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Data Analysis with SciPy

Last Updated : 15 Jul, 2025
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SciPy is a Python library useful for solving many mathematical equations and algorithms. It is designed on the top of Numpy library that gives more extension of finding scientific mathematical formulae like Matrix Rank, Inverse, polynomial equations, LU Decomposition, etc. Using its high-level functions will significantly reduce the complexity of the code and helps better in analyzing the data.

1. What is SciPy?

SciPy is an interactive Python session used as a data-processing library that is made to compete with its rivalries such as MATLAB, Octave, R-Lab, etc. It has many user-friendly, efficient and easy-to-use functions that help to solve problems like numerical integration, interpolation, optimization, linear algebra and statistics. The benefit of using the SciPy library in Python while making ML models is that it makes a strong programming language available for developing fewer complex programs and applications.

1.1 Installation of SciPy

To install SciPy in your system, you can use Python package manager pip. Before proceeding, make sure that you have Python already installed in your system. Here’s the step to install Python in your system.

Step 1: Firstly, Open terminal and Command Prompt in your system.

Step 2: Run the installation Command to install SciPy in your system.

pip install scipy

Note: Pip will be download and install SciPy along with dependencies. This process will may take some time depends on internet connection.

Step 4: To verify installation, you need to import SciPy in a Python script or interactive shell.

import scipy
print(scipy.__version__)

Now the installation of SciPy is successfully completed.

2. How does Data Analysis work with SciPy?

2.1 Data Preparation

  • Import the necessary libraries: import numpy as np and import scipy as sp.
  • Load or generate your dataset using NumPy or pandas.

2.2 Exploratory Data Analysis (EDA)

  • Exploratory Data Analysis use descriptive statistics from SciPy’s stats module to gain insights into the dataset.
  • Calculate measures such as mean, median, standard deviation, skewness, kurtosis, etc.
Python
from scipy import stats
data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
mean_val = np.mean(data)
std_dev = np.std(data)
t_stat, p_value = stats.ttest_1samp(data, popmean=5)
print("t_stat:", t_stat)
print("p_value:", p_value)

Output:

t_stat: 0.0
p_value: 1.0

2.3 Statistical Hypothesis Testing

Use SciPy’s stats module for various hypothesis tests such as t-tests, chi-square tests, ANOVA, etc.

t_stat, p_value = stats.ttest_ind(group1, group2)

2.4 Regression Analysis

Utilize the linregress function for linear regression analysis.

slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)

2.5 Signal and Image Processing

  • Use the scipy.signal module for signal processing operations.
  • Explore scipy.ndimage for image processing.

from scipy import signal, ndimage
result = signal.convolve2d(image, kernel, mode='same', boundary='wrap')

2.6 Optimization

Employ the optimization functions in SciPy to find optimal parameter values.

from scipy.optimize import minimize
def objective_function(x):
return x[0]**2 + x[1]**2

result = minimize(objective_function, [0, 0])

3. Import SciPy

Once SciPy is installed , you need to import the SciPy module(s)

Python
import numpy as np
A = [[1, 2,],
     [5, 6,]]

3.1 Linear Algebra

Determinant of a Matrix

Python
from scipy import linalg
A = [[1, 2,],
     [5, 6,]]

linalg.det(A)

Output:

-4.0

4. Compute pivoted LU decomposition of a Matrix

LU decomposition is a method that reduce matrix into constituent parts that helps in easier calculation of complex matrix operations. The decomposition methods are also called matrix factorization methods, are base of linear algebra in computers, even for basic operations such as solving systems of linear equations, calculating the inverse and calculating the determinant of a matrix. The decomposition is: A = P L U where P is a permutation matrix, L lower triangular with unit diagonal elements and U upper triangular.

Python
P, L, U = linalg.lu(A)
print(P)
print(L)
print(U)
print(np.dot(L, U))

Output:

[[0. 1. 0.]
[0. 0. 1.]
[1. 0. 0.]]
[[1. 0. 0. ]
[0.14285714 1. 0. ]
[0.57142857 0.5 1. ]]
[[7. 8. 8. ]
[0. 0.85714286 1.85714286]
[0. 0. 0.5 ]]
[[0.14285714 1. 0. ]
[0.57142857 0.5 1. ]
[1. 0. 0. ]]

5. Eigen values and Eigen vectors of this matrix

Lets find the eigen values and eigen vectors of this matrix,

Python
eigen_values, eigen_vectors = linalg.eig(A)
print(eigen_values)
print(eigen_vectors)

Output:

array([ 15.55528261+0.j, -1.41940876+0.j, -0.13587385+0.j])
array([[-0.24043423, -0.67468642, 0.51853459],
[-0.54694322, -0.23391616, -0.78895962],
[-0.80190056, 0.70005819, 0.32964312]])

Solving systems of linear equations can also be done

Python
v = np.array([[2, 4],[3,8]])
print(v)
s = linalg.solve(A,v)
print(s)

Output:

[[2 4]
[3 8]]
[[-1.5 -2. ]
[ 1.75 3. ]]

6. Sparse Linear Algebra

SciPy has some routines for computing with sparse and potentially very large matrices. The necessary tools are in the submodule scipy.sparse.

Let’s look on how to construct a large sparse matrix

Python
from scipy import sparse
A = sparse.lil_matrix((1000, 1000))
print(A)
A[0, :100] = np.random.rand(100)
A[1, 100:200] = A[0, :100]
A.setdiag(np.random.rand(1000))
print(A)

Output:

(0, 0) 0.7948113035416484
(0, 1) 0.22210781047073025
(0, 2) 0.1198653673336828
(0, 3) 0.33761517140362796
(0, 4) 0.9429097039125192
(0, 5) 0.32320293202075523
(0, 6) 0.5187906217433661
(0, 7) 0.7030189588951778
(0, 8) 0.363629602379294
(0, 9) 0.9717820827209607
(0, 10) 0.9624472949421112
(0, 11) 0.25178229582536416
(0, 12) 0.49724850589238545
(0, 13) 0.30087830981676966
(0, 14) 0.2848404943774676
(0, 15) 0.036886947354532795

6.1 Linear Algebra for Sparse Matrices

Python
from scipy.sparse import linalg
A.tocsr()
A = A.tocsr()
b = np.random.rand(1000)
ans = linalg.spsolve(A, b)
print(ans)

Output:

[-4.67207136e+01 -3.69332972e+02 3.69393775e-01 6.32141409e-02
3.33772205e+00 5.10104872e-01 3.07850190e+00 1.94608719e+01
1.49997674e+00 1.04751174e+00 9.23616608e-01 8.14103772e-01
8.42662424e-01 2.28221903e+00 4.92361307e+01 6.74574814e-01
3.06515031e-01 3.36481843e-02 9.55613073e-01 7.22911464e-01
2.70518013e+00 1.25039001e+00 1.37825326e-01 3.95005049e-01
4.04480605e+00 7.72817743e-01 2.14200400e-01 7.06283767e-01
1.12635170e-01 5.98880840e+00 4.37382510e-01 8.05571435e-01
..............................................................................................................................]

Python
from scipy import integrate
f = lambda y, x: x*y**2
i = integrate.dblquad(f, 0, 2, lambda x: 0, lambda x: 1)
print(i)

Output:

(0.6666666666666667, 7.401486830834377e-15)

There is a lot more that SciPy is capable of, such as Fourier Transforms, Bessel Functions, etc.

To learn more, click here to find the SciPy tutorial.


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