CAPTION
Height (cm)
Weight (kg)
Age (Years)
STUB
BODY OF THE TABLE
* Sources: 1. Kailasha Foundation – Fun & Learn Portal LMS Directory *Footnotes: The entire upper part of the table is called BOX HEAD.
3. Diagrammatic Mode of Presentation:
A. Non-Frequency Diagrams: Non-frequency diagrams correspond to the data which are NOT frequency data. (a) Bar Diagrams (b) Line Diagrams (Historiagram) (c) Pie Diagram or Pie Chart
B. Frequency Diagrams: Frequency Data are presented. Mostly class-intervals are presented via this mode. Three most common frequency diagrams are: (a) Histogram (b) Frequency Polygon (c) Ogives: (i) Less than type Ogives (ii) More than type Ogives
Bar Diagrams:
Line Diagram:
Multiple Bar Diagram:
Frequency Polygon:
A smooth join of all vertices of a frequency polygon. This is broadly divided into four shapes:
(i) Bell Shaped (Most Common Shape) (ii) U-Shaped (iii) J – Shaped: Simple J – shaped & Inverted J – Shaped (iv) Mixed Curve (Second Most Common Shape)
Hindi explanation:.
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Statistics deals with the collection, presentation and analysis of the data, as well as drawing meaningful conclusions from the given data. Generally, the data can be classified into two different types, namely primary data and secondary data. If the information is collected by the investigator with a definite objective in their mind, then the data obtained is called the primary data. If the information is gathered from a source, which already had the information stored, then the data obtained is called secondary data. Once the data is collected, the presentation of data plays a major role in concluding the result. Here, we will discuss how to present the data with many solved examples.
As soon as the data collection is over, the investigator needs to find a way of presenting the data in a meaningful, efficient and easily understood way to identify the main features of the data at a glance using a suitable presentation method. Generally, the data in the statistics can be presented in three different forms, such as textual method, tabular method and graphical method.
Now, let us discuss how to present the data in a meaningful way with the help of examples.
Consider the marks given below, which are obtained by 10 students in Mathematics:
36, 55, 73, 95, 42, 60, 78, 25, 62, 75.
Find the range for the given data.
Given Data: 36, 55, 73, 95, 42, 60, 78, 25, 62, 75.
The data given is called the raw data.
First, arrange the data in the ascending order : 25, 36, 42, 55, 60, 62, 73, 75, 78, 95.
Therefore, the lowest mark is 25 and the highest mark is 95.
We know that the range of the data is the difference between the highest and the lowest value in the dataset.
Therefore, Range = 95-25 = 70.
Note: Presentation of data in ascending or descending order can be time-consuming if we have a larger number of observations in an experiment.
Now, let us discuss how to present the data if we have a comparatively more number of observations in an experiment.
Consider the marks obtained by 30 students in Mathematics subject (out of 100 marks)
10, 20, 36, 92, 95, 40, 50, 56, 60, 70, 92, 88, 80, 70, 72, 70, 36, 40, 36, 40, 92, 40, 50, 50, 56, 60, 70, 60, 60, 88.
In this example, the number of observations is larger compared to example 1. So, the presentation of data in ascending or descending order is a bit time-consuming. Hence, we can go for the method called ungrouped frequency distribution table or simply frequency distribution table . In this method, we can arrange the data in tabular form in terms of frequency.
For example, 3 students scored 50 marks. Hence, the frequency of 50 marks is 3. Now, let us construct the frequency distribution table for the given data.
Therefore, the presentation of data is given as below:
| |
---|---|
10 | 1 |
20 | 1 |
36 | 3 |
40 | 4 |
50 | 3 |
56 | 2 |
60 | 4 |
70 | 4 |
72 | 1 |
80 | 1 |
88 | 2 |
92 | 3 |
95 | 1 |
|
|
The following example shows the presentation of data for the larger number of observations in an experiment.
Consider the marks obtained by 100 students in a Mathematics subject (out of 100 marks)
95, 67, 28, 32, 65, 65, 69, 33, 98, 96,76, 42, 32, 38, 42, 40, 40, 69, 95, 92, 75, 83, 76, 83, 85, 62, 37, 65, 63, 42, 89, 65, 73, 81, 49, 52, 64, 76, 83, 92, 93, 68, 52, 79, 81, 83, 59, 82, 75, 82, 86, 90, 44, 62, 31, 36, 38, 42, 39, 83, 87, 56, 58, 23, 35, 76, 83, 85, 30, 68, 69, 83, 86, 43, 45, 39, 83, 75, 66, 83, 92, 75, 89, 66, 91, 27, 88, 89, 93, 42, 53, 69, 90, 55, 66, 49, 52, 83, 34, 36.
