Today, I explored common data issues in pandas and how to handle them. Here’s what I learned: 1. Handling Empty/Null Values Definition: Empty or null values are missing data points in a dataset that can affect analysis. importpandasaspdimportnumpyasnpdata={'Name':['Ramya','Aruna',None,'Sekar'],'Age':[25,np.nan,22,28]}df=pd.DataFrame(data)# Check null values print(df.isnull())# Fill null values df['Age'].fillna(df['Age'].mean(),inplace=True)print(df) 2. Removing Duplicates Definition: Duplic...| DEV Community