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Numerical Computing in Python with NumPy
Master Numerical Computing and Data Manipulation with NumPy: Python's essential Data Science Library. Apply NumPy for data science & machine learning.Preview Numerical Computing in Python with NumPy course
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NumPy, short for "Numerical Python," is a powerful library in Python designed for numerical computations and data manipulation. It forms the foundation of many data science, scientific, and engineering applications in Python.
NumPy introduces the ndarray, a multi-dimensional array object that allows efficient storage and operations on large datasets. These arrays can be one-dimensional (vectors), two-dimensional (matrices), or even higher-dimensional. NumPy provides a vast collection of mathematical functions and routines that work seamlessly with arrays, enabling vectorized operations and speeding up computations compared to traditional loop-based approaches.
Additionally, NumPy supports broadcasting, a feature that automatically extends the shapes of smaller arrays to perform element-wise operations with larger arrays. This makes it possible to perform operations on arrays with different dimensions, simplifying code and increasing efficiency. NumPy's integration with other libraries, such as SciPy for scientific computing and Matplotlib for data visualization, creates a powerful ecosystem for data analysis and numerical computation in Python. Its widespread adoption and ease of use have made NumPy an essential tool for data scientists, researchers, engineers, and anyone involved in numerical computing tasks.
NumPy offers several features that make it incredibly useful in data science and machine learning applications:
1. N-dimensional Arrays: NumPy provides the ndarray data structure, enabling the creation and manipulation of multi-dimensional arrays. These arrays can efficiently store and process large datasets, making it easier to work with complex data structures common in data science.
2. Broadcasting: NumPy supports broadcasting, which allows element-wise operations on arrays with different shapes and dimensions. This simplifies calculations and makes code concise, enhancing performance.
3. Vectorized Operations: NumPy allows vectorized operations, where mathematical operations are applied element-wise to entire arrays. This is significantly faster than traditional loop-based operations and enhances performance.
4. Mathematical Functions: NumPy provides an extensive library of mathematical functions and routines for array computations. It includes functions for basic arithmetic, trigonometry, logarithms, statistics, linear algebra, and more.
5. Integration with other Libraries: NumPy integrates seamlessly with various Python libraries used in data science and machine learning, such as SciPy (for scientific computing), pandas (for data manipulation), and Matplotlib (for data visualization). This interoperability creates a powerful ecosystem for data analysis and exploration.
6. Efficient Memory Usage: NumPy uses contiguous memory blocks, leading to efficient memory management and reduced overhead during data operations.
7. Random Number Generation: NumPy includes facilities for random number generation, essential for simulating datasets and performing statistical analyses.
In data science and machine learning, NumPy's features play a crucial role:
1. Data Preparation: NumPy's multi-dimensional arrays are used to store and manipulate datasets, making it easier to preprocess data, perform filtering, and apply feature engineering techniques.
2. Mathematical Operations: NumPy's mathematical functions enable vectorized calculations, making complex numerical computations more efficient. This is especially beneficial when dealing with large datasets in machine learning algorithms.
3. Linear Algebra: NumPy supports linear algebra operations, such as matrix multiplication and solving systems of equations, which are fundamental to machine learning models like linear regression and neural networks.
4. Interoperability: As a fundamental library, NumPy is used alongside other data science libraries like pandas and SciPy, allowing seamless data transformations, analysis, and visualization.
In summary, NumPy's powerful array operations, efficient memory usage, and seamless integration with other libraries make it an indispensable tool for data scientists and machine learning practitioners. Its widespread adoption in the Python data science ecosystem underscores its critical role in enabling high-performance numerical computing tasks.
Course/Topic - Python Programming (advanced) - all lectures
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In this lecture session of python programming we learn about the basic introduction of Numpy and also talk about features of Numpy in python programming.
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In this lecture session we learn about Numpy tutorial basics and also talk about functions and importance of Numpy in python programming in advance.
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In this lecture session we learn about Numpy attributes and functions in python programming in python programming and also talk about features of Num py attributes.
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In this lecture session we learn about creating arrays from existing data and also talk about how we create arrays in the best way and features of array.
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In this lecture session we learn about creating arrays from ranges in python programming and also talk about features of creating arrays from ranges in brief.
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In this lecture session we learn about indexing and slicing in Numpy and also talk about features and importance of indexing and slicing in Num py.
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In this lecture session we learn about advanced slicing in Num py and also talk about features of advanced slicing in Numpy.
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In this lecture session we learn about append and resize functions in python programming and also talk about functions of append and resize functions.
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In this lecture session we learn about Nditer function and broadcasting in python programming and also talk about Nditer functions.
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In this lecture session we learn about Nditer functions in python programming and also talk about features of Nditer functions in brief.
