Course Content
Analyzing and Visualizing Real-World Data
Analyzing and Visualizing Real-World Data
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.
Task
- Select the
'Store', 'Date'
, and'Weekly_Sales'
columns. - Sort the values of the
'Weekly_Sales'
column in descending order (not ascending). - Display the first 10 rows of the obtained dataframe.
Do not worry about syntax, pandas
allows splitting methods by lines.
Task
- Select the
'Store', 'Date'
, and'Weekly_Sales'
columns. - Sort the values of the
'Weekly_Sales'
column in descending order (not ascending). - Display the first 10 rows of the obtained dataframe.
Do not worry about syntax, pandas
allows splitting methods by lines.
Everything was clear?
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.
Task
- Select the
'Store', 'Date'
, and'Weekly_Sales'
columns. - Sort the values of the
'Weekly_Sales'
column in descending order (not ascending). - Display the first 10 rows of the obtained dataframe.
Do not worry about syntax, pandas
allows splitting methods by lines.
Task
- Select the
'Store', 'Date'
, and'Weekly_Sales'
columns. - Sort the values of the
'Weekly_Sales'
column in descending order (not ascending). - Display the first 10 rows of the obtained dataframe.
Do not worry about syntax, pandas
allows splitting methods by lines.
Everything was clear?
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.
Task
- Select the
'Store', 'Date'
, and'Weekly_Sales'
columns. - Sort the values of the
'Weekly_Sales'
column in descending order (not ascending). - Display the first 10 rows of the obtained dataframe.
Do not worry about syntax, pandas
allows splitting methods by lines.
Task
- Select the
'Store', 'Date'
, and'Weekly_Sales'
columns. - Sort the values of the
'Weekly_Sales'
column in descending order (not ascending). - Display the first 10 rows of the obtained dataframe.
Do not worry about syntax, pandas
allows splitting methods by lines.
Everything was clear?
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
- Select the
'Store', 'Date'
, and'Weekly_Sales'
columns. - Sort the values of the
'Weekly_Sales'
column in descending order (not ascending). - Display the first 10 rows of the obtained dataframe.
Do not worry about syntax, pandas
allows splitting methods by lines.