Let's learn the remaining types of plots that `pandas` can build.

|Value|Kind of plot|
|---|---|
|`box`| for boxplot|
|`kde` or `density`| for density plots|
|`area`| for area plots|
|`hexbin`| for hexagonal bin plots|
|`pie`| for pie plots|

What is a boxplot? A boxplot is a method of graphical representation of data minimum, quartiles, and maximum values. The lower side of rectangle is the first quartile (such a value that 25% of data is less than this value), the top side of rectangle is the third quartile (such a value that 75% of values is less than this value). Whiskers on a plot demonstates minimum and maximum. For instance, let's build a boxplot for the `'empinch'` column (represents total household employment income).

# Importing the library
import pandas as pd

# Reading the file
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/f2947b09-5f0d-4ad9-992f-ec0b87cd4b3f/data4.csv')

# Scatter plot total income vs. dwelling price
df.empinch.plot(kind = 'box')

These types are the most popular, but you will face the remaining ones while studying visualization libraries.

The data received from different sources can be messy, and to use it in the future, you must ensure it is convenient. In the first section, you will learn what data preprocessing is and will deal with some logical inconsistencies.

Missing or NA values, outliers, and inconsistencies are other types of problematic data. Throughout the second section of the course, you will learn how to deal with such issues.

As a data analyst, you will need to draw compact conclusions based on large amounts of data. In order to achieve that, you need to understand the data grouping idea and how to apply it to examples.

Sometimes one built-in function is not enough to draw a complete conclusion, so you need to use something more complex. This section will teach you how to apply multiple functions while grouping. Also, you will learn to visualize data using the pandas library only and will be acquainted with the main plot types. As a data analyst, you will need to draw compact conclusions based on large amounts of data. In order to achieve that, you need to understand the data grouping idea and how to apply it to examples.

As mentioned before, sometimes you may need to work with data received from multiple sources. This section will teach you how to join two dataframes using different techniques.

Value	Kind of plot
`box`	for boxplot
`kde` or `density`	for density plots
`area`	for area plots
`hexbin`	for hexagonal bin plots
`pie`	for pie plots