NumPy vs Pandas: What are the differences?

Introduction

NumPy and Pandas are two popular Python libraries used for data manipulation and analysis. While both libraries have similarities, they also have key differences that make them unique in their own way.

Data Structures: NumPy is primarily focused on handling homogenous numerical data through its multi-dimensional arrays called ndarray. It provides efficient and optimized operations for numerical computations. On the other hand, Pandas is built on top of NumPy and offers data structures like Series and DataFrame, which are better suited for handling heterogeneous and tabular data with labeled axes.
Indexing: In NumPy, indexing is done using integer indices similar to standard Python lists. However, in Pandas, indexing can be done using both integer-based and label-based indices. This allows for more flexible and intuitive data selection, manipulation, and alignment.
Functionality: NumPy provides a wide range of mathematical functions and operations for numerical computations. It is excellent for numerical and array operations. On the contrary, Pandas excels in data manipulation tasks like filtering, cleaning, merging, and reshaping data. It offers tools for handling time series data and working with missing data effectively.
Time Complexity: NumPy operations are generally faster than Pandas due to its efficient array computations. For large datasets and extensive numerical computations, NumPy provides better performance. On the other hand, Pandas might be slower for complex operations involving large datasets due to its additional functionalities and data structures.
Use Cases: NumPy is more suitable for tasks that require numerical computations and mathematical operations on multi-dimensional arrays. It is commonly used in scientific computing, simulation, and linear algebra operations. On the other hand, Pandas is preferred for data cleaning, preprocessing, exploration, and analysis tasks such as data wrangling, aggregation, and visualization.

Summary

In Summary, NumPy is ideal for numerical computations with homogenous data using multi-dimensional arrays, while Pandas excels in handling heterogeneous tabular data through labeled data structures with powerful data manipulation capabilities.

For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.

We decided to use scikit-learn as our machine-learning library as provides a large set of ML algorihms that are easy to use. scikit-learn is also scalable which makes it great when shifting from using test data to handling real-world data. scikit-learn also works very well with Flask. Numpy and Pandas are used with scikit-learn for data processing and manipulation.

A large part of our product is training and using a machine learning model. As such, we chose one of the best coding languages, Python, for machine learning. This coding language has many packages which help build and integrate ML models. For the main portion of the machine learning, we chose PyTorch as it is one of the highest quality ML packages for Python. PyTorch allows for extreme creativity with your models while not being too complex. Also, we chose to include scikit-learn as it contains many useful functions and models which can be quickly deployed. Scikit-learn is perfect for testing models, but it does not have as much flexibility as PyTorch. We also include NumPy and Pandas as these are wonderful Python packages for data manipulation. Also for testing models and depicting data, we have chosen to use Matplotlib and seaborn, a package which creates very good looking plots. Matplotlib is the standard for displaying data in Python and ML. Whereas, seaborn is a package built on top of Matplotlib which creates very visually pleasing plots.

NumPy	Pandas
Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.	Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
Powerful n-dimensional arrays; Numerical computing tools; Interoperable; Performant; Easy to use	Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data;Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects;Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations;Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data;Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects;Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;Intuitive merging and joining data sets;Flexible reshaping and pivoting of data sets;Hierarchical labeling of axes (possible to have multiple labels per tick);Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format;Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc.
Statistics
GitHub Stars 30.7K	GitHub Stars -
GitHub Forks 11.7K	GitHub Forks -
Stacks 3.0K	Stacks 1.8K
Followers 799	Followers 1.3K
Votes 15	Votes 23
Pros & Cons
Pros 10 Great for data analysis 4 Faster than list	Pros 21 Easy data frame management 2 Extensive file format compatibility
Integrations
Python	Python

NumPy vs Pandas

Overview

NumPy vs Pandas: What are the differences?

Introduction

Summary

Advice on NumPy, Pandas

Detailed Comparison

What are some alternatives to NumPy, Pandas?

PyXLL

SciPy

Anaconda

Dataform

PySpark

Pentaho Data Integration

Dask

KNIME

StreamSets

Denodo

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