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In this article, we will address the following: How to use xlsxwriter to save Excel How to save multiple data frames to worksheets using Python How do use Python to stylize the Excel formatting How to ensure that specific data types such as datetime, integer, string, and floating data are saved accurately when using Python and xlsxwriter to save a data frame to Excel Question Description: Imagine that you have the following 2 data frames and you want to save them into one Excel file with different sheets and stylize the Excel formatting. Solution: Save Excel through xlsxwriter 1. Import xlsxwriter package and create Excel file Use to create a new Excel file. Workbook # import package
import xlsxwriter
# create excel file
workbook = xlsxwriter.Workbook("new_excel.xlsx") 2. Create two sheets Use to create a sheet because we have two data frames that need to be saved, we have to create two sheets. add_worksheet worksheet1 = workbook.add_worksheet('df1_sheet')
worksheet2 = workbook.add_worksheet('df2_sheet') 3. Set header format and save the header First, we set the header format and use to save our header. .write_row has four arguments, they are: worksheet.write_row(row, col, data, cell_format). .write_row header_format = workbook.add_format({
 'valign': 'top',
 'fg_color': '#002060',
 'border': 1,
 'font_color': 'white'})
worksheet1.write_row(0, 0, df1.columns, header_format)
worksheet2.write_row(0, 0, df2.columns, header_format) 4. Create some format : This method is used to define the numerical format of a number in Excel. It controls whether a number is displayed as an integer, a floating point number, a date, a currency value, or some other user-defined format. format.set_num_format() The numerical format of a cell can be specified by using a format string or an index to one of Excel’s built-in formats, for more information you can check xlsxwriter-format For example: represents , so 14 m/dd/yyyy format.set_num_format(14) = format.set_num_format('m/dd/yyyy') # set datetime format "m/d/yy"
format_datetime = workbook.add_format({'border': 1})
format_datetime.set_num_format(14) # based on above table, 14 means "m/d/yy"
format_datetime.set_font_size(12) # set font size

# set General format
format_general = workbook.add_format({'border': 1})
format_general.set_num_format(0) # 0 means general
format_general.set_font_size(12)

# set integer format "0"
format_integer = workbook.add_format({'border': 1})
format_integer.set_num_format(1)
format_integer.set_font_size(12)

# set float format "0.00"
format_float = workbook.add_format({'border':1})
format_float.set_num_format(2)
format_float.set_font_size(12)

# set integer format with thousands separators "#,##0"
format_integer_separator = workbook.add_format({'border': 1})
format_integer_separator.set_num_format(3)
format_integer_separator.set_font_size(12)

# set percent format "0.00%"
format_percent = workbook.add_format({'border':1})
format_percent.set_num_format(10)
format_percent.set_font_size(12) 5. Save the data into Excel with cell_format The zero index row already saved our header, so we start from one index row(set the first argument to 1) # .write_column(row, column, data, cell_format)

