Python Complete Notes with Raksha Shetty

Python Complete Notes for Data Analysts (300+ Pages)

These Python Complete Notes are designed specifically for Data Analysts, Data Science beginners, and students who want to learn Python from basic programming concepts to real-world data analysis and visualization.

The notes are written in simple, clear, and structured language, making them beginner-friendly while still covering practical concepts used in real data analysis projects and interviews.

The material is organized step-by-step, starting from Python fundamentals, then moving into NumPy for numerical computing, Pandas for data analysis, and Matplotlib & Seaborn for data visualization.

These notes focus on the most important concepts that cover the majority of real-world data analysis work.

1. Python Fundamentals (Core Programming Concepts)

The notes start with a strong foundation in Python programming, explaining concepts clearly with examples.

Topics covered include:

1. Introduction to Python and its key features
2. Interpreted vs compiled languages
3. Python syntax and readability principles
4. Variables and data types
5. Numeric data types (int, float, complex)
6. Strings and string operations
7. Boolean values and logical operations
8. Lists, tuples, sets, and dictionaries
9. Understanding Python operators
10. Conditional statements (if, elif, else)
11. Loops (for loops and while loops)
12. Loop control statements (break, continue, pass)

2. Functions and Functional Programming

Functions are explained in detail to help write reusable and organized code.

Topics covered:

1. What functions are and why they are used
2. Creating user-defined functions
3. Function parameters and return values

Types of arguments:

1. Positional arguments
2. Keyword arguments
3. Default arguments
4. Arbitrary positional arguments (*args)
5. Arbitrary keyword arguments (**kwargs)

Advanced function concepts:

1. Lambda functions
2. Using map() with lambda
3. Using filter() with lambda
4. Using reduce() with lambda

These concepts help perform efficient data transformations.

3. Error Handling and File Handling

Important concepts for writing robust Python programs:

1. Syntax errors vs runtime exceptions
2. Exception handling using try, except, else, and finally
3. Handling multiple exceptions

File handling topics include:

1. File paths (absolute vs relative paths)
2. Reading files
3. Writing files
4. Creating files
5. File modes (read, write, append, create)

These are important for data storage, automation, and reporting tasks.

4. Working with Python Modules

The notes also explain how to organize and reuse code using modules.

Topics include:

1. What modules are
2. Built-in modules in Python
3. Importing modules in different ways
4. Module aliases
5. Creating user-defined modules

Examples include common modules such as:

1. math
2. random
3. datetime
4. statistics
5. NumPy – Numerical Computing

NumPy is one of the most important libraries used in data science, analytics, and machine learning.

Topics covered include:

1. NumPy arrays and their advantages over Python lists
2. Speed and memory efficiency of arrays
3. Installing and importing NumPy

Array creation methods:

1. np.array()
2. np.zeros()
3. np.ones()
4. np.arange()
5. np.linspace()
6. np.full()

Random number generation

Array attributes:

1. shape
2. dtype
3. size
4. ndim

Array operations:

1. Indexing and slicing (1D and 2D arrays)
2. Boolean indexing
3. Fancy indexing

Advanced NumPy concepts:

1. Element-wise operations
2. Broadcasting and its rules
3. Aggregate functions (sum, mean, std, min, max)

Array transformations:

1. reshape()
2. flatten()
3. ravel()
4. transpose()

Combining and splitting arrays:

1. vstack()
2. hstack()
3. split()

Additional topics:

1. Copy vs view
2. Universal functions (ufuncs)
3. Set operations (unique, intersection, union)
4. Handling missing values (np.nan)
5. Using np.where() for conditional operations

6. Pandas – Data Analysis and Data Manipulation

Pandas is the most important library for data analysts.

Topics covered include:

Understanding Pandas data structures:

1. Series (1D labeled array)
2. DataFrame (2D table structure)

Creating data structures using:

a. Lists

b. Dictionaries

C) JSON files

D) SQL databases

Data reading and writing:

1. CSV files
2. Excel files
3. JSON files
4. SQL databases using SQLAlchemy

Data exploration:

1. head()
2. tail()
3. sample()
4. info()
5. describe()
6. shape
7. columns
8. dtypes

Selection and indexing:

1. Column selection
2. Row selection
3. loc vs iloc
4. Data filtering techniques:

Boolean indexing

1. Multiple conditions
2. isin()
3. between()
4. query()

Handling duplicates:

1. duplicated()
2. drop_duplicates()

Working with unique values:

1. unique()
2. nunique()
3. value_counts()

Data cleaning:

1. Handling inconsistent text data
2. Removing extra spaces
3. Standardizing categories
4. Handling missing values

Missing data handling techniques:

1. fillna()
2. forward fill
3. backward fill
4. mean / median / mode filling
5. interpolation
6. dropping rows or columns

Outlier detection methods:

1. IQR method
2. Z-score method
3. Percentile method

Data transformation:

1. astype()
2. rename()
3. sorting using sort_values()

Aggregation and grouping:

1. agg()
2. groupby()

Combining datasets:

1. merge() (SQL joins)
2. concat()
3. join()

Data reshaping:

1. pivot()
2. pivot_table()
3. melt()

Advanced Pandas functions:

1. apply()
2. map()
3. applymap()

Working with datetime:

1. dt accessor for extracting year, month, day, etc.
2. Advanced analytics functions:

ranking functions

1. shift()
2. transform() (similar to SQL window functions)

7. Data Visualization (Matplotlib & Seaborn)

Common visualization types explained:

1. Bar charts
2. Line charts
3. Scatter plots
4. Pie charts
5. Histograms
6. Heatmaps

Matplotlib

Topics covered:

1. Creating line charts
2. Bar charts and horizontal bar charts
3. Histograms
4. Scatter plots
5. Pie charts
6. Bubble charts

Chart customization:

1. markers
2. colors
3. labels
4. legends
5. axis limits
6. annotations
7. gridlines

statistical visualization.

Topics covered include:

Relational plots:

1. scatterplot()
2. lineplot()
3. relplot()

Categorical plots:

1. barplot()
2. countplot()
3. boxplot()

Distribution plots:

1. histplot()
2. kdeplot()