Now, we have 100 observations to present the data. In this case, we have more data when compared to example 1 and example 2. So, these data can be arranged in the tabular form called the grouped frequency table. Hence, we group the given data like 20-29, 30-39, 40-49, ….,90-99 (As our data is from 23 to 98). The grouping of data is called the “class interval” or “classes”, and the size of the class is called “class-size” or “class-width”.
In this case, the class size is 10. In each class, we have a lower-class limit and an upper-class limit. For example, if the class interval is 30-39, the lower-class limit is 30, and the upper-class limit is 39. Therefore, the least number in the class interval is called the lower-class limit and the greatest limit in the class interval is called upper-class limit.
Hence, the presentation of data in the grouped frequency table is given below:
| |
---|---|
20 – 29 | 3 |
30 – 39 | 14 |
40 – 49 | 12 |
50 – 59 | 8 |
60 – 69 | 18 |
70 – 79 | 10 |
80 – 89 | 23 |
90 – 99 | 12 |
|
|
Hence, the presentation of data in this form simplifies the data and it helps to enable the observer to understand the main feature of data at a glance.
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Blog Data Visualization 10 Data Presentation Examples For Strategic Communication
Written by: Krystle Wong Sep 28, 2023
Knowing how to present data is like having a superpower.
Data presentation today is no longer just about numbers on a screen; it’s storytelling with a purpose. It’s about captivating your audience, making complex stuff look simple and inspiring action.
To help turn your data into stories that stick, influence decisions and make an impact, check out Venngage’s free chart maker or follow me on a tour into the world of data storytelling along with data presentation templates that work across different fields, from business boardrooms to the classroom and beyond. Keep scrolling to learn more!
Click to jump ahead:
What should be included in a data presentation, what are some common mistakes to avoid when presenting data, faqs on data presentation examples, transform your message with impactful data storytelling.
Data presentation is a vital skill in today’s information-driven world. Whether you’re in business, academia, or simply want to convey information effectively, knowing the different ways of presenting data is crucial. For impactful data storytelling, consider these essential data presentation methods:
Ideal for comparing data across categories or showing trends over time.
Bar graphs, also known as bar charts are workhorses of data presentation. They’re like the Swiss Army knives of visualization methods because they can be used to compare data in different categories or display data changes over time.
In a bar chart, categories are displayed on the x-axis and the corresponding values are represented by the height of the bars on the y-axis.
It’s a straightforward and effective way to showcase raw data, making it a staple in business reports, academic presentations and beyond.
Make sure your bar charts are concise with easy-to-read labels. Whether your bars go up or sideways, keep it simple by not overloading with too many categories.
Great for displaying trends and variations in data points over time or continuous variables.
Line charts or line graphs are your go-to when you want to visualize trends and variations in data sets over time.
One of the best quantitative data presentation examples, they work exceptionally well for showing continuous data, such as sales projections over the last couple of years or supply and demand fluctuations.
The x-axis represents time or a continuous variable and the y-axis represents the data values. By connecting the data points with lines, you can easily spot trends and fluctuations.
A tip when presenting data with line charts is to minimize the lines and not make it too crowded. Highlight the big changes, put on some labels and give it a catchy title.
Useful for illustrating parts of a whole, such as percentages or proportions.
Pie charts are perfect for showing how a whole is divided into parts. They’re commonly used to represent percentages or proportions and are great for presenting survey results that involve demographic data.
Each “slice” of the pie represents a portion of the whole and the size of each slice corresponds to its share of the total.
While pie charts are handy for illustrating simple distributions, they can become confusing when dealing with too many categories or when the differences in proportions are subtle.
Don’t get too carried away with slices — label those slices with percentages or values so people know what’s what and consider using a legend for more categories.
Effective for showing the relationship between two variables and identifying correlations.
Scatter plots are all about exploring relationships between two variables. They’re great for uncovering correlations, trends or patterns in data.
In a scatter plot, every data point appears as a dot on the chart, with one variable marked on the horizontal x-axis and the other on the vertical y-axis.
By examining the scatter of points, you can discern the nature of the relationship between the variables, whether it’s positive, negative or no correlation at all.
If you’re using scatter plots to reveal relationships between two variables, be sure to add trendlines or regression analysis when appropriate to clarify patterns. Label data points selectively or provide tooltips for detailed information.
Best for visualizing the distribution and frequency of a single variable.
Histograms are your choice when you want to understand the distribution and frequency of a single variable.
They divide the data into “bins” or intervals and the height of each bar represents the frequency or count of data points falling into that interval.
Histograms are excellent for helping to identify trends in data distributions, such as peaks, gaps or skewness.
Here’s something to take note of — ensure that your histogram bins are appropriately sized to capture meaningful data patterns. Using clear axis labels and titles can also help explain the distribution of the data effectively.
