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Analyzing and Visualizing Real-World Data
course content

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

Comparing Dynamics

Returning to the previous section, you solved the problem of finding the most profitable stores. According to the data, these stores are numbered 20, 4, 14, 13, and 2 (the numbers are saved in the top_stores variable). Let's examine the sales dynamics of these stores.

Task

  1. Prepare the data for visualization: filter the values in the df so that only data for stores with the numbers present in the top_stores list remain. Save the resulting data in the data variable.
  2. Initialize a line plot with dates on the x-axis, weekly sales on the y-axis, using the data dataframe. Display a separate line for each store.

Task

  1. Prepare the data for visualization: filter the values in the df so that only data for stores with the numbers present in the top_stores list remain. Save the resulting data in the data variable.
  2. Initialize a line plot with dates on the x-axis, weekly sales on the y-axis, using the data dataframe. Display a separate line for each store.

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

Everything was clear?

Section 4. Chapter 2
toggle bottom row

Comparing Dynamics

Returning to the previous section, you solved the problem of finding the most profitable stores. According to the data, these stores are numbered 20, 4, 14, 13, and 2 (the numbers are saved in the top_stores variable). Let's examine the sales dynamics of these stores.

Task

  1. Prepare the data for visualization: filter the values in the df so that only data for stores with the numbers present in the top_stores list remain. Save the resulting data in the data variable.
  2. Initialize a line plot with dates on the x-axis, weekly sales on the y-axis, using the data dataframe. Display a separate line for each store.

Task

  1. Prepare the data for visualization: filter the values in the df so that only data for stores with the numbers present in the top_stores list remain. Save the resulting data in the data variable.
  2. Initialize a line plot with dates on the x-axis, weekly sales on the y-axis, using the data dataframe. Display a separate line for each store.

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

Everything was clear?

Section 4. Chapter 2
toggle bottom row

Comparing Dynamics

Returning to the previous section, you solved the problem of finding the most profitable stores. According to the data, these stores are numbered 20, 4, 14, 13, and 2 (the numbers are saved in the top_stores variable). Let's examine the sales dynamics of these stores.

Task

  1. Prepare the data for visualization: filter the values in the df so that only data for stores with the numbers present in the top_stores list remain. Save the resulting data in the data variable.
  2. Initialize a line plot with dates on the x-axis, weekly sales on the y-axis, using the data dataframe. Display a separate line for each store.

Task

  1. Prepare the data for visualization: filter the values in the df so that only data for stores with the numbers present in the top_stores list remain. Save the resulting data in the data variable.
  2. Initialize a line plot with dates on the x-axis, weekly sales on the y-axis, using the data dataframe. Display a separate line for each store.

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

Everything was clear?

Returning to the previous section, you solved the problem of finding the most profitable stores. According to the data, these stores are numbered 20, 4, 14, 13, and 2 (the numbers are saved in the top_stores variable). Let's examine the sales dynamics of these stores.

Task

  1. Prepare the data for visualization: filter the values in the df so that only data for stores with the numbers present in the top_stores list remain. Save the resulting data in the data variable.
  2. Initialize a line plot with dates on the x-axis, weekly sales on the y-axis, using the data dataframe. Display a separate line for each store.

Switch to desktop for real-world practiceContinue from where you are using one of the options below
Section 4. Chapter 2
Switch to desktop for real-world practiceContinue from where you are using one of the options below
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