NumPy and Pandas are two foundational libraries for data manipulation, analysis, and numerical computations in Python programming. While they often complement each other, they cater to distinct use cases and offer unique functionalities. Understanding their differences can help you choose the right tool for your needs. This article explores the key difference between NumPy and Pandas package.
What is Pandas?
Pandas is developed as the improved version of NumPy; it provides stacked, easy-to-use data structures for data manipulation. This brings the concept of Series and DataFrame into force as basic data types and acquiring structures, making managing and manipulating data in large structures rather simple.
Example: Panda Library
# Importing pandas library
import pandas as pd
age = [['Voldemort', 95.5, "Male"], ['Katerin', 65.7, "Female"],
['Raj Kumar ', 85.1, "Male"], ['Tonni Kakkar', 75.4, "Female"]]
# Creating a pandas dataframe
df = pd.DataFrame(age, columns=['Name', 'Marks', 'Gender'])
df
Output
Name Marks Gender
0 Voldemort 95.5 Male
1 Katerina 65.7 Female
2 RajKumar 85.1 Male
3 Tonni Kakkar 75.4 Female
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Features of Pandas
- Efficient Data Structures (Series and DataFrame): Two basic types of objects in pandas are Series and DataFrame. Some of the definitions made to explain are: A Series is an array with names, and these are one-dimensional, while a DataFrame is a two-dimensional table, and the data types that can be stored are integers, strings, floating points and many more. Each of the structures above enables easy manipulation and analysis of data.
- Automatic Data Alignment and Handling Missing Data: Pandas align data based on information labels. Originally designed for data analysis, Pandas is great at working with missing values if your dataset contains them – you can use fillna() to replace them or dropna() to exclude them from the data.
- Efficient Slicing, Indexing, and Subsetting: The flexibility of working with data using the Pandas library is that you can get records or columns of data using Pandas’ simple index. For instance, you can use labels, integer positions, or Boolean conditions (Filter or Slice).
- Advanced-Data Manipulation (Merge, Join, Pivot, Reshape): Appending data and merging is possible and simple to perform with the help of Pandas. Some of these functions include merge() and join(), allowing you to combine datasets using the matching features. In contrast, pivot() or melt() allows you to reshape your data, which is important when transforming data for analysis.
- Built-in Support for Statistical Analysis and Aggregation: Most basic stats and aggregation computation are implemented in Pandas as built-in functions. This way, you can get mean, median, and sum, or if you wish to use more complex functions, you can use groupby() to perform operations on subgroups of your data.
What is NumPy?
This is a Python library for numerical computing used as numpy, which is a powerful open-source library. It can support large, multi-dimensional arrays and matrices and a collection of mathematical functions in which these arrays can be operated. Its vast advantages of handling a large amount of data with high performance play a key role in data science, machine and engineering, and scientific computing.
Example: Numpy Library
# Importing Numpy package
import numpy as np
org_array = np.array([[00, 98, 8543],
[88, 56, 93],
[66, 33, 77]])
# Printing the Numpy array
print(org_array)
Output
[[ 0 98 8543]
[ 88 56 93]
[ 66 33 77]]
Features of NumPy
NumPy (Numerical Python) is an evolution of Python constructed to apply computation for scientific uses. It offers a great set of tools for array and matrix manipulations and contains a number of mathematical and statistical operations.
- Multidimensional Array Support: At the heart of NumPy is the array data type capable of creating and processing multi-dimensional arrays.
- Vectorized Operations: Rapid calculation of entire arrays without using the one–specific iteration through its elements, accelerating code calculation and reducing its amount.
- Broadcasting: Enables arithmetic operations between different shaped variables, for instance, adding a scalar with a matrix.
- Comprehensive Mathematical Functions: Allows arithmetic operations between arrays of different shapes, simplifying operations like adding a scalar to a matrix.
- Efficient Memory Utilization: Arrays are created and stored in successive memory regions faster than Python lists and use less area.
- Data Type Support: Compatible with most data types (integers, float, complex numbers) and can be built upon for other data type handling.
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When to Use Which?
This is where NumPy is most effective, particularly for performance-focused applications and conditions where the elements within an array or matrix are of the same data type. It outperforms others in mathematical computations and is best suited to scientific computing, simulation, and as an embedding platform for machine learning libraries.
On the other hand, Pandas is for structured and complex data, where users can learn how to easily manipulate, clean, and analyse the data. It is ideal for manipulating tabular datasets, group operations, Series structures, and direct data representations like CSV or database. Whereas NumPy offers a high value for numerical computation, Pandas offers ease in manipulating and analysing labelled or mixed-type data, which makes it suitable for EDA and Business Intelligence jobs.
Difference between Pandas vs NumPy
| Pandas | NumPy |
| It is primarily for numerical and scientific computing | It is used for data computing and analysis. |
| An array of homogeneous data types | This data frames 2D and Series 1D for heterogeneous data types. |
| This supports homogeneous numerical data types (e.g. integers, floats). | This supports a mix of data types (e.g. numbers, strings, dates). |
| This provides basic array indexing. | This advanced indexing with labels (e.g. row and column labels in DataFrames |
| It does not require built-in manual handling | This built-in support is for handling missing data (e.g., NaN). |
| It is best for numerical computations and scientific tasks. | It is best for data manipulation analysis and working with structured tabular data. |
Conclusion
NumPy is the right tool for scientific and mathematical tasks because it is fast at numerical computation with its array operations. On the other hand, Pandas has tools for handling structured data, like tables with missing values and complicated transformations. While NumPy does number processing, NumPy is for data analysis. Pandas are best for this and often combine to utilize each other’s data science and analytics strengths. Want to learn more about NumPy and Pandas? You can pursue the Certificate Program in Application Development offered by Hero Vired.
What is the main difference between NumPy and Pandas?
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Updated on December 13, 2024