Python is becoming more popular as a scientific programming language. Matrix and vector operations, critical for scientific computations, have become more accessible and efficient in Python, primarily due to libraries like NumPy and Pandas. Both libraries are renowned for their straightforward syntax and high-performance matrix calculation capabilities, making them indispensable for scientific computations, including machine learning.
Introducing Pandas and NumPy
- Definition: Pandas is an open-source Python toolkit designed for sophisticated data manipulation. It is dependent on NumPy to function.
- Origin: Derived from the term “panel data”, which refers to econometrics based on multidimensional data, Pandas was created by Wes McKinney in 2008 to enhance Python's data analysis capabilities.
- Before Pandas: While Python was adept at data preparation, it only offered limited support for data analysis. With the introduction of Pandas, data analysis in Python transformed, enabling users to efficiently load, manipulate, prepare, model, and analyze data from diverse origins.
- Definition: NumPy, primarily developed in the C language as a Python extension module, is a module designed for numerical computations on arrays, both multidimensional and single-dimensional. Its arrays are more computationally efficient than standard Python arrays.
- History: In 2005, Travis Oliphant developed NumPy by integrating the functionalities of its predecessors: Numeric and Numarray.
- Core Object: NumPy’s primary data structure is the homogeneous multidimensional array. It primarily comprises integers, with dimensions referred to as axes and the number of axes as the rank.
- Shape: Produces a tuple denoting the array size.
- Size: Yields the total number of items in the NumPy array.
- Itemsize: Specifies the byte size of each item.
- Reshape: Adjusts the shape of the NumPy array.
Significance in Scientific Computation
Due to their straightforward syntax and high-performance matrix calculation capabilities, both Pandas and NumPy are considered foundational libraries for any scientific computation, especially in machine learning. They are particularly favorable for data science applications.
Key Differences between Pandas and NumPy
- Data Handling: While Pandas is designed for tabular datasets, NumPy is optimal for numerical data.
- Tools and Objects: Pandas boasts tools like DataFrame and Series for data analysis, whereas NumPy is renowned for its powerful Array object.
- NumPy excels with datasets fewer than 50K rows.
- Pandas is superior for datasets with over 500K rows.
- Between 50K and 500K rows, performance varies based on the operation.
- Usage: Notable companies like SweepSouth utilize NumPy, whereas others such as Instacart, SendGrid, and Sighten prefer Pandas.
- Memory and Efficiency: NumPy is more memory-efficient, using less RAM than Pandas. However, indexing Series objects in Pandas can be slower than in NumPy arrays.
- Popularity: Based on referenced stacks, Pandas appears in 73 company stacks and 46 developer stacks, while NumPy appears in 62 company stacks and 32 developer stacks.