Useful for showing how different components contribute to a whole over multiple categories.
Stacked bar charts are a handy choice when you want to illustrate how different components contribute to a whole across multiple categories.
Each bar represents a category and the bars are divided into segments to show the contribution of various components within each category.
This method is ideal for highlighting both the individual and collective significance of each component, making it a valuable tool for comparative analysis.
Stacked bar charts are like data sandwiches—label each layer so people know what’s what. Keep the order logical and don’t forget the paintbrush for snazzy colors. Here’s a data analysis presentation example on writers’ productivity using stacked bar charts:
Similar to line charts but with the area below the lines filled, making them suitable for showing cumulative data.
Area charts are close cousins of line charts but come with a twist.
Imagine plotting the sales of a product over several months. In an area chart, the space between the line and the x-axis is filled, providing a visual representation of the cumulative total.
This makes it easy to see how values stack up over time, making area charts a valuable tool for tracking trends in data.
For area charts, use them to visualize cumulative data and trends, but avoid overcrowding the chart. Add labels, especially at significant points and make sure the area under the lines is filled with a visually appealing color gradient.
Presenting data in rows and columns, often used for precise data values and comparisons.
Tabular data presentation is all about clarity and precision. Think of it as presenting numerical data in a structured grid, with rows and columns clearly displaying individual data points.
A table is invaluable for showcasing detailed data, facilitating comparisons and presenting numerical information that needs to be exact. They’re commonly used in reports, spreadsheets and academic papers.
When presenting tabular data, organize it neatly with clear headers and appropriate column widths. Highlight important data points or patterns using shading or font formatting for better readability.
Utilizing written or descriptive content to explain or complement data, such as annotations or explanatory text.
Textual data presentation may not involve charts or graphs, but it’s one of the most used qualitative data presentation examples.
It involves using written content to provide context, explanations or annotations alongside data visuals. Think of it as the narrative that guides your audience through the data.
Well-crafted textual data can make complex information more accessible and help your audience understand the significance of the numbers and visuals.
Textual data is your chance to tell a story. Break down complex information into bullet points or short paragraphs and use headings to guide the reader’s attention.
Using simple icons or images to represent data is especially useful for conveying information in a visually intuitive manner.
Pictograms are all about harnessing the power of images to convey data in an easy-to-understand way.
Instead of using numbers or complex graphs, you use simple icons or images to represent data points.
For instance, you could use a thumbs up emoji to illustrate customer satisfaction levels, where each face represents a different level of satisfaction.
Pictograms are great for conveying data visually, so choose symbols that are easy to interpret and relevant to the data. Use consistent scaling and a legend to explain the symbols’ meanings, ensuring clarity in your presentation.
Looking for more data presentation ideas? Use the Venngage graph maker or browse through our gallery of chart templates to pick a template and get started!
A comprehensive data presentation should include several key elements to effectively convey information and insights to your audience. Here’s a list of what should be included in a data presentation:
1. Title and objective
2. Key data points
3. Context and significance
4. Key takeaways
5. Visuals and charts
6. Implications or actions
7. Q&A and discussion
Presenting data is a crucial skill in various professional fields, from business to academia and beyond. To ensure your data presentations hit the mark, here are some common mistakes that you should steer clear of:
Presenting too much data at once can overwhelm your audience. Focus on the key points and relevant information to keep the presentation concise and focused. Here are some free data visualization tools you can use to convey data in an engaging and impactful way.
It’s easy to assume that your audience understands as much about the topic as you do. But this can lead to either dumbing things down too much or diving into a bunch of jargon that leaves folks scratching their heads. Take a beat to figure out where your audience is coming from and tailor your presentation accordingly.
Using misleading visuals, such as distorted scales or inappropriate chart types can distort the data’s meaning. Pick the right data infographics and understandable charts to ensure that your visual representations accurately reflect the data.
Data without context is like a puzzle piece with no picture on it. Without proper context, data may be meaningless or misinterpreted. Explain the background, methodology and significance of the data.
Neglecting to cite sources and provide citations for your data can erode its credibility. Always attribute data to its source and utilize reliable sources for your presentation.
Avoid simply presenting numbers. If your presentation lacks a clear, engaging story that takes your audience on a journey from the beginning (setting the scene) through the middle (data analysis) to the end (the big insights and recommendations), you’re likely to lose their interest.
Infographics are great for storytelling because they mix cool visuals with short and sweet text to explain complicated stuff in a fun and easy way. Create one with Venngage’s free infographic maker to create a memorable story that your audience will remember.
Presenting data without first checking its quality and accuracy can lead to misinformation. Validate and clean your data before presenting it.