worksheet1.write_column(1, 0, df1.iloc[:, 0], format_datetime)
worksheet1.write_column(1, 1, df1.iloc[:, 1], format_integer)
worksheet1.write_column(1, 2, df1.iloc[:, 2], format_integer_separator)
worksheet1.write_column(1, 3, df1.iloc[:, 3], format_general)
worksheet1.write_column(1, 4, df1.iloc[:, 4], format_float)
worksheet1.write_column(1, 5, df1.iloc[:, 5], format_percent)
worksheet2.write_column(1, 0, df2.iloc[:, 0], format_datetime)
worksheet2.write_column(1, 1, df2.iloc[:, 1], format_integer)
worksheet2.write_column(1, 2, df2.iloc[:, 2], format_integer_separator)
worksheet2.write_column(1, 3, df2.iloc[:, 3], format_general)
worksheet2.write_column(1, 4, df2.iloc[:, 4], format_float)
worksheet2.write_column(1, 5, df2.iloc[:, 5], format_percent) 6. Set column width For a better display of the data, we can also use to set the column width. .set_column worksheet1.set_column('A:A', 15)
worksheet1.set_column('B:B', 15)
worksheet1.set_column('C:C', 15)
worksheet1.set_column('D:D', 15)
worksheet1.set_column('E:E', 15)
worksheet1.set_column('F:F', 15)
worksheet2.set_column('A:A', 15)
worksheet2.set_column('B:B', 15)
worksheet2.set_column('C:C', 15)
worksheet2.set_column('D:D', 15)
worksheet2.set_column('E:E', 15)
worksheet2.set_column('F:F', 15) 7. Finish workbook.close() Below is the full Python code that utilizes the xlsxwriter package to save two data frames into an Excel file with different sheets and stylized formatting. The code begins by importing the necessary pandas and xlsxwriter packages. Then, it defines the two data frames, df1 and df2. It proceeds to create a new Excel file and two sheets using and functions, respectively. xlsxwriter.Workbook add_worksheet Following this, the code sets the header format, defines various cell formats for different data types, and writes the data into the Excel file with the specified cell formats. Finally, it sets the column widths, closes the workbook, and thus completes the process of saving the data frames into an Excel file. # generate two dataframes 
import pandas as pd

df1 = pd.DataFrame(
    {'Date': ['2022/12/1', '2022/12/1', '2022/12/1', '2022/12/1'], 'Int': [116382, 227393, 3274984, 438164],
     'Int_with_seperator': [1845132, 298145, 336278, 443816], 'String': ['Tom', 'Grace', 'Luca', 'Tessa'],
     'Float': [98.45, 65.24, 30, 80.88], 'Percent': [0.8878, 0.9523, 0.4545, 0.9921]})
df2 = pd.DataFrame({'Date': ['2022/11/1', '2022/11/1', '2022/11/1', '2022/11/1'], 'Int': [233211, 24321, 35345, 23223],
                    'Int_with_seperator': [925478, 23484, 123249, 2345675],
                    'String': ['Apple', 'Huawei', 'Xiaomi', 'Oppo'], 'Float': [98.45, 65.24, 30, 80.88],
                    'Percent': [0.4234, 0.9434, 0.6512, 0.6133]})
print(df1)
print(df2)

# import package
import xlsxwriter
# create excel file
workbook = xlsxwriter.Workbook("new_excel.xlsx")

# create two sheets
worksheet1 = workbook.add_worksheet('df1_sheet')
worksheet2 = workbook.add_worksheet('df2_sheet')

# set header format and save the header
header_format = workbook.add_format({
 'valign': 'top',
 'fg_color': '#002060',
 'border': 1,
 'font_color': 'white'})
worksheet1.write_row(0, 0, df1.columns, header_format)
worksheet2.write_row(0, 0, df2.columns, header_format)

# set datetime format "m/d/yy"
format_datetime = workbook.add_format({'border': 1})
format_datetime.set_num_format(14) # based on above table, 14 means "m/d/yy"
format_datetime.set_font_size(12) # set font size

# set General format
format_general = workbook.add_format({'border': 1})
format_general.set_num_format(0) # 0 means general
format_general.set_font_size(12)

# set integer format "0"
format_integer = workbook.add_format({'border': 1})
format_integer.set_num_format(1)
format_integer.set_font_size(12)

# set float format "0.00"
format_float = workbook.add_format({'border':1})
format_float.set_num_format(2)
format_float.set_font_size(12)

# set integer format with thousands separators "#,##0"
format_integer_separator = workbook.add_format({'border': 1})
format_integer_separator.set_num_format(3)
format_integer_separator.set_font_size(12)

