Memvisualisasikan pengeluaran pengguna

Baru-baru ini, tim Produk membuat beberapa perubahan besar pada aplikasi Android dan iOS. Mereka tidak memiliki kekhawatiran langsung tentang dampak perubahan ini, tetapi ingin Anda memantau data untuk memastikan perubahan tersebut tidak merugikan pendapatan perusahaan. Selain itu, tim produk meyakini bahwa beberapa perubahan ini mungkin berdampak lebih besar pada pengguna perempuan dibandingkan laki-laki.

Dalam latihan ini, Anda akan memplot pendapatan bulanan untuk salah satu produk yang diperbarui dan mengevaluasi hasilnya.

Himpunan data user_revenue yang berisi 'device', 'gender', 'country', 'date', dan 'revenue' telah dimuat. Data tersebut telah dikelompokkan berdasarkan bulan, perangkat, dan gender. Perhatikan bahwa di sini kolom 'month' telah diekstrak dari kolom 'date'.

Latihan ini adalah bagian dari kursus

Analitik Pelanggan dan A/B Testing dengan Python

Petunjuk latihan

Pivot user_revenue sehingga kita memiliki 'month' sebagai baris (index), 'device' dan 'gender' sebagai columns, serta 'revenue' sebagai values.
Hapus baris pertama dan terakhir dari DataFrame setelah dipivot untuk mencegah ketakselanjaran yang mendistorsi hasil. Langkah ini telah dilakukan untuk Anda.
Plot pivoted_data menggunakan metode .plot() miliknya.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Pivot user_revenue
pivoted_data = pd.pivot_table(user_revenue, values =_____, columns=['device', _____], index='month')
pivoted_data = pivoted_data[1:(len(pivoted_data) -1 )]

# Create and show the plot
pivoted_data.____
plt.show()

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: 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: Bekerja dengan data deret waktu di pandas Exercise 2: Mengurai tanggal Exercise 3: Membuat grafik deret waktu dengan Matplotlib Exercise 4: Memplot data deret waktu Exercise 5: Memutar data kita (pivot)Exercise 6: Menganalisis berbagai kohort Exercise 7: Memahami dan memvisualisasikan tren Exercise 8: Musiman dan rata-rata bergerak Exercise 9: Rata-rata rolling eksponensial & pelapisan berlebihan/kekurangan Exercise 10: Peristiwa dan rilis Exercise 11: Memvisualisasikan pengeluaran pengguna

Latihan Saat Ini

Exercise 12: Mengamati pendapatan lebih saksama

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