annotate a line matplotlib

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11 Essential Python Matplotlib Visualizations for Data Science Mastery

A comprehensive guide to creating publication-quality graphs.

Matplotlib lets you make nice pictures with Python. You can use it to show data in lots of ways. It doesn’t take much code. You can make line graphs, bar charts, and many other types of charts.

Note: Specific Seaborn styles used in Matplotlib have been deprecated in version 3.6. I use updated Seaborn styles.

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Annotation Techniques for Data Visualization

• June 25, 2024
• Machine Learning

Annotations are critical in data visualization as they provide additional context, highlight key insights, and make the visual representation of data more understandable and impactful. There are some annotation techniques that every Data Scientist/Analyst should know for effective data visualization. So, in this article, I’ll take you through a guide to annotation techniques you should know for data visualization with implementation using Python.

Below are some annotation techniques you should know for data visualization:

• Text Annotations
• Arrow Annotations
• Highlighting Areas
• Trend lines

Let’s go through all these annotation techniques for data visualization with Python implementation.

Text annotations are short text notes added directly onto graphs to provide additional context or highlight important data points. They are particularly useful for drawing attention to specific events to explain trends or noting anomalies within the data. For instance, in a sales graph, text annotations can be used to mark the launch of a new product or a marketing campaign to help viewers quickly understand the cause of fluctuations in sales figures.

Below is an example of adding text annotations to a graph using Python:

Arrow annotations use arrows to point directly to specific data points or areas on a graph to highlight critical elements or trends. They are particularly effective in highlighting outliers, indicating significant changes, or drawing attention to noteworthy patterns within the data. For example, in a scatter plot of marketing spend versus sales, arrows can point to outliers where the return on investment was exceptionally high or low to make it clear which data points require further attention.

Below is an example of adding arrow annotations to a graph using Python:

Highlighting areas involves shading or colouring specific regions of a graph to draw attention to particular time periods, ranges, or zones. This technique is used to highlight critical segments within the data, such as periods of high activity, significant events, or areas that meet certain criteria. For example, a time series plot of stock prices having highlighted areas during a market crash can make it easier for viewers to identify the impact period visually.

Below is an example of highlighting areas in a graph using Python:

Trend Lines

Trend lines are lines added to graphs to indicate the general direction or pattern of the data over time or across variables. They are used to visualize trends, averages, or relationships within a dataset, which helps identify long-term movements and tendencies. For instance, in a scatter plot showing the relationship between study hours and exam scores, a trend line can illustrate whether there is a positive correlation by indicating that more study hours generally lead to higher scores.

Below is an example of adding trend lines in a graph using Python:

So, below are some annotation techniques you should know for data visualization:

I hope you liked this article on annotation techniques you should know for data visualization. Feel free to ask valuable questions in the comments section below. You can follow me on Instagram for many more resources.

Aman Kharwal

Data Strategist at Statso. My aim is to decode data science for the real world in the most simple words.

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How to annotate end of lines using python and matplotlib?

With a dataframe and basic plot such as this:

What is the best way of annotating the last points on the lines so that you get the result below?

3 Answers 3

In order to annotate a point use ax.annotate() . In this case it makes sense to specify the coordinates to annotate separately. I.e. the y coordinate is the data coordinate of the last point of the line (which you can get from line.get_ydata()[-1] ) while the x coordinate is independent of the data and should be the right hand side of the axes (i.e. 1 in axes coordinates). You may then also want to offset the text a bit such that it does not overlap with the axes.

Here is one way, or at least a method, which you can adapt to aesthetically fit in whatever way you want, using the plt.annotate method:

[EDIT]: If you're going to use a method like this first one, the method outlined in ImportanceOfBeingErnest's answer is better than what I've proposed.

For the xy argument, which is the x and y coordinates of the text, I chose the last x coordinate in plt.xticks() , and added 0.7 so that it is outside of your x axis, but you can coose to make it closer or further as you see fit.

You could also just use the right y axis, and label it with your 3 lines. For example:

This gives you the following plot:

• 2 You could remove the spines for greater effect. Very nice idea. –  Mad Physicist Commented Mar 12, 2018 at 21:11
• 1 True, it might look better that way. ax2.tick_params(length=0) should do. –  sacuL Commented Mar 12, 2018 at 21:15

I've got some tips from the other answers and believe this is the easiest solution.

Here is a generic function to improve the labels of a line chart. Its advantages are:

• you don't need to mess with the original DataFrame since it works over a line chart,
• it will use the already set legend label,
• removes the frame,
• just copy'n paste it to improve your chart :-)

You can just call it after creating any line char:

This is the original chart :

Now you just need to call the function to improve your plot:

The new chart:

Beware, it will probably not work well if a line has null values at the end.

• nice +10, but i needed to change spine.set_visible(False) to ax.spines[spine].set_visible(False) . not sure if it's a version issue (i'm on matplotlib 3.4.3), but the former was throwing 'str' object has no attribute 'set_visible' . –  tdy Commented Sep 8, 2021 at 0:04
• Thanks @tdy . Fixed it. It was a last minute modification :-( –  neves Commented Sep 8, 2021 at 0:50
• This is great! I commented out a few lines because I wanted to keep the existing legend as well (sometimes two lines end at the same y-value and the RHS labels overlap) and wanted to keep the border, but that was easy. –  ntc2 Commented Nov 5, 2021 at 15:24
• 1 Is there a way to make the labels not overlap in case the lines end at the same point? In any case, pretty cool solution. –  Geosphere Commented Aug 31, 2022 at 11:18

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Click here to download the full example code

Legend guide #

Generating legends flexibly in Matplotlib.

This legend guide is an extension of the documentation available at legend() - please ensure you are familiar with contents of that documentation before proceeding with this guide.

This guide makes use of some common terms, which are documented here for clarity:

A legend is made up of one or more legend entries. An entry is made up of exactly one key and one label.

The colored/patterned marker to the left of each legend label.

The text which describes the handle represented by the key.

The original object which is used to generate an appropriate entry in the legend.

Controlling the legend entries #

Calling legend() with no arguments automatically fetches the legend handles and their associated labels. This functionality is equivalent to:

The get_legend_handles_labels() function returns a list of handles/artists which exist on the Axes which can be used to generate entries for the resulting legend - it is worth noting however that not all artists can be added to a legend, at which point a "proxy" will have to be created (see Creating artists specifically for adding to the legend (aka. Proxy artists) for further details).

Artists with an empty string as label or with a label starting with an underscore, "_", will be ignored.

For full control of what is being added to the legend, it is common to pass the appropriate handles directly to legend() :

In some cases, it is not possible to set the label of the handle, so it is possible to pass through the list of labels to legend() :

Creating artists specifically for adding to the legend (aka. Proxy artists) #

Not all handles can be turned into legend entries automatically, so it is often necessary to create an artist which can . Legend handles don't have to exist on the Figure or Axes in order to be used.

Suppose we wanted to create a legend which has an entry for some data which is represented by a red color:

There are many supported legend handles. Instead of creating a patch of color we could have created a line with a marker:

Legend location #

The location of the legend can be specified by the keyword argument loc . Please see the documentation at legend() for more details.

