One of the most popular general-purpose programming languages of today is Python. There are a number of reasons why it has become so popular in a variety of fields like **Data Science, ****Software Engineering**, **Machine Learning**, etc. However, one of the most striking features of Python which makes it stand out amongst other programming languages is its rich set of libraries. Among these libraries, two of the most commonly used and most popular ones are Pandas and NumPy. Both Pandas and NumPy are two important tools in the Python SciPy stack that can be used for any scientific computation, for instance, performing high-performance matrix computations to Machine Learning functions and many more. In this article, we are going to discuss all these amazingly powerful libraries.

## Introduction to Pandas

Pandas is one of the most popular software **libraries of Python** which can be used for data manipulation and analytics as it provides extended data structures to hold different types of labeled and relational data and also allows a lot of operations like merging, joining, reshaping, and concatenating data. Pandas was developed by Wes McKinney in 2008. It has been built on top of the NumPy package of Python (Pandas cannot be used without the usage of NumPy). Released under the three-clause BSD license, Pandas has a variety of data structures and operations to offer for the manipulation of numerical tables and time series. The term “Pandas” comes from the term “Panel Data”. “Panel Data” is a term that is used to describe data sets that include observations over multiple time periods for the same individuals. There are several languages used to write Pandas, including Python, Cython, and C. Pandas support importing data from several file formats, including SQL, JSON, Microsoft Excel, etc. We can take a look at the repository of Pandas using the following link.

The following piece of code shows the usage of Pandas:

# Importing the pandas library (usually it is imported as "pd") import pandas as pd # Creating a nested list and initialising it age = [['Ritik', 99.5, "Male"], ['Bobby', 65.7, "Female"], ['Mona', 85.1, "Female"], ['Virat', 100.0, "Male"]] # Creating a Pandas DataFrame df = pd.DataFrame(age, columns=['Name', 'Marks', 'Sex']) # Printing the DataFrame df

## Key Features of Pandas

Now that we know a bit about what Pandas is, let us take a look at some of the key features it has to offer:

- Pandas can help us in the reshaping and pivoting of datasets.
- It can also help us in the merging and joining of datasets.
- The DataFrame object of Pandas allows the manipulation of data along with indexing.
- Good support for data alignment and integrated handling of missing data from datasets is also provided by Pandas.
- Also, a plethora of tools are provided by Pandas for reading and writing data between in-memory data structures and different file formats.
- Pandas provide support for data filtration.
- Features like label-based slicing, fancy indexing, and subsetting of large data sets are also provided by Pandas.
- Grouping by the engine, which allows split, apply and combine operations on data sets is also provided by Pandas.
- Pandas provide hierarchical axis indexing (Hierarchical indexing is a method of creating structured group relationships in data. These hierarchical indexes, or MultiIndexes, are highly flexible and offer a range of options when performing complex data queries) to work with high dimensional data in a lower dimensional data structure.

## Introduction to NumPy

NumPy is yet another powerful software library of Python which has been in heavy use in the last couple of years. **NumPy **is an open-source library that has a lot of contributors. The official site mentions that NumPy is “the fundamental package for scientific computing with Python.” Operations on big, multi-dimensional arrays and matrices can be easily performed using NumPy. Moreover, NumPy also provides us with a humongous collection of high-level mathematical functions, for instance, the sin() function, the sort() function, etc. to operate on these arrays and their elements. NumPy is a Python library that provides various derived objects (for example – masked arrays and matrices), and an assortment of routines for faster operations on arrays. This includes mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation, and many more such operations. “Numeric” is the ancestor of NumPy and was developed by Jim Hugunin.

Travis Oliphant developed NumPy in 2005 by incorporating some of the features of the competing Numarray into Numeric, with a tonne of modifications. NumPy has very quickly developed into a Python package that can very efficiently handle colossal volumes of data along with support matrix multiplication and data reshaping. NumPy has good support for the object-oriented approach, using ndarray. In other words, ndarray is a class, which consists of a lot of methods and attributes. Most of its methods are mirrored by functions in the outermost NumPy namespace. This allows the programmer to code in the paradigm of their choice. This flexibility has allowed the NumPy array dialect and NumPy ndarray class to become the de-facto language of multi-dimensional data interchange used in Python. We can take a look at the repository of NumPy using the following link.

