From 7c3b30466982483e69db3513e0afc438e982cad8 Mon Sep 17 00:00:00 2001
From: Niyonika Gaur <83643952+niyonikagaur@users.noreply.github.com>
Date: Fri, 17 May 2024 23:58:14 +0530
Subject: [PATCH] Update index.md

---
 contrib/scipy/index.md | 131 +----------------------------------------
 1 file changed, 2 insertions(+), 129 deletions(-)

diff --git a/contrib/scipy/index.md b/contrib/scipy/index.md
index 9c35f95..3ff5a7d 100644
--- a/contrib/scipy/index.md
+++ b/contrib/scipy/index.md
@@ -1,131 +1,4 @@
-## Installation of Scipy
-### Install using the command:
-#### C:\Users\Your Name>pip install scipy
- You can also use a Python distribution that already has Scipy installed like Anaconda, or Spyder.
-### Importing SciPy
-#### from scipy import constants
-## Key Features of SciPy
-### 1. Numerical Integration
-#### It helps in computing definite or indefinite integrals of functions
-```
-from scipy import integrate
+# List of sections
 
-#Define the function to integrate
-def f(x):
-    return x**2
-
-#Compute definite integral of f from 0 to 1
-result, error = integrate.quad(f, 0, 1)
-print(result)
-```
-#### Output
-```
-0.33333333333333337
-```
-### 2. Optimization
-#### It can be used to minimize or maximize functions, here is an example of minimizing roots of an equation
-```
-from scipy.optimize import minimize
-
-# Define an objective function to minimize
-def objective(x):
-    return x**2 + 10*np.sin(x)
-
-# Minimize the objective function starting from x=0
-result = minimize(objective, x0=0)
-print(result.x)
-```
-#### Output
-```
-array([-1.30644012])
-```
-### 3. Linear Algebra
-#### Solving Linear computations
-```
-from scipy import linalg
-import numpy as np
-
-# Define a square matrix
-A = np.array([[1, 2], [3, 4]])
-
-# Define a vector
-b = np.array([5, 6])
-
-# Solve Ax = b for x
-x = linalg.solve(A, b)
-print(x)
-```
-#### Output
-```
-array([-4. ,  4.5])
-```
-### 4. Statistics
-#### Performing statistics functions, like here we'll be distributing the data
-```
-from scipy import stats
-import numpy as np
-
-# Generate random data from a normal distribution
-data = stats.norm.rvs(loc=0, scale=1, size=1000)
-
-# Fit a normal distribution to the data
-mean, std = stats.norm.fit(data)
-```
-### 5. Signal Processing
-#### To process spectral signals, like EEG or MEG
-```
-from scipy import signal
-import numpy as np
-
-# Create a signal (e.g., sine wave)
-t = np.linspace(0, 1, 1000)
-signal = np.sin(2 * np.pi * 5 * t) + 0.5 * np.random.randn(1000)
-
-# Apply a low-pass Butterworth filter
-b, a = signal.butter(4, 0.1, 'low')
-filtered_signal = signal.filtfilt(b, a, signal)
-```
-The various filters applied that are applied here, are a part of signal analysis at a deeper level.
-### 6. Sparse Matrix
-#### The word ' sparse 'means less, i.e., the data is mostly unused during some operation or analysis. So, to handle this data, a Sparse Matrix is created
-#### There are two types of Sparse Matrices:
-##### 1. CSC: Compressed Sparse Column, it is used for efficient math functions and for column slicing
-##### 2. CSR: Compressed Sparse Row, it is used for fast row slicing
-#### In CSC format
-```
-from scipy import sparse
-import numpy as np
-
-data = np.array([[0, 0], [0, 1], [2, 0]])
-
-row_indices = np.array([1, 2, 1])  
-col_indices = np.array([1, 0, 2])  
-values = np.array([1, 2, 1])       
-
-sparse_matrix_csc = sparse.csc_matrix((values, (row_indices, col_indices)))
-```
-#### In CSR format
-```
-from scipy import sparse
-import numpy as np
-
-data = np.array([[0, 0], [0, 1], [2, 0]])
-sparse_matrix = sparse.csr_matrix(data)
-```
-### 7. Image Processing
-#### It is used to process the images, like changing dimensions or properties. For example, when you're doing a project on medical imaging, this library is mainly used.
-```
-from scipy import ndimage
-import matplotlib.pyplot as plt
-
-image = plt.imread('path/to/image.jpg')
-plt.imshow(image)
-plt.show()
-
-# Apply Gaussian blur to the image
-blurred_image = ndimage.gaussian_filter(image, sigma=1)
-plt.imshow(blurred_image)
-plt.show()
-```
-#### The gaussian blur is one of the properties of the ' ndimage ' package in SciPy libraries, it used for better understanding of the image.
+- [Installation of Scipy and its key uses](pandas_series_vs_numpy_ndarray.md)