Python for Data Analysis

  Data Cleaning and Preparation Handling Missing Values Missing values are entries in your dataset that have no recorded data. In Python and Pandas, missing values are typically represented as: NaN (Not a Number) — from NumPy None — a Python built-in null object Example: import pandas as pd import numpy as np data = {     "Name": ["Ramya", None, "Swathi"],     "Age": [24, 28, np.nan],     "City": ["Chennai", "Delhi", None] } df = pd.DataFrame(data) print(df) Output:     Name   Age    City 0  Ramya  24.0  Chennai 1   None  28.0    Delhi 2 Swathi   NaN     None Why Should We Handle or Remove Missing Values? Missing data can significantly affect the quality and outcome of data analysis or machine learning models. 1. Accuracy and Validity      If missing values are ignored, calculations like mean, sum, correlation, etc., can give incorrect re...