Efficient Data Merge - Pandas
Intuition\nThe problem requires combining information from two tables (Person and Address) based on a common personId. The goal is to retrieve the first name, l
# Intuition\nThe problem requires combining information from two tables (Person and Address) based on a common personId. The goal is to retrieve the first name, last name, city, and state for each person. If a person does not have an address (i.e., no corresponding entry in the Address table), we should return NULL values for city and state.\n\n# Approach\n1. Merge DataFrames: Use pd.merge() to merge the person and address DataFrames based on the personId column, using a left join.\n\n2. Select Columns: After merging, select the columns firstName, lastName, city, and state from the merged DataFrame to match the desired output format.\n\n# Complexity\n- Time complexity:\nThe time complexity of pd.merge() in pandas is O(m*n), where m and \nn are the number of rows in the person and address DataFrames, respectively. This reflects the cost of sorting and merging the data frames.\n\n- Space complexity:\nThe space complexity is O(m+n), where m and n are the sizes of the person and address DataFrames, respectively. This accounts for storing the merged DataFrame and any temporary data structures used during the merge operation.\n\n# Code\n**Pandas**\n```\nimport pandas as pd\n\ndef combine_two_tables(person: pd.DataFrame, address: pd.DataFrame) -> pd.DataFrame:\n    # Merge the person and address DataFrames using a left join on \'personId\'\n    result = pd.merge(person, address, how=\'left\', on=\'personId\')\n    \n    # Select the required columns and reorder them\n    result = result[[\'firstName\', \'lastName\', \'city\', \'state\']]\n    \n    return result\n```
['MySQL']