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.

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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

import pandas as pd

# Loading dataset
def read_csv():
    # Define the column names as a list
    names = ["mpg", "cylinders", "displacement", "horsepower", "weight", "acceleration", "model_year", "origin", "car_name"]
    # Read in the CSV file from the webpage using the defined column names
    df = pd.read_csv("auto-mpg.data", header=None, names=names, delim_whitespace=True)
    return df

# Describing data
def group_aggregation(df, group_var, agg_var):

    # Grouping the data and taking mean
    grouped_df = df.groupby([group_var])[agg_var].mean()
    return grouped_df

# Calling the function
print(group_aggregation(read_csv(),"cylinders","mpg"))

According to the problem statement, we need to group the __Auto MPG Dataset__ on the basis of a column.  Then we have to calculate the mean of the grouped data according to another column. Before doing it, we have to read the data first. There is no need to explain how to read the data, as we studied that in detail [previously](https://www.educative.io/courses/python-data-analysis-and-visualization/solution-review-reading-auto-mpg-dataset). Dataset is read from __line 4__ to __line 9__. 

Moving towards the main implementation, look at the header of the `group_aggregation(df, group_var, agg_var)` function. It takes _three_ arguments as input:
+ `df`: A dataframe containing the dataset in the form of a matrix.
+ `group_var`: A variable that will group the data  
+ `agg_var`: A variable to aggregate or describe the grouped data with statistics


__Line 15__ is the most important line. We are using a built-in function `groupby()`on `df` which takes _one_ arguments:`group_var`. It will return the data grouped according to `group_var` column. Next, we are calculating the `mean` of the grouped data with the `agg_var` column. Then at __line 16__, we are returning the result.
 

At __line 19__ we are calling the function `group_aggregation(read_csv(),"cylinders","mpg"))`. First control will transfer to `read_csv()` at __line 4__ and we'll get a dataframe. 

The next argument is `cylinders`. According to the dataset we have _five_ different values for `cylinders`: __3__,__4__,__5__,__6__, and __8__. So the dataframe` df` will be grouped into _five_ groups, each group represented by a different cylinder count. In simple words, all cars having __3__ cylinders will form one group. Similarly, all cars having __4__ cylinders will form another group, and so on.

The last argument is `mpg`. According to the dataset, `mpg` holds continuous values. According to implementation, the _mean_ of `mpg`  for all the cars (of the same group) will be returned and printed. 

According to the result, you will notice:
+ The average `mpg` of cars having __3__ `cylinders` in total is __20.550000__.
+ The average `mpg` of cars having __4__ `cylinders` in total is __29.286765__.
+ The average `mpg` of cars having __5__ `cylinders` in total is __27.366667__.
+ The average `mpg` of cars having __6__ `cylinders` in total is __19.985714__.
+ The average `mpg` of cars having __8__ `cylinders` in total is __14.963107__.

That’s it about analyzing the dataset with different statistical techniques using Pandas. The next chapter explains how to clean a dataset.

This lesson provides the solution to the previous challenge.

What is Analytics

Python Basics for Analytics

Reading Data

Describing Data

Cleaning Data

Visualizing Data

Solution Review: Group By Aggregations

Group by aggregations #