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Sign in[Submit an Article](https://write.insightmediagroup.io) [Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/) * * * [Write For TDS](https://towardsdatascience.com/submissions/) [LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search [Programming](https://towardsdatascience.com/category/programming/) # Why You Should Stop Writing Loops in Pandas How to think in columns, write faster code, and finally use Pandas like a professional [Ibrahim Salami](https://towardsdatascience.com/author/ibrahim.salami/) March 3, 20267 min read ![](../images/000.jpg) Created with Gemini AI Okay, full disclosure: when I first started using Pandas, I wrote loops like this all the time: python ``` for i in range(len(df)): if df.loc[i, "sales"] > 1000: df.loc[i, "tier"] = "high" else: df.loc[i, "tier"] = "low" ``` It worked. And I thought, _“Hey, that’s fine, right?”_ Turns out… not so much. I didn’t realize it at the time, but loops like this are a classic beginner trap. They make Pandas do way more work than it needs to, and they sneak in a mental model that keeps you thinking row by row instead of column by column. Once I started thinking in **columns** , things changed. Code got shorter. Execution got faster. And suddenly, Pandas felt like it was actually built to _help me_ , not slow me down. To show this, let’s use a tiny dataset we’ll reference throughout: python ``` import pandas as pd df = pd.DataFrame({ "product": ["A", "B", "C", "D", "E"], "sales": [500, 1200, 800, 2000, 300] }) ``` Output: markup ``` product sales0 A 5001 B 12002 C 8003 D 20004 E 300 ``` Our goal is simple: label each row as `high` if sales are greater than 1000, otherwise `low`. Let me show you how I did it _at first_ , and why there’s a better way. Learn this step by step with the interactive [Computer Science](https://roadmap.sh/computer-science) roadmap. ## The Loop Approach I Started With Here’s the loop I used when I was learning: python ``` for i in range(len(df)): if df.loc[i, "sales"] > 1000: df.loc[i, "tier"] = "high" else: df.loc[i, "tier"] = "low" print(df) ``` It produces this result: markup ``` product sales tier0 A 500 low1 B 1200 high2 C 800 low3 D 2000 high4 E 300 low ``` And yes, it works. But here’s what I learned the hard way: Pandas is doing **a tiny operation for each row** , instead of efficiently handling the whole column at once. This approach doesn’t scale — what feels fine with 5 rows slows down with 50,000 rows. More importantly, it keeps you thinking like a beginner — row by row — instead of like a professional Pandas user. ## Timing the Loop (The Moment I Realized It Was Slow) When I first ran my loop on this tiny dataset, I thought, _“No problem, it’s fast enough.”_ But then I wondered… what if I had a bigger dataset? So I tried it: python ``` import pandas as pd import time # Make a bigger dataset df_big = pd.DataFrame({ "product": ["A", "B", "C", "D", "E"] * 100_000, "sales": [500, 1200, 800, 2000, 300] * 100_000 }) # Time the loop start = time.time() for i in range(len(df_big)): if df_big.loc[i, "sales"] > 1000: df_big.loc[i, "tier"] = "high" else: df_big.loc[i, "tier"] = "low" end = time.time() print("Loop time:", end - start) ``` Here’s what I got: markup ``` Loop time: 129.27328729629517 ``` That’s **129 seconds**. Over **two minutes** just to label rows as `"high"` or `"low"`. That’s the moment it clicked for me. The code wasn’t just “a little inefficient.” It was fundamentally using Pandas the wrong way. And imagine this running inside a data pipeline, in a dashboard refresh, on millions of rows every single day. ### Why It’s That Slow The loop forces Pandas to: * Access each row individually * Execute Python-level logic for every iteration * Update the DataFrame one cell at a time In other words, it turns a highly optimized columnar engine into a glorified Python list processor. And that’s not what Pandas is built for. ### The One-Line Fix (And the Moment It Clicked) After seeing **129 seconds** , I knew there had to be a better way. So instead of looping through rows, I tried expressing the rule at the **column level** : _“If sales > 1000, label high. Otherwise, label low.”_ That’s it. That’s the rule. Here’s the vectorized version: python ``` import numpy as np import time start = time.time() df_big["tier"] = np.where(df_big["sales"] > 1000, "high", "low") end = time.time() print("Vectorized time:", end - start) ``` And the result? markup ``` Vectorized time: 0.08 ``` Let that sink in. Loop version: **129 seconds** Vectorized version: **0.08 seconds** That’s over **1,600× faster**. ### What Just Happened? The key difference is this: The loop processed the DataFrame **row by row**. The vectorized version processed the entire `sales` column **in one optimized operation**. When you write: python ``` df_big["sales"] > 1000 ``` Pandas doesn’t check values one at a time in Python. It performs the comparison at a lower level (via NumPy), in compiled code, across the entire array. Then `np.where()` applies the labels in one efficient pass. Here’s the subtle but powerful change: Instead of asking: _“What should I do with this row?”_ You ask: _“What rule applies to this column?”