What is the relation between NumPy and Pandas?

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Introduction

When it comes to the fields of data science and software development, Python is undoubtedly the best programming language. This is due to the several benefits that Python provides, including a user-friendly language and an easy-to-remember grammar. But in addition to that, Python has a substantial number of integrated libraries that let you complete a variety of jobs quickly. Two of these well-liked Python libraries are NumPy and Pandas. In this blog, we will explore the difference between NumPy and Pandas in detail, but before that, we will briefly introduce them.

What is NumPy?

NumPy stands for Numerical Python. One of the simplest and most effective Python libraries for producing and working with numerical objects is this one. The NumPy library was primarily created to accommodate massive multidimensional matrices. The use of one-dimensional and multi-dimensional arrays facilitates the execution of sophisticated mathematical operations and intricate computations. NumPy provides several features that reduce the difficult tasks of data analysis, data scientists, researchers, etc.

Key features of NumPy

Now that we know a little about what NumPy is, let's take a look at some of the key features it offers:

• The "ndarray" function for working with n-dimensional arrays and data structures is one of NumPy's most notable features.
• NumPy makes it easy to run n-dimensional array and matrix-related programs quickly.
• Based on LAPACK and BLAS (Basic Linear Algebra Subprograms), provides useful linear algebra calculations (Linear Algebra Package).
• In OpenCV, NumPy can be used as a general-purpose data structure for things like extracted function points, filter kernels, and images.
• The inability of NumPy to attach data objects to arrays as quickly as Python is one of the language's drawbacks.
• Numerous tools in NumPy are available for merging C/C++ and Fortran programming.
• In NumPy, arrays are homogeneous. includes a multidimensional container for general data (parameterized array data type).Complex operations on linear algebra, the Fourier transform, and random numbers can also be performed using NumPy.
• NumPy also consists of broadcast functions. This makes it extremely useful when working with arrays of irregular shapes, as it casts the shape of smaller arrays according to larger ones.
• NumPy has the ability to define data types to work with different databases.
Note that NumPy is not part of a standard Python installation; Consequently, you must manually install it. However, using PIP, it is quite simple to install and begin utilizing the most recent version of the NumPy library from the Python repository as demonstrated below:
“`
!pip install numpy
 “`

What are pandas?

Pandas stands for Python Data Analysis Library. It is an open-source library specifically designed for data analysis and data manipulation in Python. Pandas is built on top of the NumPy package and relies heavily on NumPy.
Pandas allows us to read from multiple sources like Excel, CSV, SQL and many more. Pandas has two types of data objects:
Pandas DataFrame: This is a mutable two-dimensional data structure with labeled rows and columns, generally compared to Excel and SQL sheets.
Pandas Series: These are one-dimensional labeled arrays for storing heterogeneous data elements, generally compared to columns in MS Excel.
Before Pandas, python supported minimal data analysis, but now it allows various data operations and time series manipulation. Pandas can perform 5 basic operations for data analysis: Load, manage, prepare, model and analyze.

Key features of pandas

Now that we know a little about what Pandas is, let's take a look at some of the key features it offers:

• Pandas can help us transform and pivot datasets.
• It can also help us merge and join datasets.
• The Pandas DataFrame object allows data manipulation along with indexing.
• Pandas also provides good support for data alignment and integrated handling of missing data from datasets.
• Pandas also provides a wealth of tools for reading and writing data between in-memory data structures and various file formats.
• Pandas provides support for data filtering.
• Pandas also provides features such as label-based partitioning, fancy indexing, and subsets of large datasets.
• Pandas also provides engine-based grouping that allows you to split, apply, and combine operations on datasets.
• Pandas provides 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) for working with high-dimensional data in a lower-dimensional data structure.

Note that individual columns in Pandas are referred to as "Series" and multiple series in a collection are called "DataFrames". Since Pandas is not included in the standard Python installation, you have to install it externally using PIP.
“`
!pip install pandas
“`

The key difference between Pandas vs. NumPy

Let's discuss some of the main key differences between Pandas and NumPy:

Data objects in NumPy and Pandas

The primary data object in NumPy is an array, more specifically an ndarray. It is an N-dimensional array that supports various computations and computations. These arrays are much faster than python list based arrays as they do not involve looping. The primary data object in Pandas is also an array. An array is a one-dimensional indexed array. By joining row objects, one can produce DataFrames, a common data type in pandas. n-dimensional indexed arrays are what DataFrames are. Very similar to NumPy's ndarrays, but indexed.

Data type supported in NumPy and Pandas

The NumPy library is mainly used to perform numerical computations and calculations. With a number of functions provided in this module, we can perform complex calculations on fields quickly and easily. At the same time, the pandas library is primarily for data analysis by allowing us to work with CSV, Excel, SQL, etc. It even has some data plotting and visualization features built in.

Uses in deep learning and machine learning

NumPy is one of the core modules on top of which most other python modules are built. The most popular machine learning tool, sci-kit learning modules, can only be fed (accept input as) NumPy arrays. The same is true for complex deep learning tools like TensorFlow. It also takes a NumPy array as input and gives an array as output. Pandas data objects cannot be used directly as input to machine learning and deep learning tools. Before we feed them into the machine learning module, we have to go through several pre-processing steps.

Performance on complex operations

NumPy performs best in complex mathematical calculations on multidimensional arrays. It is insanely faster than pandas in calculations like solving linear algebra, gradient search, matrix multiplication, data vectorization, etc. Doing these calculations on dataframes and serial objects in pandas is tedious and difficult. However, it should be noted that NumPy works best with 50,000 or fewer rows in a dataset, while pandas does best with 500,000 or more rows when manipulating data.

Indexing in Pandas and NumPy

Data rows are not indexed in NumPy arrays by default. However, this is not the case with pandas. By default, data rows are indexed or labeled. You can play with and manipulate indexes. You can use a column as an index or change the label names in the indexes. This is not entirely possible in NumPy.

Conclusion

So in conclusion, even though Pandas was built on top of NumPy, the two Python libraries have significant differences. Both Pandas and NumPy simplify matrix multiplication and are widely used in data science, especially machine learning model development. Therefore, we would recommend all current budding programmers who want to become data scientists, machine learning researchers, or machine learning practitioners to learn these libraries. This will not only open the doors for them to get a job in some of the biggest companies in the world, but also help them in their day-to-day calculations to become good experts in machine learning and data science.

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