Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Nowadays, Python is a programming language widely used by Data Scientists. Python has in-built mathematical libraries and functions, making it easier to calculate mathematical problems and to perform data analysis.
NumPy and Pandas are two most popular libraries in python programming.
NumPy and pandas are often used together in data analysis workflows. NumPy provides the numerical and mathematical backbone, while pandas simplifies data manipulation and analysis, especially when dealing with structured data like CSV files or database tables
Let’s jump into the differences.
| NumPy | Pandas |
|---|---|
| NumPy’s main data structure is the ndarray (n-dimensional array), which is a homogeneous array where all elements have the same data type. It’s suitable for numerical and mathematical operations on multi-dimensional arrays. | Pandas offers two primary data structures, Series and DataFrame. A Series is a 1D labelled array that can hold data of different types, and a DataFrame is a 2D table, essentially a collection of Series. DataFrames are designed for handling structured data with columns and rows, and they allow for heterogeneity in data types within columns. |
| NumPy provides functions for mathematical and logical operations on arrays, such as element-wise addition, multiplication, and more. It’s great for numerical calculations and array manipulations. | Pandas offers a wide range of data manipulation and analysis tools. It allows you to perform operations like filtering, grouping, aggregation, merging, and reshaping data. It’s especially well-suited for data exploration, cleaning, and preparation for analysis. |
| NumPy is more low-level and is focused on numerical computations. It doesn’t have built-in support for handling data in a tabular format. | Pandas is high-level and designed for working with structured data. It includes features for handling missing data, working with time-series data, and more, making it easier to deal with real-world data. |
| NumPy arrays are accessed by integer-based indexing and don’t have built-in support for labels or column names. | Pandas Series and DataFrame objects support both integer-based and label-based indexing. You can assign labels to rows and columns, making it easier to work with data in a more intuitive way. |
| NumPy is ideal for scientific and numerical computing, such as linear algebra, statistical analysis, and simulations. It’s the foundation for many other scientific libraries. | Pandas is well-suited for data analysis, data preparation, and data cleaning tasks. It’s commonly used in data science and machine learning for working with structured datasets. |
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