### Visualization of Data with Python (Matplotlib and Seaborn)

**Data Visualization is key to visualize**and understand what the relationship is there between the data given to us. It also helps us to convey our findings to the stake holders in a very presentable and understandable manner.

We basically use two packages Matplotlib and Seaborn for plotting of figures in python.

**Lets see the plots we can make in python:**

import matplotlib.pyplot as plt

import seaborn as sns

## 1. Line plot

A line plot or or line chart is a type of plot which displays information as a series of data points called 'markers' connected by straight line segments. Line plot should be used when we have continuous data.

Line Plots |

## 2. Area Plots

An area plot is similar to line plot, except that the area between the x-axis and the line is filled with color or shading. It represents the evolution of a numerical variable following another numerical variable.

Area Plots |

## 3. Histogram Plots

A histogram is the most commonly used graph to show frequency distribution.

Histogram Plots |

## 4. Bar Plots

A bar plot is a chart that represents categorical data with rectangular bars with heights or lengths proportional to the values that they represent. The bars can be plotted horizontally or vertically.

Bar Plots |

## 5. Pie Chart

A pie chart is a circular graphic that displays numeric proportions by dividing a circle (pie) into proportional slices.

Pie Chart |

## 6. Box Plots

A box plot is a way of statistically representing the distribution of the data through five main parameters and they are minimum - smallest number in the data set, first quartile, Median, third quartile and maximum - highest number in the data set. There are outliers also.

Box plots |

**The other plots are**scatter plots, bubble plots, Waffle charts, regression plot, residual plot, distribution plot and many more.

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