pandas

• stands for Python Data Analysis Library
• High performance library, with easy to use data structures and data analysis tools
• similar to a spreadsheet but with ore flexibility
• Used in various fields, including data science, machine learning, NNLP, big data, statistics, finance and many more

pandas Features

• has grouping and aggregation functions
• can join merge and reshape data
• built on top of numpy and matplotlib
• can import data from CSV, JSON, Excel, and others

pandas Analysis Process

data retrieval (set up the data) » selection » cleaning (chekin types, checking the blanks, and changing values) » analysis » grouping (group data) » plotting data

pandas Installation

pip install pandas
pip3 install pandas

pandas Main Data Structures

• Series
• Index
• DataFrame

Series

• A one-dimensional array
• Items share one type
• Items are indexed
• Similar to a Python dictionary

Index

• an immutable sequence for indexing and alignment
• Automatically generated when creating Series or DataFrames
• Facilitates houdn between Series and/or DataFrames

DataFrame

• pandas’ main data structure
• Two-dimensional grid similar to a database table
• Columns have labels and rows are indexed
• Similar to a list of lists/dictionaries in Python

DataFrame Example

screenshot

DataFrame Column Labels

Data Types

• objects (generalized type) it is like a pointer type and used for string type
• string type is a recent type and may not work with older versions
• categorial (is a list of user defined values, fixed numbered list of possible values)
• Numeric: integers, floats, boolean
• Datetime: date, timedelta, interval, period