NumPy and Pandas
NumPy: A Python library for fast numerical computations using powerful multi-dimensional arrays and mathematical functions. Pandas: A Python library built on NumPy for flexible, easy-to-use data manipulation and analysis with labeled data structures like Series and Data Frames.
sai rohit kappala Introduction:
Explain why data handling is important in Python:
Data is at the heart of every analysis and machine learning project. Two Python libraries — NumPy and Pandas,make data manipulation fast, flexible, and efficient. In this blog, we’ll explore what they are, how they differ, and how you can start using them right away.
Pandas:
What is Pandas:
Built on top of NumPy
Used for: Data manipulation, cleaning, and analysis
Key features:
1.Series (1D labeled array)
2.DataFrame (2D labeled data)
3.Reading/writing from CSV, Excel, JSON, SQL, etc.
Pandas Methods:
1. Creation Methods:
2. Head and Tail:
3. Info and Description:
4.Selection and Indexing:
5.Adding or Dropping Columns:
6.Filtering Data:
7.Sorting:
8.Aggregation and Statistics:
9.Handling Missing Data:
10.Reading and Writing Files:
11.Grouping and Aggregation:
12.Merge, Join, and Concat:
Numpy:
What is NumPy:
Full form: Numerical Python
Used for: Numerical computation, multi-dimensional arrays, mathematical operations.
Key features :
1.N-dimensional array object (ndarray)
2.Broadcasting
3.Linear algebra functions
4.Random number generation
NumPy Methods:
1. Array Creation:
2. Array Attributes:
3. Reshaping:
4. Indexing and Slicing:
5. Mathematical Operations:
6. Matrix Operations:
7. Random Module:
8. Concatenation and Splitting:
9. Logical and Comparison:
10. Copy and View:
When to Use Which:
Use NumPy when you need:
1.High-performance numeric computations
2.Matrix algebra, Fourier transforms, random sampling
Use Pandas when you need:
1.Data cleaning, transformation, or aggregation
2.Reading/writing structured data
3.Working with labels or time series
