Pandas

Vectorized operation

import pandas as pd
import numpy as np
import time

# Sample DataFrame
data = pd.DataFrame({
    'A': np.random.rand(1000000),
    'B': np.random.rand(1000000)
})

# Non-vectorized approach
def non_vectorized(df):
    start_time = time.time()
    df['C'] = df.apply(lambda row:
row['A'] * row['B'], axis=1)
    print(f"Non-vectorized: {time.time() - start_time:.4f} sec")

# Vectorized approach
def vectorized(df):
    start_time = time.time()
    df['C'] = df['A'] * df['B']
    print(f"Vectorized: {time.time() - start_time:.4f} secs")

# Usage example
non_vectorized(data.copy())
vectorized(data.copy())

#Non-vectorized: 7.0205 sec
#Vectorized: 0.0045 secs

Python| Pandas dataframe.append() - GeeksforGeeks

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas dataframe.append() function is used to append rows of other dataframe to the end of the given dataframe, returning a new dataframe object.

https://www.geeksforgeeks.org/python-pandas-dataframe-append/