Datumsangaben parsen

In dieser Übung übst du, Datumsangaben in Python zu parsen. Daten aus Datenbanken sind oft korrekt formatiert, andere Quellen hingegen weniger. Zu wissen, wie man Daten sauber parst, ist entscheidend, um sie in ein brauchbares Format zu bringen. Als Referenz kannst du in dieser Übung durchgehend https://strftime.org/ nutzen, um die passenden Datumsformate nachzuschlagen.

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<Kurs>Customer Analytics und A/B-Testing mit Python</Kurs>

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# Provide the correct format for the date
date_data_one = pd.to_datetime(date_data_one, format=____)
print(date_data_one)

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<Kurs>Customer Analytics und A/B-Testing mit Python</Kurs>

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This chapter provides a brief introduction to the content that will be covered throughout the course before transitioning into a discussion of Key Performance Indicators or KPIs. You'll learn how to identify and define meaningful KPIs through a combination of critical thinking and leveraging Python tools. These techniques are all presented in a highly practical and generalizable way. Ultimately these topics serve as the core foundation for the A/B testing discussion that follows.

Exercise 1: Course introduction and overview Exercise 2: Understanding the key components of an A/B test Exercise 3: Defining meaningful KPIs Exercise 4: Identifying and understanding KPIs Exercise 5: Loading & examining our data Exercise 6: Merging on different sets of fields Exercise 7: Exploratory analysis of KPIs Exercise 8: Practicing aggregations Exercise 9: Grouping & aggregating Exercise 10: Calculating KPIs - a practical example Exercise 11: Calculating KPIs Exercise 12: Average purchase price by cohort

This chapter teaches you how to visualize, manipulate, and explore KPIs as they change over time. Through a variety of examples, you'll learn how to work with datetime objects to calculate metrics per unit time. Then we move to the techniques for how to graph different segments of data, and apply various smoothing functions to reveal hidden trends. Finally we walk through a complete example of how to pinpoint issues through exploratory data analysis of customer data. Throughout this chapter various functions are introduced and explained in a highly generalizable way.

Exercise 1: Arbeiten mit Zeitreihendaten in pandas Exercise 2: Datumsangaben parsen

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Exercise 3: Zeitreihendiagramme mit Matplotlib erstellen Exercise 4: Zeitreihendaten plotten Exercise 5: Daten pivotieren Exercise 6: Untersuchung der verschiedenen Kohorten Exercise 7: Trends verstehen und visualisieren Exercise 8: Saisonalität und gleitende Durchschnitte Exercise 9: Exponentieller gleitender Durchschnitt & über/unter Glättung Exercise 10: Events und Releases Exercise 11: Ausgaben der Nutzer visualisieren Exercise 12: Umsatz genauer betrachten

In this chapter you will dive fully into A/B testing. You will learn the mathematics and knowledge needed to design and successfully plan an A/B test from determining an experimental unit to finding how large a sample size is needed. Accompanying this will be an introduction to the functions and code needed to calculate the various quantities associated with a statistical test of this type.

Exercise 1: Introduction to A/B testing Exercise 2: Good applications of A/B testing Exercise 3: General properties of an A/B Test Exercise 4: A/B test generalizability Exercise 5: Initial A/B test design Exercise 6: Experimental units: Revenue per user day Exercise 7: Preparing to run an A/B test Exercise 8: Conversion rate sensitivities Exercise 9: Sensitivity Exercise 10: Standard error Exercise 11: Calculating sample size Exercise 12: Exploring the power calculation Exercise 13: Calculating the sample size

After running an A/B test, you must analyze the data and then effectively communicate the results. This chapter begins by interleaving the theory of statistical significance and confidence intervals with the tools you need to calculate them yourself from the data. Next we discuss how to effectively visualize and communicate these results. This chapter is the culmination of all the knowledge built over the entire course.

Exercise 1: Analyzing the A/B test results Exercise 2: Confirming our test results Exercise 3: Thinking critically about p-values Exercise 4: Understanding statistical significance Exercise 5: Intuition behind statistical significance Exercise 6: Checking for statistical significance Exercise 7: Understanding confidence intervals Exercise 8: Calculating confidence intervals Exercise 9: Interpreting your test results Exercise 10: Plotting the distribution Exercise 11: Plotting the difference distribution Exercise 12: Finale