The bbox_to_anchor keyword gives a great degree of control for manual legend placement. For example, if you want your axes legend located at the figure's top right-hand corner instead of the axes' corner, simply specify the corner's location and the coordinate system of that location:

More examples of custom legend placement:

Figure legends #

Sometimes it makes more sense to place a legend relative to the (sub)figure rather than individual Axes. By using constrained_layout and specifying "outside" at the beginning of the loc keyword argument, the legend is drawn outside the Axes on the (sub)figure.

This accepts a slightly different grammar than the normal loc keyword, where "outside right upper" is different from "outside upper right".

Multiple legends on the same Axes #

Sometimes it is more clear to split legend entries across multiple legends. Whilst the instinctive approach to doing this might be to call the legend() function multiple times, you will find that only one legend ever exists on the Axes. This has been done so that it is possible to call legend() repeatedly to update the legend to the latest handles on the Axes. To keep old legend instances, we must add them manually to the Axes:

Legend Handlers #

In order to create legend entries, handles are given as an argument to an appropriate HandlerBase subclass. The choice of handler subclass is determined by the following rules:

Update get_legend_handler_map() with the value in the handler_map keyword.

Check if the handle is in the newly created handler_map .

Check if the type of handle is in the newly created handler_map .

Check if any of the types in the handle 's mro is in the newly created handler_map .

For completeness, this logic is mostly implemented in get_legend_handler() .

All of this flexibility means that we have the necessary hooks to implement custom handlers for our own type of legend key.

The simplest example of using custom handlers is to instantiate one of the existing legend_handler.HandlerBase subclasses. For the sake of simplicity, let's choose legend_handler.HandlerLine2D which accepts a numpoints argument (numpoints is also a keyword on the legend() function for convenience). We can then pass the mapping of instance to Handler as a keyword to legend.

As you can see, "Line 1" now has 4 marker points, where "Line 2" has 2 (the default). Try the above code, only change the map's key from line1 to type(line1) . Notice how now both Line2D instances get 4 markers.

Along with handlers for complex plot types such as errorbars, stem plots and histograms, the default handler_map has a special tuple handler ( legend_handler.HandlerTuple ) which simply plots the handles on top of one another for each item in the given tuple. The following example demonstrates combining two legend keys on top of one another:

The legend_handler.HandlerTuple class can also be used to assign several legend keys to the same entry:

Implementing a custom legend handler #

A custom handler can be implemented to turn any handle into a legend key (handles don't necessarily need to be matplotlib artists). The handler must implement a legend_artist method which returns a single artist for the legend to use. The required signature for legend_artist is documented at legend_artist .

Alternatively, had we wanted to globally accept AnyObject instances without needing to manually set the handler_map keyword all the time, we could have registered the new handler with:

Whilst the power here is clear, remember that there are already many handlers implemented and what you want to achieve may already be easily possible with existing classes. For example, to produce elliptical legend keys, rather than rectangular ones:

Total running time of the script: ( 0 minutes 4.624 seconds)

Download Python source code: legend_guide.py

Download Jupyter notebook: legend_guide.ipynb

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Add Labels and Text to Matplotlib Plots: Annotation Examples

Add text to plot, add labels to line plots, add labels to bar plots, add labels to points in scatter plots, add text to axes.

Used matplotlib version 3.x . View all code on this notebook

See all options you can pass to plt.text here: valid keyword args for plt.txt

Use plt.text(<x>, <y>, <text>) :

Again, zip together the data (x and y) and loop over it, call plt.annotate(<label>, (<x>,<y>))

Loop over the arrays (xs and ys) and call plt.annotate(<label>, <coords>) :

Loop over the data arrays (x and y) and call plt.annotate(<label>, <coords>) using the value itself as label:

This is useful if you have subplots (more examples here )

To add text to an individual Axes object just call ax.annotate(<text>, <xy_coords>) :

String tick labels

You want to position text in the plot but your plot also uses string tick labels , so you can't just use annotate('some-text', ('a', 4)) because you need actual integer x,y coordinates.

• Matplotlib: Pyplot.Annotate

Felipe 23 Jun 2018 26 Nov 2022 matplotlib labels annotation

Matplotlib Course for Beginners

About Course

Welcome to the  Matplotlib Course for Beginners by Studyopedia.

Matplotlib is an open-source plotting library developed by John D. Hunter. Create interactive visualizations in Python with Matplotlib. It is built on NumPy and is one of the most popular libraries for data visualization in Python.

In this tutorial, we will learn how to perform plotting with Python. Visualizations are far better than textual data. Using matplotlib, we can easily create graphs, histograms, bar graphs, etc.

The following are the features of Matplotlib:

• Free and open-source Python library
• Load and plot the data easily
• Easily Make interactive figures that can zoom, pan, update.
• Export to various file formats, such as PNG, PDF, SVG, etc.
• Use third-party packages built on Matplotlib for plotting, animations, styles, etc.
• Create graphs easily, set legends, position titles, plot, etc. with Matplotlib

Course Lessons

• Matplotlib – Introduction
• Install & Matplotlib
• Matplotlib – PyPlot Submodule (Run first Matplotlib program)

Matplotlib – Plotting

• Matplotlib – Add Grid Lines
• Matplotlib – Add Labels to a Plot
• Matplotlib – Plot Titles and Position them
• Matplotlib – Add a Legend in a Graph
• Matplotlib – Position Legends
• Matplotlib – Change the background color of the Legend
• Matplotlib – Change the font size of the Legend
• Matplotlib – Bar Graph
• Matplotlib – Pie Chart
• Matplotlib – Line Graph
• Matplotlib – Histogram
• Matplotlib – Scatter Plot

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• Learn Matplotlib for Data Science and Data Analysis
• Create interactive visualizations in Python with Matplotlib.
• Easily create graphs, histograms, bar graphs, etc.
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• Practice your skills

Course Content

Matplotlib – introduction & setup, introduction & features, install & setup matplotlib, pyplot submodule (run first matplotlib program), plot a line with dataframe, matplotlib – grid, add grid lines to a plot, matplotlib – plot settings, add labels to a plot, plot titles and position them, matplotlib – legends, add a matplotlib legend in a graph, position legends, change the background color of the legend, change the font size of the legend, plotting – data visualization, create a bar graph, create a pie chart, create a line graph, create a histogram, create a scatter plot, student ratings & reviews.

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• Matplotlib Annotate

Matplotlib is a popular Python library used for creating static, animated, and interactive visualizations in Python. One of its most useful features is the ability to annotate plots. Annotations are text boxes placed at specific locations on a plot to provide additional information about the data being presented. In this article, we will explore various aspects of annotations in Matplotlib and provide code examples to illustrate their usage.

What is Matplotlib Annotate?

Matplotlib’s annotate function is used to create annotations on a Matplotlib plot. Annotations can be used to label specific points, add descriptions to certain regions, or highlight specific features of the data.

The basic syntax for annotate is as follows:

• text : The text to display in the annotation.
• xy : The coordinates of the point being annotated.
• xytext : The coordinates of the text position.
• arrowprops : A dictionary of arrow properties (optional).

