What is the difference between NumPy and Pandas?

NumPy (Numerical Python) and Pandas are both popular libraries in Python used for data manipulation and analysis. While they have some overlapping functionalities, there are key differences between the two:

1. Data Structures: NumPy provides multi-dimensional arrays, known as ndarrays, which efficiently store and manipulate homogeneous data. It is primarily used for numerical computations and scientific computing. Pandas, on the other hand, builds upon NumPy and introduces two primary data structures: Series (1-dimensional labeled array) and DataFrame (2-dimensional labeled array). These data structures offer more flexibility and functionality for working with structured and heterogeneous data.
2. Indexing and Labeling: NumPy arrays are accessed using integer-based indexing, similar to standard Python lists. Pandas, however, introduces the concept of labeled indexing. Data in Pandas Series and DataFrame can be labeled using row and column indices, enabling more intuitive and efficient data retrieval and manipulation.
3. Data Handling: NumPy is focused on numerical computations and provides mathematical functions and operations for array manipulation, linear algebra, statistical analysis, and more. Pandas, on the other hand, is designed for data manipulation and analysis. It offers a wide range of data handling capabilities, including data cleaning, merging, reshaping, slicing, and filtering. Pandas also provides powerful tools for handling missing data and time series data.
4. Data Representation: NumPy arrays are memory-efficient and optimized for numerical computations. They are homogeneous, meaning they can only store data of a single data type. In contrast, Pandas Series and DataFrame can handle heterogeneous data, allowing for the storage and manipulation of different types of data (e.g., numeric, string, categorical).
5. Time Series and Data Alignment: Pandas provides specialized functionalities for working with time series data, including built-in date and time handling capabilities. Pandas also ensures data alignment during operations, automatically aligning data based on the row and column labels. This makes it convenient to perform computations and transformations on datasets with different dimensions or indices.

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