# set percent format "0.00%"
format_percent = workbook.add_format({'border':1})
format_percent.set_num_format(10)
format_percent.set_font_size(12)

worksheet1.write_column(1, 0, df1.iloc[:, 0], format_datetime)
worksheet1.write_column(1, 1, df1.iloc[:, 1], format_integer)
worksheet1.write_column(1, 2, df1.iloc[:, 2], format_integer_separator)
worksheet1.write_column(1, 3, df1.iloc[:, 3], format_general)
worksheet1.write_column(1, 4, df1.iloc[:, 4], format_float)
worksheet1.write_column(1, 5, df1.iloc[:, 5], format_percent)
worksheet2.write_column(1, 0, df2.iloc[:, 0], format_datetime)
worksheet2.write_column(1, 1, df2.iloc[:, 1], format_integer)
worksheet2.write_column(1, 2, df2.iloc[:, 2], format_integer_separator)
worksheet2.write_column(1, 3, df2.iloc[:, 3], format_general)
worksheet2.write_column(1, 4, df2.iloc[:, 4], format_float)
worksheet2.write_column(1, 5, df2.iloc[:, 5], format_percent)

worksheet1.set_column('A:A', 15)
worksheet1.set_column('B:B', 15)
worksheet1.set_column('C:C', 15)
worksheet1.set_column('D:D', 15)
worksheet1.set_column('E:E', 15)
worksheet1.set_column('F:F', 15)
worksheet2.set_column('A:A', 15)
worksheet2.set_column('B:B', 15)
worksheet2.set_column('C:C', 15)
worksheet2.set_column('D:D', 15)
worksheet2.set_column('E:E', 15)
worksheet2.set_column('F:F', 15)

workbook.close() Thank you for taking the time to explore data-related insights with me. I appreciate your engagement. If you find this information helpful, I invite you to follow me or connect with me on . Happy exploring!👋 LinkedIn Also published . here

