Netflix-filmlari-va-mehmon-yulduzlarni-ofis-bo‘limida tekshirish

DataCamp-da Python loyihasi

Kino va televidenie ma’lumotlarini manipulyatsiya qilish va vizualizatsiya qilish orqali Python va Intermediate Pythongdan o‘rganilgan asosiy Python ko‘nikmalarini o‘rganilgan holda qo‘llangan.

Loyiha tavsifi

Netflix filmlari vaqt oʻtishi bilan davomiyligi qisqarib borayotganini va list, loopdan tortib pandalar va matplotlibgacha boʻlgan “The Office”ning eng mashhur epizodida qaysi mehmon yulduzlar paydo boʻlishini bilish uchun “keyingi qismni tomosha qilish” tugmasini bosasiz.

Shuningdek, siz ma'lumotlar fanining muhim mahorati - ma'lumotlarni tahlil qilish bo'yicha tajribaga ega bo'lasiz. Bu sizga xom(analiz qilinmagan) ma'lumotlarni manipulyatsiya qilish va o'zingiz yaratgan ma'lumotlardan xulosalar chiqarish kabi muhim vazifalarni bajarishga imkon beradi. Notebook.ipynb faylini ochib loyihani tekshirishingiz mumkin.

Loyiha vazifalari:

1. Ma'lumotlarini lug'atga yuklash
2. Lug'atdan DataFrame yaratish
3. Ma'lumotlarimizni vizual tekshirish
4. Qolgan maʼlumotlar CSV dan yuklash
5. Filmlar uchun filtrlash!
6. Skatter grafigini yaratish
7. Chuqurroq ma’lumotlarni o‘rganish
8. Badiiy bo'lmagan filmlarni belgilash
9. Graflarni ranglar bilan to‘ldirish!
10. Va Boshqalar…?

Python project on DataCamp

Apply the foundational Python skills you learned in Introduction to Python and Intermediate Python by manipulating and visualizing movie and TV data.

Project Description

In this project, you’ll apply the skills you learned in Introduction to Python and Intermediate Python to solve a real-world data science problem. You’ll press “watch next episode” to discover if Netflix’s movies are getting shorter over time and which guest stars appear in the most popular episode of "The Office", using everything from lists and loops to pandas and matplotlib.

You’ll also gain experience in an essential data science skill — exploratory data analysis. This will allow you to perform critical tasks such as manipulating raw data and drawing conclusions from plots you create of the data.

Project Tasks

1. Loading your friend's data into a dictionary
2. Creating a DataFrame from a dictionary
3. A visual inspection of our data
4. Loading the rest of the data from a CSV
5. Filtering for movies!
6. Creating a scatter plot
7. Digging deeper
8. Marking non-feature films
9. Plotting with color!
10. What next?