Types Of Plot By Purpose - Introduction

Summary

The video introduces different types of plots used for data visualization and their purposes in data analysis. It covers six key objectives: identifying relationships, measuring deviation, ranking, understanding distribution, analyzing composition, and tracking changes, particularly in time series data. The speaker explains various plot types like scatter plots, histograms, and time series plots, and highlights how each can be used to achieve specific analytical goals. Future sessions will focus on coding these plots using a Jupyter notebook. Viewers are encouraged to follow along to learn practical visualization techniques step-by-step.

Takeaways

• 📊 Data visualization involves using different types of plots for specific purposes in data analysis.
• 📉 One primary purpose is identifying relationships between two variables using correlation plots like scatter plots and heat maps.
• 📈 Deviation plots, such as diverging bars and dot plots, help understand variance within a dataset.
• 📋 Ranking plots, like ordered bar charts and dot plots, are used to rank data within a dataset.
• 📐 Distribution plots, such as histograms, density plots, and box plots, help identify how continuous and categorical data are distributed.
• 🥧 Composition plots, like pie charts and treemaps, are used to understand the makeup of a dataset.
• 🕰️ Time series plots are important for tracking changes over time, especially for time-series data.
• 🔧 Each type of plot serves a specific purpose, from understanding correlations to analyzing changes over time.
• 📝 Future sessions will focus on coding and creating each type of plot in Jupyter notebooks.
• 👨‍💻 The session emphasized the practical application of different plotting techniques in data visualization.

Q & A

What is the primary purpose of using plots in data visualization?

The primary purpose of using plots in data visualization is to visualize data effectively, making it easier to understand patterns, trends, relationships, distributions, and compositions within a dataset.

How can plots help in identifying the relationship between two variables?

Plots such as scatter plots, correlation plots, and heatmaps help in identifying the relationship between two variables by visually representing how one variable changes with respect to another.

What type of plot is commonly used to identify variance within a dataset?

To identify variance or deviation within a dataset, diverging bar plots and diverging dot plots are commonly used. These plots help in visualizing how much a data point deviates from a central value.

How can ranking be represented within a dataset?

Ranking within a dataset can be represented using order bar charts and dot plots. These plots help in visualizing the order or rank of data points based on certain metrics, such as maximum or mean values.

What are the key plots used to visualize data distribution?

The key plots used to visualize data distribution include histograms, density plots, and box plots. These plots help in understanding how continuous or categorical variables are distributed across a dataset.

What is the role of composition plots in data visualization?

Composition plots, such as pie charts, treemaps, and bar charts, are used to visualize the composition of different categories or elements within a dataset. These plots help in understanding the proportion of each component in relation to the whole.

Which plots are most useful for analyzing time-series data?

For time-series data, time series plots and time series decomposition plots are most useful. These plots help in visualizing changes, trends, and patterns over time within the dataset.

What is a scatter plot with a line of best fit used for?

A scatter plot with a line of best fit is used to visualize the relationship between two variables while also showing the overall trend or pattern through a line that best represents the data points.

How do you visualize both continuous and categorical variables in data analysis?

Continuous variables are often visualized using histograms and density plots, while categorical variables are visualized using bar charts, pie charts, and treemaps to show distribution and composition.

What are the key topics covered in the session related to data visualization?

The session covers the purposes of data visualization, including identifying relationships, deviations, rankings, distributions, compositions, and changes within datasets. It also discusses various types of plots used for these purposes, such as scatter plots, histograms, bar charts, pie charts, and time series plots.

Outlines

00:00

📊 Introduction to Plotting and Data Visualization

In this introductory paragraph, the speaker welcomes the audience and sets the stage for a discussion on various types of plots and their uses in data visualization. The primary focus is on six key purposes for plotting in data analysis: relationship identification, deviation, ranking, distribution, composition, and changes, particularly in time series data. The speaker explains that plots help analyze relationships between variables, variance in datasets, and patterns in time series, emphasizing the importance of these visual tools in data analysis.

05:02

📉 Exploring Plot Types for Relationships, Deviation, and Ranking

This paragraph dives deeper into the specific plots used for various purposes in data visualization. For identifying relationships between variables, scatter plots and other similar plots (scatter plot with a line of best fit, counts plot, marginal box plot, correlogram, heat map, pairwise plot) are introduced. For analyzing deviations, diverging bar and dot plots are highlighted as useful tools. When it comes to ranking, the speaker mentions order bar charts and dot plots, which help rank data within a dataset. These examples set the groundwork for the detailed exploration of plot types in future sessions.

