Presenting data in graphical format is known as data visualization. It summarizes a huge amount of data understandably and helps to communicate information clearly and effectively in python for data science.

Now we will plot different charts using the data set given below:

**Histogram:**

It is there for representing the frequency of occurrence of a specific phenomenon that lies within a range of values and then arranged in consecutive and fixed intervals.

In the code below a histogram is plotted for Age, Income, Sales.

OUTPUT:

**Column Chart:**

It is there for showing a comparison between different attributes. It can also show comparison of items over time.

OUTPUT:

**Box plot chart:**

It is a graphical representation of statistical data based on the minimum, first quartile, median, third quartile, and maximum.

OUTPUT:

**Pie Chart:**

It shows a statistical number and also shows how categories represent part of a whole composition of something. A pie chart will show the number of percentages, and the sums of all the segments needed to become 100%.

OUTPUT:

**Scatter Plot:**

It shows a relationship between two variables and also can reveal the distribution trends. It’s there when there are many data points, and we want to highlight similarities in data set. It is useful in case we are looking for outliers and understanding the distribution of data.

OUTPUT:

To learn more about visualisation in python for data science, you can check this and this as well. These blogs will help you in understanding these topics better and easily.