Types Of Plot By Purpose - Introduction
Summary
TLDRThe 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
📊 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.
📉 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.
Mindmap
Keywords
💡Data Visualization
💡Correlation
💡Deviation
💡Ranking
💡Distribution
💡Composition
💡Time Series Data
💡Scatter Plot
💡Histogram
💡Heat Map
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
浏览更多相关视频
Bar Charts, Pie Charts, Histograms, Stemplots, Timeplots (1.2)
Must know Visualization in Statistics | Descriptive Statistics | Ultimate Guide !! | Part 10
Data analysis and visualization
Which is the best chart: Selecting among 14 types of charts Part II
03 Descriptive Statistics and z Scores in SPSS – SPSS for Beginners
What is Exploratory Data Analysis (EDA)? | Techcanvass
5.0 / 5 (0 votes)