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Introduction to Python for Data Analysis

bookHow Much Do We Earn

You may recognize the column 'money_spent' that corresponds to the amount of money the user spent and gained. In this chapter, we will find if there is any dependence between the day of the week and the amount of money we have!

But firstly, recall some functions:

Group Data:


              
12

            
df = df[['columns which we group']]
         .groupby(['columns on which we group'])
copy

Visualization:


              
1234

            
sns.barplot(df = DataFrame,
                 x = 'column for x-axis',
                 y = 'column for y-axis')
plt.show()
copy
Tehtävä

Swipe to start coding

  1. Group data:
  • Extract only columns 'day', 'money_spent' from the df DataFrame.
  • Group by the column 'day'.
  • Apply .mean() function to grouped df.
  • Apply .reset_index() function.
  1. Create a barplot:
  • Use df as the first argument.
  • Use column 'day' for x-axis.
  • Use the column 'money_spent' for the y-axis.
  1. Output barplot.

Ratkaisu

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bookHow Much Do We Earn

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You may recognize the column 'money_spent' that corresponds to the amount of money the user spent and gained. In this chapter, we will find if there is any dependence between the day of the week and the amount of money we have!

But firstly, recall some functions:

Group Data:


              
12

            
df = df[['columns which we group']]
         .groupby(['columns on which we group'])
copy

Visualization:


              
1234

            
sns.barplot(df = DataFrame,
                 x = 'column for x-axis',
                 y = 'column for y-axis')
plt.show()
copy
Tehtävä

Swipe to start coding

  1. Group data:
  • Extract only columns 'day', 'money_spent' from the df DataFrame.
  • Group by the column 'day'.
  • Apply .mean() function to grouped df.
  • Apply .reset_index() function.
  1. Create a barplot:
  • Use df as the first argument.
  • Use column 'day' for x-axis.
  • Use the column 'money_spent' for the y-axis.
  1. Output barplot.

Ratkaisu

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Kiitos palautteestasi!

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Completion rate improved to 2.08
Osio 4. Luku 8
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