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In this lecture session we learn about Numpy broadcasting in python programming and also talk about features of Numpy broadcasting.
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In this lecture session we learn about Numpy broadcasting in python programming and also talk about features of Numpy broadcasting.
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In this lecture session we learn about Numpy broadcasting functions in python programming and also talk about functions of Num py broadcasting.
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In this lecture session we learn about arrays manipulation functions in python programming and also talk about features of array manipulation functions.
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In this lecture session we learn about Num py unique in python programming and also talk about features of Num py uniques.
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In this lecture session we learn about Numpy delete in python programming and also talk about features of Numpy delete().
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In this lecture session we learn about containing alternate values from array deleted and also talk about double dimension arrays.
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In this lecture session we learn about Num py insert function in python programming and also talk about different types of function in python.
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In this lecture session we learn about Num py Ravel() swapaxes in python programming and also talk about features of swapaxes().
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In this lecture session we learn about split function in python programming and also talk about some functions and features of split function.
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In this lecture session we learn about HSplit functions in python programming and also talk about the importance of HSplit functions.
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In this lecture session we learn about left shift and right shift functions in python programming and also talk about features of left shift and right shift functions.
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In this lecture session we learn about left shift and right shift functions in python programming and also talk about features of left shift and right shift functions.
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In this lecture session we learn about In this lecture session we learn about and also talk about features of NumPy Trigonometric Functions.
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In this lecture session we learn about Numpy round functions in python programming and also talk about factors of round functions.
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In this lecture session we learn about Num py arithmetic functions in python programming and also talk about features of arithmetic functions.
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In this lecture session we learn about Num py power and reciprocal functions in python programming and also talk about features of Num py power and reciprocal functions.
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In this lecture session we learn about Num py power and mod function in python programming and also talk about features of mod functions.
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In this lecture session we learn about Num py Imag() and real() in python programming and also talk about features of Imag() real() functions.
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In this lecture session we learn about Numpy concatenate functions in python programming and also talk about factors and features of concatenate functions.
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In this lecture session we learn about Num py statistical functions in python programming and also talk about features of statistical functions.
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In this lecture session we learn about Numpy statistical functions in python programming and also talk about mean median Ptp functions in brief.
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In this lecture session we learn about Num py average functions in python programming and also talk about features of average functions.
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In this lecture session we learn about Num py sort search counting and also talk about factors of search counting functions in brief.
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In this lecture session we learn about Num py search sort algorithms in python programming and also talk about features of search sort algorithms in brief.
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In this lecture session we learn about Sort() functions and also talk about features of Sort() function in python programming.
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In this lecture session we learn about Numpy sort functions and also talk about features Sort function in brief.
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In this lecture session we learn about Numpy argsort in python programming and also talk about features of argsort in brief.
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In this lecture session we learn about Non zero where in python programming and also talk about function of Nonzero where in brief.
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In this lecture session we learn about Extract and Int in python programming and also talk about key features of Extract.
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In this lecture session we learn about argmax() and argmin in python programming and also talk about features of argos and argmin.
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In this lecture session we learn about Byteswap copies and views in python programming and also talk about features of Byteswap couples and views.
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In this lecture session we learn about str functions in Numpy in python programming and also talk about features of str functions.
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In this lecture session we learn about string function in numpy add and also talk about features of ADD() and Multiply() functions.
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In this lecture session we learn about Numpy centers in python programming and also talk about functions of Numpy centers.
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In this lecture session we learn about capitalize centers in python programming and also talk about features of capitalize centers.
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In this lecture session we learn about string functions in python programming and also talk about features of string functions.
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In this lecture session we learn about String functions in advance and string functions in python programming in brief.
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In this lecture session we learn about Numpy matrix library in python programming and also talk about features of Numpy matrix in brief.
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In this lecture session we learn about Numpy joining arrays in python programming and also talk about functions of arrays joining.
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In this lecture session we learn about Linear algebra in python programming and also talk about features of linear algebra.
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In this lecture session we learn about features of Linear algebra in python programming and also talk about the importance of linear algebra.
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In this lecture session we learn about Linear algebra examples and also talk about real time examples of linear algebra.
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In this lecture session we learn about Linear algebra features and real time examples of Arrays functions using linear algebra.
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In this lecture session we learn about the program of determination in matrix using linear algebra and also talk about features of determination in matrix.
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In this lecture session we learn about inverse of matrix using linear algebra and also talk about features of inverse of algebra.
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In this lecture session we learn about the program of inverse of matrix in brief and also talk about functions of inverse of matrix.
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In this lecture session we learn about random modules in python programming and also talk about features of random modules in brief.
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In this lecture session we learn about features of random modules in python programming and also talk about functions of random modules.
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In this lecture session we learn about random rand modules in python programming and also talk about Numpy import in brief.
