Difference Between Pandas and Numpy in Python
Pandas: Pandas is a powerful, open-source data analysis and manipulation library for Python. It provides data structures and functions needed to manipulate structured data, including functions for reading and writing data in various formats. Pandas' key data structures are "Series" (1-dimensional) and "DataFrame" (2-dimensional), used for manipulating numerical tables and time series data.
NumPy: NumPy, which stands for Numerical Python, is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices. It also includes a large collection of high-level mathematical functions to operate on these arrays. NumPy is particularly useful for numerical computations and is a fundamental package for scientific computing with Python.
Here are the key differences between Pandas and NumPy in Python
| Pandas | NumPy | |
| Data Types | Pandas provides two key data structures: Series (1-Dimensional) and DataFrame (2-Dimensional). | NumPy provides a single key data structure: the n-dimensional array, or ndarray. |
| Data Representation | Data is tabular, similar to Excel spreadsheets or SQL tables, which is easier to understand. | Data is represented in n-dimensional array structures, which can be more difficult to visualize. |
| Flexibility of Indexing | Provides flexible and elaborate methods of indexing data, such as .loc and .iloc for label-based and integer-based indexing respectively. | Allows only integer-based indexing. |
| Handling Missing Data | Has in-built functions for detecting, removing, and replacing missing values. | Does not handle missing values directly. Missing values may require additional handling before numerical computations. |
| Data Alignment | Ensures data alignment. If data is not present in the entire column or row, it will still keep the data with a placeholder (NaN). | Does not ensure data alignment. It simply performs element-wise operations. |
| Functionalities | Provides functionalities like group by, join, merge, and pivot, making it easy to handle relational data. | Lacks such features, and is not as convenient for manipulating data. |
| Performance | Slower when compared to NumPy, especially for array operations due to the additional features and flexibility it provides. | Faster for numerical and array computations due to its internal implementation. |
| Use Cases | Ideal for working with heterogeneous, structured data like in data analysis, data manipulation, data cleaning, etc. | Ideal for mathematical computations requiring multi-dimensional arrays, matrices, and complex mathematical functions. |
| Integration with Other Libraries | Integrates well with many other data manipulation and analysis libraries in Python. | Integral part of many other scientific and mathematical libraries due to its computational prowess. |
| Data Types of Elements | DataFrame and Series can hold elements of different data types. | Array elements must be of the same data type. |
Keep in mind that while pandas and NumPy have different features and strengths, they can and often are used together. Many pandas operations rely on NumPy functions, meaning that the two libraries complement each other in data analysis tasks.