_ That’s the line between beginner Pandas and professional Pandas. At this point, I thought I’d “leveled up.” Then I discovered I could make it even simpler. ## And Then I Discovered Boolean Indexing After timing the vectorized version, I felt pretty proud. But then I had another realization. I don’t even need `np.where()` for this. Let’s go back to our small dataset: python ``` df = pd.DataFrame({ "product": ["A", "B", "C", "D", "E"], "sales": [500, 1200, 800, 2000, 300] }) ``` Our goal is still the same: _Label each row_` _high_`_if sales > 1000, otherwise _`_low_`_._ With `np.where()` we wrote: python ``` df["tier"] = np.where(df["sales"] > 1000, "high", "low") ``` It’s cleaner and faster. Much better than a loop. But here’s the part that really changed how I think about Pandas: This line right here… python ``` df["sales"] > 1000 ``` …already returns something incredibly useful. Let’s look at it: **Output:** markup ``` 0 False1 True2 False3 True4 FalseName: sales, dtype: bool ``` That’s a Boolean Series. Pandas just evaluated the condition for the entire column at once. No loop. No `if`. No row-by-row logic. It produced a full mask of True/False values in one shot. ### Boolean Indexing Feels Like a Superpower Now here’s where it gets interesting. You can use that Boolean mask directly to filter rows: python ``` df[df["sales"] > 1000] ``` And Pandas instantly gives you: ![](../images/body-0d9ce5f2d13ed4d410a4.png) We can even build the `tier` column using Boolean indexing directly: python ``` df["tier"] = "low" df.loc[df["sales"] > 1000, "tier"] = "high" ``` I’m basically saying: * Assume everything is `"low"`. * Override only the rows where sales > 1000. That’s it. And suddenly, I’m not thinking: _“For each row, check the value…”_ I’m thinking: _“Start with a default. Then apply a rule to a subset.”_ That shift is subtle, but it changes everything. Once I got comfortable with Boolean masks, I started wondering: What happens when the logic isn’t as clean as “greater than 1000”? What if I need custom rules? That’s where I discovered `apply()`. And at first, it felt like the best of both worlds. ## Isn’t `apply()` Good Enough? I’ll be honest. After I stopped writing loops, I thought I had everything figured out. Because there was this magical function that seemed to solve everything: `apply()`. It felt like the perfect middle ground between messy loops and scary vectorization. So naturally, I started writing things like this: python ``` df["tier"] = df["sales"].apply( lambda x: "high" if x > 1000 else "low" ) ``` And at first glance? This looks great. * No `for` loop * No manual indexing * Easy to read It _feels_ like a professional solution. But here’s what I didn’t understand at the time: `apply()` is still running Python code for every single row. It just hides the loop. When you use: `df["sales"].apply(lambda x: ...)` Pandas is still: * Taking each value * Passing it into a Python function * Returning the result * Repeating that for every row It’s cleaner than a `for` loop, yes. But performance-wise? It’s much closer to a loop than to true vectorization. That was a bit of a wake-up call for me. I realized I was replacing visible loops with invisible ones. ### So When Should You Use `apply()`? * If the logic can be expressed with vectorized operations → do that. * If it can be expressed with Boolean masks → do that. * If it absolutely requires custom Python logic → then use `apply()`. In other words: _Vectorize first. Reach for_` _apply()only_`_when you must._ Not because `apply()` is bad. But because Pandas is fastest and cleanest when you think in columns, not in row-wise functions. ## Conclusion Looking back, the biggest mistake I made wasn’t writing loops. It was assuming that if the code worked, it was good enough. Pandas doesn’t punish you immediately for thinking in rows. But as your datasets grow, as your pipelines scale, as your code ends up in dashboards and production workflows, the difference becomes obvious. * Row-by-row thinking doesn’t scale. * Hidden Python loops don’t scale. * Column-level rules do. That’s the real line between beginner and professional Pandas usage. So, in summary: _Stop asking what to do with each row. Start asking what rule applies to the entire column._ Once you make that shift, your code gets faster, cleaner, easier to review and easier to maintain. And you start spotting inefficient patterns instantly, including your own. ## Register Now to Continue Reading ### Unlock Towards Data Science with a Free Account All articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more. Register for freeSign in to your account * * * ![](../images/body-542334df68a32cd56c4a.jpeg) Written By ## [Ibrahim Salami](https://towardsdatascience.com/author/ibrahim.salami/) [See all from Ibrahim Salami](https://towardsdatascience.com/author/ibrahim.salami/) [Data Analytics](https://towardsdatascience.com/tag/data-analytics/)[Data Science](https://towardsdatascience.com/tag/data-science/)[Pandas](https://towardsdatascience.com/tag/pandas/)[Python](https://towardsdatascience.com/tag/python/)[Vectorization](https://towardsdatascience.com/tag/vectorization/) Share This Article * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fstop-writing-loops-in-pandas-do-this-instead%2F&title=Why%20You%20Should%20Stop%20Writing%20Loops%20in%20Pandas) * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fstop-writing-loops-in-pandas-do-this-instead%2F&title=Why%20You%20Should%20Stop%20Writing%20Loops%20in%20Pandas) * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fstop-writing-loops-in-pandas-do-this-instead%2F&text=Why%20You%20Should%20Stop%20Writing%20Loops%20in%20Pandas) Towards Data Science is a community publication. 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