Merging and Joining are used to combine multiple datasets into one.
In real-world Data Analytics projects, data is often stored in different tables, so combining them is an essential skill.<\/p>\n\n\n\n
First, import pandas:<\/p>\n\n\n\n
import pandas as pd<\/pre>\n\n\n\nWhy Merging is Important<\/h2>\n\n\n\n
You may have:<\/p>\n\n\n\n
To perform complete analysis, you must combine them.<\/p>\n\n\n\n
employees = pd.DataFrame({
\"Emp_ID\": [1, 2, 3, 4],
\"Name\": [\"Ali\", \"Sara\", \"Ahmed\", \"Ayesha\"],
\"Department\": [\"IT\", \"HR\", \"Finance\", \"IT\"]
})<\/pre>\n\n\n\nSalary Table<\/h3>\n\n\n\nsalaries = pd.DataFrame({
\"Emp_ID\": [1, 2, 3, 5],
\"Salary\": [60000, 50000, 70000, 45000]
})<\/pre>\n\n\n\n1. Merge in Pandas<\/h2>\n\n\n\n
The merge()<\/code> function is similar to SQL JOIN.<\/p>\n\n\n\nSyntax:<\/p>\n\n\n\n
pd.merge(left_df, right_df, on=\"column_name\", how=\"type\")<\/pre>\n\n\n\nTypes of Merge<\/h2>\n\n\n\nInner Join<\/h3>\n\n\n\n
Returns only matching records from both tables.<\/p>\n\n\n\n
pd.merge(employees, salaries, on=\"Emp_ID\", how=\"inner\")<\/pre>\n\n\n\nOnly Emp_ID present in both tables will appear.<\/p>\n\n\n\n
Left Join<\/h3>\n\n\n\n
Returns all records from the left table and matching from the right.<\/p>\n\n\n\n
pd.merge(employees, salaries, on=\"Emp_ID\", how=\"left\")<\/pre>\n\n\n\nAll employees will appear. Missing salaries will show NaN.<\/p>\n\n\n\n
Right Join<\/h3>\n\n\n\n
Returns all records from the right table.<\/p>\n\n\n\n
pd.merge(employees, salaries, on=\"Emp_ID\", how=\"right\")<\/pre>\n\n\n\nOuter Join<\/h3>\n\n\n\n
Returns all records from both tables.<\/p>\n\n\n\n
pd.merge(employees, salaries, on=\"Emp_ID\", how=\"outer\")<\/pre>\n\n\n\n2. Joining Using join()<\/h2>\n\n\n\n
join()<\/code> is mainly used to join on index.<\/p>\n\n\n\nSet index first:<\/p>\n\n\n\n
employees.set_index(\"Emp_ID\", inplace=True)
salaries.set_index(\"Emp_ID\", inplace=True)<\/pre>\n\n\n\nJoin:<\/p>\n\n\n\n
employees.join(salaries, how=\"left\")<\/pre>\n\n\n\n3. Concatenation (Stacking Data)<\/h2>\n\n\n\n
Concatenate rows:<\/p>\n\n\n\n
pd.concat([employees, employees])<\/pre>\n\n\n\nConcatenate columns:<\/p>\n\n\n\n
pd.concat([employees, salaries], axis=1)<\/pre>\n\n\n\nWhen to Use What?<\/h2>\n\n\n\n
Use merge()<\/code> when joining based on a common column.
Use join()<\/code> when joining based on index.
Use concat()<\/code> when stacking data vertically or horizontally.<\/p>\n\n\n\nWhy Merging and Joining are Important<\/h2>\n\n\n\n
In Data Analytics, you often:<\/p>\n\n\n\n
Combine sales data with customer data
Merge employee records with payroll data
Join product data with inventory records
Combine multiple monthly reports<\/p>\n\n\n\n
Understanding merging helps you build complete and accurate datasets.<\/p>\n\n\n\n
Key Takeaway<\/h2>\n\n\n\n
Merging and Joining allow you to combine multiple datasets into one meaningful dataset.
Mastering these techniques is essential for real-world Data Analytics projects.<\/p>\n\n\n