📈 Understanding Distribution and Common Plotting Techniques

This section focuses on plots used to analyze distributions in datasets, particularly during data analysis. The speaker emphasizes the use of histograms, a traditional but effective method, to understand both continuous and categorical data distribution. Other tools like density plots, density curves with histograms, and box plots are also mentioned as useful for gaining insights into data distribution. The speaker emphasizes that distribution analysis is a critical step in data visualization.

📊 Analyzing Composition and Time Series Data

The final paragraph covers composition and time series analysis. Pie charts and tree maps are discussed as traditional and effective tools to identify the composition of data. Bar charts are also highlighted as a useful way to visualize composition. The paragraph closes by emphasizing the significance of time series plots and decomposition plots in analyzing trends over time in datasets. The speaker announces that in upcoming sessions, they will provide practical coding examples for each type of plot discussed.

Keywords

💡Data Visualization

Data visualization refers to the graphical representation of information and data. In the video, it is presented as a key tool in data analysis to help understand the relationship, distribution, or composition of variables. Different plots, such as scatter plots, histograms, and pie charts, are used for this purpose.

💡Correlation

Correlation is the statistical measure that describes the extent to which two variables are related. The video emphasizes correlation plots like scatter plots, heat maps, and pairwise plots to visualize the relationship between two or more variables. For example, a scatter plot with a line of best fit is used to show how one variable changes with another.

💡Deviation

Deviation refers to how much variation or difference exists within a dataset. The video mentions the importance of identifying deviation to understand variance using plots like diverging bar and dot plots. These plots help identify the spread or outliers in data.

💡Ranking

Ranking in data analysis refers to the ordering of data points based on specific criteria such as highest or lowest values. The video introduces order bar charts and dot plots as tools for visualizing the ranking of data within a dataset, which helps to understand things like maximum and mean ranges.

💡Distribution

Distribution in data visualization is the way values are spread across a dataset. The video explains that plots such as histograms, density plots, and box plots help identify how continuous or categorical variables are distributed within a dataset, which is essential for understanding data patterns.

💡Composition

Composition in data visualization refers to understanding the parts that make up a whole. The video discusses pie charts, tree maps, and bar charts as tools to display the composition of different categories within a dataset, for example, showing the proportion of different elements in a pie chart.

💡Time Series Data

Time series data refers to data points collected or recorded at specific time intervals. The video highlights the importance of time series plots and decomposition plots for visualizing changes over time, which is especially useful when analyzing trends and patterns in time-sensitive data.

💡Scatter Plot

A scatter plot is a type of graph that shows the relationship between two variables by displaying points at the intersection of their values. In the video, scatter plots are used to identify correlations between variables, often with a line of best fit added to visualize trends.

💡Histogram

A histogram is a plot used to show the distribution of a dataset by grouping data into bins or intervals. The video mentions histograms as an old yet effective method to display the distribution of continuous data, providing a visual summary of the frequency of data points.

💡Heat Map

A heat map is a data visualization tool that uses color to represent the magnitude of values in a matrix or table. In the video, heat maps are discussed in the context of showing relationships or correlations between variables, where darker or lighter shades indicate stronger or weaker correlations.

Highlights

Introduction to the session focusing on various types of plots and their usage in data visualization.

Plotting is used for multiple purposes in data analysis, with six major purposes identified.

First purpose of plotting is identifying the relationship between two variables using correlation plots like scatter plots.

Correlation plots provide insights into how one variable changes with respect to another.

The second purpose is to understand deviation within a dataset, with specific plots like diverging bars and diverging dot plots.

Third purpose is ranking within a dataset, with order bar charts and dot plots used to determine maximum, mean, and other rankings.

Distribution plots are used to examine how continuous or categorical variables are distributed within a dataset.

Histogram is an old and effective method for visualizing the distribution of data.

Other distribution plots include density plots, density curves with histograms, and box plots.

Composition is another purpose, with pie charts, tree maps, and bar charts used to understand the composition of data.

The last major purpose of plotting is identifying changes in a dataset, especially in time series data.

Time series plots and decomposition plots are effective for analyzing trends in time series data.

Scatter plots can be extended with lines of best fit, counts plots, and marginal box plots for deeper analysis.

Correlograms, heatmaps, and pairwise plots are used to explore relationships between more than two variables.

The session concludes with a promise of coding different types of plots in the next sessions, focusing on best practices for data visualization.