Fancy charts might look cool, but if they confuse people, what’s the point? Go for the simplest visual that gets your message across. Having a dilemma between presenting data with infographics v.s data design? This article on the difference between data design and infographics might help you out.
Data isn’t just about numbers; it’s about people and real-life situations. Don’t forget to sprinkle in some human touch, whether it’s through relatable stories, examples or showing how the data impacts real lives.
At the end of the day, your audience wants to know what they should do with all the data. If you don’t wrap up with clear, actionable insights or recommendations, you’re leaving them hanging. Always finish up with practical takeaways and the next steps.
Business reports often benefit from data presentation through bar charts showing sales trends over time, pie charts displaying market share,or tables presenting financial performance metrics like revenue and profit margins.
Creative data presentation ideas for academic presentations include using statistical infographics to illustrate research findings and statistical data, incorporating storytelling techniques to engage the audience or utilizing heat maps to visualize data patterns.
When choosing a chart format , consider factors like data complexity, audience expertise and the message you want to convey. Options include charts (e.g., bar, line, pie), tables, heat maps, data visualization infographics and interactive dashboards.
Knowing the type of data visualization that best serves your data is just half the battle. Here are some best practices for data visualization to make sure that the final output is optimized.
To select the right data presentation method, start by defining your presentation’s purpose and audience. Then, match your data type (e.g., quantitative, qualitative) with suitable visualization techniques (e.g., histograms, word clouds) and choose an appropriate presentation format (e.g., slide deck, report, live demo).
For more presentation ideas , check out this guide on how to make a good presentation or use a presentation software to simplify the process.
To enhance data presentations, use compelling narratives, relatable examples and fun data infographics that simplify complex data. Encourage audience interaction, offer actionable insights and incorporate storytelling elements to engage and inform effectively.
The opening of your presentation holds immense power in setting the stage for your audience. To design a presentation and convey your data in an engaging and informative, try out Venngage’s free presentation maker to pick the right presentation design for your audience and topic.
Data presentation typically involves conveying data reports and insights to an audience, often using visuals like charts and graphs. Data visualization , on the other hand, focuses on creating those visual representations of data to facilitate understanding and analysis.
Now that you’ve learned a thing or two about how to use these methods of data presentation to tell a compelling data story , it’s time to take these strategies and make them your own.
But here’s the deal: these aren’t just one-size-fits-all solutions. Remember that each example we’ve uncovered here is not a rigid template but a source of inspiration. It’s all about making your audience go, “Wow, I get it now!”
Think of your data presentations as your canvas – it’s where you paint your story, convey meaningful insights and make real change happen.
So, go forth, present your data with confidence and purpose and watch as your strategic influence grows, one compelling presentation at a time.
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Think about a scenario where your report cards are printed in a textual format. Your grades and remarks about you are presented in a paragraph format instead of data tables. Would be very confusing right? This is why data must be presented correctly and clearly. Let us take a look.
Presentation of data.
Presentation of data is of utter importance nowadays. Afterall everything that’s pleasing to our eyes never fails to grab our attention. Presentation of data refers to an exhibition or putting up data in an attractive and useful manner such that it can be easily interpreted. The three main forms of presentation of data are:
Here we will be studying only the textual and tabular presentation, i.e. data tables in some detail.
The discussion about the presentation of data starts off with it’s most raw and vague form which is the textual presentation. In such form of presentation, data is simply mentioned as mere text, that is generally in a paragraph. This is commonly used when the data is not very large.
This kind of representation is useful when we are looking to supplement qualitative statements with some data. For this purpose, the data should not be voluminously represented in tables or diagrams. It just has to be a statement that serves as a fitting evidence to our qualitative evidence and helps the reader to get an idea of the scale of a phenomenon .
For example, “the 2002 earthquake proved to be a mass murderer of humans . As many as 10,000 citizens have been reported dead”. The textual representation of data simply requires some intensive reading. This is because the quantitative statement just serves as an evidence of the qualitative statements and one has to go through the entire text before concluding anything.
Further, if the data under consideration is large then the text matter increases substantially. As a result, the reading process becomes more intensive, time-consuming and cumbersome.
A table facilitates representation of even large amounts of data in an attractive, easy to read and organized manner. The data is organized in rows and columns. This is one of the most widely used forms of presentation of data since data tables are easy to construct and read.
There are many ways for construction of a good table. However, some basic ideas are:
Qualitative classification.
In this classification, data in a table is classified on the basis of qualitative attributes. In other words, if the data contained attributes that cannot be quantified like rural-urban, boys-girls etc. it can be identified as a qualitative classification of data.
200 | 390 | |
167 | 100 |
In quantitative classification, data is classified on basis of quantitative attributes.