Creating Matplotlib Annotations

To create an annotation, you first need to create a Matplotlib figure and axes. Let’s start by importing the necessary libraries and creating a simple line plot:

Now, let’s add an annotation to the plot. We’ll place the annotation at the point (3, 9), with the text “Important Point”:

The resulting plot will display an arrow pointing to the specified location with the text “Important Point” nearby.

Matplotlib Annotation Positions

Annotations can be placed at different positions relative to the point being annotated. The xy and xytext parameters control the position of the point and text, respectively. Here are some examples of different annotation positions:

• Above and to the right of the point:

• Above and to the left of the point:

• Below and to the right of the point:

• Below and to the left of the point:

Customizing Matplotlib Annotations

You can customize the appearance of annotations by modifying the arrowprops dictionary. This dictionary allows you to change the color, style, and other properties of the annotation arrow. Here’s an example that changes the arrow color to red and the arrow style to a curved arrow:

Using Axes Coordinates in Matplotlib

By default, the xy and xytext coordinates are specified in data coordinates. However, you can also use axes coordinates by setting the xycoords and textcoords parameters to "axes fraction" . This allows you to position annotations relative to the axes rather than the data:

Matplotlib Multiple Annotations

You can add multiple annotations to a plot by calling the annotate function multiple times. Each annotation can have its own position, text, and formatting. Here’s an example with two annotations:

Connecting Annotations with Arrows in Matplotlib

Annotations can be connected with arrows to indicate relationships between different points on the plot. You can control the appearance of the arrow using the arrowprops parameter. Here’s an example that connects two annotations with an arrow:

Matplotlib Annotation Text Styling

You can also style the text of annotations using the fontsize , fontweight , and fontfamily parameters. These parameters allow you to specify the font size, weight (e.g., bold), and family (e.g., serif) of the annotation text. Here’s an example that changes the font size and weight of the annotation:

In conclusion, Matplotlib annotations are a powerful tool for adding descriptive information to plots. Whether you need to label specific points, highlight important features, or create informative visualizations, annotations can enhance the clarity and impact of your plots.

Matplotlib Articles

• Matplotlib Inline
• Matplotlib Legend
• Matplotlib Scatter
• Matplotlib Subplot
• Matplotlib Subplots
• Pip Install Matplotlib
• Axis Labels in Matplotlib
• ax.figure.savefig in matplotlib
• Blue Color in Matplotlib
• Box Plot in Matplotlib
• Matplotlib Axis Range
• Matplotlib bar plot multiple columns
• Matplotlib boxplot multiple columns
• Matplotlib colorbar limits
• Matplotlib Colorbar Range
• Matplotlib Errorbar Style
• Matplotlib Figure Save
• Matplotlib Figure Set Title
• Matplotlib Figure Title
• Matplotlib Grid Behind Bars

• Plot Styles
• Contour Plots
• Quiver and Stream Plots
• 3D Surface Plots
• Review Questions

Plot annotations

Sometimes it is useful for problem solvers to annotate plots. Text can be included on a plot to indicate a point of interest or highlight a specific feature of a plot. The code section below builds a simple line plot and applies three annotations (three arrows with text) on the plot. Matplotlib's ax.annotate() method creates the annotations. Multiple keyword arguments can be passed to ax.annotate() method to specify the annotation location and style the annotation.

The keyword argument to pay attention to in the next code section is xycoords= . Each of the three annotations in the next figure has a different xycoords= keyword argument. The annotation can be located relative to the plot data, located relative to the axis, or located relative to the figure window.

In the first annotation, xycoords='data' . This means the annotation is placed relative to the data. Since xy=(0, 0) , the annotation arrow points to the data point 0,0 .

In the second annotation, xycoords='axes fraction' . This means the second annotation is placed relative to the axis. Since xy=(0, 0.5) , the annotation arrow points all the way to the left edge of the x-axis and half way up the y-axis.

In the third annotation, xycoords='figure pixels' . This means the third annotation is placed relative to the figure window. Since xy=(20, 75) , the third annotation arrow points 20 pixels to the right and 75 pixels up from the bottom left corner of the figure window.

The chart below summarizes Matplotlib's ax.annotate() keyword arguments.

The next code section builds a figure with three annotation arrows. In [1]: import numpy as np import matplotlib.pyplot as plt # if using a Jupyter notebook, include: % matplotlib inline

x = np . arange ( - 5 , 5 , 0.01 ) y = x ** 2

fig , ax = plt . subplots ()

# Plot a line ax . plot ( x , y )

# first annotation relative to the data ax . annotate ( 'function minium \n relative to data' , xy = ( 0 , 0 ), xycoords = 'data' , xytext = ( 2 , 3 ), arrowprops = dict ( facecolor = 'black' , shrink = 0.05 ), horizontalalignment = 'left' , verticalalignment = 'top' )

# second annotation relative to the axis limits bbox_props = dict ( boxstyle = "round,pad=0.5" , fc = "w" , ec = "k" , lw = 2 )

ax . annotate ( 'half of range \n relative to axis limits' , xy = ( 0 , 0.5 ), xycoords = 'axes fraction' , xytext = ( 0.2 , 0.5 ), bbox = bbox_props , arrowprops = dict ( facecolor = 'black' , shrink = 0.05 ), horizontalalignment = 'left' , verticalalignment = 'center' )

# third annotation relative to the figure window bbox_props = dict ( boxstyle = "larrow,pad=0.5" , fc = "w" , ec = "k" , lw = 2 )

ax . annotate ( 'outside the plot \n relative to figure window' , xy = ( 20 , 75 ), xycoords = 'figure pixels' , horizontalalignment = 'left' , verticalalignment = 'top' , bbox = bbox_props )

ax . set_xlim ( - 5 , 5 ) ax . set_ylim ( - 1 , 10 ) ax . set_title ( 'Parabolic Function with Text Notation' )

plt . show ()

Pipeline visualization: How to visualize your pipeline data and insights using matplotlib and seaborn

1. setting the stage for pipeline visualization, 2. preparing your pipeline data for visualization, 3. an overview of matplotlib and seaborn, 4. creating line plots, scatter plots, and bar charts, 5. leveraging seaborn for better aesthetics, 6. adjusting colors, labels, and other plot properties, 7. adding interactivity to your pipeline plots, 8. real-world examples of pipeline visualization, 9. wrapping up and next steps.

1. Why Visualize Pipelines?

- Clarity and Understanding : Data pipelines are intricate networks of data transformations, extractions, and loading processes. Visualizing these pipelines helps us comprehend their structure, dependencies, and flow.

- Debugging and Troubleshooting : When a pipeline fails or produces unexpected results, visualization aids in pinpointing bottlenecks, faulty components, or data inconsistencies.

- Performance Optimization : By visualizing execution times, resource utilization, and data volume at each stage, we can identify areas for improvement .

- Communication : Visual representations facilitate communication between team members, stakeholders, and domain experts. A well-designed pipeline diagram can convey complex ideas succinctly.