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In this lecture session we learn about the program of random rand modules in python programming and also talk about features of random modules.
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In this lecture session we learn about python tags, copyright and credits in randam modules and also talk about features of tags, copyrights and credit.
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In this lecture session we learn about the difference between randn and rand() functions in python programming.
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In this lecture session we learn about random integers and also talk about features of random integers modules in brief.
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In this lecture session we learn about how we use dimension of arrays in random modules in brief.
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In this lecture session we learn about permutation in random modules in python programming and also talk about functions of permutations.
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In this lecture session we learn about python random modules in python programming and also talk about python modules.
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In this lecture session we learn about random choice sequences and also talk about features of random choices.
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In this lecture session we learn about random select modules in python programming and also talk about functions of random select modules.
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In this lecture session we learn about how we get a random boolean in python using random choices and also talk about features of choice from the tuples.
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In this lecture session we learn about how we generate random strings and passwords in python and also talk about import random.
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In this lecture session we learn about how to generate an uppercase random string of fixed length and also talk about features of uppercase random string module.
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In this lecture session we learn about how we generate a random alphanumeric string letter and also talk about features of alphanumeric string.
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In this lecture session we learn about random seed in python programming and also talk about functions of random need modules.
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In this lecture session we learn about random use, random seed and shuffle in python programming.
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In these lecture sessions we learn about generating a random alphanumeric string with a fixed count of letters.
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In this lecture session we learn secrets modules in python programming and also talk about features of secrets modules in brief.
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In this lecture session we learn about secret python modules to generate secure random numbers and also talk about the importance of secrets modules.
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In this lecture session we learn about random module uniform functions and also talk about features of random module uniforms functions.
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In this lecture session we learn about random modules that generate numbers except K and also talk about features of random modules that generate numbers except K.
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In this lecture session we learn about secrets module generate tokens in python programming.
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In this lecture session we learn about random modules that generate binary strings in brief.
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In this lecture session we learn about Numpy module revision in python programming and also talk about features of Numpy module revision in brief.
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In this lecture session we learn about Numpy module revision features in python programming and also talk about importing Numpy.
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In this lecture session we learn about Numpy indexing in python programming and also talk about the importance of indexing.
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In this lecture session we learn about Numpy basic operations in python programming and also talk about features of basic operations.
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In this lecture session we learn about Unary operators in Numpy in python programming and also talk about features of unary operators in Numpy.
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In this lecture session we learn about binary operators in Numpy in python programming and also talk about the importance of binary operators.
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In this lecture session we learn about universal operators in Numpy and also talk about features of universal operators in Numpy.
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In this lecture session we learn about Numpy filter arrays and also talk about the importance of filter arrays in python programming.
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In this lecture session we learn about Numpy module projects in python programming and also talk about other module projects.
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In this lecture session we learn about how to remove from one array those items that exist in another module projects.
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In this lecture session we learn the Numpy program to find the max and mine in python programming.
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In this lecture session we learn about compute mean, STD, variance of a given array along the second axis in python programming.
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In this lecture session we learn about the covariance matrix of 2 arrays and also talk about features of the covariance matrix.
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In this lecture session we learn about covert Numpy dtypes to native python types also talk about features of covert dtypes.
1. Understand NumPy Basics: Gain a solid understanding of NumPy's fundamental concepts, including multi-dimensional arrays, array creation, indexing, and slicing.
2. Perform Array Operations: Learn to perform various mathematical, logical, and statistical operations on NumPy arrays, utilizing vectorized computations for efficiency.
3. Explore Broadcasting and Universal Functions: Explore NumPy's broadcasting feature to handle operations on arrays with different shapes and dimensions. Utilize universal functions (ufuncs) for element-wise operations.
4. Manipulate Multi-dimensional Arrays: Learn advanced techniques for reshaping, stacking, and splitting arrays to efficiently organize and process data.
5. Implement Linear Algebra with NumPy: Understand how to perform matrix operations, solve linear equations, and utilize NumPy's linear algebra capabilities for machine learning algorithms.
6. Handle Missing Data: Learn to work with masked arrays, allowing efficient handling of missing or invalid data in datasets.
7. Optimize Numerical Computations: Gain insights into optimizing code using NumPy's efficient memory management and vectorized operations for improved performance.
8. Integrate NumPy with Data Science Libraries: Explore seamless integration with other Python data science libraries like pandas and SciPy, empowering advanced data analysis and exploration.
9. Apply NumPy in Data Science and Machine Learning: Apply NumPy's numerical computing capabilities to real-world data science and machine learning tasks, including data preprocessing, feature engineering, and model implementation.
10. Foster Good Coding Practices: Emphasize writing clean, efficient, and well-documented code using NumPy's best practices to ensure maintainability and readability.