0-50 | 29 |
51-100 | 64 |
Here data is classified according to time. Thus when data is mentioned with respect to different time frames, we term such a classification as temporal.
2016 | 10,000 |
2017 | 12,500 |
When data is classified according to a location, it becomes a spatial classification.
India | 139,000 |
Russia | 43,000 |
Q: The classification in which data in a table is classified according to time is known as:
Ans: The form of classification in which data is classified based on time frames is known as the temporal classification of data and tabular presentation.
Which class are you in.
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It is the simplest form of data Presentation often used in schools or universities to provide a clearer picture to students, who are better able to capture the concepts effectively through a pictorial Presentation of simple data.
It is a simplified version of the pictorial Presentation which involves the management of a larger amount of data being shared during the presentations and providing suitable clarity to the insights of the data.
Pie charts provide a very descriptive & a 2D depiction of the data pertaining to comparisons or resemblance of data in two separate fields.
A bar chart that shows the accumulation of data with cuboid bars with different dimensions & lengths which are directly proportionate to the values they represent. The bars can be placed either vertically or horizontally depending on the data being represented.
It is a perfect Presentation of the spread of numerical data. The main differentiation that separates data graphs and histograms are the gaps in the data graphs.
Box plot or Box-plot is a way of representing groups of numerical data through quartiles. Data Presentation is easier with this style of graph dealing with the extraction of data to the minutes of difference.
Map Data graphs help you with data Presentation over an area to display the areas of concern. Map graphs are useful to make an exact depiction of data over a vast case scenario.
All these visual presentations share a common goal of creating meaningful insights and a platform to understand and manage the data in relation to the growth and expansion of one’s in-depth understanding of data & details to plan or execute future decisions or actions.
Data Presentation could be both can be a deal maker or deal breaker based on the delivery of the content in the context of visual depiction.
Data Presentation tools are powerful communication tools that can simplify the data by making it easily understandable & readable at the same time while attracting & keeping the interest of its readers and effectively showcase large amounts of complex data in a simplified manner.
If the user can create an insightful presentation of the data in hand with the same sets of facts and figures, then the results promise to be impressive.
There have been situations where the user has had a great amount of data and vision for expansion but the presentation drowned his/her vision.
To impress the higher management and top brass of a firm, effective presentation of data is needed.
Data Presentation helps the clients or the audience to not spend time grasping the concept and the future alternatives of the business and to convince them to invest in the company & turn it profitable both for the investors & the company.
Although data presentation has a lot to offer, the following are some of the major reason behind the essence of an effective presentation:-
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A side-by-side comparison of eight tools using multiple kinds of documents, from documentcloud.
By Sanjin Ibrahimovic
Posted on: August 27, 2024
Tools like Tabula can help journalists extract tabular data from digitally created and scanned documents.
Editor’s note: This article is published in collaboration with MuckRock . You may also be interested in their 2023 review of OCR tools !
Extracting tabular data from documents presents a persistent challenge to reporters and researchers alike. In a perfect world, agencies would always provide data in a tabular format, but we’re not there just yet. They often supply it in PDFs, Word documents and even images.
There are some free tools available, like Tabula , that extract rows and columns from documents that already contain tidy, machine-generated tables. But when documents are handwritten, image-based, or otherwise complicated, free tools simply won’t cut it. And if you have dozens of documents, your project is even more challenging.
Over the past year, we at DocumentCloud have shared our guide to self-hosted maps as well as a comprehensive review of optical character recognition ( OCR ) platforms . Now, we decided to review the options available for tabular data extraction.
We assembled a collection of documents to test, including:
These test documents are available in a DocumentCloud project, if you’d like to flip through them on your own.
Select results for each tool are linked below each review if they are especially noteworthy or different than the others. All the free tools struggle with handwritten analysis, thus there is no way to compare apples to apples for each document. Each technology has its strengths and weaknesses, use cases and costs.
Tabula works really well on text-based, machine-generated PDFs. If you have a lot of documents with relatively clean text, and they all follow the same format, Tabula makes it easy to extract this structured data. Tabula will even auto-detect tables across multiple pages, which produces decent results for uncomplicated documents.
Sometimes the table auto-detection gets the boundaries of tables or columns incorrect and it produces more accurate results with the highlight feature. If you have a lot of documents with the same format, you can save it as a template to use.
If you generate a good template and all of the documents are the same structure, you can even apply the template to all the documents to extract data in bulk. DocumentCloud’s Add-On allows you to run Tabula on a set of documents using autodetection of tables or by providing a template for data extraction. The Add-On will produce a zip file with the tables you are looking for. We found Tabula to work well on the Annual Tax Increment Financing Report from the City of Chicago ( results ).
