Q1. How do you load a CSV file into a Pandas DataFrame? A) dataFrame_name = pd.read_csv(file_name/file_path) Q2. How do you check the data type of a column in a Pandas DataFrame? A) dataFrame_name['column_name'].dtype Q3. How do you select rows from a Pandas DataFrame based on a condition? A) new_dataFrame_name = dataFrame_name[condition] Q4. How do you rename columns in a Pandas DataFrame? A) new_dataFrame_name = dataFrame_name.rename(columns={'old_column_name':'new_column_name'}) Q5. How do you drop columns in a Pandas DataFrame? A) new_dataFrame_name = dataFrame_name.drop(columns=['column_name']) Q6. How do you find the unique values in a column of a Pandas DataFrame? A) unique_values = dataFrame_name['column_name'].unique() Q7. How do you find the number of missing values in each column of a Pandas DataFrame? A) missingvalues = dataFrame_name.isna.sum() Q8. How do you fill missing values in a Pandas DataFrame with a specific value? A) dataFrame_name = dataFrame_name.fillna(value) Q9. How do you concatenate two Pandas DataFrames? A) new_dataFrame_name = pd.concat([dataFrame1,dataFrame2]) Q10. How do you merge two Pandas DataFrames on a specific column? A) dataFrame1.merge(dataframe2,on='column_name') Q11. How do you group data in a Pandas DataFrame by a specific column and apply an aggregation function? A) dataFrame_name.grupby('column_name')['column_name'].agg(aggregation_function) Q12. How do you pivot a Pandas DataFrame? A) dataFrame_name.pivot(index='index_column', columns='columns_to_pivot', values='values_to_show') Q13. How do you change the data type of a column in a Pandas DataFrame? A) dataFrame_name['column_name'] = dataFrame_name['column_name'].astype('new_data_type') Q14. How do you sort a Pandas DataFrame by a specific column? A) dataFrame_name.sort_values(by='column_name', ascending=True/False, inplace=True) Q15. How do you create a copy of a Pandas DataFrame? A) new_dataFrame_name = dataFrame_name.copy() Q16. How do you filter rows of a Pandas DataFrame by multiple conditions? A) filtered_dataFrame_name = dataFrame_name[(condition1) & (condition2)] Q17. How do you calculate the mean of a column in a Pandas DataFrame? A) dataFrame_name = dataFrame['column_name'].mean() Q18. How do you calculate the standard deviation of a column in a Pandas DataFrame? A) std_weight = dataFrame['column_name'].std() Q19. How do you calculate the correlation between two columns in a Pandas DataFrame? A) dataFrame_name['column_name'].corr(dataFrame_name['column_name']) Q20. How do you select specific columns in a DataFrame using their labels? A) dataFrame_name[['label1', 'label2', 'labeln']] Q21. How do you select specific rows in a DataFrame using their indexes? A) dataFrame_name.loc[index(es)] Q22. How do you sort a DataFrame by a specific column? A) dataFrame_name.sort_values('column_name') Q23. How do you create a new column in a DataFrame based on the values of another column? A) dataFrame_name['new_column_name'] = dataFrame_name['existing_column_name'].apply(function) Q24. How do you remove duplicates from a DataFrame? A) df.drop_duplicates() Q25. What is the difference between .loc and .iloc in Pandas? A) .loc is label-based indexing while .iloc is integer-based indexing