2. Types of Pipeline Visualizations :

- Flowcharts : Flowcharts depict the sequence of steps in a pipeline. Nodes represent tasks (e.g., data extraction, transformation, loading), and arrows indicate data flow. For example:

[Extract] -> [Clean] -> [Transform] -> [Load]

- Sankey Diagrams : Sankey diagrams visualize flow between stages, emphasizing data volume changes. Width of the arrows represents data quantity. Imagine a Sankey diagram showing how customer data flows from CRM to recommendation engines.

- Gantt Charts : Gantt charts display task durations and dependencies. In pipeline visualization, they reveal execution times for each step.

- Dependency Graphs : These graphs illustrate task dependencies. Nodes represent tasks, and edges indicate dependencies. For instance, a machine learning pipeline might require feature engineering before model training.

- Heatmaps : Heatmaps display metrics (e.g., execution time, memory usage) across pipeline stages. Color intensity reflects values. A heatmap could reveal that data validation takes longer than expected.

- Tree Maps : Tree maps divide the pipeline into nested rectangles, with each rectangle representing a stage. The area of each rectangle corresponds to resource usage or data volume.

3. Examples :

- Imagine a retail recommendation system pipeline:

- Data Extraction : Extract customer behavior data from databases and APIs.

- Data Transformation : Clean, preprocess, and engineer features (e.g., purchase history, browsing patterns).

- Model Training : Train collaborative filtering or deep learning models .

- Recommendation Generation : Generate personalized recommendations.

- Evaluation : Evaluate recommendation quality using metrics like precision or recall.

- Visualizing this pipeline as a flowchart or dependency graph would reveal bottlenecks (e.g., slow data extraction) and guide optimization efforts.

4. Best Practices :

- Simplicity : Keep visualizations clear and concise. Avoid clutter.

- Annotations : Label stages, data sources, and key metrics.

- Interactive Visualizations : Use tools like D3.js or Plotly to create interactive pipeline diagrams.

- Version Control : Maintain versioned pipeline visualizations alongside code.

- Automate Updates : As your pipeline evolves, update visualizations automatically.

In summary, pipeline visualization is more than aesthetics—it's a powerful tool for understanding , optimizing, and communicating the intricacies of data pipelines. Whether you're building ETL pipelines, ML workflows, or business processes, visualizing the journey from raw data to insights is essential.

Setting the stage for pipeline visualization - Pipeline visualization: How to visualize your pipeline data and insights using matplotlib and seaborn

### 1. data Cleaning and preprocessing :

Before you even think about visualization, ensure that your data is clean and well-structured. Here are some key steps:

- Handling Missing Values:

- Identify missing values (NaNs or nulls) in your dataset.

- Decide how to handle them: impute with mean, median, mode, or drop rows/columns.

- Example: Suppose you're analyzing customer data , and some entries have missing age values. You might impute missing ages with the median age of the entire dataset.

- Removing Outliers:

- Outliers can distort visualizations. Detect them using statistical methods (e.g., Z-score, IQR).

- Decide whether to remove or transform outliers.

- Example: In a sales dataset, unusually high transaction amounts might be outliers. You could cap them at a reasonable threshold.

- Data Transformation:

- Normalize or standardize numerical features.

- Encode categorical variables (one-hot encoding, label encoding).

- Example: If you're visualizing product ratings across different categories, normalize the ratings to a common scale (e.g., 0 to 1).

### 2. Feature Engineering:

Feature engineering involves creating new features from existing ones. It enhances the richness of your data for visualization:

- Creating Derived Features:

- Combine existing features to create meaningful ones.

- Example: In a time-series dataset, create a "day of the week" feature from the timestamp.

- Feature Scaling:

- Ensure that features are on similar scales.

- StandardScaler or MinMaxScaler can help.

- Example: When visualizing customer spending across different product categories, scale the spending amounts.

### 3. Aggregation and Grouping:

Sometimes, you need to aggregate data to a higher level for better visualization:

- Grouping Data:

- Aggregate data by a categorical variable (e.g., group sales by region).

- Compute summary statistics (mean, sum, count) within each group.

- Example: Visualize average monthly sales per product category.

### 4. Data Sampling:

- Large datasets can overwhelm visualizations. Consider sampling:

- Random Sampling:

- Randomly select a subset of data points.

- Example: If you have millions of customer reviews, visualize a random sample to avoid clutter.

- Stratified Sampling:

- Preserve the distribution of a categorical variable.

- Example: When visualizing survey responses, ensure that each age group is represented proportionally.

### 5. Data Format and Type:

- Ensure your data is in the right format for visualization libraries (e.g., pandas DataFrames, NumPy arrays).

- Convert data types (e.g., dates to datetime objects).

- Example: If you're plotting a time series, convert date strings to datetime objects.

Remember, data preparation is an iterative process. Continuously refine your data as you explore and visualize it. By investing time in thoughtful data preparation, you'll create more insightful and accurate visualizations for your pipeline.

Feel free to ask if you'd like further examples or details!

1. The Role of Visualization Tools: A Multifaceted Perspective

Data visualization serves various purposes, and different stakeholders view it from distinct angles:

- exploratory Data analysis (EDA) : As a data scientist, you'll often use visualization tools during EDA. Matplotlib and seaborn allow you to quickly generate plots to understand data distributions, relationships, and potential outliers. For instance, scatter plots, histograms, and box plots help reveal patterns and anomalies.

- Storytelling and Communication : When presenting insights to non-technical audiences, effective visualizations are paramount. Managers, executives, and clients appreciate clear, concise plots that convey complex information . Matplotlib and seaborn provide customizable options for creating visually appealing charts and graphs.

- Customization and Aesthetics : While both libraries offer default styles, you can customize visual elements extensively. Matplotlib allows fine-grained control over plot components, such as axes, labels, and colors. Seaborn, built on top of matplotlib, simplifies customization with high-level functions and attractive default themes.

2. Matplotlib: The swiss Army knife of Visualization

Matplotlib, the foundational library, provides a wide range of plotting capabilities. Here's why it's a go-to choice:

- Versatility : Matplotlib supports various plot types, including line plots, scatter plots, bar charts, and heatmaps. You can create subplots, annotate points, and add legends effortlessly.

- Low-Level Control : With Matplotlib, you have complete control over plot elements. For instance:

Import matplotlib.pyplot as plt

Plt.plot(x, y, label='Data')

Plt.xlabel('X-axis')

Plt.ylabel('Y-axis')

Plt.title('My Plot')

Plt.legend()

This snippet generates a simple line plot with labeled axes and a legend.

- Integration with Other Libraries : Matplotlib integrates seamlessly with numpy, pandas, and other data manipulation tools. You can plot directly from arrays or dataframes.

3. Seaborn: A High-Level Wrapper for Stylish Visualizations

Seaborn builds upon Matplotlib, emphasizing simplicity and aesthetics. Here's why it's popular:

- Statistical Plotting : Seaborn simplifies creating statistical plots, such as regression lines, kernel density estimates, and violin plots. For instance:

Import seaborn as sns

Sns.regplot(x='Age', y='Income', data=df)

This code generates a scatter plot with a regression line.

- Built-in Themes : Seaborn offers attractive themes (e.g., darkgrid, whitegrid, and ticks) that enhance plot aesthetics. You can set the theme globally or for specific plots.

