Python is one of the most popular languages for Machine Learning, Data Analysis, and Deep learning tasks. It is powerful because of its libraries that provide the user full command over the data. Today, we will look into the most popular libraries i.e. Numpy and Pandas, and then we will compare them.
Pandas
Pandas is an open-source, BSD-licensed library written in Python Language. Pandas provide high performance, fast, easy-to-use data structures, and data analysis tools for manipulating numeric data and time series. Pandas is built on the numpy library and written in languages like Python, Cython, and C. In pandas, we can import data from various file formats like JSON, SQL, Microsoft Excel, etc.
Example:
Python3
# Importing pandas libraryimport pandas as pd# Creating and initializing a nested listage = [['Aman', 95.5, "Male"], ['Sunny', 65.7, "Female"], ['Monty', 85.1, "Male"], ['toni', 75.4, "Male"]]# Creating a pandas dataframedf = pd.DataFrame(age, columns=['Name', 'Marks', 'Gender'])# Printing dataframedf |
Output:
Name Marks Gender
0 Aman 95.5 Male
1 Sunny 65.7 Female
2 Monty 85.1 Male
3 toni 75.4 Male
For diving more deep into Pandas look at this Pandas Tutorial.
Numpy
Numpy is the fundamental library of python, used to perform scientific computing. It provides high-performance multidimensional arrays and tools to deal with them. A numpy array is a grid of values (of the same type) that are indexed by a tuple of positive integers, numpy arrays are fast, easy to understand, and give users the right to perform calculations across arrays.
Example:
Python3
# Importing Numpy packageimport numpy as np# Creating a 3-D numpy array using np.array()org_array = np.array([[23, 46, 85], [43, 56, 99], [11, 34, 55]])# Printing the Numpy arrayprint(org_array) |
Output:
[[23 46 85]
[43 56 99]
[11 34 55]]
To get more information about Numpy take a look at this Numpy tutorial.
Difference between Pandas and Numpy
Table of Differences Between Pandas and NumPy is as follows:
|
PANDAS |
NUMPY |
|---|---|
|
When we have to work on Tabular data, we prefer the pandas module. |
When we have to work on Numerical data, we prefer the numpy module. |
|
The powerful tools of pandas are Data frame and Series. |
Whereas the powerful tool of numpy is Arrays. |
| Pandas consume more memory. | Numpy is memory efficient. |
| Pandas have a better performance when the number of rows is 500K or more. | Numpy has a better performance when number of rows is 50K or less. |
|
Indexing of the pandas series is very slow as compared to numpy arrays. |
Indexing of numpy arrays is very fast. |
| Pandas have 2d table object called DataFrame. | Numpy is capable of providing multi-dimensional arrays. |
|
It was developed by Wes McKinney and was released in 2008. |
It was developed by Travis Oliphant and was released in 2005. |
|
It is used in a lot of organizations like Kaidee, Trivago, Abeja Inc. , and a lot more. |
It is being used in organizations like Walmart Tokopedia, Instacart, and many more. |
|
It has a higher industry application. |
It has a lower industry application. |
