Beginner's guide to Python Pandas for Data Analysis

Name: Beginner's guide to Python Pandas for Data Analysis
Rating: 4.8 (16 reviews)

Learn how to code with pandas library

Highest Rated

Created byRaphael Asghar

Last updated 10/2020

English

What you'll learn

This course will introduce to the student how to use a Python analytical tool called Pandas. With this technology, the student will be able to import and analyze data from a variety of data sources, such as Excel, CSV, SQL Server, URLs, Big Data, and much more. With the aid of Juypter Notebook editor, the student will be able to interact with pandas library and learn to code. The pandas library will introduce to the student on how to import and export data, how to manage, manipulate, configure data, and how to filter, add, delete, concatenate, group data and visualize data with charts and much more.

Course content

1 section • 50 lectures • 12h 50m total length

INTRODUCTION TO PYTHON PANDAS12:24
Import and cleanse raw data from csv files, excel files, and sql server tables in a Jupyter notebook, create data frames, and visualize results with pandas for informed decisions.
DOWNLOADING PYTHON AND JUPYTER7:45
INSTALLING SSMS4:54
INSTALLING SQL SERVER 20199:21
Install and configure SQL Server 2019 Developer Edition on Windows 10, verify prerequisites, and connect via GUI to support data analysis with Python Pandas in the course.
NAVIGATE JUPYTER INTERFACE14:16
WHAT IS PANDAS17:45
QUIZ 17:24
Rafael guides learners through a practical quiz on Python and pandas, covering insert and delete cell shortcuts, list creation, and building a data frame from a cars dictionary.
IMPORTING AND READING DATA FROM A CSV FILE12:06
PANDAS COMMON KEYWORDS FOR INFO12:31
QUIZ 23:48
ILOC AND LOC FILTERING AND SLICING21:12
Learn to slice and filter large sales data with pandas iloc and loc, selecting rows and columns by position or label.
ILOC AND LOC FILTERING AND SLICING PART 225:29
Explore iloc and loc filtering and slicing in pandas, using start and end row and column indices and negative indices. Understand label versus position indexing, accessing data frames and series.
WORKING WITH INDEXES19:10
Learn to work with indexes in pandas by loading an Excel file, slicing and filtering with df.loc, selecting columns, and creating a permanent index (email) with set_index and reset_index.
QUIZ 313:02
Master pandas data selection in Python through quiz 3: pull specific columns and top 10 rows, use iloc and loc for indexing, filter by California, and set state as index.
CONDITIONAL FILTERING WITH OPERATORS13:20
CONDITIONAL FILTERING WITH OPERATORS PART 226:24
Apply conditional filtering in pandas using or, and, not, and is in to select rows by name and score. Wrap filters in a data frame to view results.
PANDAS SORTING COLUMNS22:40
QUIZ 47:27
PANDAS SORTING COLUMNS30:55
REPLACE COLUMNS ROWS AND STRING15:21
QUIZ 56:28
PANDAS STR FUNCTION34:59
PANDAS REGEX12:35
PANDAS REGEX PART 226:35
GROUPING DATA27:45
QUIZ 615:32
Explore core pandas concepts through quiz 6, focusing on group by, data filtering with get_group, regex find and replace, unique values, youngest selection, and earnings per year.
LAMBDA FUNCTION21:15
PIVOT TABLE15:08
Explore how the pivot_table function in pandas reshapes data by region, rep, and item, then aggregate unit cost and units with mean or sum.
MISSING VALUES34:02
DATES AND DATETIMES21:11
IMPORT HTML URL DATA5:21
QUIZ 79:20
CONNECTING TO SQL SERVER13:24
JOINS EXPLAINED9:18
SQL AND PANDAS JOINS18:44
EXAMPLES OF SQL VS PANDAS QUERIES6:22
SQL SERVER AND PANDAS COMMANDS24:52
Learn how to connect to SQL Server from the pandas environment in Jupiter, compare SQL and pandas commands, and run live data operations using select, where, group by, and more.
QUIZ 85:23
MAPLOT LINE GRAPHS6:29
FULL MAPLOT LINE GRAPHS25:52
MAPPLOT PIE CHART13:05
MATPLOT BAR CHART10:44
PYTHON PANDAS PROJECT PART 110:39
PYTHON PANDAS PROJECT PART 219:13
PYTHON PANDAS PROJECT PART 321:58
PYTHON PANDAS PROJECT PART 419:26
PYTHON PANDAS PROJECT PART 519:13
IMPORT ALL TABLES VIA SINGLE TSQL COMMAND2:55
QUIZ 910:14
SELF LEARNING PROJECT5:41

Requirements

This course is designed with the beginner in mind; as such, we introduce the fundamentals of pandas library commands to manage data, However, my course called An Absolute Beginner’s Guide to Python is a highly recommended course or a fundamental understanding of Python language.

Description

This course will introduce to the student how to use a Python analytical tool called Pandas. With this technology, the student will be able to import and analyze data from a variety of data sources, such as Excel, CSV, SQL Server, URLs, Big Data, and much more. With the aid of Juypter Notebook editor, the student will be able to interact with pandas library and learn to code. The pandas library will introduce to the student on how to import and export data, how to manage, manipulate, configure data, and how to filter, add, delete, concatenate, group data and visualize data with charts and much more.

Who this course is for:

As this is a beginner guide to pandas library, it is designed for the student who wants to learn about how to manipulate, configure, manage, import, and export data from a variety of sources such as Excel, CSV, SQL databases, URLs, and many more resources. These students can be developers, database admins, beginning data scientists, financial analysts, Excel students, or anyone who works with data.