16.7 Matplotlib Bars

Duration: 3 min

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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 Python programming tutorial focused on creating bar charts using the Matplotlib library. The instructor begins by introducing the `plt.bar()` function, demonstrating its basic syntax with a simple example that plots categories A, B, C, and D with corresponding values 13, 8, 12, and 7. The lesson progresses to show how to create horizontal bar charts by using the `plt.barh()` function, which swaps the x and y axes. The tutorial then covers customization options, including setting the color of the bars using the `color` parameter and adjusting the width of the bars with the `width` parameter. Finally, the instructor explains how to control the height of the bars using the `height` parameter, which is particularly relevant for horizontal bars. The entire lesson is presented on a digital whiteboard with code snippets and a live graph, providing a clear, step-by-step guide for beginners.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video starts with a title slide for a Python tutorial on 'Matplotlib Bars'. The instructor introduces the topic and then transitions to a slide titled 'Creating Bars'. On this slide, the instructor explains that the `plt.bar()` function is used to draw bar graphs. The code example shown includes importing `matplotlib.pyplot` as `plt` and `numpy` as `np`. The example defines two arrays: `x` for the categories (['A', 'B', 'C', 'D']) and `y` for the values ([13, 8, 12, 7]). The instructor then demonstrates how to create the bar chart with `plt.bar(x, y)` and display it with `plt.show()`. The resulting vertical bar chart is visible on the right, with the x-axis labeled A, B, C, D and the y-axis showing the values. The instructor points to the code and the chart, explaining the process.

  2. 2:00 – 2:41 02:00-02:41

    The instructor moves to a new slide titled 'Horizontal Bars'. The code on the screen is updated to use the `plt.barh()` function, which creates horizontal bars. The instructor explains that this function is used to draw horizontal bar graphs, and the x and y axes are swapped. The same data arrays `x` and `y` are used, but the chart now displays the bars horizontally. The instructor then transitions to a slide titled 'Bar Color', where the `color` parameter is introduced. The code is modified to `plt.bar(x, y, color='red')`, and the instructor points to the code, explaining that this will color the bars red. Next, the 'Bar Width' slide is shown, where the `width` parameter is introduced. The code is updated to `plt.bar(x, y, width=0.1)`, and the instructor draws a diagram to illustrate how the width of the bars is reduced. Finally, the 'Bar Height' slide is presented, where the `height` parameter is explained for horizontal bars. The code is updated to `plt.bar(x, y, height=0.1)`, and the instructor explains that this parameter controls the height of the bars, which is the vertical dimension in a horizontal bar chart.

The video provides a comprehensive, step-by-step tutorial on creating and customizing bar charts in Python using Matplotlib. It begins with the fundamental concept of the `plt.bar()` function for vertical bars, then logically progresses to the `plt.barh()` function for horizontal bars. The lesson effectively demonstrates key customization parameters, including `color`, `width`, and `height`, showing how each one modifies the appearance of the chart. The clear progression from basic to more advanced features, supported by live code and visual examples, makes it an effective learning resource for understanding data visualization with Matplotlib.

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