Practise Questions Set 1 (Pandas) (Q21 - 30)

Duration: 4 min

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Inside: a video lesson and guided study material.

Module outline

  1. HPSC Interview Preparation: Interview Preparation
  2. Python Language: Introduction to Python, Tokens, Variables, Data Types, I/O, Operators & Expressions, Control Flow & Looping, Collections & Strings, Functions & Modules, File Handling & Data Analysis, DSA using Python
  3. Data Structure & Algorithms: Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing, Algorithm Analysis, Sorting Algorithms, Minimum Spanning Trees, Time Complexity Analysis, Graph Theory, Shortest Path Algos
  4. Computer Networks: Introduction to CN, Data Communication, DLL: Access Control, DLL: Flow Control, Data Link Layer - Ethernet, Net Layer: IPv4 & Proto, Net Layer: IP Addressing, Transport Layer Services, TL: Congestion & UDP, Application Layer, Hardware basics, Network Security
  5. Computer System & Organisation: Basics of COA, Cache Memory Organization, Input Output Organisation, Pipelining, Instr Formats & Modes, Control Unit Design, Programming the Basic Computer
  6. Database Management System: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, SQL, File Organization & Indexing, Transaction Management, Concurrency Control
  7. Operating Systems: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Deadlock, Threads & Process Creation, Memory Management, Virtual Memory, Disc Scheduling
  8. Digital Logic & Circuits: Digital Systems & Boolean Basics, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Combinational Circuit, Sequential Circuits, Number System, Number Representation
  9. Theory Of Computation: Introduction to TOC, Deterministic FA (DFA), Non-Deterministic FA, Regular Expressions, Grammar, Regular Language Properties, Pushdown Automata & CFG, Turing Machines, Moore & Mealy Machines
  10. Compiler Design: Intro to Compilers, Lexical Analysis, Grammar & CFG, Syntax Analysis: Top-Down, Syntax Analysis: Bottom-Up, Semantic Analysis & SDT, Intermediate Code Gen, Code Optimization, Run Time Environment
  11. Software Engineering: Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management, Data Warehousing & Mining
  12. Introduction to Emerging Trends: Emerging Tech Trends
  13. Digital Society & Cyber Law: Society, Law & Ethics
  14. HPSC PGT Previous Year Paper:
  15. HPSC Assistant Professor Previous Year Paper:
  16. Live Sessions: HPSC Live Sessions, EMRS TGT/PGT Live Class
  17. Full Mock Test:
  18. HPSC PGT - MOCK Test Series:
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

This video is a lecture on Python's pandas library, focusing on multiple-choice questions (MCQs) related to DataFrame operations. The instructor, standing in front of a digital screen, explains the output of various pandas functions. The first section covers question 21, where a DataFrame with duplicate values is created, and the `drop_duplicates()` method is used to remove duplicate rows, resulting in the same DataFrame. The second section, from 2:00 onwards, covers questions 23 through 30. It explains the `iloc` method for selecting the first column, the `loc` method for selecting a specific value, the `empty` attribute to check if a DataFrame is empty, the `rename` method to change column names, the `count()` method which counts non-null values, the `ndim` attribute for the number of dimensions, and the `values` attribute which returns a NumPy array. The instructor uses on-screen code and handwritten annotations to clarify the concepts.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video begins with a discussion of question 21. The instructor displays the code `df = pd.DataFrame({'A':[1,2,3]})` and `print(df.drop_duplicates())`. He explains that the `drop_duplicates()` method removes duplicate rows. Since the DataFrame has no duplicate rows, the output is the same DataFrame. The instructor uses a yellow marker to write the values [1, 2, 3] under the column 'A' and circles the number 2 to emphasize the data. The correct answer is A: Same DataFrame.

  2. 2:00 – 3:35 02:00-03:35

    The instructor moves to question 23, explaining that `df.iloc[:,0]` selects the first column of the DataFrame, which is the column 'A'. For question 24, he explains that `df.loc[0,'B']` selects the value at row 0 and column 'B', which is 3. For question 25, he explains that `df.empty` returns False because the DataFrame is not empty. For question 26, `df.empty` returns True because the DataFrame is empty. For question 27, he explains that `df.rename(columns={'A':'X'}, inplace=True)` renames the column 'A' to 'X'. For question 28, he explains that `df.count()` counts non-null values, which is 3. For question 29, `df.ndim` returns 2, the number of dimensions. For question 30, `df.values` returns a NumPy array.

The video provides a comprehensive review of fundamental pandas DataFrame operations through a series of MCQs. It systematically covers data selection (iloc, loc), data manipulation (drop_duplicates, rename), and data inspection (empty, count, ndim, values). The instructor uses a clear, step-by-step approach, demonstrating the code and explaining the output, which helps students understand the behavior of these essential pandas functions.

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