# Short introduction of python libraries which are used widely for Machine Learning like NumPy, SciPy, matplotlib, scikit-learn, pandas

 Category: Machine Learning Tags: Machine Learning Libraries Code Files

Till today I have written all tutorials without libraries and now I’m taking our journey to next level where we will use python libraries for classification, visualization and clustering. In this article, we will have a short introduction of NumPy, SciPy, matplotlib, scikit-learn, pandas.

## NumPy

NumPy basically provides n-dimensional array object. NumPy also provides mathematical functions which can be used in many calculations.

Command to install: pip install numpy

```import numpy as np
arr = np.array([[1,2,3],[4,5,6]])
print("Numpy array\n {}".format(arr))```

Output

Output

Numpy array

[[1 2 3]

[4 5 6]]

## SciPy

SciPy is collection of scientific computing functions. It provides advanced linear algebra routines, mathematical function optimization, signal processing, special mathematical functions, and statistical distributions.

Command to install: pip install scipy

```from scipy import sparse
# Create a 2D NumPy array with a diagonal of ones, and zeros everywhere else
eye = np.eye(3)
print("NumPy array:\n{}".format(eye))
sparse_matrix = sparse.csr_matrix(eye)
print("\nSciPy sparse CSR matrix:\n{}".format(sparse_matrix))```

Output

NumPy array:

[[1. 0. 0.]

[0. 1. 0.]

[0. 0. 1.]]

SciPy sparse CSR matrix:

(0, 0)        1.0

(1, 1)        1.0

(2, 2)        1.0

## matplotlib

matplotlib is scientific plotting library usually required to visualize data. Importantly visualization is required to analyze the data. You can plot histograms, scatter graphs, lines etc.

Command to install: pip install matplotlib

```import matplotlib.pyplot as plt
x = [1,2,3]
y = [4,5,6]
plt.scatter(x,y)
plt.show()```

Output

## scikit-learn

scikit-learn is built on NumPy, SciPy and matplotlib provides tools for data analysis and data mining. It provides classification and clustering algorithms built in and some datasets for practice like iris dataset, Boston house prices dataset, diabetes dataset etc.

Command to install: pip install scikit-learn

```from sklearn import datasets
sample = iris_data['data'][:3]
print("iris dataset sample data: \n{}".format(iris_data['feature_names']))
print("{}".format(sample))```

Output

iris dataset sample data:

['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']

[[5.1 3.5 1.4 0.2]

[4.9 3.  1.4 0.2]

[4.7 3.2 1.3 0.2]]

## pandas

pandas is used for data analysis it can take multi-dimensional arrays as input and produce charts/graphs. pandas may take a table with columns of different datatypes. It may ingest data from various data files and database like SQL, Excel, CSV etc.

Command to install: pip install pandas

```import pandas as pd
age = {'age': [4, 6, 8, 34, 5, 30, 41] }
dataframe = pd.DataFrame(age)
print("all age:\n{}".format(dataframe))
filtered = dataframe[dataframe.age > 20]
print("age above 20:\n{}".format(filtered))```

Output

all age:

age

0    4

1    6

2    8

3   34

4    5

5   30

6   41

age above 20:

age

3   34

5   30

6   41

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