Back to: Robotics & Artificial Intelligence (Class X)

What is a Module?

A Module is simply a Python file (.py) that contains functions, variables, or classes which we can reuse.

Why Use Modules?

Saves time
Avoids rewriting code
Organizes programs

Create a file named math_tools.py

def add(a, b):
    return a + b

def sub(a, b):
    return a - b

Now use it in another file:

import math_tools

print(math_tools.add(5, 3))
print(math_tools.sub(10, 4))

What is a Package?

A Package is a folder that contains multiple modules.

myPackage/
   __init__.py
   calc.py
   greet.py

Use package:

from myPackage import calc
print(calc.add(2,3))

Library

What is a Library?

A Library is a collection of many packages and modules created by developers for specific tasks like math, data analysis, graphs, etc.

Example Libraries:

NumPy
Pandas
SciPy
Matplotlib

Matplotlib

Matplotlib is a Python library used to draw graphs and charts. It helps us see data visually instead of only numbers.

Why Do We Use Matplotlib?

Sometimes numbers are hard to understand. Graphs make information clear, fast, and attractive.

What Can Matplotlib Create?

Line Graph 📈
Bar Chart 📊
Pie Chart 🥧
Scatter Plot
Histograms

Import Library

import matplotlib.pyplot as plt

Line Graph

import matplotlib.pyplot as plt
x = [1, 2, 3, 4]
y = [10, 20, 25, 30]

plt.plot(x, y, label="Sales Growth")

plt.xlabel("Month")
plt.ylabel("Sales")
plt.title("Monthly Sales Report")
plt.show()

import → brings graph tools
x, y → data values
plot() → draws line
xlabel / ylabel → axis names
title() → heading
show() → display graph

This code creates a simple line graph with proper labels and a title.

Bar Graph

import matplotlib.pyplot as plt

subjects = ["Math", "Science", "English"]
marks = [80, 75, 90]

plt.bar(subjects, marks)

plt.xlabel("Subjects")
plt.ylabel("Marks")
plt.title("Student Marks Report")
plt.show()

plt.bar(subjects, marks)
Draws the bar chart.
plt.xlabel(“Subjects”)
Name of the bottom (X) axis.
plt.ylabel(“Marks”)
Name of the side (Y) axis.
plt.title(“Student Marks Report”)
Heading shown at the top of the chart.
plt.show()
Displays the bar graph on the screen.

Now the bar chart will look clear, labeled, and professional.

Pie Chart

import matplotlib.pyplot as plt

data = [40, 30, 20, 10]
labels = ["Math", "Science", "English", "Hindi"]

plt.pie(data, labels=labels)
plt.show()

import → bring graph tool
data → numbers (slice size)
labels → names of slices
pie() → draw pie chart
show() → display chart

This code visually shows how much each subject contributes out of the total.

Scatter Plot

A Scatter Plot is a graph used to show the relationship between two sets of numbers. It displays points (dots) instead of lines or bars.

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]      # Study Hours
y = [50, 55, 65, 70, 80] # Marks

plt.scatter(x, y)

plt.xlabel("Study Hours")
plt.ylabel("Marks")
plt.title("Study Hours vs Marks")

plt.show()

import matplotlib.pyplot as plt
Imports graph library.

x list
Data for X-axis (Study Hours).

y list
Data for Y-axis (Marks).

plt.scatter(x, y)
Draws dots on graph.

xlabel() / ylabel()
Names of axes.

title()
Heading of the graph.

show()
Displays the graph.

Histogram

A Histogram is a graph used to show the distribution of numerical data. It tells how many values fall into a particular range.

What are Bins?

Bins are ranges or groups in which data is divided.

Example:
Marks of students = 10, 20, 30, 40, 50, 60

If we use 3 bins, Python may divide data like:

Bin 1: 0 – 20
Bin 2: 21 – 40
Bin 3: 41 – 60

Each bin shows how many values fall inside that range.

import matplotlib.pyplot as plt

marks = [35, 45, 55, 65, 70, 75, 80]

plt.hist(marks, bins=5)

plt.xlabel("Marks Range")
plt.ylabel("Number of Students")
plt.title("Marks Distribution Histogram")

plt.show()

import matplotlib.pyplot as plt
Imports graph library.
marks list
Numerical data to analyze.
plt.hist(marks, bins=5)
- hist() creates histogram.
- bins=5 means data is divided into 5 ranges.
xlabel() → Name of bottom axis.
ylabel() → Name of side axis.
title() → Heading of graph.
show() → Displays graph.

How Histogram Works

Suppose marks are from 35 to 80 and bins = 5.
Matplotlib automatically creates ranges like:

35–44
45–54
55–64
65–74
75–84

Then it counts how many students fall in each range.

NumPy

NumPy (Numerical Python) is a Python library used for working with numbers and arrays. It is very useful in Data Science, AI, Machine Learning, and Robotics because it makes mathematical calculations fast and easy.

Key Features of NumPy

Array Object – Similar to lists but faster and more powerful.
High Performance – Works faster than normal Python lists.
Mathematical Functions – Supports operations like addition, subtraction, mean, square root, etc.
Multi-Dimensional Arrays – Can handle 1D, 2D, 3D arrays.

1. Creating Array

import numpy as np

arr = np.array([1, 2, 3, 4])
print(arr)

2. Zeros and Ones

import numpy as np

a = np.zeros(5)   # 5 zeros
b = np.ones(3)    # 3 ones

print(a)
print(b)

import numpy as np → Imports the NumPy library. np is a short name (alias).

np.zeros(5) → Creates an array with 5 zero values.

np.ones(3) → Creates an array with 3 one values.

print(a) and print(b) → Display the arrays on the screen.