11. Final Project: Apply the acquired skills to a comprehensive final project, solving a real-world numerical computing problem, and showcasing the ability to leverage NumPy effectively in data science tasks.
By the end of this course, participants will have a strong foundation in numerical computing with NumPy, enabling them to confidently use this essential library for various data science and machine learning applications.
Numerical Computing in Python with NumPy - Course Syllabus
Module 1: Getting Started with NumPy
- Introduction to NumPy and its features
- Installing NumPy and setting up the Python environment
- NumPy arrays: Creating, indexing, and slicing arrays
- Broadcasting: Understanding how NumPy handles array shapes
- Basic array operations: Arithmetic, aggregation, and element-wise functions
Module 2: Working with Multi-dimensional Arrays
- Multi-dimensional arrays and their properties
- Array reshaping and stacking
- Universal functions (ufuncs): Applying functions element-wise
- Array broadcasting: Understanding how broadcasting works
- Masked arrays: Handling missing or invalid data
Module 3: Advanced NumPy Operations
- Array manipulation: Concatenation, splitting, and resizing arrays
- Advanced array indexing and slicing techniques
- Fancy indexing: Selecting specific elements or subsets from arrays
- Linear algebra operations with NumPy: Dot products, matrix operations
- Statistical computing with NumPy: Mean, median, variance, and more
Module 4: Data Processing and Visualization with NumPy
- Reading and writing data using NumPy
- Introduction to NumPy's subpackage 'numpy.random'
- Simulation and sampling using random numbers
- Vectorized computation: Benefits of using NumPy over loops
- Basic data visualization with NumPy and Matplotlib
Certifications in Python programming and related fields can be valuable for both beginners and experienced professionals looking to enhance their skills and boost their careers.
Some popular certifications in Python and related domains include:
1. Python Institute Certifications:
• PCAP (Certified Associate in Python Programming): A beginner-level certification focusing on basic Python programming concepts.
• PCPP (Certified Professional in Python Programming): An advanced certification for experienced Python programmers, covering more in-depth topics and best practices.
2. Microsoft Certifications:
• Microsoft Certified: Python for Data Science: This certification focuses on using Python for data analysis, data visualization, and machine learning with Microsoft technologies.
• Microsoft Certified: Azure AI Engineer Associate: Includes Python programming for building AI solutions on Microsoft Azure.
3. Google Cloud Certifications:
• Google Data Engineer: Includes Python for data processing and building data pipelines on Google Cloud Platform (GCP).
4. AWS Certifications:
• AWS Certified Developer: Includes Python for developing applications on the Amazon Web Services (AWS) cloud platform.
5. Data Science and Machine Learning Certifications:
• Uplatz offer various data science and machine learning certification programs that often include Python programming as a core component.
6. Data Analysis and Visualization Certifications:
• Tableau, a popular data visualization tool, offers Tableau Desktop Specialist and Tableau Desktop Certified Associate certifications, which involve working with Python data.
Remember that certifications are not the only way to showcase your skills. Building practical projects and contributing to open-source projects can also be valuable for demonstrating your proficiency in Python and related fields. Additionally, employers often value hands-on experience and real-world projects alongside certifications. Choose certifications that align with your career goals and interests, and consider combining certifications with practical experience to stand out in the job market.
Python offers a wide range of career opportunities and job roles due to its versatility, ease of use, and popularity across various industries. Here are some common career paths and job opportunities in Python:
1. Software Developer/Engineer: Python developers work on designing, developing, and maintaining software applications using Python. They may focus on web development, back-end development, data analysis, or automation.
2. Data Scientist/Data Analyst: Python is a popular language for data analysis and manipulation. Data scientists and data analysts use Python to extract insights, analyze data, build predictive models, and create data visualizations.
3. Machine Learning Engineer: Python is the language of choice for many machine learning projects. Machine learning engineers use Python libraries like TensorFlow and scikit-learn to build and deploy machine learning models.
4. DevOps Engineer: Python is widely used in the DevOps domain for automation, configuration management, and infrastructure-as-code. DevOps engineers use Python to streamline development, testing, and deployment processes.
5. Web Developer: Python, along with frameworks like Django and Flask, is commonly used for web development. Web developers use Python to build dynamic and interactive web applications.
6. Full-Stack Developer: Full-stack developers work on both front-end and back-end development of web applications, and Python is often used for the back-end component.
7. Software Tester/Automation Engineer: Python is extensively used for test automation. Software testers and automation engineers use Python to develop automated test scripts and perform testing tasks.
8. Data Engineer: Data engineers use Python to design, build, and manage data pipelines, enabling the extraction, transformation, and loading of data into data warehouses or analytical systems.
9. Cybersecurity Analyst: Python is used in cybersecurity for tasks like network scanning, vulnerability testing, and automating security tasks.
