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

# Group by aggregations

We'll use the same function we created in the [Reading Data exercise](https://www.educative.io/courses/python-data-analysis-and-visualization/exercise-reading-auto-mpg-dataset) to read in the `pandas` data frame.

We will create an additional function `group_aggregation` which will take a data frame, a group variable, and an aggregation variable. The function will group by the group variable and calculate the mean of the aggregation variable for each group. Your function will return the grouped data frame.

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

This exercise will test your abilitiy to do group by aggregations on a pandas data frame

What is Analytics

Python Basics for Analytics

Reading Data

Describing Data

Cleaning Data

Visualizing Data

Exercise: Group By Aggregations