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Lära How Much Do We Earn | Becoming an Analyst
Introduction to Python for Data Analysis
course content

Kursinnehåll

Introduction to Python for Data Analysis

Introduction to Python for Data Analysis

1. Introduction to Python 1/2
2. Introduction to Python 2/2
3. Explore Dataset
4. Becoming an Analyst

book
How 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
Uppgift

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.

Lösning

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 4. Kapitel 8
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book
How 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
Uppgift

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.

Lösning

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 4. Kapitel 8
Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Vi beklagar att något gick fel. Vad hände?
some-alt