10. Game Developer: Python can be used for game development, particularly for scripting and game logic.
11. Scientific Computing and Research: Python is a popular language in scientific computing and research, used for simulations, data analysis, and computational research.
12. Teaching and Training: With Python's popularity as a beginner-friendly language, there are opportunities for Python instructors and trainers.
Python's versatility and widespread adoption across industries make it a valuable skill for individuals seeking diverse career opportunities. Whether you're interested in software development, data analysis, machine learning, or any other field, Python proficiency can open doors to a rewarding and fulfilling career.
Q.1. What is Python?
Python is a high-level, interpreted, interactive, and object-oriented scripting language. Python is designed to be highly readable. It uses English keywords frequently, whereas the other languages use punctuation, and it has fewer syntactical constructions than the other languages.
Q.2. Compare between Java and Python.
|
Criteria |
Java |
Python |
|
Ease of use |
Good |
Excellent |
|
Speed of coding |
Average |
Excellent |
|
Data types |
Statically typed |
Dynamically typed |
|
Data Science and Machine Learning applications |
Average |
Excellent |
Q.3. What are the key features of Python?
· Python is an interpreted language, so it doesn’t need to be compiled before execution, unlike languages such as C.
· Python is dynamically typed, so there is no need to declare a variable with the data type. Python Interpreter will identify the data type on the basis of the value of the variable.
For example, in Python, the following code line will run without any error:
a = 100
a = "Uplatz"
· Python follows an object-oriented programming paradigm with the exception of having access specifiers. Other than access specifiers (public and private keywords), Python has classes, inheritance, and all other usual OOPs concepts.
· Python is a cross-platform language, i.e., a Python program written on a Windows system will also run on a Linux system with little or no modifications at all.
· Python is literally a general-purpose language, i.e., Python finds its way in various domains such as web application development, automation, Data Science, Machine Learning, and more.
Q.4. What is the purpose of PYTHONPATH environment variable?
PYTHONPATH has a role similar to PATH. This variable tells Python Interpreter where to locate the module files imported into a program. It should include Python source library directory and the directories containing Python source code. PYTHONPATH is sometimes preset by Python Installer.
Q.5. What is the purpose of PYTHONSTARTUP, PYTHONCASEOK, and PYTHONHOME environment variables?
· PYTHONSTARTUP: It contains the path of an initialization file having Python source code. It is executed every time we start the interpreter. It is named as .pythonrc.py in Unix, and it contains commands that load utilities or modify PYTHONPATH.
· PYTHONCASEOK: It is used in Windows to instruct Python to find the first case-insensitive match in an import statement. We can set this variable with any value to activate it.
· PYTHONHOME: It is an alternative module search path. It is usually embedded in PYTHONSTARTUP or PYTHONPATH directories to make switching of module libraries easy.
Q.6. Which data types are supported in Python?
Python has five standard data types:
· Numbers
· Strings
· Lists
· Tuples
· Dictionaries
Q.7. What is the difference between lists and Tuples?
Lists are mutable, i.e., they can be edited.
Tuples are immutable (they are lists that cannot be edited).
Lists are usually slower than tuples.
Tuples are faster than lists.
Syntax:
list_1 = [10, ‘Uplatz’, 20]
Syntax:
tup_1 = (10, ‘Uplatz’ , 20)
Q.8. How is memory managed in Python?
· Memory in Python is managed by Python private heap space. All Python objects and data structures are located in a private heap. This private heap is taken care of by Python Interpreter itself, and a programmer doesn’t have access to this private heap.
· Python memory manager takes care of the allocation of Python private heap space.
· Memory for Python private heap space is made available by Python’s in-built garbage collector, which recycles and frees up all the unused memory.
Q.9. Explain Inheritance in Python with an example.
As Python follows an object-oriented programming paradigm, classes in Python have the ability to inherit the properties of another class. This process is known as inheritance. Inheritance provides the code reusability feature. The class that is being inherited is called a superclass and the class that inherits the superclass is called a derived or child class. Following types of inheritance are supported in Python:
· Single inheritance: When a class inherits only one superclass
· Multiple inheritance: When a class inherits multiple superclasses
· Multilevel inheritance: When a class inherits a superclass and then another class inherits this derived class forming a ‘parent, child, and grandchild’ class structure
· Hierarchical inheritance: When one superclass is inherited by multiple derived classes
Q.10. What is a dictionary in Python?
Python dictionary is one of the supported data types in Python. It is an unordered collection of elements. The elements in dictionaries are stored as key–value pairs. Dictionaries are indexed by keys.
For example, below we have a dictionary named ‘dict’. It contains two keys, Country and Capital, along with their corresponding values, India and New Delhi.
dict={‘Country’:’India’,’Capital’:’New Delhi’, }
Q.11. Can you write an efficient code to count the number of capital letters in a file?
