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First Look at the Data | Visualizing Data
Analyzing and Visualizing Real-World Data

First 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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course content

Course Content

Analyzing and Visualizing Real-World Data

First 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
toggle bottom row
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