Gain insights into performing predictive data analysis using Python. Learn about tools like NumPy, Pandas, Matplotlib, and Seaborn, and applying techniques to real-world financial and advertisement projects.

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

# Reshaping

Reshaping is considered a powerful tool for getting the data ready for analysis. It transforms the initial data model into the desired shape to ease the cleaning and manipulation of data for further analysis. This technique is usually performed on a `Series` or `DatFrame` with [multiple indexes](https://www.educative.io/courses/predictive-data-analysis-with-python/additional-functions-and-features#multilevel-indexing).

Three techniques are mostly used to reshape data, and pandas provide built-in functions for all of them.

* **Stacking**

* **Unstacking**

* **Pivoting**

## Stack
Stacking a pandas `DataFrame` means taking the innermost level of column index from a multi-indexed `DataFrame` and adding it as another level to the innermost row index. If the `DataFrame` is not multi-indexed, it takes the first column and moves it to the innermost row level. 

The following example explains this concept.

In this lesson, some data reshaping techniques are discussed.

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

Reshaping Data

Reshaping

Stack