- Color Palettes : Choosing the right color palette matters . Seaborn provides predefined palettes (e.g., "viridis," "coolwarm," and "husl") for categorical and continuous data.

4. Examples in Practice

Let's consider an example: visualizing the distribution of exam scores for two groups (A and B). We'll use seaborn's violin plot:

Import pandas as pd

Data = pd.DataFrame({'Group': ['A'] 50 + ['B'] 50,

'Score': np.random.normal(loc=70, scale=10, size=100)})

Sns.violinplot(x='Group', y='Score', data=data)

Plt.xlabel('Group')

Plt.ylabel('Score')

Plt.title('Exam Score Distribution')

This violin plot shows the density of scores for each group.

In summary, both matplotlib and seaborn have their strengths. Matplotlib provides low-level control, while seaborn simplifies common tasks. Consider your audience, goals, and desired aesthetics when choosing between them. Happy visualizing!

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1. Line Plots :

- Line plots are commonly used to display trends over time or continuous data. They connect data points with straight lines, making it easy to observe changes.

- Insight : Line plots are excellent for visualizing stock prices, temperature variations, or any other continuous data series.

- Example :

Import numpy as np

# Generate some sample data

X = np.linspace(0, 10, 100)

Y = np.sin(x)

# Create a line plot

Plt.plot(x, y, label='sin(x)')

Plt.title('Sine Function')

In this example, we create a simple sine wave using NumPy and plot it using Matplotlib. The resulting line plot shows the periodic behavior of the sine function.

2. Scatter Plots :

- Scatter plots are ideal for visualizing the relationship between two numerical variables. Each data point is represented as a dot, allowing us to identify patterns, clusters, or outliers.

- Insight : Scatter plots are commonly used in exploratory data analysis (EDA) to identify correlations or anomalies.

# Generate random data

Np.random.seed(42)

X = np.random.rand(50)

Y = 2 * x + np.random.normal(0, 0.1, 50)

# Create a scatter plot

Plt.scatter(x, y, label='Data points')

Plt.title('Scatter Plot')

Here, we simulate data with a linear relationship and add some noise. The scatter plot reveals the positive correlation between `x` and `y`.

3. Bar Charts :

- bar charts are effective for comparing categorical data or showing the distribution of a variable across different categories.

- Insight : Bar charts are commonly used for visualizing survey results , sales by product category, or population demographics.

# Sample data: sales by product category

Categories = ['Electronics', 'Clothing', 'Books', 'Toys']

Sales = [1200, 800, 1500, 600]

# Create a bar chart

Plt.bar(categories, sales, color='skyblue')

Plt.xlabel('Product Category')

Plt.ylabel('Sales')

Plt.title('Sales by Product Category')

This bar chart displays sales data for different product categories. It's evident that books have the highest sales.

Remember, these examples are just the tip of the iceberg! Matplotlib offers extensive customization options, including color palettes, annotations, and subplots. As you explore more complex datasets, you'll appreciate the versatility and expressiveness of Matplotlib. Happy plotting!

Creating line plots, scatter plots, and bar charts - Pipeline visualization: How to visualize your pipeline data and insights using matplotlib and seaborn

## The Power of Seaborn: A Multifaceted Approach

Seaborn, built on top of Matplotlib, is a python data visualization library that provides a high-level interface for creating attractive statistical graphics. Its simplicity, versatility, and integration with Pandas make it a popular choice for data visualization tasks. Let's explore some key insights about Seaborn:

1. Unified Aesthetics and Color Palettes :

- Seaborn offers a consistent and visually appealing default style that instantly improves the aesthetics of your plots. By invoking `sns.set()` at the beginning of your script, you activate Seaborn's style settings.

- The choice of color palette significantly impacts the readability and emotional impact of your visualizations. Seaborn provides a wide range of color palettes, such as "viridis," "coolwarm," and "husl." For example:

Sns.set(style="whitegrid") # Set the overall style

Sns.set_palette("viridis") # choose a color palette

# Example: Create a scatter plot

Tips = sns.load_dataset("tips")

Sns.scatterplot(x="total_bill", y="tip", data=tips)

- Experiment with different palettes to find the one that best suits your data and context.

2. Facet Grids for Multidimensional Exploration :

- Seaborn's `FacetGrid` allows you to create a grid of subplots based on one or more categorical variables. Each subplot represents a subset of your data, making it ideal for exploring relationships across multiple dimensions.

- For instance, consider visualizing the relationship between total bill amount, tip, and day of the week:

G = sns.FacetGrid(tips, col="day", height=4)

G.map(sns.scatterplot, "total_bill", "tip")

- This grid of scatter plots reveals patterns specific to each day, providing deeper insights.

3. Customizing Plot Elements :

- Seaborn allows fine-grained customization of plot elements. You can adjust the font size, axis labels, titles, and more.

- For instance, to set custom axis labels:

Plt.xlabel("Total Bill Amount ($)")

Plt.ylabel("Tip Amount ($)")

Plt.title("Scatter Plot: Total Bill vs. Tip")

4. Statistical Estimation with Confidence Intervals :

- Seaborn integrates statistical estimation directly into its plotting functions. For example, `sns.regplot()` not only creates a scatter plot but also fits a regression line with confidence intervals.

- Consider the following code snippet:

Sns.regplot(x="total_bill", y="tip", data=tips)

Plt.title("Regression Plot: Total Bill vs. Tip")

- The regression line provides insights into the relationship between total bill and tip, along with uncertainty.

5. Pair Plots for Multivariate Exploration :

- Seaborn's `pairplot` generates pairwise scatter plots for multiple numerical variables in your dataset. It's a powerful tool for identifying correlations and distributions.

- Example usage:

Sns.pairplot(tips, hue="day", diag_kind="kde")

- The diagonal plots show kernel density estimates (KDEs) for each variable, while the off-diagonal plots reveal relationships between pairs of variables.

In summary, Seaborn empowers you to create expressive, informative, and visually appealing visualizations. By mastering its features, you'll enhance your ability to communicate insights effectively. Remember, the art of data visualization lies not only in the data but also in the thoughtful design of your plots. Happy visualizing!

Leveraging seaborn for better aesthetics - Pipeline visualization: How to visualize your pipeline data and insights using matplotlib and seaborn

1. Color customization: One way to enhance the visual appeal of your plots is by adjusting the colors. You can specify custom colors for different elements such as lines, markers, and bars. For instance, you can use the RGB color model to define precise colors or choose from predefined color palettes provided by matplotlib and seaborn.

2. Label customization: Labels play a crucial role in conveying information effectively. You can customize the labels of axes, legends, and titles to provide clear context to your plots. This includes modifying the font size, font style, and alignment of the labels. Additionally, you can add units or symbols to the labels to enhance their clarity.

3. Plot properties customization: Apart from colors and labels, you can customize various other properties of your plots. This includes adjusting the plot size, aspect ratio, and margins to ensure optimal visual representation. You can also modify the grid lines, tick marks, and spines to align with your desired aesthetics.

4. Annotations and text customization: Annotations and text can be used to highlight specific insights or provide additional information within the plot. You can customize the position, font style, and formatting of annotations and text to make them visually appealing and informative. This can be particularly useful when explaining complex concepts or highlighting important data points.

