There are other kinds of graphs `pandas` allows us to use for visualization. Among them are boxplots, bar plots, scatter plots, etc. To build a respective plot, apply the `.plot()` method to selected data, and set the `kind` parameter to plot type you want to build.

Possible values for the `kind` parameter are the following.

|Value|Kind of plot|
|---|---|
|`bar` or `barh`| for bar plots|
|`hist`| for histogram|
|`scatter`| for scatter plots|


For instance, to build a scatter plot you also need to set two parameters: `x` and `y`. Let's build a scatter plot of total income vs. dwelling price.

# 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.plot(kind = 'scatter', x = 'totinch', y = 'valueh')

The same parameters must be set to build a bar plot. For instance, let's build a bar plot with address on the x-axis and total income on the y-axis for the first 20 dwellings.

# 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.iloc[:20].plot(kind = 'bar', x = 'address', y = 'totinch')

This course covers intermediate topics on pandas, a must-have tool for each data analyst. During the course, you will learn how to prepare data for further interactions and how to group it using different techniques. You will learn the easiest data visualization and be acquainted with data joining.

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.

Bar and Scatter Plots

Awesome!

Bar and Scatter Plots

Value	Kind of plot
`bar` or `barh`	for bar plots
`hist`	for histogram
`scatter`	for scatter plots