Pengelompokan & agregasi

Anda akan sering menggunakan .groupby() dan .agg() dalam kursus ini, jadi penting untuk merasa nyaman dengannya. Pada latihan ini, tugas Anda adalah menghitung serangkaian statistik ringkas tentang data pembelian yang dipisahkan berdasarkan 'device' (Android atau iOS) dan 'gender' (Male atau Female).

Setelah itu, Anda akan membandingkan nilai antar subkumpulan ini, yang akan memberi Anda tolok ukur awal untuk nilai-nilai ini sebagai calon KPI yang dapat dioptimalkan ke depannya.

DataFrame purchase_data dari latihan sebelumnya telah dimuat untuk Anda. Sebagai pengingat, data tersebut berisi pembelian yang digabung dengan demografi pengguna.

Latihan ini adalah bagian dari kursus

Analitik Pelanggan dan A/B Testing dengan Python

Petunjuk latihan

Kelompokkan DataFrame purchase_data berdasarkan 'device' dan 'gender' (dalam urutan tersebut).
Agregasikan grouped_purchase_data dengan menghitung 'mean', 'median', dan deviasi standar ('std') dari harga pembelian, dalam urutan tersebut, untuk setiap grup.
Periksa hasilnya. Apakah mean berbeda jauh dari median? Seberapa besar variasi pada setiap grup?

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

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

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

# Examine the results
print(purchase_summary)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Analitik Pelanggan dan A/B Testing dengan Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

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: Pendahuluan dan ikhtisar kursus Exercise 2: Memahami komponen utama uji A/B Exercise 3: Mendefinisikan KPI yang bermakna Exercise 4: Mengidentifikasi dan memahami KPI Exercise 5: Memuat & menelaah data kita Exercise 6: Menggabungkan pada himpunan kolom yang berbeda Exercise 7: Analisis eksploratif terhadap KPI Exercise 8: Latihan agregasi Exercise 9: Pengelompokan & agregasi

Latihan Saat Ini

Exercise 10: Menghitung KPI - contoh praktis Exercise 11: Menghitung KPI Exercise 12: Rata-rata harga pembelian per kohort

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