Data Cleaning and Manipulation in Pandas:
Data Cleaning and Manipulation in Pandas: Handling missing values: In real-world datasets, missing values are quite common. Pandas provides various functions to handle missing values, such as isna() , fillna() , and dropna() . For example: python Copy code import pandas as pd data = { 'Name' : [ 'John' , 'Alice' , 'Bob' , 'Mary' ], 'Age' : [ 25 , 30 , None , 35 ]} df = pd.DataFrame(data) df.fillna( 0 , inplace= True ) print (df) This will fill all the missing values in the DataFrame with 0. Data filtering: Data filtering is the process of selecting rows or columns based on some condition. Pandas provides various ways to filter data, such as using boolean indexing, query() method, and loc[] and iloc[] methods. For example: bash Copy code import pandas as pd data = { 'Name' : [ 'John' , 'Alice' , 'Bob' , 'Mary' ], 'Age' : [25, 30, 35, 40]} df = pd.DataFrame(data) filtered_data =