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.
Lösung
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Completion rate improved to 3.45First Look at the Data
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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.
Lösung
Wechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwendenDanke für Ihr Feedback!
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Completion rate improved to 3.45single
