PYTHON PROGRAMMING UNIT-V : Introduction to Data Science: Functional Programming, JSON and XML in Python, NumPy with Python, Pandas.
Sample Experiments:
1. Python program to check whether a JSON string contains complex object or not. 2. Python Program to demonstrate NumPy arrays creation using array () function. 3. Python program to demonstrate use of ndim, shape, size, dtype. 4. Python program to demonstrate basic slicing, integer and Boolean indexing. 5. Python program to find min, max, sum, cumulative sum of array 6. Create a dictionary with at least five keys and each key represent value as a list where this list contains at least ten values and convert this dictionary as a pandas data frame and explore the data through the data frame as follows: 7. Apply head () function to the pandas data frame 8. Perform various data selection operations on Data Frame
9. Select any two columns from the above data frame, and observe the change in one attribute with respect to other attribute with scatter and plot operations in matplotlib
It covers the fundamental concepts and tools required for data analysis and scientific computing using Python. It introduces functional programming, which helps in writing efficient and reusable code using functions such as map(), filter(), and lambda. The unit also explains JSON and XML in Python, which are widely used data formats for storing and exchanging information between applications. Python provides built-in libraries to read, write, and manipulate these structured data formats effectively.
The unit further focuses on powerful Python libraries such as NumPy and Pandas, which are essential for data science. NumPy is used for numerical computations, array operations, and mathematical functions, making data processing faster and more efficient.Machine Learning Projects for Final Year Pandas provides flexible data structures like Series and DataFrames for handling, cleaning, analyzing, and visualizing data. Together, these tools enable students to perform data manipulation, statistical analysis, and prepare datasets for machine learning and real-world data science applications.
It covers the fundamental concepts and tools required for data analysis and scientific computing using Python. It introduces functional programming, which helps in writing efficient and reusable code using functions such as map(), filter(), and lambda. The unit also explains JSON and XML in Python, which are widely used data formats for storing and exchanging information between applications. Python provides built-in libraries to read, write, and manipulate these structured data formats effectively.
ReplyDeleteThe unit further focuses on powerful Python libraries such as NumPy and Pandas, which are essential for data science. NumPy is used for numerical computations, array operations, and mathematical functions, making data processing faster and more efficient.Machine Learning Projects for Final Year Pandas provides flexible data structures like Series and DataFrames for handling, cleaning, analyzing, and visualizing data. Together, these tools enable students to perform data manipulation, statistical analysis, and prepare datasets for machine learning and real-world data science applications.
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