Lecture: Python Pandas Overview and Features

Introduction to Pandas

• Pandas is a powerful, open-source library built on Python.
• Popular for data analysis and manipulation.
• Widely used in various fields: Statistics, Finance, Neuroscience, Economics, Web Analytics, Advertising, etc.
• Key for working with datasets: cleaning, analyzing, and manipulating data.

Features of Pandas

1. Data Analysis and Manipulation
   • Reading and cleaning datasets.
   • Handling duplicate data.
2. File Handling
   • Supports various file formats: Excel, CSV, JSON, XML.
3. Data Structures
   • DataFrame: Two-dimensional tabular data with rows and columns.
   • Series: One-dimensional array, like a column in a table.
4. Handling Missing Data
   • Easy handling of null or incomplete data.
5. Data Operations
   • Insert or delete columns.
   • Grouping data (rows/columns).
6. Development and History
   • Developed by Wes McKinney in 2008.

Setting up Pandas

Installation

1. Python and Pip
   • Python installation (e.g., Python 3.12).
   • Pip for managing Python packages.
2. PyCharm IDE
   • Community edition is open-source and freely available.
3. Numpy Installation
   • Pandas relies on Numpy.

Example Installation Steps

1. Install Python and Pip.
2. Install PyCharm Community Edition.
3. Connect Python with PyCharm.
4. Install Numpy and Pandas in PyCharm.
5. Verify installations using command prompt (e.g., python --version).

Examples and Code Demonstrations

Creating a DataFrame

1. Import pandas.
2. Create a dataset (example provided: student records).
3. Load data into a DataFrame.
4. Operate with DataFrame: load, clean, analyze.

Handling DataFrames

1. Accessing Rows and Columns
   • By integer positions (using .iloc).
   • By labels (using .loc).
2. Manipulations
   • Removing duplicates.
   • Handling missing data.
   • Deleting columns or rows.
3. Iterating DataFrames
   • Using loops for iteration.
4. Dataframe Attributes and Methods
   • .dtype, .ndim, .size, .shape, .index, .T, etc.

Grouping and Aggregation

1. Grouping Data
   • Using groupby for splitting data into groups.
2. Aggregation
   • Applying aggregations (e.g., mean, size).
3. Example Tasks
   • Iterate over groups, view groups, perform aggregation.

Data Cleaning Operations

1. Finding and Removing Duplicates
   • Using duplicated() and drop_duplicates().
2. Handling Missing Values
   • .isnull(), .notnull(), .dropna(), .fillna().
3. Cleaning Data
   • Replace missing values or handle incorrect data.
   • Example methods for cleaning data explained.

Visualization and Plotting with Pandas

1. Plotting DataFrames
2. Matplotlib Library
   • Essential for plotting with Pandas.
3. Types of Plots
   • DataFrame plot, Histogram, Pie Chart, Scatter Plot, Area Plot.

Plotting Examples

1. Plot a DataFrame
2. Histogram
   • Representing frequency distributions.
3. Pie Chart
   • Dividing data into slices.
4. Scatter Plot
   • Relationship between two variables.
5. Area Plot
   • Quantitative data visually.