Raggruppare e aggregare

Userai spesso .groupby() e .agg() in questo corso, quindi è importante prenderci confidenza. In questo esercizio, il tuo compito è calcolare un insieme di statistiche descrittive sui dati degli acquisti, suddivisi per 'device' (Android o iOS) e 'gender' (Maschio o Femmina).

Successivamente, confronterai i valori tra questi sottoinsiemi, così da ottenere un riferimento di base per questi valori come potenziali KPI da ottimizzare in futuro.

Il DataFrame purchase_data dell'esercizio precedente è già stato caricato per te. Come promemoria, contiene gli acquisti uniti alle informazioni demografiche degli utenti.

Questo esercizio fa parte del corso

Customer Analytics and A/B Testing in Python

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Istruzioni dell'esercizio

Raggruppa il DataFrame purchase_data per 'device' e 'gender', in quest'ordine.
Aggrega grouped_purchase_data calcolando, in quest'ordine, 'mean', 'median' e la deviazione standard ('std') del prezzo di acquisto per questi gruppi.
Esamina i risultati. La media differisce molto dalla mediana? Quanta variabilità c'è in ciascun gruppo?

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Group the data 
grouped_purchase_data = purchase_data.____(____ = ['____', '____'])

# Aggregate the data
purchase_summary = grouped_purchase_data.____({'price': ['____', '____', '____']})

# Examine the results
print(purchase_summary)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Customer Analytics and A/B Testing in Python

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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: Introduzione e panoramica del corso Exercise 2: Comprendere i componenti chiave di un test A/B Exercise 3: Definire KPI significativi Exercise 4: Identificare e comprendere i KPI Exercise 5: Caricare ed esaminare i dati Exercise 6: Unire su insiemi diversi di campi Exercise 7: Analisi esplorativa delle KPI Exercise 8: Esercitarsi con le aggregazioni Exercise 9: Raggruppare e aggregare

Esercizio in corso

Exercise 10: Calcolare i KPI: un esempio pratico Exercise 11: Calcolo dei KPI Exercise 12: Prezzo medio di acquisto per coorte

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: Working with time series data in pandas Exercise 2: Parsing dates Exercise 3: Creating time series graphs with Matplotlib Exercise 4: Plotting time series data Exercise 5: Pivoting our data Exercise 6: Examining the different cohorts Exercise 7: Understanding and visualizing trends Exercise 8: Seasonality and moving averages Exercise 9: Exponential rolling average & over/under smoothing Exercise 10: Events and releases Exercise 11: Visualizing user spending Exercise 12: Looking more closely at revenue

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