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

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

# Remving outliers from the data
def outlier_detection(df):
    df = df.quantile([.90, .10])
    return df

print(outlier_detection(read_csv()))

According to the problem statement, we need to find percentile from the data __Auto MPG Dataset__ of all columns. 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 8__. 

Moving towards the main implementation, look at the header of the `outlier_detection(df)` function at __line 11__. It takes _one_ arguments as input:
+ `df`: A dataframe containing the dataset in the form of a matrix.


__Line 12__ is the most important line. We are using a built-in function `quantile()`on `df` which takes _one_ argument:`[.90, .10]`. We are specifying the range for quartile that it should be between __0.90__ and __0.10__. It will return _two_ numbers in a list for each column.
 

At __line 15__ we are calling the function `outlier_detection(read_csv())`. First control will transfer to `read_csv()` at __line 3__ and we'll get a dataframe. Then control will go to __line 11__ and 90th and 10th percentile will be returned for each column.

For the result, you will notice that percentiles are printed. For example, the 90th and 10th percentile for `mpg` is __34.33__ and __14.00__ respectively.

That’s it about the main concepts regarding cleaning the dataset using Pandas. The next chapter explains how to visualize a dataset using the `seaborn` library.

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: Cleaning Auto MPG Dataset

Cleaning the dataset