Visualization Techniques- Box plot, Line Chart, Scatter plot, Bar chart.

1. Data Science
Exploratory Data Analysis (EDA)
(Box plot, Scatter plot, Bar plot, Line plot)
Part-II

2. Box plots (Box-and-Whisker Plots):
• A box plot, also known as a box-and-whisker plot, is a graphical
representation of the distribution of a dataset based on five summary
statistics: minimum, first quartile (Q1), median (Q2), third quartile (Q3),
and maximum.
• It provides a visual summary of the central tendency, spread, and
skewness of the data, as well as identifying outliers.
• The boxplot() function in Matplotlib is used to create boxplot.

3. Box Plot (Box-and-Whisker Plots):
• Minimum and Maximum: The smallest and largest exam scores in the
dataset.
• Quartiles (Q1, Q2, Q3): These divide the dataset into four equal parts.
Q1 represents the 25th percentile, Q2 is the median (50th percentile), and
Q3 is the 75th percentile.
• Interquartile Range (IQR): The range between the first and third
quartiles (Q3 - Q1).
• Whiskers: Lines extending from the box to the minimum and maximum
values within 1.5 times the IQR from the first and third quartiles.
• Outliers: Data points outside the whiskers, indicating potential anomalies
or extreme values.

5. Interpreting the Box Plot:
• If the Median is at the center of the Box and the whiskers are almost the
same on both the ends, then the data is Normally Distributed.
• If the Median lies closer to the First Quartile and if the whisker at the lower
end is shorter then it has a Positive Skew (Right Skew).
• If the Median lies closer to the Third Quartile and if the whisker at the
upper end is shorter than it has a Negative Skew (Left Skew).
• If there are values that fall above or below the end of the whiskers, they are
plotted as dots. These points are often called outliers.
• In the example of exam scores, the box plot helps visualize the
performance of students and understand the spread of scores across the
class.

6. Skewness

7. Scatter plots
• Scatter plots visualize the relationship between two numerical variables
by plotting each observation as a point on a two-dimensional graph. They
help in identifying patterns, trends, and correlations between variables.
• The scatter() function in Matplotlib is used to create scatter plots.
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9. Interpreting the Scatter plots
• If the data points tend to move upwards from left to right, it indicates a
positive relationship. Conversely, if the data points tend to move
downwards from left to right, it indicates a negative relationship.
• If the data points form a roughly straight line, it suggests a linear
relationship. If the data points follow a curved pattern or do not conform
to a straight line, it suggests a nonlinear relationship.
• We can Calculate the correlation coefficient (such as Pearson's correlation
coefficient) to quantify the strength and direction of the relationship
between the variables. A correlation coefficient close to +1 indicates a
strong positive relationship, close to -1 indicates a strong negative
relationship, and close to 0 indicates no linear relationship.

10. Bar Plots
• Bar plots represent the frequency or count of categorical variables by
displaying bars of varying heights. They are effective for comparing the
distribution of categories or groups.
• In a bar chart, the x-axis represents the categories or groups, and the y-
axis represents the frequency or count of observations in each category.
The bars are separated from each other to represent distinct categories.
• The bar() function in Matplotlib is used to create bar plot.
• Bar charts are versatile and can be used to compare categorical data
across different groups or time periods, such as sales by product category,
votes by political party, or average temperatures by month.

12. Line plots
• Line plots show the trend or pattern of a numerical variable over time or
another continuous variable. They are commonly used for time series data
analysis.
• Line charts are used to visualize changes in one continuous numerical
variable over time or another ordered categorical variable.
• Example: Stock prices over time: We can plot stock prices (continuous
numerical variable) against time (ordered categorical variable) to see how
they change over different time periods.
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