�ݺ�ߣ

Powering Innovation with Python's Versatile Libraries
PYTHON

Python is a high-level, interpreted programming language known
for its simplicity, versatility, and readability. It is widely used across
various fields, including web development, data science, artificial
intelligence, scientific computing, and automation.

Python Libraries
Python is a versatile programming language, and its library
ecosystem is one of its greatest strengths.
Libraries provide pre-written code that simplifies complex
tasks, allowing developers to focus on logic.
Python libraries cater to a wide range of domains, including
web development, data analysis, machine learning, and more.
These libraries save time, enhance productivity, and foster
innovation.

Python simplifies repetitive tasks through scripting
and automation. Libraries: Selenium, PyAutoGUI,
Requests, BeautifulSoup. Example: Automating
data entry, web scraping, or sending emails.
Python powers cutting-edge technologies in AI
and ML. Libraries: TensorFlow, PyTorch, Keras,
OpenCV, NLTK.Example: Developing chatbots,
image recognition systems, or natural language
processing (NLP) models.
NumPy, Pandas: Data manipulation and numerical operations.
Matplotlib, Seaborn : Data visualization tools.
Example: Analyzing customer behavior, predicting trends, or
building recommendation systems.
Python is used to develop scalable and secure web
applications. Django and Flask: Popular web frameworks
for building web apps. FastAPI: Used for building APIs
with high performance. Example: Building websites, e-
commerce platforms, or RESTful APIs.

1. Libraries are collections of pre-written modules and functions that extend Python's capabilities.
2. They streamline coding by reducing the need for repetitive or boilerplate code.
3. Libraries can be built-in (like os, math) or external (like NumPy, pandas).
4. They are often open-source, maintained by vibrant communities, and continuously updated

Pandas (data manipulation), NumPy
(numerical operations), SciPy (scientific
computing).
Matplotlib (basic plots), Seaborn
(statistical visualization), Plotly (interactive
graphs).
TensorFlow (deep learning), PyTorch (AI
frameworks), Scikit-learn (machine
learning)
Flask (microservices), Django (full-stack),
FastAPI (modern APIs).
OS (operating system interaction),
Requests (HTTP requests), OpenCV
(image processing).

A
Time-saving: Libraries provide pre-written solutions to common
problems.
B
Reliability: Most libraries are tested extensively, reducing bugs and
errors.
C
Community Support: A large community ensures continuous updates
and extensive documentation.
D
Integration: Libraries easily integrate into existing projects, enhancing
functionality.

Learning: Refer to official documentation
and tutorials for detailed usage examples.
Installation: Use pip to install libraries:
Example: `pip install library-name`.
Updating: Regularly update libraries to
access new features and security fixes.
Importing: Import the library in your Python
script:
Example: `import library-name`.

Read documentation to understand
advanced usage and avoid errors.
Regularly update libraries and avoid
unnecessary installations.
Ensure that the library versions match your
Python version.
Some libraries depend on others, so ensure
all are installed correctly.

Lets Jump into Code and See some Examples

1. Arrays: We define two 1D arrays array1 and array2.
2. Element-wise Operations:
• Addition (+) : Adds corresponding elements: 1+4, 2+5, 3+6.
• Multiplication (*): Multiplies corresponding elements : 1 x
4,2x5,3x6.
NumPy (Numerical Python) is a powerful, open-source Python library
primarily used for numerical and scientific computing. It provides a high-
performance multidimensional array object and tools for working with arrays
efficiently. NumPy serves as the foundation for many data science and
machine learning libraries.

1. Arrays: We define 2D arrays matrix1 and matrix2.
2. Matrix 1 is a 2 x 3 matrix.
3. Matrix 2 is a 3 x 2 matrix
4. The matrix multiplication result will be a 2 x 2 matrix, computed by taking the
dot product of rows from Matrix 1 with columns from Matrix 2.
NumPy's np.dot function makes matrix multiplication efficient and easy to
perform!