5. Examples: Let's consider an example to illustrate the customization process. Suppose you have a line plot representing the performance of different pipelines over time. You can customize the color of each line to differentiate between the pipelines. Additionally, you can add labels to the x-axis and y-axis to provide clear context. To highlight a specific insight, you can annotate a data point with a text box containing relevant information.

Remember, these are just a few examples of how you can customize plots in the context of pipeline visualization. By leveraging the flexibility offered by matplotlib and seaborn, you can tailor your plots to effectively communicate your data and insights .

Adjusting colors, labels, and other plot properties - Pipeline visualization: How to visualize your pipeline data and insights using matplotlib and seaborn

1. Why Interactivity Matters :

Interactive visualizations empower users to engage with data dynamically. Instead of static plots, where viewers are passive observers, interactive plots allow users to explore different aspects of the data, zoom in on specific regions, filter data points, and gain deeper insights . Here are some reasons why interactivity matters:

- Exploration : Interactivity enables users to drill down into details, identify patterns, and discover hidden relationships. For example, in a scatter plot, users can hover over points to reveal additional information (e.g., labels, values).

- Communication : Interactive plots make presentations more engaging. During a live demo or a client meeting, you can interactively highlight specific data points or trends .

- User-Centric : Different users have different questions. Interactivity allows them to tailor their exploration based on their interests.

2. Adding Interactivity with Matplotlib :

Matplotlib, a popular Python library for creating static plots, also provides tools for adding interactivity. Here's how:

- Hover Information : Use the `mplcursors` library to display tooltips when hovering over data points. For instance, in a scatter plot, you can show the corresponding data values or labels.

- Zooming and Panning : Enable zooming and panning in your plots using the built-in navigation toolbar. Users can focus on specific regions of interest.

- Widgets : Matplotlib supports widgets like sliders, buttons, and checkboxes. These widgets allow users to dynamically adjust parameters (e.g., filtering data by date range).

Import mplcursors

Fig, ax = plt.subplots()

Ax.scatter(x_data, y_data, label="Data Points")

Mplcursors.cursor(hover=True).connect("add", lambda sel: sel.annotation.set_text(f"({sel.target[0]:.2f}, {sel.target[1]:.2f})"))

3. Seaborn and Interactivity :

Seaborn, built on top of Matplotlib, simplifies creating statistical visualizations. While Seaborn doesn't provide direct interactivity, you can combine it with Matplotlib's features:

- Pair Plots : Use Seaborn's `pairplot` to create scatter plots for multiple variables. Enhance it with Matplotlib's hover information.

- Facet Grids : Seaborn's `FacetGrid` allows you to create a grid of subplots based on categorical variables. You can add interactivity to individual subplots.

Sns.set(style="ticks")

G = sns.pairplot(df, hue="species")

G.map_offdiag(mplcursors.cursor(hover=True).connect("add", lambda sel: sel.annotation.set_text(f"({sel.target[0]:.2f}, {sel.target[1]:.2f})")))

4. Web-Based Interactive Visualizations :

For web applications, consider libraries like Plotly or Bokeh . These allow you to create interactive plots that users can explore directly in their browsers.

- Plotly : Generates interactive plots (e.g., scatter plots, heatmaps) with built-in zoom, pan, and hover capabilities.

- Bokeh : Provides a flexible framework for creating web-based visualizations. You can embed Bokeh plots in web pages.

Example (Plotly):

Import plotly.express as px

Fig = px.scatter(df, x="x", y="y", color="category", hover_name="label")

Fig.update_layout(title="Interactive Scatter Plot")

In summary, interactive visualizations enhance data exploration, communication, and user engagement. Whether you're using Matplotlib, Seaborn, or web-based libraries, consider adding interactivity to your pipeline plots for a richer data experience!

Adding interactivity to your pipeline plots - Pipeline visualization: How to visualize your pipeline data and insights using matplotlib and seaborn

1. supply Chain optimization :

- Imagine a large retail company managing its supply chain . They receive products from suppliers, process them in warehouses, and distribute them to stores. By visualizing the entire supply chain pipeline , they can:

- Identify Delays : Plotting delivery times at each stage reveals bottlenecks. For instance, if products spend too much time in transit, it might be worth optimizing shipping routes.

- Monitor Inventory Levels : Visualizing inventory levels helps maintain optimal stock levels . A sudden spike in inventory might indicate overstocking, while low levels could lead to shortages.

- predict demand : Historical sales data combined with visualization can help predict future demand, allowing the company to adjust production and distribution accordingly.

2. Data Processing Pipelines :

- Data scientists and engineers often work with complex data pipelines. Consider a sentiment analysis pipeline for social media data :

- Data Collection : Scraping tweets or posts.

- Preprocessing : Tokenization, stop-word removal, etc.

- Feature Extraction : Creating features for machine learning models .

- model training : Training classifiers.

- Visualization Insights :

- Word Clouds : Visualize frequently occurring words in tweets/posts.

- Sentiment Trends : Plot sentiment scores over time.

- Feature Importance : Feature visualization helps understand model decisions.

3. Financial Trading Algorithms :

- Algorithmic trading relies on efficient pipelines. A trading algorithm might involve:

- Data Retrieval : Fetching stock prices, news, etc.

- Feature Engineering : Creating indicators (moving averages, RSI, etc.).

- Model Prediction : Using machine learning to predict stock movements .

- Trade Execution : Placing buy/sell orders.

- Candlestick Charts : Displaying stock price patterns.

- Portfolio Value : Plotting portfolio value over time.

- Trading Signals : Highlighting buy/sell signals.

4. Healthcare Pipelines :

- In healthcare, pipelines handle patient data, diagnostics, and treatment plans:

- Patient Journey : Visualize patient pathways from diagnosis to treatment.

- Medical Imaging Pipelines : Visualize MRI, CT scans, etc., for diagnosis.

- Drug Development Pipelines : Track drug discovery stages.

- Heatmaps : Show areas of high disease prevalence.

- Treatment Efficacy : Compare outcomes for different treatments.

- Clinical Trial Progress : Monitor drug development milestones.

5. Manufacturing Process Optimization :

- Factories have intricate production pipelines:

- Assembly Lines : Visualize production steps.

- Quality Control Pipelines : Inspect product quality.

- Resource Allocation Pipelines : Optimize machine usage.

- Process Flowcharts : Map out assembly steps.

- Defect Heatmaps : Identify problem areas.

- Resource Utilization Trends : Optimize machine schedules.

Remember, effective visualization not only provides insights but also communicates complex information to stakeholders. Whether you're optimizing supply chains , analyzing data, trading stocks, treating patients, or manufacturing products, pipeline visualization is your compass in the data-driven world .

Real world examples of pipeline visualization - Pipeline visualization: How to visualize your pipeline data and insights using matplotlib and seaborn

Conclusion: Wrapping Up and Next Steps

In this comprehensive guide on pipeline visualization using matplotlib and seaborn , we've explored various techniques to create insightful visualizations for your data pipelines. As we reach the end of our journey, let's summarize the key takeaways and discuss what lies ahead.

1. The Power of Visualization :

- Visualizations play a crucial role in understanding complex data and conveying insights effectively.

