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Analyzing and Visualizing Real-World Data

1. Preprocessing Data: Part I

Loading the DataData TypesAnalyzing Columns [1/4]Removing Characters: Method 1Removing Characters: Method 2Analyzing Columns [2/4]Replacing Characters

2. Preprocessing Data: Part II

Analyzing Columns [3/4]Removing and ReplacingAnalyzing Columns [4/4]Wrong 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

Analyzing and Visualizing Real-World Data

First Look at the DataFirst Look at the Data

Welcome to the last section! In the previous section, we investigated that the most profitable week, according to sales data, is the 'pre-Christmas' week, while Christmas week itself is significantly worse.

We want to start with some exploratory analysis: let's see revenues over weeks using matplotlib and seaborn.

Task

  1. Import the matplotlib.pyplot with the alias plt, and seaborn with the alias sns.
  2. Prepare data for visualization: calculate the total revenue for all shops across weeks. To do it, group the values of the df dataframe by the 'Date' column, select the 'Weekly_Sales' column, calculate total values, and reset indexes. Save the obtained data within the data variable.
  3. Initialize a line plot with the 'Date' values on the x-axis, 'Weekly_Sales' values on the y-axis, using the data dataframe.
  4. Display the plot.

Everything was clear?

Section 4. Chapter 1
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