Pandas is an open-source Python library used for working with and analyzing structured data. It provides easy-to-
use tools to handle data in the form of tables (like rows and columns), making it simple to clean, manipulate, and
explore datasets.
Data Structures :
• Series: A one-dimensional labeled array (like a single column of data).
• DataFrame: A two-dimensional table-like structure (like an Excel spreadsheet).
Data Handling :
• Easily read and write data from files like CSV, Excel, or databases.
Data Manipulation:
• Filter, sort, group, merge, and reshape data quickly.
Data Cleaning:
• Handle missing values and prepare datasets for analysis.

Creating the Dictionary:
• A dictionary called data is created with two keys: "Name" and "Age".
• "Name": Contains a list of names (["Alice", "Bob"]).
• "Age": Contains a list of ages ([25, 30])..
Creating the DataFrame :
• The dictionary data is converted into a DataFrame using pd.DataFrame().
• A DataFrame is a table-like structure with rows and columns:
• The keys of the dictionary ("Name" and "Age") become the column headers.
• The values of each key become the column data.
Row indices (0, 1): Automatically generated by Pandas to uniquely identify
each row.

Creating the DataFrame:
• A dictionary (data) is used to define the columns and their values.
• The pd.DataFrame(data) converts this dictionary into a table-like structure.
Adding a New Column:
• A new column, "Profession," is added to the DataFrame with specified
values.
Filtering Rows:
• Rows where the "Age" column has values greater than 28 are filtered using
df[df["Age"] > 28].
Displaying Data:
• The original, modified, and filtered DataFrames are printed for comparison.

Matplotlib is a popular Python library used for creating static, animated, and interactive visualizations in Python. It
provides a wide range of plotting options, including line plots, bar charts, histograms, scatter plots, and more. The
most commonly used part of Matplotlib is the pyplot module, which allows for simple, high-level plotting with
commands
• Extensive support for different types of plots (e.g., line, bar, pie, scatter).
• Customization of plots, including titles, labels, colors, and grids.
• Ability to save plots to various file formats (e.g., PNG, PDF).
• Integration with other libraries like NumPy and Pandas for data visualization.
Seaborn is a Python data visualization library built on top of Matplotlib. It simplifies the creation of visually
appealing, informative statistical graphics. Seaborn provides high-level functions for creating complex
visualizations like violin plots, box plots, heatmaps, and pair plots. It is especially useful for statistical plotting and
works well with Pandas DataFrames.
• Simplifies the creation of complex statistical plots (e.g., regression lines, correlation heatmaps).
• Aesthetically pleasing plots with better default themes and color palettes than Matplotlib.
• Direct integration with Pandas for plotting data stored in DataFrames.
• Built-in functions for statistical analysis and visualization (e.g., sns.lmplot for linear models).
• Integration with other libraries like NumPy and Pandas for data visualization.

• Data:
categories: A list of categorical labels for the x-axis.
values: A list of numerical values corresponding to each category.
• plt.bar(): This function is used to create a bar chart, where categories are plotted on the x-axis and values on the y-axis.
• Labels and Title:
The plt.title(), plt.xlabel(), and plt.ylabel() functions are used to add a title to the chart and label the x and y axes.
• plt.show(): Displays the chart in a window.
• Around 15 - 20 categories of plot in Matplotlib

• Seaborn is used to create a scatter plot with a regression line using the lmplot function.
• The data comes from the built-in tips dataset, which contains information about restaurant bills and tips.
• x='total_bill' and y='tip' specify the columns to plot.
• The regression line shows the relationship between the total bill and the tip.
• Matplotlib (Simple): A basic line plot showing a linear relationship between two variables.
• Seaborn (Complex): A scatter plot with a regression line, using a built-in dataset to analyze the relationship between two variables (total bill and tip).

Python libraries are essential tools for
developers, enhancing efficiency and
functionality.
They enable Python's application in diverse
domains, from AI to web development.
By mastering libraries, developers can focus
on creativity and problem-solving.
Conclusion

Feel free to ask any questions or
share your thoughts.

�ݺ�ߣ

Advance Programming �ݺ�ߣs lect.pptx.pdf

Recommended

More Related Content

Similar to Advance Programming �ݺ�ߣs lect.pptx.pdf (20)

Recently uploaded (20)

Advance Programming �ݺ�ߣs lect.pptx.pdf