How to Specify Data Format in Excel with Python

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The phrase “complexity is the enemy of security” has floated around IT teams for decades. The general belief is that an overly complicated approach to will lead to greater vulnerabilities. Since most organizations are scrambling to advance their defenses against new threats, the debate on whether it is true matters now more than ever. cybersecurity What Does “Complexity Is the Enemy of Security” Mean? The “complexity is the enemy of security” mantra means the more complicated a system is, the more challenging it will be to safeguard against threats. The more components, dependencies, integrations, and configurations an IT team has, the more time they’ll spend maintaining, managing, and securing everything. Instead of sunsetting and replacing legacy systems, many organizations keep adding to their tech stack. Layering security to strengthen defenses for years. Rather than navigate intricate dependencies and configurations, they adopt more technology. has been the industry-standard approach While many IT teams believe adapting is essential to security, most can’t upgrade all their technology whenever a new threat appears. Instead, they rely on well-established systems. However, having a mix of legacy and modern devices because it creates gaps — one of the reasons people consider complexity the enemy. increases the risk of cyberattacks Why Cybersecurity Complexity Exists Most IT teams manage dozens of software, hardware, vendors, and apps. Globally, every business on average in 2022. That number has risen annually for nearly a decade and will likely continue increasing for years. used 130 separate SaaS applications Even though the general sentiment of complexity being detrimental has existed for ages, IT teams continue to layer security. They’ve mainly had to because it’s the only way they can safeguard against constantly changing cyberthreats. Organizations to keep pace with the ever-evolving threat landscape. With the sudden rise of technologies like artificial intelligence and the Internet of Things, professionals have had to modernize or risk being vulnerable. must constantly adapt How Does Cybersecurity Complexity Impact IT Teams? Cybersecurity complexity has ripple effects upstream and downstream — it affects end users as much as chief information security officers. That said, the IT team feels the brunt of the impact. They must manage an increased attack surface, constant updates, misconfigurations, troubleshooting, unintended component interactions, and convoluted compliance monitoring. Consequently, IT professionals experience fatigue. Many neglect security in response, increasing their vulnerabilities. Research shows human error causes in the United States and the United Kingdom. People are already prone to careless and malicious behavior, so these consistently large workloads aren’t ideal. 90% of cybersecurity incidents When systems become too complicated to manage, people naturally look for convenient workarounds. For example, they might skip pressing updates, refuse to implement multifactor authentication, or ignore essential alerts. As a result, their overall security suffers. Attackers have used complexity to their advantage. They’ve exploited outdated systems, negligence, and misconfigurations to find gaps and vulnerabilities. Notably, IT professionals might also have more difficulty locating threats when maintaining dozens of security tools simultaneously. Is “Complexity Is the Enemy of Security” True? On the one hand, the phrase “complexity is the enemy of security” is true because overly complicated setups require more effort to maintain. They inherently create an increased attack surface and have a higher probability of failure. The chance for human error rises because people find workarounds to make their lives more convenient. Additionally, complexity means more overhead and operational costs. The more organizations spend to maintain and manage their systems, the less they’ll have to strengthen security more meaningfully. The same concept applies to time — the longer they focus on upgrades and troubleshooting, the less attention they can devote to more pressing concerns. On the other hand, complexity doesn’t automatically translate to difficulty. Often, complicated configurations result in more straightforward management for end users. Additionally, multilayered security offers comprehensive protection from every potential threat. Moreover, having more hardware, software, and applications allows IT teams to adapt to any threat. Continuously layering security as the cybersecurity landscape evolves helps them fine-tune their defenses. Even if they only have redundant systems, they’re automatically more protected because they have backups. Complexity isn’t inherently harmful to IT teams. Instead, subpar planning is the real threat. Professionals who balance their technology needs with manageability and convenience can better protect themselves. Sometimes, layering security translates to simplicity. Complexity Doesn’t Translate to Better Security For years, IT teams have upped their investments in security. Worldwide spending on cybersecurity , an increase of 12.1% compared to 2022. However, none of those expenses have meaningfully slowed cyberthreats. reached $219 billion in 2023 Rather, cybercriminals have become more brazen and have launched more attacks. Annual data breaches in 2019 to 1,802 in 2022. The fact these cybersecurity incidents increased by 40% in the same period that spending rose $30.3 billion proves the industry’s approach to layering security isn’t working out as expected. went from 1,279 instances Even though most organizations have been spending more for years, they haven’t made a meaningful dent in the threat landscape. It seems complexity doesn’t result in marked improvement by itself. Although subpar planning is the real threat, IT teams shouldn’t layer security just to modernize or adapt. Organizations can strengthen security with proper planning. Instead of continuously adopting a multilayered approach because it’s easier than reconfiguring or phasing out legacy systems, they must prioritize. Whether they need to safeguard personally identifiable information, proprietary data or critical systems, they must build their strategy around protecting core systems instead of expanding to fill every potential gap. Organizations Can Make Complexity Work for Them Organizations have grown too comfortable, making their systems overly complicated to compensate for their vulnerabilities. Instead of spreading themselves thin by expanding their protections to cover every endpoint, they should seriously consider reprioritizing. Strengthening the most critical systems and dropping unnecessary hardware can be very rewarding. Even though having dozens of tools and systems isn’t inherently risky, simplifying their approach to security will undoubtedly improve IT professionals’ work lives and increase organizational safety. Although many IT leaders believe they must continue adopting new technology to protect themselves against the ever-evolving threat landscape, balancing complexity and manageability is much safer in the long term.

Is Complexity the Enemy of Security?