- From scatter plots to heatmaps, each type of visualization serves a specific purpose. Choosing the right one depends on your data and the story you want to tell.

- Remember that a well-designed visualization can reveal patterns, outliers, and relationships that might remain hidden in raw data.

2. Matplotlib and Seaborn: A Dynamic Duo :

- Matplotlib is a versatile library for creating static, interactive, and animated visualizations. It provides fine-grained control over plot elements.

- Seaborn , built on top of Matplotlib, simplifies the creation of statistical visualizations. Its high-level functions allow you to create elegant plots with minimal code.

- Together, these libraries empower data scientists and analysts to produce professional-quality visualizations.

3. Customizing Your Plots :

- Customize your plots by adjusting colors, fonts, labels, and axes. Use the `plt` and `sns` functions to modify plot elements.

- Explore different color palettes (e.g., categorical, sequential, diverging) to enhance readability and convey meaning.

- Don't forget to add titles, axis labels, and legends to provide context for your audience.

4. Exploratory Data Analysis (EDA) :

- EDA involves visualizing data distributions , correlations, and trends. Use histograms, box plots, and pair plots to explore relationships.

- For time series data , line plots and bar plots can reveal temporal patterns.

- Example: Suppose you're analyzing stock prices. A candlestick chart can show open, close, high, and low prices over time.

5. Comparing Multiple Groups :

- When comparing groups (e.g., different product categories, regions, or user segments), consider bar plots, violin plots, or grouped scatter plots.

- Highlight differences and similarities effectively. For instance, compare sales performance across different quarters using a grouped bar plot.

6. Heatmaps and Correlation Matrices :

- Heatmaps visualize relationships between variables. Use them to display correlation matrices, confusion matrices, or any 2D data.

- Color intensity represents the strength of the relationship. For example, a heatmap of customer ratings can reveal which features drive satisfaction.

7. Interactive Visualizations :

- Interactive plots engage users and allow exploration. Consider using libraries like Plotly or Bokeh for interactive dashboards.

- Create tooltips, zoomable plots, and interactive legends. For instance, build a scatter plot where hovering over points reveals additional information.

8. Next Steps :

- Keep learning! Explore advanced visualization techniques, such as 3D plots, animations, and geospatial visualizations.

- Dive into domain-specific visualizations (e.g., bioinformatics, finance, social networks).

- Collaborate with domain experts to refine your visualizations and extract deeper insights.

Remember that visualization is both an art and a science. Practice, experiment, and iterate to create compelling visuals that resonate with your audience. Happy plotting!

Wrapping up and next steps - Pipeline visualization: How to visualize your pipeline data and insights using matplotlib and seaborn

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How to Annotate a Graph with Matplotlib and Python

The Matplotlib package is great for visualizing data. One of its many features is the ability to annotate points on your graph. You can use annotations to explain why a particular data point is significant or interesting.

If you haven’t used Matplotlib before, you should check out my introductory article, Matplotlib – An Intro to Creating Graphs with Python or read the official documentation .

Let’s get started!

Installing Matplotlib

If you don’t have Matplotlib on your computer, you must install it. Fortunately, you can use pip, the Python package manager utility that comes with Python.

Open up your terminal or command prompt and run the following command:

Pip will now install Matplotlib and any dependencies that Matplotlib needs to work properly. Assuming that Matplotlib installs successfully, you are good to go!

Annotating Points on a Graph

Matplotlib comes with a handy annotate() method that you can use. As with most of Matplotlib’s methods, annotate() can take quite a few different parameters.

For this example, you will be using the following parameters:

• text – The label for the annotation
• xy – The x/y coordinate of the point of interest
• arrowprops – A dictionary of arrow properties
• xytext – Where to place the text for the annotation

Now that you know what you’re doing, open up your favorite Python IDE or text editor and create a new Python file. Then enter the following code:

Here, you are creating a simple line graph. You want to annotate three points on the graph. The arrowprops define the arrowstyle and, in the latter two points, the connectionstyle . These properties tell Matplotlib what type of arrow to use and whether it should be connected to the text as a straight line, an arc, or a 90-degree turn.

When you run this code, you will see the following graph:

You can see how the different points are located and how the arrowprops lines are changed. You should check out the full documentation to learn all the details about the arrows and annotations.

Wrapping Up

Annotating your graph is a great way to make your plots more informative. Matplotlib allows you to add many different labels to your plots, and annotating the interesting data points is quite nice.

You should spend some time experimenting with annotations and learning all the different parameters it takes to fully understand this useful feature.

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violin plots using matplotlib in plotting-visualization section #1223

RajKhanke commented Jun 21, 2024

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Matplotlib: line chart with annotations.

In this tutorial, we’ll create an annotated line chart with the help of Pandas and Matplotlib libraries.

Prerequisites

Getting started, data preparation.

To create a line chart with annotations, we’ll need the following:

• Python installed on your machine
• Pip: package management system (it comes with Python)
• Jupyter Notebook: an online editor for data visualization
• Pandas: a library to create data frames from data sets and prepare data for plotting
• Matplotlib: a plotting library
• Seaborn: a plotting library (we’ll only use part of its functionally to add a grid to the plot and get rid of Matplotlib borders)

You can download the latest version of Python for Windows on the official website .

To get other tools, you’ll need to install recommended Scientific Python Distributions. Type this in your terminal:

Create a folder that will contain your notebook (e.g. “matplotlib-line-chart”) and open Jupyter Notebook by typing this command in your terminal (don’t forget to change the path):

This will automatically open the Jupyter home page at http://localhost:8888/tree. Click on the “New” button in the top right corner, select the Python version installed on your machine, and a notebook will open in a new browser window.

In the first line of the notebook, import all the necessary libraries:

You’ll need the last line ( %matplotlib notebook ) to display plots in input cells.

Let’s create a Matplotlib line chart with annotations showing Google trends related to immigration. We’ll use a .csv file for plotting. You can download the file on GitHub ( imm_trends.csv ).

On the second line in your Jupyter notebook, type this code to read the file. We delete all columns except “date” and “immigration”.

Here’s the output:

Let’s also find maximum values — we’ll need to know them to create annotations:

Our first annotation would be for values in rows #66 (2016-11-06), the second #78 (2017-01-29), and the third #150 (2018-06-17). 

We also must tell Matplotlib that the dates in our data set are indeed dates:

We’ll need this to change the format from “year-month-day” to “year-month” on our graph.

We’ll need the following variables for plotting:

We’ll create a Matplotlib line chart with annotations in 6 steps. All the code snippets below should be placed inside one cell in your Jupyter Notebook.

1. Create a figure and subplots

fig, ax = plt.subplots(facecolor='#f0eeee') — this would create a figure with one subplot. Here, facecolor would set the color of the figure as a whole.

fig.set_size_inches(10, 5) creates a 1000 × 500 px figure.

plt.tight_layout() adjusts subplot params so that subplots are nicely fit in the figure.

2. Format dates

This would change the date format from “year-month-day” to “year-month”, set date frequency on the graph, and rotate the dates 90 degrees. Note that mdates.DateFormatter would work only if you used pd.to_datetime to convert dates earlier (see the Data Preparation section for more details).

