Concatenation dataframe
IntuitionTo combine data from multiple sources with the same schema, vertical concatenation ensures all rows are included while maintaining consistency in colum
# **Intuition**  
To combine data from multiple sources with the same schema, vertical concatenation ensures all rows are included while maintaining consistency in columns.



# **Approach**  
1. Use `pandas.concat()` to concatenate the two DataFrames vertically.  
2. Set `ignore_index=True` to reset the index in the resulting DataFrame for a clean, continuous index.  



# **Complexity**  
- **Time Complexity**: $$O(n)$$, where \(n\) is the total number of rows from both DataFrames.  
- **Space Complexity**: $$O(n)$$, as the new DataFrame requires storage for all rows.  

# Code
```pythondata []
import pandas as pd

def concatenateTables(df1: pd.DataFrame, df2: pd.DataFrame) -> pd.DataFrame:
   
    newdf = pd.concat([df1, df2], ignore_index=True)
    return newdf


data1 = {
    "student_id": [1, 2, 3, 4],
    "name": ["Mason", "Ava", "Taylor", "Georgia"],
    "age": [8, 6, 15, 17]
}
df1 = pd.DataFrame(data1)


data2 = {
    "student_id": [1, 2, 3, 4],
    "name": ["Mason", "Ava", "Taylor", "Georgia"],
    "age": [8, 6, 15, 17]
}
df2 = pd.DataFrame(data2)

result = concatenateTables(df1, df2)
print(result)

```
['Pandas']