Penyamaan mata uang

Dalam latihan ini dan sepanjang bab ini, Anda akan bekerja dengan himpunan data perbankan ritel yang disimpan dalam DataFrame banking. Himpunan data ini memuat data tentang jumlah uang di akun (acct_amount), mata uangnya (acct_cur), jumlah investasi (inv_amount), tanggal pembukaan akun (account_opened), dan tanggal transaksi terakhir (last_transaction) yang dikonsolidasikan dari cabang Amerika dan Eropa.

Tugas Anda adalah memahami ukuran rata-rata akun dan bagaimana investasi bervariasi menurut ukuran akun. Namun, agar analisis ini akurat, Anda perlu terlebih dahulu menyeragamkan jumlah mata uang menjadi dolar. Paket pandas telah diimpor sebagai pd, dan DataFrame banking sudah tersedia di lingkungan Anda.

Latihan ini adalah bagian dari kursus

Membersihkan Data di Python

Petunjuk latihan

Temukan baris pada acct_cur di banking yang bernilai sama dengan 'euro' dan simpan dalam variabel acct_eu.
Temukan semua baris acct_amount di banking yang memenuhi kondisi acct_eu, lalu konversikan ke USD dengan mengalikannya dengan 1.1.
Temukan semua baris acct_cur di banking yang memenuhi kondisi acct_eu, lalu ubah nilainya menjadi 'dollar'.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Find values of acct_cur that are equal to 'euro'
acct_eu = banking['____'] == '____'

# Convert acct_amount where it is in euro to dollars
banking.loc[____, '____'] = banking.loc[____, '____'] * ____

# Unify acct_cur column by changing 'euro' values to 'dollar'
banking.loc[____, '____'] = ____

# Assert that only dollar currency remains
assert banking['acct_cur'].unique() == 'dollar'

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Membersihkan Data di Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

In this chapter, you'll learn how to overcome some of the most common dirty data problems. You'll convert data types, apply range constraints to remove future data points, and remove duplicated data points to avoid double-counting.

Exercise 1: Data type constraints Exercise 2: Common data types Exercise 3: Numeric data or ... ?Exercise 4: Summing strings and concatenating numbers Exercise 5: Data range constraints Exercise 6: Tire size constraints Exercise 7: Back to the future Exercise 8: Uniqueness constraints Exercise 9: How big is your subset?Exercise 10: Finding duplicates Exercise 11: Treating duplicates

Categorical and text data can often be some of the messiest parts of a dataset due to their unstructured nature. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into one, and reformat strings for consistency.

Exercise 1: Membership constraints Exercise 2: Members only Exercise 3: Finding consistency Exercise 4: Categorical variables Exercise 5: Categories of errors Exercise 6: Inconsistent categories Exercise 7: Remapping categories Exercise 8: Cleaning text data Exercise 9: Removing titles and taking names Exercise 10: Keeping it descriptive

In this chapter, you'll dive into more advanced data cleaning problems, such as ensuring that weights are all written in kilograms instead of pounds. You'll also gain invaluable skills that will help you verify that values have been added correctly, and that missing values don't negatively impact your analyses.

Exercise 1: Keseragaman Exercise 2: Tanggal ambigu Exercise 3: Penyamaan mata uang

Latihan Saat Ini

Exercise 4: Tanggal seragam Exercise 5: Validasi lintas bidang Exercise 6: Lintas kolom atau bukan lintas kolom?Exercise 7: Bagaimana integritas data kita?Exercise 8: Kelengkapan Exercise 9: Apakah ini missing at random?Exercise 10: Investor yang hilang Exercise 11: Ikuti aliran dana

Record linkage is a powerful technique used to merge multiple datasets together, used when values have typos or different spellings. In this chapter, you'll learn how to link records by calculating the similarity between strings—you'll then use your new skills to join two restaurant review datasets into one clean master dataset.

Exercise 1: Comparing strings Exercise 2: Minimum edit distance Exercise 3: The cutoff point Exercise 4: Remapping categories II Exercise 5: Generating pairs Exercise 6: To link or not to link?Exercise 7: Pairs of restaurants Exercise 8: Similar restaurants Exercise 9: Linking DataFrames Exercise 10: Getting the right index Exercise 11: Linking them together!Exercise 12: Congratulations!