{"id":10548,"date":"2023-11-07T16:17:35","date_gmt":"2023-11-07T16:17:35","guid":{"rendered":"https:\/\/www.blopig.com\/blog\/?p=10548"},"modified":"2023-11-07T16:23:41","modified_gmt":"2023-11-07T16:23:41","slug":"converting-pandas-dataframes-into-publication-ready-tables","status":"publish","type":"post","link":"https:\/\/www.blopig.com\/blog\/2023\/11\/converting-pandas-dataframes-into-publication-ready-tables\/","title":{"rendered":"Converting pandas DataFrames into Publication-Ready Tables"},"content":{"rendered":"\n

Analysing, comparing and communicating the predictive performance of machine learning models is a crucial component of any empirical research effort. Pandas, a staple in the Python data analysis stack, not only helps with the data wrangling itself, but also provides efficient solutions for data presentation. Two of its lesser-known yet incredibly useful features are df.to_markdown()<\/code> and df.to_latex()<\/code>, which allow for a seamless transition from DataFrames to publication-ready tables. Here\u2019s how you can use them!<\/p>\n\n\n\n\n\n\n\n

Exporting DataFrames to Markdown<\/h2>\n\n\n\n

Markdown is widely used for its simplicity and readability, making it a go-to format for rendering your GitHub README or rebuttals on OpenReview. With the df.to_markdown()<\/code> method, you can turn any DataFrame into a Markdown table with a single line of code.<\/p>\n\n\n\n

import pandas as pd\n\n# construct example DataFrame\nresults = pd.DataFrame(\n    {\n        \"model\": [\n            \"random forest\", \n            \"support vector machine\", \n            \"multi-layer perceptron\"\n            ],\n        \"AUC-ROC\": [0.83, 0.79, 0.81],\n        \"AUC-PRC\": [0.46, 0.48, 0.49],\n        \"ECE\": [0.04, 0.09, 0.05],\n        \"runtime\": [0.004, 0.003, 0.01],\n    }\n)\n\n# convert it to Markdown\nprint(results.to_markdown(index=False))<\/pre>\n\n\n\n
This Markdown table can then be copied into any Markdown editor or platform that supports it (such as this website) and will be rendered as a neat table.<\/p>\n\n\n\n
\n\n\n\n\n\n\n
model<\/th>\nAUC-ROC<\/th>\nAUC-PRC<\/th>\nECE<\/th>\nruntime<\/th>\n<\/tr>\n<\/thead>\n
random forest<\/td>\n0.83<\/td>\n0.46<\/td>\n0.04<\/td>\n0.004<\/td>\n<\/tr>\n
support vector machine<\/td>\n0.79<\/td>\n0.48<\/td>\n0.09<\/td>\n0.003<\/td>\n<\/tr>\n
multi-layer perceptron<\/td>\n0.81<\/td>\n0.49<\/td>\n0.05<\/td>\n0.01<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\n\n\n
This function uses the tabulate<\/code> library, which additionally allows you to specify a range of different table styles<\/a> using the tablefmt<\/code> argument – e.g. a text grid like this:<\/p>\n\n\n\n
+------------------------+-----------+-----------+-------+-----------+\n| model                  |   AUC-ROC |   AUC-PRC |   ECE |   runtime |\n+========================+===========+===========+=======+===========+\n| random forest          |      0.83 |      0.46 |  0.04 |     0.004 |\n+------------------------+-----------+-----------+-------+-----------+\n| support vector machine |      0.79 |      0.48 |  0.09 |     0.003 |\n+------------------------+-----------+-----------+-------+-----------+\n| multi-layer perceptron |      0.81 |      0.49 |  0.05 |     0.01  |\n+------------------------+-----------+-----------+-------+-----------+<\/code><\/pre>\n\n\n\n
Exporting DataFrames to LaTeX<\/h2>\n\n\n\n
LaTeX is the de facto standard for the typesetting of machine learning papers. The df.to_latex()<\/code> method can convert a DataFrame into LaTeX tabular format which can be included directly in your LaTeX documents. Using the same example as above<\/p>\n\n\n\n
import pandas as pd\n\n# construct example DataFrame\nresults = pd.DataFrame(\n    {\n        \"model\": [\n            \"random forest\", \n            \"support vector machine\", \n            \"multi-layer perceptron\"\n            ],\n        \"AUC-ROC\": [0.83, 0.79, 0.81],\n        \"AUC-PRC\": [0.46, 0.48, 0.49],\n        \"ECE\": [0.04, 0.09, 0.05],\n        \"runtime\": [0.004, 0.003, 0.01],\n    }\n)\n\n# convert it to LaTeX\nprint(results.to_latex(index=False))<\/pre>\n\n\n\n
we can generate the following table:<\/p>\n\n\n\n
\"\"<\/a><\/figure>\n\n\n\n
Similar to the df.to_markdown()<\/code> function, df.to_latex()<\/code> is quite flexible and allows you to customize the LaTeX table output to a great extent. Here are some of the specialized formatting options you can use with df.to_latex()<\/code> to e.g. align columns, add captions and labels and standardise number formatting:<\/p>\n\n\n\n
# Custom LaTeX table with specialized formatting\nlatex_output = results.to_latex(index=False,\n                                column_format='|l|r|r|r|r|',\n                                caption='Model Performance Metrics.',\n                                label='tab:model_performance',\n                                multicolumn_format='c',\n                                escape=False,\n                                header=[\n                                    'Model', 'AUC-ROC', 'AUC-PRC', \n                                    'ECE', 'Runtime (s)'\n                                    ],\n                                float_format=\"%.4f\")\n\nprint(latex_output)<\/pre>\n\n\n\n
resulting in the following table:<\/p>\n\n\n\n
\"\"<\/a><\/figure>\n\n\n\n
Conclusion<\/h2>\n\n\n\n
Over are the days of having to manually copy-paste your results into Overleaf! Both df.to_markdown()<\/code> and df.to_latex()<\/code> are straightforward yet highly customisable tools that allow you to easily compile and present your results for papers, blog posts and GitHub documentation. <\/p>\n","protected":false},"excerpt":{"rendered":"
Analysing, comparing and communicating the predictive performance of machine learning models is a crucial component of any empirical research effort. Pandas, a staple in the Python data analysis stack, not only helps with the data wrangling itself, but also provides efficient solutions for data presentation. Two of its lesser-known yet incredibly useful features are df.to_markdown() […]<\/p>\n","protected":false},"author":86,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"nf_dc_page":"","wikipediapreview_detectlinks":true,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"ngg_post_thumbnail":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[361,14,463,48,227],"tags":[],"ppma_author":[616],"class_list":["post-10548","post","type-post","status-publish","format-standard","hentry","category-data-science","category-howto","category-latex","category-publication","category-python-code"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"authors":[{"term_id":616,"user_id":86,"is_guest":0,"slug":"leo","display_name":"Leo Klarner","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/8a288902cdb15c98aa887d33d06a4061fa3ebe87388f89f76734cf2be40ec362?s=96&d=mm&r=g","0":null,"1":"","2":"","3":"","4":"","5":"","6":"","7":"","8":""}],"_links":{"self":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts\/10548","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/users\/86"}],"replies":[{"embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/comments?post=10548"}],"version-history":[{"count":5,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts\/10548\/revisions"}],"predecessor-version":[{"id":10581,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts\/10548\/revisions\/10581"}],"wp:attachment":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/media?parent=10548"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/categories?post=10548"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/tags?post=10548"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=10548"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}