Delve into Python data analysis and visualization using NumPy, SciPy, Pandas, and Seaborn. Gain insights into reading, cleaning, and visualizing data to generate actionable insights.

mlxtend.tar.gz

visualization

This course assumes no prior experience and can teach anyone how to use Python to analyze and visualize data. Stop using Excel and start harnessing the power of Python.

Data analysis is one of the hottest careers of the 21st century. As an analyst, your goal is to peel back layers of data in order to answer questions of interest; that is the power of analytics. It allows you to take raw data and create meaningful, actionable insights.

In this course, you’ll learn how to use Python, NumPy, SciPy, Pandas, and Seaborn to perform data analysis and visualization. You’ll explore the four crucial steps for any data analysis project: reading, describing, cleaning, and visualizing data. In each step, you will work with the most common and popular tools that data analysts use every day. By the end of the course, you will be able to confidently extract knowledge and answers from data.

Python Data Analysis and Visualization

# Detect outliers

In this exercise, we will use the same data frame as read in from the [Reading Data exercise](https://www.educative.io/courses/python-data-analysis-and-visualization/exercise-reading-auto-mpg-dataset).

We will then create an additional function `outlier_detection` that takes a data frame `df` and returns 2 numbers in a list:

+ The 90<sup>th</sup> percentile for every column
+ The 10<sup>th</sup> percentile for every column

Try to implement the function below. Feel free to view the solution, after giving it a few shots. Good Luck!

This exercise will test a simple way to start investigating potential outliers.

What is Analytics

Python Basics for Analytics

Reading Data

Describing Data

Cleaning Data

Visualizing Data

Exercise: Cleaning Auto MPG Dataset

Detect outliers