20 Python DataFrame Programs for Practice

If you’re working with data in Python, Pandas DataFrames are an essential tool. Below is a list of 20 Python DataFrame questions that can help you improve your data manipulation and analysis skills.

Questions

1. Create a Pandas DataFrame from a dictionary of lists.
2. Create a DataFrame from a list of dictionaries.
3. Display the first and last five rows of a DataFrame.
4. Access specific rows and columns in a DataFrame using `loc[]` and `iloc[]`.
5. Filter rows of a DataFrame based on a condition.



6. Sort the DataFrame by one or more columns.
7. Rename columns in a DataFrame.
8. Handle missing data by replacing or dropping NaN values.
9. Group data by a specific column and apply an aggregate function.
10. Merge two DataFrames based on a common column.
11. Concatenate two or more DataFrames along rows or columns.
12. Convert a column in a DataFrame to datetime format.
13. Filter a DataFrame based on multiple conditions using logical operators.
14. Remove duplicate rows from a DataFrame.



15. Pivot a DataFrame using the `pivot_table()` function.
16. Calculate the mean, median, and standard deviation for a DataFrame column.
17. Apply a function to each element in a DataFrame column using `apply()`.
18. Join two DataFrames with a common key column using different types of joins (inner, outer, left, right).
19. Create a DataFrame with a multi-index (hierarchical indexing).
20. Export a DataFrame to a CSV file and read it back into Python.

These questions cover essential operations you can perform with Pandas DataFrames. Practicing these will help you become proficient in handling and analyzing data in Python.