[0. 0. 0. 0. 0.]
[1. 1. 1.]

3. Range

import numpy as np

arr = np.arange(1, 10)   # 1 to 9
print(arr)

import numpy as np → Imports the NumPy library with short name np.

np.arange(1, 10) → Creates an array starting from 1 up to 9.

The first value (1) is included.
The last value (10) is not included.

print(arr) → Displays the array.

[1 2 3 4 5 6 7 8 9]

4.Reshape Array

import numpy as np

arr = np.array([1,2,3,4,5,6])
new_arr = arr.reshape(2,3)

print(new_arr)

np.array([1,2,3,4,5,6]) → Creates a 1-Dimensional array with 6 elements.

reshape(2,3) → Converts it into a 2 rows × 3 columns array.

print(new_arr) → Displays the reshaped array.

[[1 2 3]
 [4 5 6]]

5. Mathematical Operations

import numpy as np

a = np.array([1,2,3])
b = np.array([4,5,6])

print(a + b)   # Addition
print(a * b)   # Multiplication

np.array([1,2,3]) and np.array([4,5,6]) → Create two arrays.

a + b → Adds each element of array a with the corresponding element of array b.

a * b → Multiplies each element of array a with the corresponding element of array b.

This is called Element-wise Operation.

[5 7 9]
[ 4 10 18]

6. Mean, Sum, Max, Min

import numpy as np

arr = np.array([5,10,15,20])

print(np.sum(arr))    # Sum
print(np.mean(arr))   # Average
print(np.max(arr))    # Maximum
print(np.min(arr))    # Minimum

np.array([5,10,15,20]) → Creates an array of numbers.

np.sum(arr) → Adds all values in the array.

np.mean(arr) → Finds the average (mean) value.

np.max(arr) → Finds the largest number.

np.min(arr) → Finds the smallest number.

Sum: 5 + 10 + 15 + 20 = 50
Average: 50 ÷ 4 = 12.5
Maximum: 20
Minimum: 5

These functions are very useful in Data Analysis, AI, and Robotics for analyzing sensor values, marks, or any numerical data quickly.

7. Random Numbers

import numpy as np

arr = np.random.rand(3)
print(arr)

np.random.rand(3) → Creates an array with 3 random decimal numbers.

The numbers are between 0 and 1.

print(arr) → Displays the random values.

[0.23 0.78 0.51]

Pandas

Pandas is a Python library used for data handling and data analysis. It is mainly used in Data Science, AI, Machine Learning, and Robotics projects to work with tables of data like Excel sheets.

In simple words, Pandas helps Python read, organize, and analyze data easily.

Main Data Structures in Pandas

1. Series

A one-dimensional array (like a single column).

import pandas as pd

data = [10, 20, 30]
s = pd.Series(data)
print(s)

import pandas as pd → Imports the Pandas library with short name pd.

data = [10, 20, 30] → A normal Python list.

pd.Series(data) → Converts the list into a Pandas Series.

print(s) → Displays the Series.

0    10
1    20
2    30
dtype: int64

Understanding the Output

0, 1, 2 → These are index numbers (row labels).
10, 20, 30 → Actual data values.
dtype: int64 → Data type of values (integer).

2. DataFrame

A two-dimensional table (rows and columns like Excel).

import pandas as pd

data = {
    "Name": ["Aman", "Riya", "John"],
    "Marks": [80, 75, 90]
}

df = pd.DataFrame(data)
print(df)

import pandas as pd → Imports the Pandas library.

data = { ... } → A dictionary containing columns.

"Name" → Column 1
"Marks" → Column 2

pd.DataFrame(data) → Converts the dictionary into a DataFrame (table).

print(df) → Displays the table.

   Name  Marks
0  Aman     80
1  Riya     75
2  John     90

Common Pandas Functions

1. Read CSV File

df = pd.read_csv("data.csv")

`pd.read_csv("data.csv")`

Reads a CSV file (Excel-like file) into a DataFrame.
"data.csv" is the file name.
df is the variable where data is stored.

2.Show First Rows

print(df.head())

Shows the first 5 rows of the dataset.
Useful to quickly check how the data looks.

Example Output

   Name  Marks
0  Aman     80
1  Riya     75
2  John     90

3.Show Last Rows

print(df.tail())

Shows the last 5 rows of the dataset. Useful to check the ending records.

4. Information About Data

print(df.info())

Gives information about the dataset, such as:

Number of rows
Column names
Data types (int, float, object)
Null (missing) values

5. Statistical Summary

print(df.describe())

Shows statistical summary of numeric columns:

Count
Mean (Average)
Standard Deviation
Minimum value
Maximum value

SciPy

SciPy is a Python library used for scientific and mathematical calculations. It works along with NumPy and is mainly used in engineering, data science, AI, and research work.

What SciPy is Used For

Advanced Mathematics
Optimization problems
Statistics
Signal Processing
Linear Algebra
Integration & Differentiation
Image Processing

Important Modules in SciPy

scipy.linalg → Linear Algebra
scipy.stats → Statistics
scipy.optimize → Optimization
scipy.integrate → Integration
scipy.signal → Signal Processing

1. scipy.linalg

import numpy as np
from scipy import linalg

A = np.array([[1, 2], [3, 4]])
inv_A = linalg.inv(A)
print(inv_A)

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