3. Set a title

4. create annotations.

Next, we’ll create three annotations for date values placed in rows #66, 78, and 150 in our dataframe ( df ). 

Annotations can be created with ax.annotate , which takes several parameters.

1. Indicate the text that will be used for the annotation (e. g. 'November 2016' ).

2. Set the arrow position (e. g. (df['date'][60], 75) , where df['date'][60] — the date in the row #60 — is a timestamp on the x-axis (Timestamp('2016-09-25 00:00:00')) and 75 is the y-axis coordinate).

3. Set the annotation text position (e. g. (df['date'][10], 60) , where df['date'][10] is a timestamp where the text begins and 60 is the y-axis coordinate).

4. Set other parameters: text color, arrow properties, the font size and family. Check Matplotlib documentation for more.

5. Create labels and ticks, set their color and font

6. save the file.

You might need to repeat facecolor in savefig() . Otherwise, plt.savefig might ignore it.

That’s it, our Matplotlib line chart is ready. You can download the notebook on GitHub to get the full code.

→ Matplotlib Pie Charts

→ Matplotlib: Error Bar Chart

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How to Plot Confusion Matrix with Labels in Sklearn?

Confusion matrices are a crucial tool in evaluating the performance of classification models. They provide a detailed breakdown of the model’s predictions, showing the counts of true positives, true negatives, false positives, and false negatives. This article will guide you through the process of plotting a confusion matrix with labels using Scikit-Learn, a popular machine learning library in Python.

Table of Content

Introduction to Confusion Matrix

Visualizing confusion matrix with labels, building a classification model, generating predictions, plotting the confusion matrix with labels, customizing the confusion matrix plot, 1. adding percentages, 2. changing color maps, 3. adding titles and axis labels.

A confusion matrix is a table used to evaluate the performance of a classification algorithm. It compares the actual target values with those predicted by the model.

Each row of the matrix represents the instances in an actual class, while each column represents the instances in a predicted class.

Understanding the Components of a Confusion Matrix

Before diving into the implementation, let’s understand the components of a confusion matrix:

• True Positives (TP):  The number of instances correctly predicted as positive.
• True Negatives (TN):  The number of instances correctly predicted as negative.
• False Positives (FP):  The number of instances incorrectly predicted as positive.
• False Negatives (FN):  The number of instances incorrectly predicted as negative.

The confusion matrix helps in calculating various performance metrics such as accuracy, precision, recall, and F1-score.

To get started, you need to have Python installed on your system along with the necessary libraries. You can install Scikit-Learn and Matplotlib using pip:

Let’s start by building a simple classification model. For this example, we’ll use the Iris dataset , which is included in Scikit-Learn.

Next, we’ll use the trained model to make predictions on the test set.

Now, let’s plot the confusion matrix with labels. Scikit-Learn provides a convenient function  ConfusionMatrixDisplay  to plot the confusion matrix.

Confusion Matrix with Labels

You can customize the confusion matrix plot to make it more informative and visually appealing.

You can add percentages to the confusion matrix to make it easier to interpret.

Percentages to the confusion matrix

You can change the color map to suit your preferences.

CM with Changing Color Maps

You can add titles and axis labels to make the plot more descriptive.

CM with Titles and axis labels

In this article, we have covered how to plot a confusion matrix with labels using Scikit-Learn. We started by understanding the components of a confusion matrix and then built a simple classification model using the Iris dataset.

We generated predictions and plotted the confusion matrix with labels. Additionally, we explored various customization options to make the plot more informative and visually appealing.

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5 Best Ways to Annotate the End of Lines Using Python and Matplotlib

💡 Problem Formulation: When visualizing data with line plots, it can be informative to annotate the last data point on each line, effectively highlighting the most up-to-date value. This article discusses various methods of annotating the ends of lines in Python using Matplotlib. For instance, given a time-series plot, the reader may want to mark the final data point on each line with text indicating the value and date.

Method 1: Using text() Function

Matplotlib’s text() function allows for the placement of text on plots at specified coordinates. By using the data’s x and y coordinates, text can be dynamically placed at the end of lines. The function’s parameters enable precise control over the text’s appearance, including its alignment and style.

Here’s an example:

Output: A line plot with an annotation at the end of the line displaying ‘(5, 11)’.

This code snippet creates a simple line plot and uses the text() function to place textual annotation at the final data point. The use of x[-1] and y[-1] ensures that the text is aligned with the last point on the line. The horizontal alignment is set to ‘right’ so that the text doesn’t overlap with the line.

Method 2: Using annotate() Function

Matplotlib’s annotate() function offers advanced text annotation with optional arrows. It is beneficial for creating annotations that point to a specific data point with customizable arrow properties, allowing for clear and informative visuals where the text can be offset from the data point.

Output: A line plot with an annotation at the end of the line ‘End: 15’ with an arrow pointing to (5, 15).

In this code example, the annotate() function is utilized to point out the end of the line with an arrow. The text is offset by 10 points in both x and y directions (left and upwards relative to the data point), ensuring it doesn’t interfere with the line or data point. The arrowprops dictionary defines the arrow’s appearance.

Method 3: Using Custom Legend

Creating a custom legend entry for the last data point can serve as an indirect way to annotate the end of a line. This technique employs the use of a legend to display the final value and can be advantageous in situations where multiple lines are present, and space is limited.

Output: A line plot with a custom legend entry ‘End (12)’, with the last data point highlighted in red.

The code snippet employs plotting the last data point separately with a different style and then using it to create a custom legend entry, which indirectly annotates the end of the line. The red ‘o’ ([ro]) denotes the last data point visually, and the legend explains it.

Method 4: Combining Text with Markers

Annotating the end of line plots can be performed by combining text annotations with data point markers. This approach allows for the annotation to be visually distinct and can be particularly useful when needing to emphasize the last point along with its annotation.

Output: A line plot with each point marked with a circle, and the final point including a text annotation ’10’ above it.

This code snippet illustrates a line plot with ‘o’ markers at each data point. The last data point is emphasized with a space-prefixed text annotation, providing a clear and clutter-free visualization. The positioning is handled via the ‘verticalalignment’ parameter to set the text above the marker.

Bonus One-Liner Method 5: Lambda Function with Annotate

A succinct method to annotate the end of a line plot leverages a lambda function within a call to annotate() . This one-liner is ideal for minimally intrusive and quick annotations when developing plots on the fly.

Output: A line plot with a textual annotation ’40’ at the end of the line corresponding to the last data point (5, 40).

The code features an immediately-invoked lambda function that calls annotate() with the last data points as parameters for a quick and clean annotation. The benefit of this approach is how it streamlines annotation into a succinct line of code.

Summary/Discussion

Emily Rosemary Collins is a tech enthusiast with a strong background in computer science, always staying up-to-date with the latest trends and innovations. Apart from her love for technology, Emily enjoys exploring the great outdoors, participating in local community events, and dedicating her free time to painting and photography. Her interests and passion for personal growth make her an engaging conversationalist and a reliable source of knowledge in the ever-evolving world of technology.

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matplotlib - how many graph types are available in library? and can you show me some examples how to use them?