# NumPy vs Pandas in 2024: Which Library is Better?

11 min read

## Table of contents

- Key Takeaways
- Numpy Overview
- Features of NumPy
- Multidimensional Arrays
- 2. Broadcasting
- 3. Vectorized Operations
- 4. Mathematical Functions
- 5. Indexing and Slicing
- 6. Integration with Other Libraries
- Key Attributes of a NumPy Array
- Common NumPy Functions
- 1. np.array()
- 2. np.zeros()
- 3. np.ones()
- 4. np.full()
- 5. np.arange()
- 6. np.linspace()
- 7. np.eye()
- 8. np.random.rand()
- 9. np.random.randint()

- Pandas Overview
- Features of Pandas
- Main Differences Between NumPy and Pandas
- Conclusion

Python is famous for easy versatility, ease of use, and flexibility. It stands at the forefront of data science, machine learning, and artificial intelligence. Its intuitive syntax and powerful capabilities make it an ideal choice for performing sophisticated data manipulations and extracting meaningful insights from diverse datasets.

Python has an extensive array of libraries such as NumPy and Pandas designed to simplify and enhance the complexity of data-related tasks. NumPy offers robust structures for numerical computing, while Pandas brings ease and efficiency to data manipulation and analysis, particularly with structured data.

In this article, we will explore the distinct features between NumPy and Pandas and their role in the data science ecosystem. We'll also understand how they compare in various aspects of data handling and analysis.

## Key Takeaways

Both NumPy and Pandas are Python libraries used in data manipulation and analytics. Both NumPy and Pandas are designed for efficient data handling and manipulation in Python. Specifically, Pandas is built on top of NumPy, meaning it uses NumPy's array processing capabilities.

NumPy is a Python library that performs various numerical computations and array processing for single and multidimensional array items.

Pandas is a high-performance library used to perform operations on both tabular and non-tabular types of data.

## Numpy Overview

NumPy, which stands for Numerical Python, is a popular Python library for efficient matrix and vector computations. It is an open-source library that stands out for its high-performance multidimensional arrays. It also provides a comprehensive collection of tools to work with these arrays. Unlike base Python, which is not inherently vectorized, NumPy introduces vectorized operations. This enhances the efficiency and speed of numerical analyses and computations.

NumPy provides an extensive range of mathematical functions, including but not limited to transpose, reshape, sum, and dot products. These functions simplify array and matrix operations, making NumPy an ideal choice for scientific computing tasks. Numpy can handle single and multi-dimensional arrays with ease. This is because it's written in C and is amazingly fast and efficient.

It's important to note that NumPy is not part of the standard Python installation and needs to be installed separately. However, its installation is straightforward, typically using Python's package manager, PIP. NumPy's influence extends beyond its own library; it is the foundational library upon which other significant Python data handling and analysis libraries, such as Pandas, are built. Besides, it has an intuitive syntax and robust computational capabilities which makes it a top choice for data analytics, data science, and machine learning, among other scientific computing fields.

## Features of NumPy

Here are some of the key features that make NumPy ideal for data analysis and machine learning:

**Multidimensional Arrays**

**ndarray Class**: At the core of NumPy is the ndarray (n-dimensional array) class. This feature allows for the creation and manipulation of arrays with varying numbers of dimensions (1D, 2D, 3D, etc.), offering great flexibility and efficiency in data handling.**Efficient Storage and Computation**: These arrays provide a more efficient storage and computation mechanism than traditional Python lists, especially for large datasets.**Homogeneous Data**: The elements in a NumPy array are of the same data type, enhancing computational efficiency.

**2. Broadcasting**

**Array Operations**: Broadcasting is a powerful mechanism that allows NumPy to perform arithmetic operations on arrays of different shapes and sizes. This is done without the need for explicit element-by-element loops.**Rules**: Broadcasting follows a set of rules to apply binary operations (like addition, and multiplication) on arrays with different shapes, making coding simpler and faster.

**3. Vectorized Operations**

**Performance**: NumPy enables vectorized operations, meaning operations are applied to entire arrays instead of individual elements. This not only makes code more concise but also significantly faster, as loop overheads in Python are minimized.**Examples**: Operations like adding two arrays, multiplying arrays element-wise, etc., are done with simple syntax and high performance.

**4. Mathematical Functions**

**Comprehensive Library**: NumPy includes an extensive collection of mathematical functions to perform computations on arrays. These include linear algebra routines, statistical functions, Fourier transforms, and more.**Random Number Generation**: It provides various tools for generating random numbers, which are useful in simulations and algorithm development.**Compatibility with SciPy and others**: NumPy’s mathematical capabilities are often extended by libraries like SciPy, which builds upon NumPy arrays.

