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Analyzing and Visualizing Real-World Data
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Course Content

Analyzing and Visualizing Real-World Data

Analyzing and Visualizing Real-World Data

1. Preprocessing Data: Part I
Loading the DataData TypesColumns OverviewRemoving Characters: Method 1Removing Characters: Method 2Checking ColumnsReplacing Characters
2. Preprocessing Data: Part II
Analyzing ColumnsRemoving and ReplacingProcessing DatesWrong Convertation?Proper ConvertingThat's All?
3. Analyzing Data
Superficial AnalysisThe Most Profitable StoreDate RangeHow Periodic the Data is?The Most Profitable PeriodsThe Best CombinationHolidays EffectWhat Holidays are we Considering?The Most Profitable Holiday
4. Visualizing Data
First Look at the DataComparing DynamicsFinding RelationDoes Temperature Affect?Or is it Holiday Effect?Comparing ShopsAdding Holidays to Comparison

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

  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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Everything was clear?

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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

  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

  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

  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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