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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

bookComparing Shops

As you can see, all of these points are related to the 'pre-Christmas' periods, as we noticed before. Now, let's use visualizing tools to compare the revenue for shops. We already know the top 5 selling stores, but are other shops significantly worse? We'll find out using a bar chart.

Task
test

Swipe to show code editor

  1. Prepare the data: group the values of the df DataFrame based on the 'Store' column, then select the 'Weekly_Sales' column, calculate sum values across groups, and reset the indexes. Save the result within the data variable.
  2. Initialize a bar plot. Use the 'Store' column values of data for the x-axis, 'Weekly_Sales' for the y-axis, and make bars 'blue'.
  3. Display the plot.

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Section 4. Chapter 6
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bookComparing Shops

As you can see, all of these points are related to the 'pre-Christmas' periods, as we noticed before. Now, let's use visualizing tools to compare the revenue for shops. We already know the top 5 selling stores, but are other shops significantly worse? We'll find out using a bar chart.

Task
test

Swipe to show code editor

  1. Prepare the data: group the values of the df DataFrame based on the 'Store' column, then select the 'Weekly_Sales' column, calculate sum values across groups, and reset the indexes. Save the result within the data variable.
  2. Initialize a bar plot. Use the 'Store' column values of data for the x-axis, 'Weekly_Sales' for the y-axis, and make bars 'blue'.
  3. Display the plot.

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 4. Chapter 6
toggle bottom row

bookComparing Shops

As you can see, all of these points are related to the 'pre-Christmas' periods, as we noticed before. Now, let's use visualizing tools to compare the revenue for shops. We already know the top 5 selling stores, but are other shops significantly worse? We'll find out using a bar chart.

Task
test

Swipe to show code editor

  1. Prepare the data: group the values of the df DataFrame based on the 'Store' column, then select the 'Weekly_Sales' column, calculate sum values across groups, and reset the indexes. Save the result within the data variable.
  2. Initialize a bar plot. Use the 'Store' column values of data for the x-axis, 'Weekly_Sales' for the y-axis, and make bars 'blue'.
  3. Display the plot.

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!

As you can see, all of these points are related to the 'pre-Christmas' periods, as we noticed before. Now, let's use visualizing tools to compare the revenue for shops. We already know the top 5 selling stores, but are other shops significantly worse? We'll find out using a bar chart.

Task
test

Swipe to show code editor

  1. Prepare the data: group the values of the df DataFrame based on the 'Store' column, then select the 'Weekly_Sales' column, calculate sum values across groups, and reset the indexes. Save the result within the data variable.
  2. Initialize a bar plot. Use the 'Store' column values of data for the x-axis, 'Weekly_Sales' for the y-axis, and make bars 'blue'.
  3. Display the plot.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 4. 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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