Pandas and NumPy are two Python libraries that stand out by their great utility and the excellent functionality they add to Python.
What are Pandas?
Developers and industry professionals who need to interact with data daily find Pandas to be an extremely useful tool and obviously it enjoys great popularity among them. Pandas has emerged as one of the most if not most flexible and powerful open-source tool for data-related professionals. The whole Pandas tool centers on Data Frames which have a structure akin to spreadsheets or tables. In Pandas DataFrames users can perform separate operations on rows and columns by virtue of specific indexes possessed by both.
Users can easily modify and manipulate DataFrames. Not only that Pandas has specific functions that address issues like handling missing data, transforming data and as we mentioned a while ago perform independent operations on rows and columns. Further many if not most SQL functions have a Pandas counterpart including popular SQL functions like merge, join, group by and filter by. Considering the great flexibility, diversity of functions and powerful tools that it provides, the great popularity of Pandas among data scientists should come as little surprise.
The growing popularity of Python in the scientific community is partly due to how NumPy and Pandas libraries in python aid scientific computations by facilitating vector and matrix manipulations. The two libraries are today almost indispensable for Machine Learning thanks to the great performance they exhibit while performing matrix computations and the intuitive syntax of the technologies. The two technologies share many important toolbox components with other Data Science technologies like MATLAB and R Programming adding to both their use and popularity.
What is NumPy?
NumPy is a short name for “Numeric” or “Numerical Python”. NumPy is an open-source Python module which can carry out swift mathematical computation on data-related structures like matrices or simply arrays. Matrices and arrays are a critically important part of the larger ML ecosystem. The Python ML Ecosystem finds breadth and completion with the several ML modules that include Pandas, Scikit-learn, TensorFlow, Matplotlib and of course NumPy amongst others.
Comparison - Pandas & NumPy
Through both Pandas and NumPy both are data-related Python libraries there are crucial differences that separate Pandas from NumPy. Let’s illustrate that through the following table:
Parameter
Developer
NumPy
Travis Oliphant
Pandas
Wes McKinney
Release year
2005
2008
Primary use
Working with numeric values to apply math functions
Data analysis with Python
Data Compatibility
Meant especially for numeric data and fast math operations involving arrays and matrices
Meant for simultaneous use of heterogenous data like numerical and alphabets
Performance
Suitable for smaller datasets with less than 50K rows
Pandas works better for large datasets (>500,000 rows)
Primary Tools
Arrays
Series and DataFrames
Memory usage
Less memory hogging than Pandas
Consumes greater memory than NumPy
Objects
Data type, n-dimensional arrays etc. are NumPy objects
Special 2d DataFrames objects
Indexing
Array indexing is comparatively faster in NumPy
Series indexing in Pandas is lower than NumPy array indexing
Usage
Enterprises like Walmart, Instacart, Tokopedia, SendGrid amongst others use NumPy. It features in 32 developer and 62 company stacks.
Many apps use the technology like Abeja, Trivago, Kaidee etc. It enjoys greater industry use being a part of 46 developer and 73 company stacks
Core features of Pandas and NumPy
NumPy
NumPy stands out through its following features:
- Handles data structures and n-dimensional arrays exceptionally well.
- Fast performance while handling apps with n-dimensional arrays and matrices.
- Uses the LAPACK linear algebra and BLAS Basic linear algebra subprograms to effectively compute linear algebra calculations.
- Works as an OpenCV universal data structure
- Comes with several tools facilitating integration with Fortran and C/C++ code.
- Features multidimensional generic containers for homogenous arrays.
- It can perform complex Fourier transform, linear algebra random number operations.
- It also has broadcasting features.
- Ability to handle data type definitions for smooth functioning with different databases.
Pandas
Pandas is characterized by the following features:
- Let’s you reshape and pivot datasets.
- Let’s you merge and join datasets.
- You can index and manipulate DataFrame object data.
- Smooth support for data alignment
- Integrated handling of missing data
- The framework comes with several built-in tools for data read/write functions between a variety of file formats and in-memory data structures.
- Supports filtration of data
- Support for fancy indexing
- You can perform label-based slicing.
- You can sub-set large datasets
- Engine-wise grouping is possible while allowing apply, split and combine operations on data sets.
- You can perform hierarchical axis indexing.
Use cases of Pandas and NumPy
NumPy finds widespread use in:
- The financial industry
- When you need exhaustive Linear Algebra computation
- When you are dealing with statistics
- When Polynomials are involved
- When extensive sorting is necessary
- Easy search functions are needed.
Pandas is the top choice in situations involving:
Economics
Recommendation Systems
Stock Prediction
Statistics
Neuroscience
Analytics
Advertising
Natural Language Processing
Data Science
Big Data
Pandas project case studies
Pandas is perfect for preliminary data analysis as you prepare and explore data. So, it finds many use cases and here are three examples of live Pandas-based projects:
Pandas project case studies
The recommended videos/shows that you see on Netflix are based on recommendation systems designed by data scientists. But before they can create their recommendation model and train it, they must pre-process the existing data in order to understand it. Such pre-analysis and data exploration is performed to perfection by Pandas.
Determining Banking Churn Rates
Customer churn is the business term used to measure ration of customers that stop using a product or service or in o0ther words it captures the number of lost customers. For banks that means people who closed their accounts or switched specific banking products. Banks need data scientists to log such metrics and determine characteristics of the lost customers, things like demographic data, payment medium and others. Data Scientists will also examine the available data on the customer segments that continued to use certain product offerings.
Retail Sales Data Analytics
Customer data holds great promise for retailers too and promise key findings that will contribute significantly to improving existing products or services. Data Analysts and Data Scientists hired by retailers use Pandas to pull all sorts of customer data. With Pandas it is possible for them draw insights from data trends observed across departments which ultimately lead to more aware and better decision making.
Expertise over Big Data Apps :
iSummation has an in-house team of data science and python programming professionals with decades of collective experience. We can help your business leverage the maximum potential of your data using NumPy and Pandas frameworks.