Difference Between NumPy and Pandas

Both pandas and NumPy are validly used powerful open-source libraries in python. These packages have their own applicability. A lot of pandas functionalities are built on top of NumPy, and they are both part of the SkiPy Analytics world.

Numpy stands for Numerical Python. NumPy is the core library for scientific computing. it can deal with multidimensional data, which is nothing but n-dimensional numerical data. Numpy array is a powerful N-dimensional array object which is in the form of rows and columns.

Many NumPy operations are implemented in the C language. It is fast and it requires less memory than pandas.

Numpy allows you to do every numerical task like linear algebra and many other advanced linear algebra tasks. These include tasks like inverting a matrix, Singular value decomposition, determinant estimation, etc.

Let’s take an example and see how we gonna do mathematical operations.

Example

import numpy as np
arr = np.array([[2,12,3], [10,5,7],[9,8,11]])
print(arr)
arr_inv = np.linalg.inv(arr)
print(arr_inv)

Explanation

The first line of the above block imports the NumPy module and np is representing the alias name for the NumPy module. The variable arr is a 2-Dimensional array and it has 3 rows and 3 columns. After that, we are calculating the inverse matrix of our array arr by using the inv() function available in the numpy.linalg (linear algebra) module.

Output

[[ 2 12 3]
 [10 5 7]
 [ 9 8 11]]
[[ 0.0021692 0.23427332 -0.14967462]
 [ 0.10195228 0.01084599 -0.03470716]
 [-0.07592191 -0.19956616 0.23861171]]

This output block has two arrays first one is representing the array of values from the arr variable and the second one is an inverted matrix of arr (variable arr_inv).

Pandas provides high-performance data manipulation in Python and it requires NumPy for operating as it is built on the top of NumPy. The name of Pandas is derived from the word Panel Data, which means Econometrics from Multidimensional data.

Pandas allows you to do most of the things that you can do with the spreadsheet with Python code, and NumPy majorly works with numerical data whereas Pandas works with tabular data. This tabular data can be any form like it may be CSV file or SQL data.

The Pandas provides powerful tools like DataFrame and Series that are mainly used for analyzing the data.

Let’s take an example and see how pandas will handle tabular data.

Example

data = pd.read_csv('titanic.csv')
print(data.head())

Explanation

Pandas provides a number of functions to read any type of data into a pandas DataFrame or Series, in this above example we read the titanic data set as pandas dataframe. And displayed the output using the head() method.

Output

  PassengerId   Survived   Pclass \
0           1          0        3
1           2          1        1
2           3          1        3
3           4          1        1
4           5          0        3

                                               Name   Gender   Age   SibSp \
0                           Braund, Mr. Owen Harris     male  22.0       1
1 Cumings, Mrs. John Bradley (Florence Briggs Th...   female  38.0       1
2                            Heikkinen, Miss. Laina   female  26.0       0
3      Futrelle, Mrs. Jacques Heath (Lily May Peel)   female  35.0       1
4                          Allen, Mr. William Henry     male  35.0       0

   Parch            Ticket     Fare Cabin   Embarked
0      0         A/5 21171   7.2500   NaN          S
1      0          PC 17599  71.2833   C85          C
2      0  STON/O2. 3101282   7.9250   NaN          S
3      0            113803  53.1000  C123          S
4      0            373450   8.0500   NaN          S

As we can see, a pandas data frame can store any type of data whereas NumPy is only dealing with a numerical value.