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Analyzing and Visualizing Real-World Data
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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

Comparing 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.

Tarefa

  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.

Tarefa

  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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Tudo estava claro?

Seção 4. Capítulo 6
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Comparing 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.

Tarefa

  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.

Tarefa

  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.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

Seção 4. Capítulo 6
toggle bottom row

Comparing 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.

Tarefa

  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.

Tarefa

  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.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

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.

Tarefa

  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.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Seção 4. Capítulo 6
Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
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