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.

Завдання

Swipe to show code editor

Import the matplotlib.pyplot with the alias plt, and seaborn with the alias sns.
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.
Initialize a line plot with the 'Date' values on the x-axis, 'Weekly_Sales' values on the y-axis, using the data dataframe.
Display the plot.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

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Дякуємо за ваш відгук!

Секція 4. Розділ 1

First Look at the Data

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

Завдання

Swipe to show code editor

Import the matplotlib.pyplot with the alias plt, and seaborn with the alias sns.
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.
Initialize a line plot with the 'Date' values on the x-axis, 'Weekly_Sales' values on the y-axis, using the data dataframe.
Display the plot.

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 4. Розділ 1

First Look at the Data

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

Завдання

Swipe to show code editor

Import the matplotlib.pyplot with the alias plt, and seaborn with the alias sns.
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.
Initialize a line plot with the 'Date' values on the x-axis, 'Weekly_Sales' values on the y-axis, using the data dataframe.
Display the plot.

Все було зрозуміло?

Дякуємо за ваш відгук!

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

Завдання

Swipe to show code editor

Import the matplotlib.pyplot with the alias plt, and seaborn with the alias sns.
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.
Initialize a line plot with the 'Date' values on the x-axis, 'Weekly_Sales' values on the y-axis, using the data dataframe.
Display the plot.

Секція 4. Розділ 1