving real-world data analysis problems with Python Pandas! (Lego dataset analysis)

- Task #1: What percentage of all licensed sets ever released were Star Wars Themed?

Solving real-world data analysis problems with Python Pandas! (Lego dataset analysis)
2022年03月01日
00:09:45 - 00:24:23
how did you change 'Star wars' text immediately? - Solving real-world data analysis problems with Python Pandas! (Lego dataset analysis)

how did you change 'Star wars' text immediately?

Solving real-world data analysis problems with Python Pandas! (Lego dataset analysis)
2022年03月01日
00:16:52 - 00:43:37
- Task #2: In which year was Star Wars not the most popular licensed theme? - Solving real-world data analysis problems with Python Pandas! (Lego dataset analysis)

- Task #2: In which year was Star Wars not the most popular licensed theme?

Solving real-world data analysis problems with Python Pandas! (Lego dataset analysis)
2022年03月01日
00:24:23 - 00:34:00
- Bonus Task: How many unique sets were released each year (1955-2017)? - Solving real-world data analysis problems with Python Pandas! (Lego dataset analysis)

- Bonus Task: How many unique sets were released each year (1955-2017)?

Solving real-world data analysis problems with Python Pandas! (Lego dataset analysis)
2022年03月01日
00:34:00 - 00:42:26
- Conclusion! - Solving real-world data analysis problems with Python Pandas! (Lego dataset analysis)

- Conclusion!

Solving real-world data analysis problems with Python Pandas! (Lego dataset analysis)
2022年03月01日
00:42:26 - 00:43:37
- What we’ll be doing in this video - How to Schedule & Automatically Run Python Code!

- What we’ll be doing in this video

How to Schedule & Automatically Run Python Code!
2020年11月27日
00:00:00 - 00:00:56
- Check out Skillshare! (sponsored) - How to Schedule & Automatically Run Python Code!

- Check out Skillshare! (sponsored)

How to Schedule & Automatically Run Python Code!
2020年11月27日
00:00:56 - 00:01:56
).hours.at("").do(func). but this is not working. - How to Schedule & Automatically Run Python Code!

).hours.at("").do(func). but this is not working.

How to Schedule & Automatically Run Python Code!
2020年11月27日
00:01:00 - 01:20:23
- How can we automate scripts? Overview of local, cloud, and serverless methods - How to Schedule & Automatically Run Python Code!

- How can we automate scripts? Overview of local, cloud, and serverless methods

How to Schedule & Automatically Run Python Code!
2020年11月27日
00:01:56 - 00:05:18
- Simple example of local script automation w/ cronjobs & windows task scheduler - How to Schedule & Automatically Run Python Code!

- Simple example of local script automation w/ cronjobs & windows task scheduler

How to Schedule & Automatically Run Python Code!
2020年11月27日
00:05:18 - 00:18:32
- How to schedule code on a cloud machine (use cronjobs) - How to Schedule & Automatically Run Python Code!

- How to schedule code on a cloud machine (use cronjobs)

How to Schedule & Automatically Run Python Code!
2020年11月27日
00:18:32 - 00:18:51
- Simple example of cloud script automation w/ AWS Lambda & Cloudwatch - How to Schedule & Automatically Run Python Code!

- Simple example of cloud script automation w/ AWS Lambda & Cloudwatch

How to Schedule & Automatically Run Python Code!
2020年11月27日
00:18:51 - 00:27:09
- Schedule & automate sending an email locally - How to Schedule & Automatically Run Python Code!

- Schedule & automate sending an email locally

How to Schedule & Automatically Run Python Code!
2020年11月27日
00:27:09 - 00:45:12
- Schedule & automate sending an email on the cloud w/ Lambda & Cloudwatch - How to Schedule & Automatically Run Python Code!

- Schedule & automate sending an email on the cloud w/ Lambda & Cloudwatch

How to Schedule & Automatically Run Python Code!
2020年11月27日
00:45:12 - 00:50:18
- Installing python packages in a serverless environment (zip uploads) - How to Schedule & Automatically Run Python Code!

- Installing python packages in a serverless environment (zip uploads)

How to Schedule & Automatically Run Python Code!
2020年11月27日
00:50:18 - 00:55:50
- Generate & schedule sending analytics reports (locally) - How to Schedule & Automatically Run Python Code!

- Generate & schedule sending analytics reports (locally)

How to Schedule & Automatically Run Python Code!
2020年11月27日
00:55:50 - 01:02:45
- Limitations of lambda (max file upload size) - How to Schedule & Automatically Run Python Code!

- Limitations of lambda (max file upload size)

How to Schedule & Automatically Run Python Code!
2020年11月27日
01:07:03 - 01:09:00
- Generate & schedule sending analytics reports in AWS Lambda - How to Schedule & Automatically Run Python Code!

- Generate & schedule sending analytics reports in AWS Lambda

How to Schedule & Automatically Run Python Code!
2020年11月27日
01:09:00 - 01:18:32
- Final thoughts & video recap! - How to Schedule & Automatically Run Python Code!

- Final thoughts & video recap!

How to Schedule & Automatically Run Python Code!
2020年11月27日
01:18:32 - 01:20:23
- What we will be doing in this video - How to Generate an Analytics Report (pdf) in Python!

