Ikuti aliran dana

Dalam latihan ini, Anda bekerja dengan versi lain dari DataFrame banking yang berisi nilai hilang untuk kolom cust_id dan acct_amount.

Anda ingin melakukan analisis tentang berapa banyak pelanggan unik yang dimiliki bank, rata-rata jumlah yang disimpan oleh pelanggan, dan lainnya. Anda tahu bahwa baris dengan cust_id yang hilang tidak banyak membantu, dan bahwa secara rata-rata acct_amount biasanya 5 kali lipat dari inv_amount.

Dalam latihan ini, Anda akan menghapus baris banking dengan cust_id yang hilang, dan mengimputasi nilai hilang pada acct_amount menggunakan pengetahuan domain.

Latihan ini adalah bagian dari kursus

Membersihkan Data di Python

Petunjuk latihan

Gunakan .dropna() untuk menghapus nilai hilang pada kolom cust_id di banking dan simpan hasilnya ke banking_fullid.
Gunakan inv_amount untuk menghitung estimasi nilai akun untuk banking_fullid dengan menetapkan nilainya sama dengan inv_amount * 5, lalu tetapkan hasilnya ke acct_imp.
Imputasi nilai hilang pada acct_amount di banking_fullid dengan acct_imp yang baru dibuat menggunakan .fillna().

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Drop missing values of cust_id
banking_fullid = banking.____(subset = ['____'])

# Compute estimated acct_amount
acct_imp = ____

# Impute missing acct_amount with corresponding acct_imp
banking_imputed = banking_fullid.____({'____':____})

# Print number of missing values
print(banking_imputed.isna().sum())

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 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

Latihan Saat Ini

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!