“ ” What AI model maker would take the risk to make any powerful model if they could be responsible for anything someone might possibly do with it? This – author, futurist, engineer, and systems architect. I’ve had the pleasure of conversing with him a couple of times, first about the , which he leads, and then about . I’ve noticed some of Jeffries’ recent shares about OpenAI, AI doomers, and upcoming regulations, most notably the EU AI Act. question is posed by Dan Jeffries AI Infrastructure Alliance Generative AI I’ve and generally consider it and its tiered approach a step in the right direction. I also thought that introducing the notion of general-purpose AI systems powered by foundation models made sense. As far as I understood, uses of the system may or may not be regulated, depending on how they are categorized. covered the EU AI Act https://hackernoon.com/what-the-eu-ai-act-effect-means-for-the-bloc?embedable=true In addition, there are specific requirements for foundation models, including for things such as data governance and compute. That means and will probably converge toward compliance. foundation models can be evaluated In , Jeffries makes some good points about fearmongering and innovation. He also lays out a simple framework for regulation, which looks reasonable. It certainly made me think long and hard about finding the right balance between innovation and regulation in AI. this post How can we find the right balance between innovation and regulation in AI? I’ve laid out what i believe are some important questions to ask here are, along with answers i’ve arrived at. 1. If foundation models can be used in a number of settings, including in ones that would be regulated if dedicated models were developed for them, does it make sense to regulate foundation models, too? Probably yes, and we don’t need to include AI doomerism in this conversation. Let’s take surveillance, for example. I picked the example on purpose because you’d think surveillance would be regulated. It is not, and that’s a good point Jeffries makes – it probably should be. If surveillance is regulated, and foundation models can be used for that or any other regulated application, I don’t see why they should be exempt from regulation. However, there are lots of nuances here in defining “use.” See the next point. 2. What is the threshold that distinguishes a foundation model from a non-foundation one? Capabilities, but it’s not straightforward at all. I’ve seen ideas like , which I don’t think will work. using the amount of compute used to train models as a proxy for capabilities being floated If a model can be *directly* used to power regulated applications such as surveillance, then it should be subject to the same regulation dedicated models are. The key word here is *directly*. A surveillance system contains many components, software, and hardware, from cameras to databases and image recognition algorithms. None of these should or could be regulated in isolation. But if a model does have image recognition capabilities and can *directly* connect to a camera and a database and automate surveillance, then it makes sense to regulate it. Emphasis on *automate* here. See the next point. 3. If foundation models are to be regulated, what type of oversight should be applied to them, and how will it be enforced? High-risk systems, as defined in the EU AI Act, seem about right. There’s a syllogism to unpack here. The EU AI Act originally adopted the definition proposed by the OECD regarding what it applies to: “The OECD defines an Artificial Intelligence (AI) System as a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments”. The , but the emphasis remains on decisions. That is the whole point of specifically regulating AI systems – the automation part. A camera or a database can be used by people to make decisions. An AI system can make decisions autonomously. OECD has issued a revised definition In the EU AI Act, : Unacceptable risk systems are banned entirely (although some exceptions apply), high-risk systems are subject to rules of traceability, transparency, and robustness, low-risk systems require transparency on the part of the supplier and minimal risk systems for which no requirements are set. AI systems are classified into 4 categories according to the perceived risk they pose https://linkeddataorchestration.com/2022/05/12/the-eu-ai-act-what-you-need-to-know/?embedable=true Requiring traceability, transparency, and robustness from foundation models seems reasonable. , so we have an idea of what this would look like in practice. Stanford researchers have evaluated foundation model providers like OpenAI and Google for their compliance with the EU AI Act It does not look like asking foundation model developers to provide this information would impose excessive overhead or stifle competition. But it does look like having them comply with these requirements would create a level playing field and benefit everyone using them directly or indirectly. 4. Should there be a distinction between regulating big tech models vs. open source models? Probably not, counter-intuitive as it may seem. First, because it’s hard to draw a line, case in point – does OpenAI count as big tech, for example? Second, because big tech, open source, and innovation are not mutually exclusive. Case in point – Meta, IBM, and the . newly announced AI Alliance Third, because . Case in point – and . open source is also a nuanced concept Llama AI model weights The question of whether governments should regulate open foundation models and how to do so is the key to finding the right balance between innovation and regulation in AI. Experts from industry, academia, and government convened to outline a path forward at a . Princeton-Stanford workshop on How to Promote Responsible Open Foundation Models This is the first in a series of short posts about important questions posed by people who think deeply and far about technology and its impact, focusing on AI in particular. The idea is to introduce a question and tease some answers as provided and/or inspired by the person who posed the question. Feedback welcome. Also published . here

Balancing AI Innovation & Regulation: Perspectives on Foundation Models and Responsible Development

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