Tanggal seragam

Setelah menyeragamkan mata uang pada berbagai saldo rekening, Anda ingin menambahkan dimensi temporal ke analisis Anda dan melihat bagaimana pelanggan menginvestasikan uang mereka berdasarkan ukuran saldo rekening setiap tahun. Kolom account_opened merepresentasikan kapan pelanggan membuka rekening mereka dan merupakan proksi yang baik untuk membagi aktivitas dan investasi pelanggan dari waktu ke waktu.

Namun, karena data ini dikonsolidasikan dari berbagai sumber, Anda perlu memastikan semua tanggal memiliki format yang sama. Anda akan melakukannya dengan mengonversi kolom ini menjadi objek datetime, sambil memastikan formatnya diinferensikan dan format yang berpotensi salah diubah menjadi nilai hilang. DataFrame banking sudah tersedia di lingkungan Anda dan pandas telah diimpor sebagai pd.

Latihan ini adalah bagian dari kursus

Membersihkan Data di Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Print the header of account_opened
print(____)

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

Latihan Saat Ini

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!