https://github.com/ieshreya/30-days-of-pandas

Solutions to the 30 days of Pandas study plan on Leetcode
https://github.com/ieshreya/30-days-of-pandas

Last synced: 24 days ago
JSON representation

Solutions to the 30 days of Pandas study plan on Leetcode

• Host: GitHub
• URL: https://github.com/ieshreya/30-days-of-pandas
• Owner: ieshreya
• Created: 2023-08-06T00:43:30.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-08-06T01:04:21.000Z (over 1 year ago)
• Last Synced: 2024-10-15T07:46:12.216Z (2 months ago)
• Size: 1000 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# 30 days of pandas

This repository contains solutions to the [30 Days of Pandas](https://leetcode.com/studyplan/30-days-of-pandas/) study plan on Leetcode.

[1. Big Countries](https://leetcode.com/problems/big-countries)

```py

def big_countries(world: pd.DataFrame) -> pd.DataFrame:

    df = world[(world['area'] >= 3000000) | (world['population'] >= 25000000)]

    return df[['name', 'population', 'area']]

```

[2. Recyclable and Low Fat Products](https://leetcode.com/problems/recyclable-and-low-fat-products/)

```py

def find_products(products: pd.DataFrame) -> pd.DataFrame:

    df = products[(products['low_fats'] == 'Y') & (products['recyclable'] == 'Y')]

    return df[['product_id']]

```

[3. Customers Who Never Order](https://leetcode.com/problems/customers-who-never-order/)

```py

def find_customers(customers: pd.DataFrame, orders: pd.DataFrame) -> pd.DataFrame:

    df = customers.merge(orders, how='left', left_on='id', right_on='customerId')

    df = df[df['customerId'].isna()]

    df = df[['name']].rename(columns={'name': 'Customers'})

    return df

```

[4. Article Views I](https://leetcode.com/problems/article-views-i/)

```py

def article_views(views: pd.DataFrame) -> pd.DataFrame:

    df = views[views['author_id'] == views['viewer_id']]

    df = df['author_id'].unique()

    df = sorted(df)

    result_df = pd.DataFrame({'id': df})

    return result_df

```

5.