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Introduction to Python for Data Analysis
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Kursinhalt

Introduction to Python for Data Analysis

Introduction to Python for Data Analysis

1. Introduction to Python 1/2
First Step in ProgrammingDeal with VariablesPractice Math SkillsChallengeDeal with PercentsHow to Attract One User?ChallengeLearn NumbersBoolean NumbersChallengeWork with StringsGet Familiar with ListsHow to Append New Element?
2. Introduction to Python 2/2
If/ElseChallengeIf/Elif/ElseChallengeFor LoopWhile LoopChallenge
3. Explore Dataset
Data StudyOperations with the FileHow to Explore the DataSorting DataMax, min, mean, medianChallengeGroup DataDive Deeper into Grouping DataGroup Data 2.0Introduction to NumPyPivot TablesRecall Loops.loc FunctionDeal with Several ConditionsChallengeVisualization: First StepsDive Deeper into Visualization
4. Becoming an Analyst
Data StudyExamine DatasetChallengeСAC 1/2CAC 2/2Costly CustomersOrganic TrafficHow Much Do We EarnIs Our Project Profitable? ROI 1/2ROI 2/2Visualize ROI

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
Aufgabe

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.

Lösung

Switch to desktopWechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden
War alles klar?

Wie können wir es verbessern?

Danke für Ihr Feedback!

Abschnitt 4. Kapitel 8
toggle bottom row

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
Aufgabe

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.

Lösung

Switch to desktopWechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden
War alles klar?

Wie können wir es verbessern?

Danke für Ihr Feedback!

Abschnitt 4. Kapitel 8
Switch to desktopWechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden
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