Tabula works well for:
Tabula does not work as well for:
pdfplumber is a tool every budding data journalist and data wrangler should be familiar with. It works really well on clean, machine-generated PDFs with strong underlying text layer accuracy, like the WARN report from our tests. pdfplumber does an exceptional job at extracting lines, intersections, cells, and tables from documents. We especially like the library’s ability to visually show you the table and cell outlines it was able to extract.
Another factor we really liked: pdfplumber’s table extraction functions include several parameters that can be fine-tuned to find better table fits. The table can quickly be captured and stored in a pandas dataframe, which then allows you to export it as a CSV or convert into a JSON string. pdfplumber is in active development, and the documentation is kept up to date.
The limitations of pdfplumber are that it does not provide any form of OCR and offers less support for table extraction on OCR ’d documents. If you are looking to extract structured data from a bunch of clean forms with machine-generated text, pdfplumber should be near the top of your list. It’s also a relatively lightweight tool to integrate into a replicable workflow that costs nothing to run.
pdfplumber works well for:
pdfplumber does not work as well for:
PaddleOCR is free, open source program. It holds a lot of promise, especially in the evaluation of image-based documents, where Tabula and pdfplumber struggle. Because PaddleOCR takes quite a bit more setup, and it struggles to analyze handwritten text, it won’t perform as well as the paid options on the most difficult documents. But it is definitely one tool to keep in the arsenal.
We think that PaddleOCR, much like docTR from our OCR review , is well-suited for creating a larger ecosystem of customizable OCR tools broadly available to the public. PaddleOCR is best-suited for image-based PDFs and multilingual documents. For those interested in training your own models on labeled data and fine-tuning the extraction, we recommend taking a look at PaddleOCR.
For those with privacy concerns related to cloud services, training and fine-tuning your own model within PaddleOCR might also be your best bet for tabular data extraction and analysis.
PaddleOCR works well for:
PaddleOCR does not work as well for:
This is a hack that we like to recommend for a one-off document that seems to give you formatting problems in other software. Many users already have Excel installed and it is accessible online, which gives it an advantage over PaddleOCR.
You can screenshot or take a picture of the table(s) of interest and import them directly into Excel . You can import the picture by clicking Data > From Picture > Picture From File .
Excel works really well on images like scans, photographs, and screenshots that aren’t supported by Tabula or pdfplumber.
It does not work on handwritten text, so it is not a replacement for paid options that will perform the OCR necessary. Additionally, we would not consider it a replacement for programmatic data extraction on a mass set of documents, which often requires more tuning. We found that it performed well on this statement of financial position .
Excel works well for:
Excel does not work as well for:
Pinpoint works well on the extraction of tabular data from text-based, image-based or handwritten documents. It performed admirably on even the most challenging documents we threw at it, such as our Nigerian election report.
Pinpoint, however, has several weaknesses. Similar to Tabula, it sometimes fails to auto-detect tables, and therefore requires human intervention for table detection. For very large document sets, this is not a trivial time expense.
Pinpoint recently added a feature to extract similar tables from a set of documents and combine into one spreadsheet. This is great, with one caveat:
Considering the costs for table extraction for both Azure Document Intelligence and Amazon Textract, we might question how sustainable it is for Google to offer this feature long term. Google’s propensity for killing its own products should be considered as a possibility.
Finally, being required to use the Pinpoint UI is a weakness. The Google Cloud Vision API , which is used for other document analysis tasks such as OCR , does not offer table extraction . Tables aren’t mentioned in its pricing either. Pinpoint itself is not available programmatically. There are no endpoints you can reach or an API to call.
This is something our team has concerns about when users try to bulk export documents from Pinpoint and upload them to DocumentCloud. This also means that table extraction isn’t available programmatically. Researchers and journalists know the importance of maintaining security and an access regime for sensitive data and documents. Walled gardens have failed before and often leave us to clean up and migrate.
Google Pinpoint works well for:
Pinpoint does not work as well for:
Amazon textract.
Amazon Textract performed well on all of the documents in our test set. Amazon Textract is more efficient than Azure Document Intelligence in one way: its Python library, Textractor , makes it dead simple to go from image to table to CSV or Excel file. As far as programmatic tools go, it was the simplest to use and implement into an Add-On. Amazon has a free tier that offers three months of some usage, but it is more expensive than Azure Document Intelligence for bulk usage. At $15 per 1,000 pages for the first million pages, there is a significant price difference.
The issue we observed with Amazon Textract (and Azure Document Intelligence) is that what you see is what you get with regard to table extraction. If you want to tune the model to your document set with pretrained tables, the costs add up quickly. For Amazon Textract, the cost increases to $30 per 1,000 pages for the first million pages and $20 per 1,000 pages after that. If you’re planning to analyze tens of thousands or hundreds of thousands of tables, this cost can be an issue.