The normal solution for this problem statement would be as follows:
with open(SOME_LARGE_FILE) as countletter:
count = 0
text = countletter.read()
for character in text:
if character.isupper():
count += 1
To make this code more efficient, the whole code block can be converted into a one-liner code using the feature called generator expression. With this, the equivalent code line of the above code block would be as follows:
count sum(1 for line in countletter for character in line if character.isupper())
Q.12. Write a code to sort a numerical list in Python.
The following code can be used to sort a numerical list in Python:
list = ["2", "5", "7", "8", "1"]
list = [int(i) for i in list]
list.sort()
print (list)
Q.13. How will you reverse a list in Python?
The function list.reverse() reverses the objects of a list.
Q.14. How will you remove the last object from a list in Python?
list.pop(obj=list[-1]):
Here, −1 represents the last element of the list. Hence, the pop() function removes the last object (obj) from the list.
Q.15. What are negative indexes and why are they used?
To access an element from ordered sequences, we simply use the index of the element, which is the position number of that particular element. The index usually starts from 0, i.e., the first element has index 0, the second has 1, and so on.
When we use the index to access elements from the end of a list, it’s called reverse indexing. In reverse indexing, the indexing of elements starts from the last element with the index number ‘−1’. The second last element has index ‘−2’, and so on. These indexes used in reverse indexing are called negative indexes.
Q.16. What are split(), sub(), and subn() methods in Python?
These methods belong to Python RegEx ‘re’ module and are used to modify strings.
· split(): This method is used to split a given string into a list.
· sub(): This method is used to find a substring where a regex pattern matches, and then it replaces the matched substring with a different string.
· subn(): This method is similar to the sub() method, but it returns the new string, along with the number of replacements.
Q.17. How are range and xrange different from one another?
Functions in Python, range() and xrange() are used to iterate in a for loop for a fixed number of times. Functionality-wise, both these functions are the same. The difference comes when talking about Python version support for these functions and their return values.
|
The range() Method |
The xrange() Method |
|
In Python 3, xrange() is not supported; instead, the range() function is used to iterate in for loops. |
The xrange() function is used in Python 2 to iterate in for loops. |
|
It returns a list. |
It returns a generator object as it doesn’t really generate a static list at the run time. |
|
It takes more memory as it keeps the entire list of iterating numbers in memory. |
It takes less memory as it keeps only one number at a time in memory. |
Q.18. Define pickling and unpickling in Python.
Pickling is the process of converting Python objects, such as lists, dicts, etc., into a character stream. This is done using a module named ‘pickle’, hence the name pickling.
The process of retrieving the original Python objects from the stored string representation, which is the reverse of the pickling process, is called unpickling.
Q.19. What is a map function in Python?
The map() function in Python has two parameters, function and iterable. The map() function takes a function as an argument and then applies that function to all the elements of an iterable, passed to it as another argument. It returns an object list of results.
For example:
def calculateSq(n):
return n*n
numbers = (2, 3, 4, 5)
result = map( calculateSq, numbers)
print(result)
Q.20. Write a code to get the indices of N maximum values from a NumPy array.
We can get the indices of N maximum values from a NumPy array using the below code:
import numpy as np
ar = np.array([1, 3, 2, 4, 5, 6])
print(ar.argsort()[-3:][::-1])
Q.21. What is a Python module?
Modules are independent Python scripts with the .py extension that can be reused in other Python codes or scripts using the import statement. A module can consist of functions, classes, and variables, or some runnable code. Modules not only help in keeping Python codes organized but also in making codes less complex and more efficient. The syntax to import modules in Python is as follows:
import module_name # include this code line on top of the script
Q.22. What do file-related modules in Python do? Can you name some file-related modules in Python?
Python comes with some file-related modules that have functions to manipulate text files and binary files in a file system. These modules can be used to create text or binary files, update their content, copy, delete, and more.
Some file-related modules are os, os.path, and shutil.os. The os.path module has functions to access the file system, while the shutil.os module can be used to copy or delete files.
Q.23. Explain the use of the 'with' statement and its syntax.
In Python, using the ‘with’ statement, we can open a file and close it as soon as the block of code, where ‘with’ is used, exits. In this way, we can opt for not using the close() method.
with open("filename", "mode") as file_var:
Q.24. Explain all file processing modes supported in Python.
Python has various file processing modes.
· For opening files, there are three modes:
o read-only mode (r)
o write-only mode (w)
o read–write mode (rw)
· For opening a text file using the above modes, we will have to append ‘t’ with them as follows:
o read-only mode (rt)
o write-only mode (wt)
o read–write mode (rwt)
· Similarly, a binary file can be opened by appending ‘b’ with them as follows:
o read-only mode (rb)
o write-only mode (wb)
o read–write mode (rwb)
· To append the content in the files, we can use the append mode (a):
o For text files, the mode would be ‘at’
o For binary files, it would be ‘ab’
25. Is indentation optional in Python?
Indentation in Python is compulsory and is part of its syntax.
