Fast and efficient solution, step by step explanation.
Intuition\n Describe your first thoughts on how to solve this problem. \nThe problem requires finding the unique product IDs and names from the first quarter sa
# Intuition\n<!-- Describe your first thoughts on how to solve this problem. -->\nThe problem requires finding the unique product IDs and names from the first quarter sales that are not present in the rest of the sales data.\n# Approach\n<!-- Describe your approach to solving the problem. -->\nTo solve the problem of finding unique product IDs and names from the first quarter sales that are not present in the rest of the sales data, the following approach is taken:\n- Filter the `sales` DataFrame to obtain the sales data for the first quarter, defined as the period from `"2019-01-01"` to `"2019-03-31"`. This is done by creating a boolean mask using the `>=` and `<=` operators on the `"sale_date"` column.\n- Drop any duplicate rows based on the `product_id` in the first quarter sales using the `drop_duplicates` method. This ensures that only unique product IDs are considered.\n- Separate the rest of the sales data by filtering the `sales` DataFrame based on the condition that the `"sale_date"` is either before the start of the first quarter or after the end of the first quarter. This is done using the `|` (`OR`) operator.\n- Use the `isin` method to create **a boolean mask** that identifies the product IDs from the first quarter sales that are also present in the rest of the sales data.\n- Invert the boolean mask using the `~` operator to select the product IDs from the first quarter sales that are not present in the rest of the sales data.\n- Merge the filtered first quarter sales with the `product` DataFrame based on the `product_id` using the `merge` method. This ensures that the resulting DataFrame contains the corresponding product names.\n- Return the resulting DataFrame with only the `product_id` and `product_name` columns.\n\nThis approach efficiently filters and merges the data to obtain the desired result. By dropping duplicate rows and using the `isin` method, we ensure that only unique product IDs and names are returned. The use of boolean masks allows for easy identification of product IDs that are present or not present in the rest of the sales data.\n# Complexity\n#### Time complexity:\n<!-- Add your time complexity here, e.g. $$O(n)$$ -->\n$$O(n + m)$$ The time complexity of this approach is dependent on the size of the `sales` DataFrame. The filtering and merging operations have a time complexity of $$O(n)$$, where $$n$$ is the number of rows in the DataFrame. The use of `isin` has a time complexity of $$O(m)$$, where $$m$$ is the number of unique product IDs in the first quarter sales (`first_q_sales`). \n#### Space complexity:\n<!-- Add your space complexity here, e.g. $$O(n)$$ -->\nIn the given approach, the space complexity can be analyzed as follows:\n- The `sales` DataFrame for the first quarter is filtered and stored in memory. The size of this DataFrame depends on the number of rows and columns in the original data, but for the purpose of space complexity analysis, we can assume it takes $$O(n)$$ space, where $$n$$ is the number of records in the first quarter sales data.\n- The duplicate rows are dropped from the first quarter `sales` DataFrame, resulting in a new DataFrame with unique product IDs. The space required for this operation is proportional to the number of unique product IDs, which we can assume to be $$O(m)$$, where $$m$$ is the number of unique product IDs in the first quarter sales data.\n- Another DataFrame is created to store the rest of the sales data that is not in the first quarter. The space required for this DataFrame is again proportional to the number of records in the rest of the sales data, which we can assume to be $$O(k)$$, where $$k$$ is the number of records in the rest of the sales data.\n- A boolean mask is created to identify the product IDs from the first quarter sales that are also present in the rest of the sales data. This boolean mask requires additional space to store the boolean values for each product ID. Since the size of the mask is equal to the number of unique product IDs in the first quarter sales data, we can assume its space complexity to be $$O(m)$$.\n- The inverted boolean mask is used to select the product IDs from the first quarter sales that are not present in the rest of the sales data. This selection operation does not require any additional memory as it operates on the existing boolean mask.\n- Finally, the resulting DataFrame with the selected product IDs and names is returned. The space required for this DataFrame is again proportional to the number of unique product IDs in the first quarter sales data, which we can assume to be $$O(m)$$.\n\n**TL;DR** \nOverall, the space complexity of the approach can be approximated as $$O(n + m + k)$$, where $$n$$ is the number of records in the first quarter sales data, $$m$$ is the number of unique product IDs in the first quarter sales data, and $$k$$ is the number of records in the rest of the sales data.\n# Running & Memory\n![Screenshot 2023-09-09 at 15.34.45.png](https://assets.leetcode.com/users/images/acc345a4-fde5-4d4a-81c9-0a580ec5360c_1694256298.5338826.png)\n\n# Code\n```\nimport pandas as pd\n\n\ndef sales_analysis(product_df: pd.DataFrame, sales_df: pd.DataFrame) -> pd.DataFrame:\n    """\n    Perform sales analysis for a specific quarter.\n\n    Parameters:\n    - product_df (pd.DataFrame): DataFrame containing product information.\n    - sales_df (pd.DataFrame): DataFrame containing sales data.\n\n    Returns:\n    - pd.DataFrame: DataFrame with product_id and product_name for products\n                    that had sales in the specified quarter.\n    """\n    # Define the start and finish dates for the analysis quarter\n    start_date, finish_date = "2019-01-01", "2019-03-31"\n\n    # Select products with sales within the specified quarter\n    first_q_sales = sales_df[\n        (sales_df["sale_date"] >= start_date) & (sales_df["sale_date"] <= finish_date)\n    ][["product_id"]].drop_duplicates()\n\n    # Select products with sales outside the specified quarter\n    other_sales = sales_df[\n        (sales_df["sale_date"] < start_date) | (sales_df["sale_date"] > finish_date)\n    ][["product_id"]]\n\n    # Create a boolean mask to identify products that had sales outside the first quarter\n    mask = first_q_sales["product_id"].isin(other_sales["product_id"])\n\n    # Identify products with sales only in the specified quarter\n    selected_products = first_q_sales[~mask].merge(product_df, on="product_id")[\n        ["product_id", "product_name"]\n    ]\n\n    return selected_products\n\n```
['Database', 'Python', 'Python3', 'Pandas']