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.
Swipe to start coding
- Import the
matplotlib.pyplotwith the aliasplt, andseabornwith the aliassns. - Prepare data for visualization: calculate the total revenue for all shops across weeks. To do it, group the values of the
dfdataframe by the'Date'column, select the'Weekly_Sales'column, calculate total values, and reset indexes. Save the obtained data within thedatavariable. - Initialize a line plot with the
'Date'values on the x-axis,'Weekly_Sales'values on the y-axis, using thedatadataframe. - Display the plot.
Solución
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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.
Swipe to start coding
- Import the
matplotlib.pyplotwith the aliasplt, andseabornwith the aliassns. - Prepare data for visualization: calculate the total revenue for all shops across weeks. To do it, group the values of the
dfdataframe by the'Date'column, select the'Weekly_Sales'column, calculate total values, and reset indexes. Save the obtained data within thedatavariable. - Initialize a line plot with the
'Date'values on the x-axis,'Weekly_Sales'values on the y-axis, using thedatadataframe. - Display the plot.
Solución
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