All programming languages have some way of defining the scope and extent of the block of codes; in Python, it is indentation. Indentation provides better readability to the code, which is probably why Python has made it compulsory.
Q.26. How are Python arrays and Python lists different from each other?
In Python, when we say ‘arrays’, we are usually referring to ‘lists’. It is because lists are fundamental to Python just as arrays are fundamental to most of the low-level languages.
However, there is indeed a module named ‘array’ in Python, which is used or mentioned very rarely. Following are some of the differences between Python arrays and Python lists.Lists
Arrays can only store homogeneous data (data of the same type).
Lists can store heterogeneous and arbitrary data.
Since only one type of data can be stored, arrays use memory for only one type of objects. Thus, mostly, arrays use lesser memory than lists.
Lists can store data of multiple data types and thus require more memory than arrays.
The length of an array is pre-fixed while creating it, so more elements cannot be added.
Since the length of a list is not fixed, appending items to it is possible.
Q.27. Write a code to display the contents of a file in reverse.
To display the contents of a file in reverse, the following code can be used:
for line in reversed(list(open(filename.txt))):
print(line.rstrip())
Q.28. Differentiate between NumPy and SciPy.SciPy
NumPy stands for Numerical Python.
SciPy stands for Scientific Python.
It is used for efficient and general numeric computations on numerical data saved in arrays. E.g., sorting, indexing, reshaping, and more.
This module is a collection of tools in Python used to perform operations such as integration, differentiation, and more.
There are some linear algebraic functions available in this module, but they are not full-fledged.
Full-fledged algebraic functions are available in SciPy for algebraic computations.
Q.29. Which of the following is an invalid statement?
1. xyz = 1,000,000
2. x y z = 1000 2000 3000
3. x,y,z = 1000, 2000, 3000
4. x_y_z = 1,000,000
Answer: 2
Q.30. Can we make multiline comments in Python?
Python does not have a specific syntax for including multiline comments like other programming languages. However, programmers can use triple-quoted strings (docstrings) for making multiline comments as when a docstring is not used as the first statement inside a method, it gets ignored by Python parser.
Q.31. What would be the output if I run the following code block?
list1 = [2, 33, 222, 14, 25]
print(list1[-2])
1. 14
2. 33
3. 25
4. Error
Answer: 14
Q.32. Write a command to open the file c:\hello.txt for writing.
f= open(“hello.txt”, “wt”)
Q.33. What is __init__ in Python?
Equivalent to constructors in OOP terminology, __init__ is a reserved method in Python classes. The __init__ method is called automatically whenever a new object is initiated. This method allocates memory to the new object as soon as it is created. This method can also be used to initialize variables.
Q.34. What do you understand by Tkinter?
Tkinter is an in-built Python module that is used to create GUI applications. It is Python’s standard toolkit for GUI development. Tkinter comes with Python, so there is no installation needed. We can start using it by importing it in our script.
Q.35. Is Python fully object oriented?
Python does follow an object-oriented programming paradigm and has all the basic OOPs concepts such as inheritance, polymorphism, and more, with the exception of access specifiers. Python doesn’t support strong encapsulation (adding a private keyword before data members). Although, it has a convention that can be used for data hiding, i.e., prefixing a data member with two underscores.
Q.36. What is lambda function in Python?
A lambda function is an anonymous function (a function that does not have a name) in Python. To define anonymous functions, we use the ‘lambda’ keyword instead of the ‘def’ keyword, hence the name ‘lambda function’. Lambda functions can have any number of arguments but only one statement.
Q.37. What is self-keyword in Python?
Self-keyword is used as the first parameter of a function inside a class that represents the instance of the class. The object or the instance of the class is automatically passed to the method that it belongs to and is received in the ‘self-keyword.’ Users can use another name for the first parameter of the function that catches the object of the class, but it is recommended to use ‘self-keyword’ as it is more of a Python convention.
Q.38. What are control flow statements in Python?
Control flow statements are used to manipulate or change the execution flow of a program. Generally, the flow of the execution of a program runs from top to bottom, but certain statements (control flow statements) in Python can break this top-to-bottom order of execution. Control flow statements include decision-making, looping, and more.
Q.39. What is the difference between append() and extend() methods?
Both append() and extend() methods are methods used to add elements at the end of a list.
· append(element): Adds the given element at the end of the list that called this append() method
· extend(another-list): Adds the elements of another list at the end of the list that called this extend() method
Q.40. What are loop interruption statements in Python?
