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

# Split-Apply-Combine method
In this technique, we split the data into specific groups, like in the previous [lesson](https://www.educative.io/courses/predictive-data-analysis-with-python/data-grouping). Then certain operations are applied to those groups separately. Finally, all the groups are again combined to form the final required dataset. Let's review the following example.

The initial data set is first split into three groups, `A`, `B`, and `C`. Then, the `sum` operation is applied to every element of each group. Finally, the results are combined at the end, and a dataset with concise required information is formed.

Let's perform this technique on *air quality index* data and see what type of useful information can be extracted. 


import pandas as pd

df = pd.read_csv('air.csv') # reading data from file

print(df)

As can be seen from the output, the file contains the `Date`, `Time`, and the number of different pollutants that are in the air in that time frame. Pollutant data for every hour of each day is mentioned. Our task is to find that time of each day when the concentration of `CO(GT)` and `NO2(GT)` are the highest as these two are the major components that play an important role in air pollution.

The following steps are performed to obtain the required result:

* First, the data needs to be sorted on the amounts of `CO(GT)` and `NO2(GT)` in *descending* order.

* Now, the data is split or grouped on `Date`.

* A function that ranks the data on the basis of the most polluted time of the day is applied.

* Finally, those results are combined where the rank of the specific time of day is highest.

## Sorting

The data is sorted using the `sort_values` function explained in this [lesson](https://www.educative.io/courses/predictive-data-analysis-with-python/sort-and-rank-in-pandas).

In this lesson, the split-apply-combine can be found of pandas is 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

Split-Apply-Combine Technique

Split-Apply-Combine method