2. INTRODUCTION TO MATPLOTLIB
● Matplotlib is a popular Python library used for creating visualizations,
charts, and plots.
● It helps in understanding data patterns, trends, and relationships
through graphical representation.
● Importing Matplotlib:
import matplotlib.pyplot as plt
● Matplotlib provides a wide range of plot types, from basic line plots
to complex 3D visualizations. This versatility makes it suitable for
various data analysis and presentation needs, catering to different
domains such as science, engineering, finance, and more.
● Matplotlib seamlessly integrates with other popular Python libraries
such as NumPy and Pandas. This integration allows you to visualize
data structures like arrays and data frames directly without extensive
data manipulation, enhancing productivity and workflow efficiency.
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3. CATEGORIZATION OF PLOTS
Matplotlib can be categorized into different types based on the number of
variables included in plots. Here are the categories:
1. Univariate Plots:
● These plots involve analyzing a single variable.
● Examples: Histograms, density plots, box plots, violin plots, bar
plots (for categorical data).
2. Bivariate Plots:
● These plots visualize relationships between two variables.
● Examples: Scatter plots, line plots (connecting two variables), bar
plots (comparing two variables), hexbin plots.
3. Multivariate Plots:
● These plots explore relationships involving more than two
variables.
● Examples: 3D scatter plots, bubble plots, pair plots (showing
relationships among multiple variables), parallel coordinate plots.
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4. CATEGORIZATION OF PLOTS
4. Categorical Plots:
● These plots focus on categorical data and its distribution.
● Examples: Bar plots (for categorical data), stacked bar plots,
grouped bar plots, mosaic plots, dendrogram plots.
5. Time Series Plots:
● These plots specifically deal with time-related data.
● Examples: Line plots (over time), area plots, candlestick plots,
seasonal decomposition plots.
6. Statistical Plots:
● These plots emphasize statistical relationships and summaries.
● Examples: Regression plots, distribution plots (showing
distributions and fit), QQ plots (quantile-quantile plots).
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5. CATEGORIZATION OF PLOTS
7. Network Plots:
● These plots depict relationships within networks or graphs.
● Examples: Network graphs, directed graphs, node-link diagrams.
8. Interactive Plots:
● These plots allow user interaction for exploring data.
● Examples: Interactive scatter plots, interactive line plots,
interactive heat maps.
9. Composite Plots:
● These plots combine different types of plots to show complex
relationships.
● Examples: Facet grids (grid of subplots), composite heat maps
with annotations.
Matplotlib versatility makes it suitable for creating a wide range of plots,
catering to various data visualization needs.
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6. LINE PLOTS IN MATPLOTLIB
● A line plot is a fundamental type of visualization in Matplotlib that
displays data points connected by straight lines.
● It's often used to show the trend or progression of data over a
continuous interval.
● For example:
Suppose you have collected temperature data over a week. You can
create a line plot to visualize how the temperature changes each day.
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7. LINE PLOTS IN MATPLOTLIB
● In this example, days represent the x-axis values (days of the week), and
temperatures represent the y-axis values (temperature in degrees Celsius). The
marker, linestyle, and color arguments customize the appearance of the line plot.
● The output of the mentioned program is as follows:
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8. BAR PLOTS IN MATPLOTLIB
● A bar plot is a visualization in Matplotlib that uses rectangular bars to
represent categorical data.
● It's commonly used to compare values across different categories or groups.
● For example:
Suppose you want to compare the sales of different products in a store. You
can create a bar plot to visualize the sales for each product
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9. BAR PLOTS IN MATPLOTLIB
● In this example, products represent the x-axis categories (product names), and sales
represent the heights of the bars (sales amounts). The color argument specifies the color
of the bars, and xticks(rotation=45) rotates the x-axis labels for better readability.
● The output of the mentioned program is as follows:
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10. HISTOGRAMS IN MATPLOTLIB
● A histogram is a graphical representation in Matplotlib that displays
the distribution of continuous data by dividing it into intervals (bins)
and showing the frequency of data points in each bin.
● For example:
Suppose you have a dataset of exam scores and want to visualize
their distribution. You can create a histogram to show how many
students scored within specific score ranges:
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11. HISTOGRAMS IN MATPLOTLIB
● In this example, exam_scores is the dataset of scores. The bins parameter specifies
the number of intervals to divide the data into. The histogram displays the
frequency of scores in each bin, helping you understand the distribution of exam
performance.
● The output of the mentioned program is as follows:
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12. SCATTER PLOTS IN MATPLOTLIB
● A scatter plot is a visualization in Matplotlib that displays individual
data points as dots on a 2D plane.
● It's used to showcase relationships between two continuous
variables.
● For example:
Imagine you're analyzing the relationship between the study hours
and exam scores of a group of students. A scatter plot can help you
visualize whether there's a correlation between these variables
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13. SCATTER PLOTS IN MATPLOTLIB
● In this example, study_hours and exam_scores are the two continuous variables. Each
point on the scatter plot represents a student's study hours and their corresponding exam
score. The marker argument determines the shape of the data points.
● The output of the mentioned program is as follows:
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14. SUBPLOTS IN MATPLOTLIB
● Subplots in Matplotlib allow you to create multiple plots within a
single figure, enabling side-by-side comparisons or visualizing related
data.
● For example:
Suppose you want to display two line plots comparing two sets of
data in separate subplots
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15. SUBPLOTS IN MATPLOTLIB
● In this example, subplot(1, 2, 1) creates the first subplot, and subplot(1, 2, 2) creates the
second. The tight_layout() call improves spacing between subplots for better presentation.
● The output of the mentioned program is as follows:
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