Exercises - Jupyter Notebook
Number (Angka)
Python mendukung tipe data angka, termasuk integer (int) dan floating-point (float).
Variable Assignment (Penugasan Variabel)
Variabel digunakan untuk menyimpan data.
String (Teks)
String adalah tipe data yang digunakan untuk teks.
Printing (Cetak)
Menggunakan fungsi print() untuk mencetak data ke layar.
List (Daftar)
List adalah tipe data yang digunakan untuk menyimpan koleksi elemen.
Dictionaries (Kamus)
Dictionary adalah tipe data yang digunakan untuk menyimpan pasangan kunci-nilai.
Booleans (Boolean)
Tipe data boolean hanya memiliki dua nilai, True atau False.
Tuples (Tuple)
Tuple adalah tipe data yang mirip dengan list, tetapi bersifat tidak dapat diubah.
Sets (Himpunan)
Set adalah tipe data yang digunakan untuk menyimpan elemen unik tanpa urutan tertentu.
Comparison Operators (Operator Perbandingan)
Operator perbandingan digunakan untuk membandingkan nilai.
Logical Operators (Operator Logika)
Operator logika digunakan untuk menggabungkan kondisi.
If, Elif, Else (Percabangan)
Percabangan digunakan untuk mengambil keputusan berdasarkan kondisi.
For Loops (Perulangan For)
Perulangan for digunakan untuk mengulangi sejumlah elemen.
While Loops (Perulangan While)
Perulangan while digunakan untuk mengulangi kode selama kondisi tertentu terpenuhi.
Range() (Rentang)
Fungsi range() digunakan untuk menghasilkan rentang bilangan.
List Comprehension
List comprehension adalah cara singkat untuk membuat list.
Functions (Fungsi)
Fungsi digunakan untuk mengelompokkan kode yang dapat dipanggil ulang.
Lambda Expressions
Lambda expressions digunakan untuk membuat fungsi anonim.
Map dan filter
Fungsi map() dan filter() digunakan untuk memproses list.
Methods (Metode)
Metode adalah fungsi yang terkait dengan objek.
Exercises (UTS)
1. What is 7 to the power of 4?
2. Split this string:
s = "Hi there Sam!"
into a list.
3. ['Hi', 'there', 'dad!']
Given the variables:
planet = "Earth"
diameter = 12742
Use .format() to print the following string:
The diameter of Earth is 12742 kilometers.
4.
Given this nested list, use indexing to grab the word "hello"
5. Given this nested dictionary grab the word "hello". Be prepared, this will be annoying/tricky
![](data:image/png;base64,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)
6. What is the main difference between a tuple and a list?
![](data:image/png;base64,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)
7. Create a function that grabs the email website domain from a string in the form: **
user@domain.com
So for example, passing "user@domain.com" would return: domain.com
![](data:image/png;base64,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)
8. Create a basic function that returns True if the word 'dog' is contained
in the input string. Don't worry about edge cases like a punctuation
being attached to the word dog, but do account for capitalization.
![](data:image/png;base64,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)
9. Create a function that counts the number of times the word "dog" occurs in a string. Again ignore edge cases.
![](data:image/png;base64,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)
10. Use lambda expressions and the filter() function to filter out words
from a list that don't start with the letter 's'. For example:**
seq = ['soup','dog','salad','cat','great']
Use lambda expressions and the filter() function to filter out words
from a list that don't start with the letter 's'. For example:**
seq = ['soup','dog','salad','cat','great']
should be filtered down to:
['soup','salad']
![](data:image/png;base64,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)
11. Final Problem
You are driving a little too fast, and a police officer stops you.
Write a function
to return one of 3 possible results: "No ticket", "Small ticket", or
"Big Ticket".
If your speed is 60 or less, the result is "No Ticket". If speed is
between 61
and 80 inclusive, the result is "Small Ticket". If speed is 81 or
more, the result is "Big Ticket". Unless it is your birthday (encoded
as a boolean value in the parameters of the function) -- on your
birthday, your speed can be 5 higher in all
cases.
Posting Komentar untuk "Exercises - Jupyter Notebook"