Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Lære Comparing Shops | Visualizing Data
Analyzing and Visualizing Real-World Data
course content

Kursinnhold

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

book
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.

Oppgave

Swipe to start coding

  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.

Løsning

Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
Alt var klart?

Hvordan kan vi forbedre det?

Takk for tilbakemeldingene dine!

Seksjon 4. Kapittel 6
toggle bottom row

book
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.

Oppgave

Swipe to start coding

  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.

Løsning

Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
Alt var klart?

Hvordan kan vi forbedre det?

Takk for tilbakemeldingene dine!

Seksjon 4. Kapittel 6
Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
Vi beklager at noe gikk galt. Hva skjedde?
some-alt