Menyaring DataFrame

Fungsi filter sangat berguna untuk dengan cepat memperoleh subset data berdasarkan kondisi tertentu. Anda dapat menyaring vektor, tuple, DataFrame, dan struktur data lainnya. Namun, pada latihan ini, kita akan fokus pada tuple dan DataFrame.

Sintaks untuk filter adalah:

filter(source => f::Function, df)

Untuk sebuah DataFrame: - source adalah kolom yang akan digunakan sebagai dasar penyaringan - f::Function adalah kondisi penyaringan kita sebagai fungsi anonim - df adalah DataFrame yang akan disaring

Latihan ini adalah bagian dari kursus

Julia Tingkat Menengah

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Use an anonymous function to filter the tuple nums
nums = ____
filter(____, nums)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Julia Tingkat Menengah

SkillTag.level.beginnerSkillTag.label

4.7+

Mulai Kursus Gratis

Loops are one of the core concepts that underpin Julia. In this chapter, you'll learn about for loops and while loops, and how to use them to iterate over data structures that you are already familiar with. You will also cover ranges, a useful tool for generating sequences of data.

Exercise 1: For loops Exercise 2: For loops question Exercise 3: Loop over a vector Exercise 4: Loops and enumerate Exercise 5: Looping over nested structures Exercise 6: While loops Exercise 7: Writing a while loop Exercise 8: While loops for iteration Exercise 9: Ranges Exercise 10: Ranges - iteration Exercise 11: Defining ranges Exercise 12: Looping over ranges Exercise 13: Splat unpacking

This chapter focuses on expanding your knowledge of the data structures available in Julia. Learn how to use tuples, dictionaries, multi-dimensional arrays, and structures to store and traverse data quickly and efficiently.

Exercise 1: Tuples Exercise 2: When should you use a tuple?Exercise 3: Create, index, and slice a tuple Exercise 4: Create a NamedTuple for a person Exercise 5: Dictionaries Exercise 6: Create untyped dict, iterate Exercise 7: Create typed dict, iterate Exercise 8: Modify keys/values in dict, use get()Exercise 9: Multi-dimensional arrays Exercise 10: Create 1D and 2D arrays Exercise 11: Index and slice a 2D array Exercise 12: Array merging Exercise 13: Structs Exercise 14: Define a structure Exercise 15: Mutable and typed structs

In this chapter, you’ll extend your understanding of functions, exploring positional, keyword, and default function arguments. You will also cover code execution timing, getting a strong understanding of how to measure the time your code takes to run. This chapter will round off with a capstone on writing your own functions to solve real-world problems.

Exercise 1: Execution time and measurement Exercise 2: Built-in time macro Exercise 3: Timing a function Exercise 4: Positional and Default Arguments, Type Declarations Exercise 5: Positional arguments recap Exercise 6: Default arguments Exercise 7: Type declarations Exercise 8: Keyword Arguments Exercise 9: Keyword arguments Exercise 10: Variable number of arguments Exercise 11: Writing Your Own Function Exercise 12: Writing your own functions Exercise 13: Writing your own functions - structs

This final chapter will introduce anonymous functions and will recap one of the powerful features of Julia; multiple dispatch. You will learn how to use functions from Python and R packages within Julia and discover how to clean and modify data within dataframes.

Exercise 1: Fungsi Anonim dan Multiple Dispatch Exercise 2: Multiple dispatch Exercise 3: Fungsi anonim Exercise 4: Menyaring DataFrame

Latihan Saat Ini

Exercise 5: Mengimpor Fungsi dari Python dan R Exercise 6: Mengimpor Python dan R Exercise 7: Fungsi Python di Julia Exercise 8: Fungsi R di Julia Exercise 9: Membersihkan Data Exercise 10: Mengganti nama kolom Exercise 11: Data hilang Exercise 12: Data hilang tingkat lanjut Exercise 13: Selamat!