- What we will be doing in this video

How to Generate an Analytics Report (pdf) in Python!
2020年11月11日
00:00:00 - 00:01:30
- Check out Skillshare! (sponsored) - How to Generate an Analytics Report (pdf) in Python!

- Check out Skillshare! (sponsored)

How to Generate an Analytics Report (pdf) in Python!
2020年11月11日
00:01:30 - 00:03:00
- Source code & Setup - How to Generate an Analytics Report (pdf) in Python!

- Source code & Setup

How to Generate an Analytics Report (pdf) in Python!
2020年11月11日
00:03:00 - 00:06:37
- Python FPDF library basics - How to Generate an Analytics Report (pdf) in Python!

- Python FPDF library basics

How to Generate an Analytics Report (pdf) in Python!
2020年11月11日
00:06:37 - 00:09:42
- Choosing our paper format (A4, Letter, etc) - How to Generate an Analytics Report (pdf) in Python!

- Choosing our paper format (A4, Letter, etc)

How to Generate an Analytics Report (pdf) in Python!
2020年11月11日
00:09:42 - 00:11:54
- Adding and resizing images in our PDF! - How to Generate an Analytics Report (pdf) in Python!

- Adding and resizing images in our PDF!

How to Generate an Analytics Report (pdf) in Python!
2020年11月11日
00:11:54 - 00:18:52
- Helper method (which states & countries can we plot?) - How to Generate an Analytics Report (pdf) in Python!

- Helper method (which states & countries can we plot?)

How to Generate an Analytics Report (pdf) in Python!
2020年11月11日
00:18:52 - 00:21:48
- Continuing to build out our report (exploring source code) - How to Generate an Analytics Report (pdf) in Python!

- Continuing to build out our report (exploring source code)

How to Generate an Analytics Report (pdf) in Python!
2020年11月11日
00:21:48 - 00:27:17
- Adding additional pages to the report - How to Generate an Analytics Report (pdf) in Python!

- Adding additional pages to the report

How to Generate an Analytics Report (pdf) in Python!
2020年11月11日
00:27:17 - 00:29:09
- Adding a title to our report - How to Generate an Analytics Report (pdf) in Python!

- Adding a title to our report

How to Generate an Analytics Report (pdf) in Python!
2020年11月11日
00:29:09 - 00:32:37
- Adding a professional letterhead to report - How to Generate an Analytics Report (pdf) in Python!

- Adding a professional letterhead to report

How to Generate an Analytics Report (pdf) in Python!
2020年11月11日
00:32:37 - 00:35:00
- Plotting geographic maps with covid-19 data (plotly) - How to Generate an Analytics Report (pdf) in Python!

- Plotting geographic maps with covid-19 data (plotly)

How to Generate an Analytics Report (pdf) in Python!
2020年11月11日
00:35:00 - 00:40:02
- Using datetime library to automatically grab & format yesterday’s date - How to Generate an Analytics Report (pdf) in Python!

- Using datetime library to automatically grab & format yesterday’s date

How to Generate an Analytics Report (pdf) in Python!
2020年11月11日
00:40:02 - 00:43:46
Hey, Keith awesome tutorial! At  to remove leading "0" you can do "%#m/%#d/%y" instead. The "#" will remove leading "0" - How to Generate an Analytics Report (pdf) in Python!

Hey, Keith awesome tutorial! At to remove leading "0" you can do "%#m/%#d/%y" instead. The "#" will remove leading "0"

How to Generate an Analytics Report (pdf) in Python!
2020年11月11日
00:42:23 - 00:49:15
- Finalizing our report - How to Generate an Analytics Report (pdf) in Python!

- Finalizing our report

How to Generate an Analytics Report (pdf) in Python!
2020年11月11日
00:43:46 - 00:46:41
- Where are the colors set? - How to Generate an Analytics Report (pdf) in Python!

- Where are the colors set?

How to Generate an Analytics Report (pdf) in Python!
2020年11月11日
00:46:41 - 00:48:11
- Final thoughts - How to Generate an Analytics Report (pdf) in Python!

- Final thoughts

How to Generate an Analytics Report (pdf) in Python!
2020年11月11日
00:48:11 - 00:49:15
...OK, maybe the second best , right after the time spent yesterday building play dough dinosaurs with my 2 y.o. son after a 1 week business trip. But you were really close from 1st place, I promised :)More seriously, absolutly stunning tutorial! Highly valuable and extremly clearly explained.Thanks for that ! - How to Generate an Analytics Report (pdf) in Python!

...OK, maybe the second best , right after the time spent yesterday building play dough dinosaurs with my 2 y.o. son after a 1 week business trip. But you were really close from 1st place, I promised :)More seriously, absolutly stunning tutorial! Highly valuable and extremly clearly explained.Thanks for that !

How to Generate an Analytics Report (pdf) in Python!
2020年11月11日
00:49:14 - 00:49:15
This were the best  minutes I spent this week... - How to Generate an Analytics Report (pdf) in Python!

This were the best minutes I spent this week...

How to Generate an Analytics Report (pdf) in Python!
2020年11月11日
00:49:14 - 00:49:14