This week I was looking around on r/datasets and stumbled across a really cool dataset that had collected a ton of tech layoff data and collated it into a beautifully simple Google sheet. 

It presented a straightforward yet insightful overview: key details about the layoffs' scope, the companies involved, and the exact dates of occurrence.

What had piqued my interest for some time was the uncanny timing of significant tech layoffs, often occurring in rapid succession. This dataset seemed tailor-made for delving into this phenomenon, to explore whether it was a consistent trend.

Quickly exporting the data to CSV, I promptly uploaded it to DataChat, excited to unravel its insights and reveal the potential patterns that lay within.

Data Cleaning

Upon loading the dataset, my eagerness to dive into monthly statistics was met with an unexpected hurdle—the Data Cleaning process. To my dismay, it became evident that the Date column contained some unprocessed entries truncated by ellipses ("..."), rendering them as strings rather than recognizable date formats. This compelled me to embark on an intricate journey of Data Cleaning.

Spanning from 2020 to 2023, the dataset's limited time range left me with no feasible approach to fill in missing years. Thus, I made the decision to prune these rows, resulting in around 1900 remaining entries out of the initial ~2200. This retained data volume felt sufficiently robust for analysis.

To ensure a comprehensive understanding, I began with preliminary column-level statistics. An interesting observation emerged: approximately 30% of the layoffs lacked information about headcount. However, despite this gap, the dataset still held substantial valuable insights.

Peering into the horizon of my analysis, I noted  that there wasn’t much information about the featured companies themselves. Recognizing the significance of comparing big versus small companies, I created a derived "Total Employees" column using the "percent laid off" and "total laid off" columns. A cleaning step was required to eliminate the "%" character from the percentage column.

Data Exploration

My initial focus was to examine the monthly distribution of total layoffs. I graphed the count of employees laid off per month, revealing a notable outlier in January 2023. Interesting, this aberration followed a pattern that appeared akin to a recovery in the third quarter of 2022.

Digging into the data, I uncovered a significant insight: six out of the ten largest layoffs happened in January 2023, predominantly at prominent companies. This concentration of sizable layoffs contributed to the remarkable spike within that month. However, venturing beyond these large layoffs unveiled an unusual surge in smaller layoffs as well.

Further investigation online revealed a compelling trend: January emerged as the most common month for layoffs  for layoffs across various industries. This timing coincided with companies realigning their strategies based on the prior year's performance. Notably, a similar surge occurred in January 2021, backing this observation.

While the Q3 2022 upswing was intriguing, it prompted me to probe whether this trend was consistent across all companies. Size emerged as a key variable, categorizing companies into small, medium, and large based on percentiles. Plotting layoffs per month for each group, I was intrigued to find that the timing of layoffs remained relatively the same, irrespective of company size.

To determine whether this trend was unique to the United States, I conducted a separate analysis for international companies. Surprisingly, there were no apparent timing variations for companies outside the United States. This indicated that the January layoff trend was not constrained by geographical boundaries.

Wrapping up my exploration, I turned my attention to potential differences in layoff behavior among various industries. With numerous categories to consider, I narrowed my focus to the top four. Notably, I observed intriguing disparities: Transportation tech companies appeared to initiate their layoffs earlier in a cycle, while Consumer tech companies tended to be positioned on the later side of the layoff cycle.


In closing, delving into this dataset was an engaging and enlightening experience.  The insights I gained provided a deep understanding of companies' layoff practices. Notably, I discovered that January holds the distinction of being the most prevalent month for layoffs—a fact that sheds light on common industry practices. Moreover, the trend of widespread industry layoffs transcending company size was an eye-opener.

I must highlight the effectiveness of DataChat's free account as a tool for this analysis. With its support, I completed the analysis within an hour, inclusive of extensive exploration and tangential investigations not covered in this summary. This process truly underscored the power of efficient data analysis tools in gaining swift and meaningful insights. If you're keen to embark on your own data analysis journey, I highly recommend signing up for a free trial at

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