Concatenating DataFrames using Pandas Method and Analysis
CodeIntuitionThe problem requires us to merge two tables by stacking them vertically while preserving all data.The pd.concat() function in Pandas allows us to c
# Code
```pythondata []
import pandas as pd

def concatenateTables(df1: pd.DataFrame, df2: pd.DataFrame) -> pd.DataFrame:
    return pd.concat([df1,df2], ignore_index=True)
```
# Intuition
The problem requires us to merge two tables by stacking them vertically while preserving all data.

The pd.concat() function in Pandas allows us to combine multiple DataFrames along a specified axis.

Since we are stacking the tables, we will concatenate along axis=0 (row-wise).

We use ignore_index=True to reset the index after concatenation.

# Approach
Use pd.concat():

The function pd.concat([df1, df2], ignore_index=True) efficiently combines the tables.

ignore_index=True ensures the resulting DataFrame has a continuous index.

Return the concatenated DataFrame:

Since we only need to merge the tables and return the result, pd.concat() is sufficient.

Complexity Analysis
Time Complexity: 𝑂(𝑛+𝑚)


Where 𝑛 is the number of rows in df1 and 𝑚 is the number of rows in df2.

Since pd.concat() needs to copy both DataFrames, it runs in linear time relative to the total number of rows.

Space Complexity: 𝑂(𝑛+𝑚)

A new DataFrame is created to store the result, requiring additional memory proportional to the sum of the input sizes.


['Pandas']