Amazon Textract performed admirably on even the challenging flight log and election document , which contained handwritten information and photographed tables.
Amazon Textract works well for:
Textract does not work as well for:
Azure performs well on all of the same types of documents that Textract excels on. Although the data returned from Azure requires some processing to get it into a dataframe, CSV , or JSON string, it isn’t as challenging as GPT –4 Vision to get into the right format. Table extraction uses the “layout” model of form analyzer and costs $10 per 1,000 pages , with some discounts available for bulk analysis.
As noted in our OCR review , we found Azure resources to be more straightforward to get started with, compared to Amazon web services or Google applications.
But for custom table extraction with pre-trained models, the costs are much higher: two to four times the cost, depending on whether you pay up front and whether you use an Azure function or a connected container. Connected containers are usually recommended for large workloads, as their pricing scales better on Azure. We found Azure performed wonderfully on even the most difficult documents, including the Nigerian election document and the Jeffrey Epstein flight log .
Azure Document Intelligence works well for:
Azure does not work as well for:
GPT –4 Vision, which initially seemed promising, has several weaknesses that make it less reliable for the task of table extraction compared to other tools.
When trying to analyze the Nigerian election document in our preliminary trials, we bumped against guardrails that prevented us from extracting tables at first.
We aren’t the only users who bumped into this problem . From our experience, this form of denial of access is both annoying, unpredictable, and requires more energy to work around.
Another issue we ran into while running the GPT –4 Vision Add-On for table extraction is coercing the results from GPT into a suitable format. The gpt–4-vision-preview model does not support specifying a response format like JSON , which would ensure the provided results are machine parseable. You are therefore stuck goading GPT into replying with something parseable. Our Add-On uses Instructor , an open-source library, to guarantee a parseable result, but even then we ran into an issue getting started and things may change in the future. Even after guaranteeing machine-parseable results, it was still a bit clunky to get these results into an exportable result like JSON or a CSV .
After all of this work, we still experienced inaccuracies in the responses that didn’t happen with the other paid tools. For example, it wasn’t great at keeping rows together on pages that weren’t straight like in the Nigeria elections document, and it inaccurately identified handwritten numbers on the polling tables.
Trying to calculate the cost of extracting tables from an image or document is also not nearly as straightforward as it was with other tools. At its current price point, we think it makes more sense to use a dedicated tool like Azure Document Intelligence or Amazon Textract, which will also provide more reliable results.
If you are looking for customized results, it is cheaper to use the GPT -Vision API than a custom trained model on Amazon Textract or Azure Document Intelligence, but note that it likely won’t be as reliable. Overall, the most frustrating part of working with GPT –4 Vision for the task of table extraction is that every time we ran the same extraction prompt we received significantly different results .
GPT –4 Vision works well for:
GPT –4 Vision does not work as well for:
Camelot/excalibur.
The last code change on GitHub for Camelot was five years ago, and Excalibur is a web interface for Camelot. In our preliminary testing, we did not get better results than Tabula, which is free and still receiving code maintenance.
Although Nanonets produced promising results during our trial, we decided that Nanonets’ pricing is cost-prohibitive for most newsrooms and journalists.
The DocumentCloud team has released a number of Add-Ons that empower users to run powerful data-extraction tools against individual documents and sets of documents. Because some of them rely on expensive proprietary extraction tools, it helps to know what you can expect from each. We designed our Add-Ons ecosystem to make it easy to build reusable, no-code tooling for data-driven reporting.
Tabula , Azure , Textract , and GPT –4 Vision table extractors are all available as DocumentCloudAdd-Ons , and you can review the code for each on MuckRock’s GitHub . The Tabula Add-On is free to use for any verified DocumentCloud newsroom, while Azure , Textract and GPT –4 Vision require a paid plan on MuckRock. (We charge for these because they cost us money on each run.)
Sanjin is MuckRock’s Developer Experience Engineer. He develops new add-ons, updates documentation of DocumentCloud add-ons and the DocumentCloud API , hosts trainings for both users and developers to get plugged into the DocumentCloud add-on platform, and recruits developers and organizations to use existing add-ons and develop their own in an open-source and collaborative way.
Search this site, from our archives:, our search for the best tabular-data extraction tool in 2024, and what we found.
COMMENTS
As a result of this, it is simple to remember the statistical facts. Cost-effective: Tabular presentation is a very cost-effective way to convey data. It saves time and space. Provides Reference: As the data provided in a tabular presentation can be used for other studies and research, it acts as a source of reference.
Definition: Data presentation is the art of visualizing complex data for better understanding. Importance: Data presentations enhance clarity, engage the audience, aid decision-making, and leave a lasting impact. Types: Textual, Tabular, and Graphical presentations offer various ways to present data.