Transcripts

hello guys welcome back now from this

session onwards we are going to discuss

about various types of plot and their

usage so let's start our discussion so

guys we already know about that lot is

being used for data visualization and we

can use these lots for different

purposes so let's discuss about what are

the purpose we are having in data

analysis so easily we are having six

type of purpose for which we are using

the different kind of plot the first

thing we want to identify the

relationship between two variables and

the plots under correlation is used to

visualize the relationship between two

or more variables and correlation plot

gives us information like how does one

variable change with respect to another

variable now proceeding to the another

purpose that is deviation we want to

identify how much variance we are having

within the given data set then we use

various kinds of Aviation plots now

let's proceed further and discuss about

another purpose that is ranking sometime

it is required to get the information

about the ranking within a given data

set information like what is the maximum

range what is the mean range so there

are certain plots which help us to

identify the ranking of the data within

the given data set so let's proceed

further and discuss about distribution

so guys while doing the data analysis

sometime we required to know the

distribution of the data within the

given data set how the continuous

variable is distributed within the given

data set same way if you want to

identify the distribution of the

categorical variable we will be using

various type of plot to identify the

distribution within the given data set

now let's discuss about another purpose

that is composition sometime within data

analysis it is required to identify the

composition detail within the given data

set so to get the information about the

composition we will be using various

plots that we will look into now at last

but not the least we will be using

various plots to identify the changes

within the given data set and when we

are dealing with any kind of Time series

data and on that time we will be using

various plots which will be captured

sharing or say which will be visualizing

the changes within the data set and this

is one of the important purpose when we

are dealing with the time series data so

guys this is about the purpose of the

data visualization or say purpose of the

plotting now let's discuss about what

are the law which falls into each

category so let's jump into the Jupiter

notebook and we will discuss one by one

in this session and next session onward

we will write the code for different

types of plots so guys the first purpose

we have discussed about correlation and

the plots which belongs to this is the

scatter plot we are going to write the

code for a scatter plot in the next

session in this session we will discuss

about what are the plots available in

each of the category one by one so with

the scatter plot and other plots which

belongs to this category like a scatter

plot with line of best fit counts plot

marginal box plot correlogram heat map

pairwise plot so all this plot is used

to identify the relationship between two

variables and more than two variables

which we will look into but remember

that whenever you want to identify the

relationship between two variables then

these are the plots is going to be very

helpful to visualize the relationship

now let's proceed further there is other

purpose which we have discussed is the

deviation within deviation there is

something called diverging bus and

diverging Dot Plot so this is very

helpful to identify the deviation within

the data set that means suppose if you

want to identify the variance within the

data set then it is going to be very

helpful these two plots and next we have

discussed about ranking so identify the

ranking within the given data set we are

going to look into order bar chart and

Dot Plot so these two plot is very

helpful to identify the ranking within

the given data set now let's proceed

further and we have also discussed about

the distribution distribution generally

we use very much within data analysis we

need to identify the distribution of the

data within the given data set so for

that reason you will find there are

various plots we will be using two major

thing we generally do first one is the

correlation identification when we do

the data analysis and the second one we

generally do the distribution where we

want to identify the distribution of the

continuous variable then we want to

identify the distribution of the

categorical variable then we are going

to use histogram histogram is one of the

old methods and very effective way to

identifying the distribution within the

given data set along with that we will

also discuss about density plot we will

look into how to create density plot we

will also look into density curves with

histogram and at last we will also

discuss about the box plot box plot is

very helpful also identify the

distribution within the given data set

for the next purpose we have discussed

composition where we want to identify

the composition detail within the given

data set so for that reason we are going

to use pie chart pie chart is also old

methodology which you have seen many

places where you want to know the

composition of different kind of data

within the given data set so we use

ichart there is another method which we

use for composition that is tree map

tree map is also very useful way of

identifying the composition within the

given data set the third category of

chart we are going to look into our plot

we are going to look into is the bar

chart which you already aware about most

of the places you have seen the bar

chart that is also very useful method to

identify the composition and the last

purpose we have discussed that is very

useful when we are dealing with the time

series data set and on that time

basically we are using time series plot

we will look into how to create time

series plot we are also going to discuss

about time series decomposition plot it

is very effective rotting technique to

identify the trend within the given data

set that we will look into so overall

these number of plotting Technique we

are going to look into or these number

of plots we are going to look into and

we will look into the best practices to

write the code or data visualization so

on this note I am stopping over here

next session onwards I am going to start

writing the code for each of the plots

we want to follow along you can follow

with me so see you in the next session

till then bye bye take care