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 # calling pandas module

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.shape

# Calling the function and printing the result
print(read_csv())

According to the problem statement, we need to get the __shape__ of __Auto MPG Dataset__ as an output. In the code above, at __line 1__, we imported the `pandas` module for reading the dataset. Next, we implemented the function `read_csv()`.  

Look at its header at __line 3__. At __line 6__, we set the name of the _columns_ in a list called `names`. We get the name of the columns from the [documentation](https://archive.ics.uci.edu/ml/datasets/Auto+MPG). Notice that `names` hold __9__ string values:
+ `mpg`
+ `cylinders`
+ `displacement`
+ `horsepower`
+ `weight`
+ `acceleration`
+ `model_year`
+ `origin`
+ `car_name`

Notice that we replace whitespace with `_` in `model year` and `car name`.

__Line 8__ is the most important line. We are using a built-in function `read_csv()` from `pandas`. As you are familiar, it takes some arguments as follows:
+ `path`: The path to the dataset 
+ `header`: In case the columns are provided explicitly, it's set to `None`.
+ `names`: List of columns' names to be used
+ `delim_whitespace`: Specifies whether or not whitespace should be used as a sep

So for `path`, we give the name of the file: __auto-mpg.data__. As we know the columns' names and provided them explicitly to the function as `names=names`, we set `header` as `None`. It was mentioned that there are whitespaces, and we have to use them as sep, so `delim_whitespace` is set to `true`. This built-in function returns a _dataframe_ `df` as a result. At __line 9__, we are returning `shape`, a property of the dataframe `df`.
 

Now, look at __line 12__, where we are calling the function `read_csv()`. It returns a tuple in the form of `(rows, columns)`, which is printed at the end. For this dataset, the function will return `(398, 9)` as there are __398__ rows and __9__ columns. 

That’s it about the basics of reading the dataset of different formats using Pandas. The next chapter explains how to describe any dataset in statistical terms.

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: Reading Auto MPG Dataset

Reading the Dataset