Explain the Main Parts of a Table: Following are the main parts of a table: (1) Table number. Table number is the very first item mentioned on the top of each table for easy identification and further reference. (2) Title. Title of the table is the second item that is shown just above the table.
Related: 14 Data Modelling Tools For Data Analysis (With Features) Tabular Tabular presentation is using a table to share large amounts of information. When using this method, you organise data in rows and columns according to the characteristics of the data. Tabular presentation is useful in comparing data, and it helps visualise information.
The objectives of tabular data presentation are as follows. The tabular data presentation helps in simplifying the complex data. It also helps to compare different data sets thereby bringing out the important aspects. The tabular presentation provides the foundation for statistical analysis. The tabular data presentation further helps in the ...
Creating Tables and Graphs with results. 6. Preparation of oral presentation or conference poster. 7. Preparation of final tables and graphs for publication (usually 2‐6 for a journal article). 8. Write the final manuscript.
In statistics, tabular data refers to data that is organized in a table with rows and columns. Within the table, the rows represent observations and the columns represent attributes for those observations. For example, the following table represents tabular data: This dataset has 9 rows and 5 columns. Each row represents one basketball player ...
Data Presentation - Tables. Tables are a useful way to organize information using rows and columns. Tables are a versatile organization tool and can be used to communicate information on their own, or they can be used to accompany another data representation type (like a graph). Tables support a variety of parameters and can be used to keep ...
Data sets can be presented either by listing all the elements or by giving a table of values and frequencies. This page titled 1.3: Presentation of Data is shared under a CC BY-NC-SA 3.0 license and was authored, remixed, and/or curated by Anonymous via source content that was edited to the style and standards of the LibreTexts platform. In ...
In this article, the techniques of data and information presentation in textual, tabular, and graphical forms are introduced. Text is the principal method for explaining findings, outlining trends, and providing contextual information. A table is best suited for representing individual information and represents both quantitative and ...
In statistics, tabular data refers to data that is organized in a table with rows and columns. Within the table, the rows represent observations and the columns represent attributes for those observations. For example, the following table represents tabular data: This dataset has 9 rows and 5 columns. Each row represents one basketball player ...
Tabular Ways of Data Presentation and Analysis. To avoid the complexities involved in the textual way of data presentation, people use tables and charts to present data. In this method, data is presented in rows and columns - just like you see in a cricket match showing who made how many runs. Each row and column have an attribute (name, year ...
Understanding Data Presentations (Guide + Examples) Design • March 20th, 2024. In this age of overwhelming information, the skill to effectively convey data has become extremely valuable. Initiating a discussion on data presentation types involves thoughtful consideration of the nature of your data and the message you aim to convey.
In tabular representation of data, the given data set is presented in rows and columns. When a table is used to represent a large amount of data in an arranged, organised, engaging, coordinated and easy to read form it is called the tabular representation of data. The main parts of a Table are table number, title, headnote, captions or column ...
Data can be presented in three ways: 1. Textual Mode of presentation is layman's method of presentation of data. Anyone can prepare, anyone can understand. No specific skill (s) is/are required. 2. Tabular Mode of presentation is the most accurate mode of presentation of data. It requires a lot of skill to prepare, and some skill (s) to ...
In this method, we can arrange the data in tabular form in terms of frequency. For example, 3 students scored 50 marks. Hence, the frequency of 50 marks is 3. Now, let us construct the frequency distribution table for the given data. Therefore, the presentation of data is given as below:
Tabular data presentation is all about clarity and precision. Think of it as presenting numerical data in a structured grid, with rows and columns clearly displaying individual data points. A table is invaluable for showcasing detailed data, facilitating comparisons and presenting numerical information that needs to be exact. They're commonly ...
collected through enquiry. A table represen ts sum mary of the data by usin g columns and rows. entering figures in the body of table. 12.2 PURPOSE OF THE TABULATION. The purposes of tables and ...
Data Tables or Tabular Presentation. A table facilitates representation of even large amounts of data in an attractive, easy to read and organized manner. The data is organized in rows and columns. This is one of the most widely used forms of presentation of data since data tables are easy to construct and read.
5. Histograms. It is a perfect Presentation of the spread of numerical data. The main differentiation that separates data graphs and histograms are the gaps in the data graphs. 6. Box plots. Box plot or Box-plot is a way of representing groups of numerical data through quartiles. Data Presentation is easier with this style of graph dealing with ...
Pinpoint works well on the extraction of tabular data from text-based, image-based or handwritten documents. It performed admirably on even the most challenging documents we threw at it, such as our Nigerian election report. Pinpoint, however, has several weaknesses. Similar to Tabula, it sometimes fails to auto-detect tables, and therefore ...