Python Pandas Library for Data Science

Python Pandas Library for Data Science, available at $19.99, has an average rating of 4.25, with 9 lectures, based on 50 reviews, and has 11259 subscribers.

You will learn about You will learn the basics of Pandas Library You will have clarity on Pandas Data structures – Series & Dataframes You will Play with Dataframes, Selecting columns & rows from a dataframe You will understand Subsetting of dataframes – df[start_index:end_index] You will get insights on Indexing You will get clarity on Dataframes merging and concatenating This course is ideal for individuals who are Data analysts and business analysts or Excel users looking to learn a more powerful software for data analysis It is particularly useful for Data analysts and business analysts or Excel users looking to learn a more powerful software for data analysis.

Enroll now: Python Pandas Library for Data Science

Summary

Title: Python Pandas Library for Data Science

Price: $19.99

Average Rating: 4.25

Number of Lectures: 9

Number of Published Lectures: 9

Number of Curriculum Items: 10

Number of Published Curriculum Objects: 10

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will Learn

You will learn the basics of Pandas Library

You will have clarity on Pandas Data structures – Series & Dataframes

You will Play with Dataframes, Selecting columns & rows from a dataframe

You will understand Subsetting of dataframes – df[start_index:end_index]

You will get insights on Indexing

You will get clarity on Dataframes merging and concatenating

Who Should Attend

Data analysts and business analysts

Excel users looking to learn a more powerful software for data analysis

Target Audiences

Data analysts and business analysts

Excel users looking to learn a more powerful software for data analysis

Pandas Background:

When working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool for you. pandas will help you to explore, clean and process your data. In pandas, a data table is called a DataFrame. Pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,. . . ). Importing data from each of these data sources is provided by function with the prefix read_*. Similarly, the to_* methods are used to store data.

Selecting or filtering specific rows and/or columns? Filtering the data on a condition? Methods for slicing, selecting, and extracting the data you need are available in pandas. There is no need to loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise. Adding a column to a DataFrame based on existing data in other columns is straightforward.

Pandas has great support for time series and has an extensive set of tools for working with dates, times, and timeindexed data. Data sets do not only contain numerical data. pandas provides a wide range of functions to cleaning textual data and extract useful information from it.

In this course we cover:

Basics of Pandas Library

Pandas Data structures – Series & Dataframes

Playing with Dataframes, Selecting columns & rows from a dataframe

Subsetting of dataframes – df[start_index:end_index]

Indexing

Dataframes merging and concatenating

Python programming has become one of the most sought after programming languages in the world, with its extensive amount of features and the sheer amount of productivity it provides. Therefore, being able to code Pandas in Python, enables you to tap into the power of the various other features and libraries which will use with Python. Some of these libraries are NumPy, SciPy, MatPlotLib, etc.

Testimonials:

Excellent course ~ Kwizerimana Amedee

This is one good Course for learning Pandas Lib. ~ Kumesh Rana

muy bueno! (Very Good) ~ Osvaldo Falabella

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Pandas Data Structures – Series

Lecture 1: Pandas Data Structures – Series

Chapter 3: Pandas Data Structures – Dataframes

Lecture 1: Dataframes

Chapter 4: Playing with Dataframes

Lecture 1: Playing with Dataframes

Chapter 5: Selecting columns & rows from a Dataframe

Lecture 1: Selecting columns & rows from a Dataframe

Chapter 6: Subsetting of Dataframes

Lecture 1: Subsetting of Dataframes

Chapter 7: Indexing

Lecture 1: Indexing Part1

Lecture 2: Indexing Part2

Chapter 8: Dataframes Merging and Concatenating

Lecture 1: Dataframes Merging and Concatenating

Instructors

Sai Acuity Institute of Learning Pvt Ltd Enabling Learning Through Insight!
Cybersecurity, Data Science & Human Capital Practitioners!

Rating Distribution

1 stars: 6 votes

2 stars: 2 votes

3 stars: 10 votes

4 stars: 10 votes

5 stars: 22 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!