The following piece of code shows the usage of NumPy:

# Importing the Numpy package (Usually it is imported as "np") import numpy as np # Creating a Three Dimensional numpy array using np.array() marks_array = np.array([[63, 66, 65], [23, 76, 91], [81, 44, 52]]) # Printing the marks_array array created in NumPy print(marks_array)

## Key Features of NumPy

Now that we know a bit about what NumPy is, let us take a look at some of the key features it has to offer:

- One of the most striking features of NumPy is the “ndarray” for dealing with n-dimensional arrays and data structures.
- Programs related to matrices and n-dimensional arrays can be run really very fast using NumPy.
- It provides effective linear algebra computations by relying on BLAS (Basic Linear Algebra Subprograms) and LAPACK (Linear Algebra Package).
- NumPy can be addressed as a universal data structure in OpenCV for images, filter kernels, extracted feature points, etc.
- One of the not-so-good features of NumPy is that it does not allow easy appending of data entries to arrays as quickly as Python does.
- NumPy contains a lot of tools for the integration of code from C/C++ and Fortran.
- The arrays in NumPy are of homogenous nature. It contains a multidimensional container for generic data (parameterized data type of arrays).
- Complex operations on linear algebra, Fourier transform, and random numbers can also be performed using NumPy.
- NumPy also consists of Broadcasting functions. This makes it extremely useful while dealing with arrays of uneven shapes as it broadcasts the shape of smaller arrays according to the larger ones.
- NumPy has data type definition capability to work with varied databases.

## Pandas Vs Numpy: Comparison and Difference

Now that we have a clear understanding of what Pandas and NumPy are, let us take a look at the major Differences Between NumPy and Pandas:

COMPARISON PARAMETER | PANDAS | NUMPY |

Developed By | Pandas was developed by Wes McKinney. | NumPy was developed by Travis Oliphant. |

Year Of Release | Pandas was released in the year – 2008. | NumPy was released in the year – 2005. |

Primary Objective to Use | Pandas is mostly used for data analysis tasks in Python. | NumPy is mostly used for working with Numerical values as it makes it easy to apply mathematical functions. |

Data Compatibility | Pandas library works well for numeric, alphabets, and heterogeneous types of data simultaneously. | Numpy library works better with only numerical data, has efficient storage, and fastly performs mathematical operations on array-based and array-based matrix-based numeric values. |

Performance | If the number of rows of the dataset is more than five hundred thousand (500K), then the performance of Pandas is better than NumPy. | NumPy can be said to be faster in performance than Pandas, up to fifty thousand (50K) rows and less of the dataset. (The performance between fifty thousand rows to five hundred thousand rows mostly depends on the type of operation Pandas, and NumPy are going to have to perform.) |

Tools | DataFrames and Series are the most powerful tools for Pandas. | Arrays are the most powerful tool of NumPy. |

Memory Usage | Pandas consume more memory compared to NumPy. | NumPy has lesser memory consumption compared to Pandas. |

Objects | DataFrames are the two-dimensional (2d) Objects provided by Pandas. | NumPy provides n-dimensional arrays, Data Type (dtype), etc. as objects. |

Indexing | The indexing of pandas series is significantly slower than the indexing of NumPy arrays. | The indexing of NumPy arrays is much faster than the indexing of Pandas arrays. |

Usage or Application in Organisations | Pandas is being used in a lot of popular organizations like Trivago, Kaidee, Abeja Inc., and many more. | Instacart, SendGrid, Walmart, Tokopedia, and many more organizations make use of NumPy. |

Industrial Coverage | Pandas have a higher industry application compared to NumPy as mentioned in 73 company stacks and 46 developer stacks. | NumPy has a lower industry application compared to Pandas as mentioned in 62 company stacks and 32 developer stacks. |

## Conclusion

So, in conclusion, we can say that even though Pandas has been built on top of NumPy, both Python libraries have significant differences. Both Pandas and NumPy simplify matrix multiplication and therefore are being heavily used in the field of Data Science, especially model developments in Machine Learning. Hence, we would recommend all the budding programmers of today who want to become Data Scientists or Machine Learning Researchers, or Machine Learning Practitioners to learn both these libraries. This will not only open gates for them to grab a job at some of the biggest companies in the world but also help them in their day-to-day calculations to become good Machine Learning and Data Science experts.

## Frequently Asked Questions

**Q: What are some of the alternatives to Pandas?**

Answer: Some of the alternatives to Pandas could be as follows:

- NumPy
- PySpark
- R Language
- Apache Spark
- Anaconda
- SciPy

**Q: Is Pandas faster than Numpy?**

Answer: If the number of rows in the dataset is more than five hundred thousand, then the performance of Pandas is better than NumPy. However, NumPy can be said to be faster in performance than Pandas, up to fifty thousand rows and fewer. The performance between fifty thousand rows to five hundred thousand rows mostly depends on the type of operation Pandas, and NumPy is going to have to perform.

**Q: Should I learn Numpy or Pandas first?**

Answer: In our opinion, one should learn NumPy first and then Pandas as Pandas is built on top of NumPy and therefore learning NumPy before Pandas could prove to be advantageous.

**Q: How long does it take to learn Pandas?**

Answer: It mostly depends upon the learner as to how long it will take to learn Pandas or any other topic in general. However, we can say that if a few hours are spent daily on learning Pandas, one should be able to learn it in about two or three weeks.