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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

The 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.

Tarefa

  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.

Tarefa

  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.

Tudo estava claro?

Seção 3. Capítulo 6
toggle bottom row

The 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.

Tarefa

  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.

Tarefa

  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.

Tudo estava claro?

Seção 3. Capítulo 6
toggle bottom row

The 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.

Tarefa

  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.

Tarefa

  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.

Tudo estava claro?

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.

Tarefa

  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.

Seção 3. Capítulo 6
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