seaborn.tar.gz

seaborn

In this course, you will learn how to perform predictive data analysis using Python. The ideal audience is those who want to start their careers as data analysts. The main goal of this course is to show you how to use statistics to draw useful insights from data which can help in predicting future behavior or patterns.

Beyond that, you’ll learn all the tools of the trade that data scientists use everyday including: NumPy, Pandas, Matplotlib, and Seaborn. You’ll learn not only how to extract meaningful insights from data, but you’ll also learn how to create stunning visualizations that you can use for reports.

Various datasets of real-world scenarios are used in each lesson to get you accustomed to handling any type of data. At the end of the course, you will work on two real-world projects that demonstrate how data analysis techniques are being used in the financial and advertisement sector to generate revenue.

Predictive Data Analysis with Python

## DataFrame
A `DataFrame` can be described as a 2-D array or matrix. It has data stored in it just like an excel spreadsheet with multiple columns and rows. Unlike `Series` which only has one index, each value in a `DataFrame` object is associated with a _row_ index as well as a *column* index.

In the following code snippet, a *CSV* file containing cancer registration statistics of 2017 is read in a `DataFrame`.

# DataFrame
A `DataFrame` can be described as a 2-D array or matrix. It has data stored in it just like an excel spreadsheet with multiple columns and rows. Unlike `Series` which only has one index, each value in a `DataFrame` object is associated with a _row_ index as well as a *column* index.

In the following code snippet, a *CSV* file containing cancer registration statistics of 2017 is read in a `DataFrame`.

In this lesson, the DataFrame object of pandas is explained.

Getting Started

Numpy for Python

Pandas for Python

Statistics for Data Analysis

Data Wrangling

Visualizing the Data

Data Scraping

Project #1

Project #2

Conclusion

Predictive Data Analysis Exam

DataFrame in Pandas

DataFrame