**5. Indexing and Slicing**

**Advanced Indexing**: NumPy supports complex indexing and slicing operations, allowing for efficient access and modification of array data.**Slicing**: Similar to Python lists, NumPy arrays can be sliced, enabling operations on sub-arrays.**Fancy Indexing**: This includes using index arrays and boolean indexing for more sophisticated data manipulation.

**6. Integration with Other Libraries**

**Ecosystem Compatibility**: NumPy integrates seamlessly with other Python libraries, such as Pandas for data analysis, Matplotlib for plotting, and SciPy for advanced scientific computations.

**Attributes and Functions of NumPy Arrays**

NumPy arrays are powerful tools in Python for numerical computing. Understanding their attributes and the commonly used functions to create and manipulate them is crucial. Here's an overview:

**Key Attributes of a NumPy Array**

**Shape**: This attribute provides a tuple indicating the dimensions of the array. For example, a 1D array with 5 elements has a shape`(5,)`

, while a 2D array with 3 rows and 4 columns has a shape`(3, 4)`

.**dtype**: This indicates the data type of the array's elements, such as`int`

,`float`

,`complex`

, etc. NumPy supports a wide range of data types.**ndim**: This gives the number of dimensions (axes) of the array. For instance, a 1D array has`ndim`

of 1, a 2D array has`ndim`

of 2, and so forth.**size**: It returns the total number of elements in the array, calculated as the product of the shape's elements.**itemsize**: This represents the size (in bytes) of each element in the array.**nbytes**: It provides the total memory size occupied by the array, calculated as`itemsize`

multiplied by`size`

.**data**: A buffer containing the actual data of the array. It's infrequently used directly but can be vital for low-level data manipulation.**flags**: Contains information about the memory layout of the array, like if it's C-contiguous or Fortran-contiguous, or if it's read-only.

**Common NumPy Functions**

Here are some of the most commonly used NumPy functions:

`1. np.array()`

This function creates an array from a Python list or tuple. Here is an example

```
import numpy as np
numpy_array = np.array([1, 2, 3, 4, 5])
print(numpy_array)
```

`2. np.zeros()`

This function generates an array filled with zeros.

```
import numpy as np
zeros_array = np.zeros((3, 4))
print(zeros_array)
```

`3. np.ones()`

This function creates an array where all elements are ones.

```
import numpy as np
ones_array = np.ones((2, 3))
print(ones_array)
```

`4. np.full()`

This function produces an array filled with a specified value.

```
import numpy as np
full_array = np.full((2, 2), 7)
print(full_array)
```

`5. np.arange()`

This function generates an array with a range of values.

```
import numpy as np
range_array = np.arange(0, 10, 2)
print(range_array)
```

`6. np.linspace()`

This function creates an array with evenly spaced values over a specific interval.

```
import numpy as np
linspace_array = np.linspace(0, 1, 5)
print(linspace_array)
```

`7. np.eye()`

This function constructs an identity matrix.

```
import numpy as np
identity_matrix = np.eye(3)
print(identity_matrix)
```

`8. np.random.rand()`

This function creates an array with random values from a uniform distribution between 0 and 1.

```
import numpy as np
random_array = np.random.rand(2, 3)
print(random_array)
```

`9. np.random.randint()`

This function generates an array with random integers within a specified range.

```
import numpy as np
random_int_array = np.random.randint(1, 10, size=(3, 3))
print(random_int_array)
```

Each of these attributes and functions plays a crucial role in the manipulation and analysis of data using NumPy.

## Pandas Overview

Pandas is an open-source data analysis and manipulation library for Python that provides ease of use, efficiency, and versatility in handling data. Developed in 2008, Pandas enables users to perform a wide array of data manipulation tasks with minimal effort.

The term "Pandas" is derived from "Panel Data", an econometric term for multidimensional structured data sets. It is built on top of the NumPy library, meaning it integrates closely with NumPy's array-based computational functionalities.

## Features of Pandas

Pandas, a feature-rich Python library, is specifically designed for data manipulation and analysis. Some of its top features include:

**1. Handling Missing Data**

Pandas provides sophisticated means for detecting and handling missing data (NaN values). It can fill missing values with specified data, drop rows or columns with missing values, and perform calculations that intelligently ignore NaNs.

**2. Data Visualization**

With built-in support for plotting, Pandas can generate a variety of commonly used graphs and charts directly from data frames, leveraging its integration with plotting libraries like Matplotlib.

**3. Grouping and Sorting**

The "group by" functionality in Pandas allows for segmenting data into groups and applying functions like aggregation, transformation, or filtration. Pandas also provides advanced sorting capabilities, enabling sorting by index, by one or more columns, and even within groups.

**4. Hierarchical Indexing**

Pandas supports hierarchical or multi-level indexing, allowing more complex data representation and manipulation. This is particularly useful for working with higher dimensional data in a lower dimensional form.

