Pandas1 Solutions

big-countries.py

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+"""
+
+Table: World
+
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| name        | varchar |
+| continent   | varchar |
+| area        | int     |
+| population  | int     |
+| gdp         | bigint  |
++-------------+---------+
+name is the primary key (column with unique values) for this table.
+Each row of this table gives information about the name of a country, the continent to which it belongs, its area, the population, and its GDP value.
+
+
+A country is big if:
+
+it has an area of at least three million (i.e., 3000000 km2), or
+it has a population of at least twenty-five million (i.e., 25000000).
+Write a solution to find the name, population, and area of the big countries.
+
+Return the result table in any order.
+
+The result format is in the following example.
+
+
+Example 1:
+
+Input: 
+World table:
++-------------+-----------+---------+------------+--------------+
+| name        | continent | area    | population | gdp          |
++-------------+-----------+---------+------------+--------------+
+| Afghanistan | Asia      | 652230  | 25500100   | 20343000000  |
+| Albania     | Europe    | 28748   | 2831741    | 12960000000  |
+| Algeria     | Africa    | 2381741 | 37100000   | 188681000000 |
+| Andorra     | Europe    | 468     | 78115      | 3712000000   |
+| Angola      | Africa    | 1246700 | 20609294   | 100990000000 |
++-------------+-----------+---------+------------+--------------+
+Output: 
++-------------+------------+---------+
+| name        | population | area    |
++-------------+------------+---------+
+| Afghanistan | 25500100   | 652230  |
+| Algeria     | 37100000   | 2381741 |
++-------------+------------+---------+
+
+"""
+
+# This function filters countries based on their area and population thresholds.  
+# It selects countries with an area of at least 3,000,000 square kilometers or a population of at least 25,000,000.  
+# The resulting DataFrame contains only the 'name', 'population', and 'area' columns.  
+
+import pandas as pd
+
+def big_countries(world: pd.DataFrame) -> pd.DataFrame:
+    return world[(world["area"] >= 3000000) | (world["population"] >= 25000000)][["name", "population", "area"]]
+

customers-who-never-order.py

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+"""
+
+Table: Customers
+
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| id          | int     |
+| name        | varchar |
++-------------+---------+
+id is the primary key (column with unique values) for this table.
+Each row of this table indicates the ID and name of a customer.
+
+
+Table: Orders
+
++-------------+------+
+| Column Name | Type |
++-------------+------+
+| id          | int  |
+| customerId  | int  |
++-------------+------+
+id is the primary key (column with unique values) for this table.
+customerId is a foreign key (reference columns) of the ID from the Customers table.
+Each row of this table indicates the ID of an order and the ID of the customer who ordered it.
+
+
+Write a solution to find all customers who never order anything.
+
+Return the result table in any order.
+
+The result format is in the following example.
+
+
+
+Example 1:
+
+Input: 
+
+Customers table:
++----+-------+
+| id | name  |
++----+-------+
+| 1  | Joe   |
+| 2  | Henry |
+| 3  | Sam   |
+| 4  | Max   |
++----+-------+
+Orders table:
++----+------------+
+| id | customerId |
++----+------------+
+| 1  | 3          |
+| 2  | 1          |
++----+------------+
+
+Output: 
++-----------+
+| Customers |
++-----------+
+| Henry     |
+| Max       |
++-----------+
+
+"""
+
+# This function identifies customers who have never placed an order by performing a left join between the customers and orders tables.  
+# It filters out customers whose 'id' does not match any 'customerId' in the orders table (i.e., those with NaN values in 'customerId').  
+# Finally, it selects only the 'name' column, renaming it to 'Customers', and returns the resulting DataFrame.  
+
+def find_customers(customers: pd.DataFrame, orders: pd.DataFrame) -> pd.DataFrame:
+    df = customers.merge(orders, left_on='id', right_on='customerId', how='left')
+    df = df[df['customerId'].isna()]
+    df = df[['name']].rename(columns={'name': 'Customers'})
+    return df

recyclable-and-low-fat-products.py

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+"""
+
+Table: Products
+
++-------------+---------+
+| Column Name | Type    |
++-------------+---------+
+| product_id  | int     |
+| low_fats    | enum    |
+| recyclable  | enum    |
++-------------+---------+
+product_id is the primary key (column with unique values) for this table.
+low_fats is an ENUM (category) of type ('Y', 'N') where 'Y' means this product is low fat and 'N' means it is not.
+recyclable is an ENUM (category) of types ('Y', 'N') where 'Y' means this product is recyclable and 'N' means it is not.
+
+
+Write a solution to find the ids of products that are both low fat and recyclable.
+
+Return the result table in any order.
+
+The result format is in the following example.
+
+
+Example 1:
+
+Input: 
+Products table:
++-------------+----------+------------+
+| product_id  | low_fats | recyclable |
++-------------+----------+------------+
+| 0           | Y        | N          |
+| 1           | Y        | Y          |
+| 2           | N        | Y          |
+| 3           | Y        | Y          |
+| 4           | N        | N          |
++-------------+----------+------------+
+Output: 
++-------------+
+| product_id  |
++-------------+
+| 1           |
+| 3           |
++-------------+
+Explanation: Only products 1 and 3 are both low fat and recyclable.
+
+"""
+
+# Approach:
+# 1. Filter the DataFrame to include only rows where 'low_fats' is 'Y' and 'recyclable' is 'Y'.
+# 2. Select only the 'product_id' column from the filtered DataFrame.
+# 3. Return the resulting DataFrame containing product IDs that meet both conditions.
+
+import pandas as pd
+
+def find_products(products: pd.DataFrame) -> pd.DataFrame:
+    df = products[(products['low_fats'] == 'Y') & (products['recyclable'] == 'Y')]
+    return df[['product_id']]