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The Best Combination | Analyzing Data
Analyzing and Visualizing Real-World Data
course content

Course Content

Analyzing and Visualizing Real-World Data

Analyzing and Visualizing Real-World Data

1. Preprocessing Data: Part I
2. Preprocessing Data: Part II
3. Analyzing Data
4. Visualizing Data

bookThe Best Combination

Interesting! The most profitable weeks are the second to last weeks of December and November. Now, let's answer the question: What was the best selling week and store? Since our data is already weekly, we won't need to group observations this time.

Task
test

Swipe to show code editor

  1. Select the 'Store', 'Date', and 'Weekly_Sales' columns.
  2. Sort the values of the 'Weekly_Sales' column in descending order (not ascending).
  3. Display the first 10 rows of the obtained dataframe.

Do not worry about syntax, pandas allows splitting methods by lines.

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Section 3. Chapter 6
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bookThe Best Combination

Interesting! The most profitable weeks are the second to last weeks of December and November. Now, let's answer the question: What was the best selling week and store? Since our data is already weekly, we won't need to group observations this time.

Task
test

Swipe to show code editor

  1. Select the 'Store', 'Date', and 'Weekly_Sales' columns.
  2. Sort the values of the 'Weekly_Sales' column in descending order (not ascending).
  3. Display the first 10 rows of the obtained dataframe.

Do not worry about syntax, pandas allows splitting methods by lines.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 3. Chapter 6
toggle bottom row

bookThe Best Combination

Interesting! The most profitable weeks are the second to last weeks of December and November. Now, let's answer the question: What was the best selling week and store? Since our data is already weekly, we won't need to group observations this time.

Task
test

Swipe to show code editor

  1. Select the 'Store', 'Date', and 'Weekly_Sales' columns.
  2. Sort the values of the 'Weekly_Sales' column in descending order (not ascending).
  3. Display the first 10 rows of the obtained dataframe.

Do not worry about syntax, pandas allows splitting methods by lines.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Interesting! The most profitable weeks are the second to last weeks of December and November. Now, let's answer the question: What was the best selling week and store? Since our data is already weekly, we won't need to group observations this time.

Task
test

Swipe to show code editor

  1. Select the 'Store', 'Date', and 'Weekly_Sales' columns.
  2. Sort the values of the 'Weekly_Sales' column in descending order (not ascending).
  3. Display the first 10 rows of the obtained dataframe.

Do not worry about syntax, pandas allows splitting methods by lines.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 3. Chapter 6
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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