There are two types of loop interruption statements in Python that let users terminate a loop iteration prematurely, i.e., not letting the loop run its full iterations.
Following are the two types of loop interruption statements:
· Python break statement: This statement immediately terminates the loop entirely, and the control flow of the program is shifted directly to the outside of the loop.
· Python continue statement: Continue statement terminates the current loop iteration and moves the control flow of the program to the next iteration of the loop, letting the user skip only the current iteration.
Q.41. What is docstring in Python?
Python lets users include a description (or quick notes) for their methods using documentation strings or docstrings. Docstrings are different from regular comments in Python as, rather than being completely ignored by the Python Interpreter like in the case of comments, Python documentation strings can actually be accessed at the run time using the dot operator when docstring is the first statement in a method or function.
Q.42. What is the output of the following?
x = [‘ab’, ‘cd’]
print(len(list(map(list, x))))
Output:
[[‘a’, ‘b’], [‘c’, ‘d’]].
Explanation: Each element of x is converted into a list.
Q.43. Which one of the following is not the correct syntax for creating a set in Python?
1. set([[1,2],[3,4],[4,5]])
2. set([1,2,2,3,4,5])
3. {1,2,3,4}
4. set((1,2,3,4))
Answer: set([[1,2],[3,4],[4,5]])
Explanation: The argument given for the set must be an iterable.
Q.44. What is functional programming? Does Python follow a functional programming style? If yes, list a few methods to implement functionally oriented programming in Python.
Functional programming is a coding style where the main source of logic in a program comes from functions.
Incorporating functional programming in our codes means writing pure functions.
Pure functions are functions that cause little or no changes outside the scope of the function. These changes are referred to as side effects. To reduce side effects, pure functions are used, which makes the code easy-to-follow, test, or debug.
Python does follow a functional programming style. Following are some examples of functional programming in Python.
filter(): Filter lets us filter some values based on a conditional logic.
list(filter(lambda x:x>6,range(9))) [7, 8]
map(): Map applies a function to every element in an iterable.
list(map(lambda x:x**2,range(5))) [0, 1, 4, 9, 16, 25]
reduce(): Reduce repeatedly reduces a sequence pair-wise until it reaches a single value.
from functools import reduce >>> reduce(lambda x,y:x-y,[1,2,3,4,5]) -13
Q.45. How does Python Flask handle database requests?
Flask supports a database-powered application (RDBS). Such a system requires creating a schema, which needs piping the schema.sql file into the sqlite3 command. So, we need to install the sqlite3 command in order to create or initiate the database in Flask.
Flask allows to request for a database in three ways:
· before_request(): They are called before a request and pass no arguments.
· after_request(): They are called after a request and pass the response that will be sent to the client.
· teardown_request(): They are called in a situation when an exception is raised and responses are not guaranteed. They are called after the response has been constructed. They are not allowed to modify the request, and their values are ignored.
Q.46. Write a Python program to check whether a given string is a palindrome or not, without using an iterative method. Note: A palindrome is a word, phrase, or sequence that reads the same backward as forward, e.g., madam, nurses run, etc.
def fun(string):
s1 = string
s = string[::-1]
if(s1 == s):
return true
else:
return false
print(fun(“madam”))
47. Write a Python program to calculate the sum of a list of numbers.
def sum(num):
if len(num) == 1:
return num[0] #with only one element in the list, sum result will be equal to the element.
else:
return num[0] + sum(num[1:])
print(sum([2, 4, 5, 6, 7]))
Output:
24
Q.48. Do we need to declare variables with data types in Python?
No. Python is a dynamically typed language, I.E., Python Interpreter automatically identifies the data type of a variable based on the type of value assigned to the variable.
Q.49. How will you read a random line in a file?
We can read a random line in a file using the random module.
For example:
import random
def read_random(fname):
lines = open(fname).read().splitlines()
return random.choice(lines)
print(read_random (‘hello.txt’))
Q.50. Write a Python program to count the total number of lines in a text file.
def file_count(fname):
with open(fname) as f:
for i, 1 in enumerate(f):
paas
return i+1
print(“Total number of lines in the text file: ”, file_count(“file.txt”))
Q.51. Why would you use NumPy arrays instead of lists in Python?
NumPy arrays provide users with three main advantages as shown below:
· NumPy arrays consume a lot less memory, thereby making the code more efficient.
· NumPy arrays execute faster and do not add heavy processing to the runtime.
· NumPy has a highly readable syntax, making it easy and convenient for programmers.
Q.52. What is the easiest way to calculate percentiles when using Python?
The easiest and the most efficient way you can calculate percentiles in Python is to make use of NumPy arrays and its functions.
Consider the following example:
import numpy as np
a = np.array([1,2,3,4,5,6,7])
p = np.percentile(a, 50) #Returns