**5. Diverse Data Input/Output Formats**

Pandas can read and write data in various formats, including CSV, Excel, SQL databases, JSON, and more. This makes it highly flexible in data intensive operations.

**6. Data Merging, Joining, and Reshaping**

Pandas facilitates merging and joining data sets, similar to SQL operations. This makes it easy to combine data from different sources. It also provides tools for reshaping, pivoting, and transposing datasets, allowing for flexible data reorganization.

**7. Subsetting and Indexing**

Pandas provides the `loc`

and `iloc`

functions that enable accessing subsets of rows and columns using labels and integer positions, respectively. This allows for precise and easy data selection. Also, it supports selecting data based on conditions, similar to SQL's WHERE clause.

**8. Custom Functionality**

Using Pandas **apply and lambda functions**, you can apply custom functions to data, either to entire data frames, to rows, or columns. This enhances its ability to handle user-specific requirements. Also, it supports vectorized operations, enabling efficient calculations across entire datasets.

**9. Handling NULL and MISSING Values**

Pandas comes with built-in functions for identifying, summarizing, and operating on NULL and MISSING values These functions are crucial for data cleaning and preparation.

**10. Joining and Appending DataFrames**

Pandas provides an easy way to join and append different DataFrame objects. This facilitates the consolidation of data from multiple sources.

## Main Differences Between NumPy and Pandas

Understanding the differences between NumPy and Pandas is crucial, as these libraries are foundational yet serve distinct purposes. Here are the top differences between NumPy and Pandas:

**1. Data Object**

**NumPy**: Central to NumPy is the**ndarray**(n-dimensional array), a powerful data structure that is efficient for numerical computations. These arrays are homogenous, meaning all elements are of the same data type, which optimizes both storage and computation, especially when compared to Python's native list structures.**Pandas**: The main data structures in Pandas are**DataFrames**and**Series**. A DataFrame resembles a spreadsheet with rows and columns, suitable for representing real-world data in a tabular format. A Series is a one-dimensional labeled array capable of holding any data type, making it more flexible.

**2. Industry Usage**

**NumPy**: Widely employed for numerical and scientific computing tasks. Its speed and efficiency in array manipulations make it a staple in fields requiring high-performance numerical computations.**Pandas**: Favored in data analysis and visualization, especially with structured data such as CSV files, Excel sheets, etc. Its data structures and functionalities align well with the needs of data analysts and scientists.

**3. Type of Data Supported**

**NumPy**: Tailored for handling numerical data in arrays and matrices, NumPy excels in mathematical operations on homogeneous datasets.**Pandas**: Designed with versatility for data analysis, Pandas supports a wide range of data, from tabular data to time series and heterogeneous datasets, offering more functionality for real-world data manipulation.

**4. Usage in Machine Learning and Deep Learning**

**NumPy**: Its arrays are often used as inputs for machine learning and deep learning frameworks due to their efficiency and compatibility with numerical data.**Pandas**: Although Pandas data structures are rich in features, they typically need to be converted to NumPy arrays or undergo preprocessing before being used in machine learning models.

**5. Performance**

**NumPy**: Generally exhibits better performance with smaller datasets, particularly those with fewer than 50,000 rows.**Pandas**: More suited for handling larger datasets. Its performance advantages become more apparent with datasets exceeding 500,000 rows.

**6. Indexing**

**NumPy**: Lacks the default indexing feature for its arrays, focusing instead on the positional access of data.**Pandas**: Provides default indexing for its**Series**and**DataFrames**, allowing more sophisticated and intuitive data manipulation, akin to database operations.

**7. Core Language**

**NumPy**: Primarily written in C, it's designed for high-performance numerical computing.**Pandas**: Developed with inspiration from the R language, it offers functions similar to R for data manipulation and analysis.

**8. Memory Usage**

**NumPy**: More memory-efficient due to its focus on homogeneous numerical data and optimized array structures.**Pandas**: Tends to be more memory-intensive, especially with large datasets, due to the more complex nature of its data structures.

**9. Data Handling Capabilities**

**NumPy**: Excellently handles homogeneous numerical data for mathematical and statistical operations.**Pandas**: Offers superior capabilities for handling heterogeneous data and complex tasks like data cleaning, grouping, pivoting, and high-level preparation of datasets for analysis.

## Conclusion

NumPy excels in numerical and array-oriented computing with high performance and memory efficiency, Pandas is more suited for complex data manipulation, particularly with structured data. The choice between NumPy and Pandas largely depends on the specific requirements of the task, such as the type of data being handled, the size of the dataset, and the nature of the operations to be performed. In practice, they are often used together, leveraging their individual strengths in different stages of data analysis and processing.