{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Ibrahim Salami",
  "title_listed": "Stop Writing Messy Boolean Masks: 10 Elegant Ways to Filter Pandas DataFrames",
  "keywords": "",
  "domain": "数据处理",
  "content_type": "深度技术解析",
  "is_paid_flag": "否",
  "url": "https://towardsdatascience.com/stop-writing-messy-boolean-masks-10-elegant-ways-to-filter-pandas-dataframes/",
  "publish_date": "2026-01-22",
  "corpus_stage": "",
  "notes": "",
  "id": "5df231235b12",
  "collected_at": "2026-10-02T11:54:22",
  "title": "Stop Writing Messy Boolean Masks: 10 Elegant Ways to Filter Pandas DataFrames | Towards Data Science",
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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[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/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# Stop Writing Messy Boolean Masks: 10 Elegant Ways to Filter Pandas DataFrames\nMaster the art of readable, high-performance data selection using .query(), .isin(), and advanced vectorized logic.\n[Ibrahim Salami](https://towardsdatascience.com/author/ibrahim.salami/)\nJanuary 22, 20267 min read\n![](../images/000.jpg)\nImage by Author. Created with Gemini\nIn a recent article, I discussed [how to create your first DataFrame](https://towardsdatascience.com/the-absolute-beginners-guide-to-pandas-dataframes/) using Pandas. I mentioned that the first thing you need to master is Data structures and arrays before moving on to data analysis with Python.\nPandas is an excellent library for data manipulation and retrieval. Combine it with Numpy and Seaborne, and you’ve got yourself a powerhouse for data analysis.\nIn this article, I’ll be walking you through **practical ways to filter data in pandas** , starting with simple conditions and moving on to powerful methods like `.isin()`, `.str.startswith()`, and `.query()`. By the end, you’ll have a toolkit of filtering techniques you can apply to any dataset.\nWithout further ado, let’s get into it!\n## Importing our data\nOk, to start, I’ll import our pandas library\npython\n\n```\n# importing the pandas library\nimport pandas as pd\n```\n\nThat’s the only library I’ll need for this use case\nNext, I’ll import the dataset. The dataset comes from ChatGPT, btw. It consists of basic sales transaction records. Let’s take a look at our dataset.\npython\n\n```\n# checking out our data\ndf_sales = pd.read_csv('sales_data.csv')\ndf_sales\n```\n\nHere’s a preview of the data\n![](../images/001.webp)\nIt consists of basic sales records with columns OrderId, Customer, Product, Category, Quantity, Price, OrderDate and Region.\nAlright, let’s begin our filtering!\n## Filtering by a single condition\nLet’s try to select all records from a particular category. For instance, I want to know how many unique orders were made in the Electronics category. To do that, it’s pretty straightforward\npython\n\n```\n# Filter by a single condition\n# Example: All orders from the “Electronics” category.\ndf_sales[‘Category’] == ‘Electronics’\n```\n\nIn Python, you need to distinguish between the `=` operator and the `==` operator.\n`=` is used to assign a value to a variable.\nFor instance\npython\n\n```\nx = 10 # Assigns the value 10 to the variable x\n```\n\n`==` on the other hand is used to compare two values together. For instance\npython\n\n```\na = 3\nb = 3\nprint(a == b) # Output: True\n\nc = 5\nd = 10\nprint(c == d) # Output: False\n```\n\nWith that said, let’s apply the same notion to the filtering I did above\npython\n\n```\n# Filter by a single condition\n# Example: All orders from the “Electronics” category.\ndf_sales[‘Category’] == ‘Electronics’\n```\n\nHere, I’m basically telling Python to search through our entire record to find a category named Electronics. When it finds a match, it displays a Boolean result, True or False. Here’s the result\n![](../images/body-67e50f11cb719dcc4c89.webp)\nAs you can see. We are getting a Boolean output. True means Electronics exists, while False means the latter. This is okay and all, but it can become confusing if you’re dealing with a large number of records. Let’s fix that.\npython\n\n```\n# Filter by a single condition\n# Example: All orders from the “Electronics” category.\ndf_sales[df_sales[‘Category’] == ‘Electronics’]\n```\n\nHere, I just wrapped the condition in the DataFrame. And with that, we get this output\n![](../images/body-8ccff2f83c87546bf195.webp)\nMuch better, right? Let’s move on\n## Filter rows by numeric condition\nLet’s try to retrieve records where the order quantity is greater than 2. It’s pretty straightforward.\npython\n\n```\n# Filter rows by numeric condition\n# Example: Orders where Quantity > 2\ndf_sales[‘Quantity’] > 2\n```\n\nHere, I’m using the greater than > operator. Similar to our output above, we’re gonna get a Boolean result with True and False values. Let’s fix it up real quick.\n![](../images/body-8de36a23bc91c5c0ebb9.webp)\nAnd there we go!\n## Filter by date condition\nFiltering by date is straightforward. For instance.\npython\n\n```\n# Filter by date condition\n# Example: Orders placed after “2023–01–08”\ndf_sales[df_sales[“OrderDate”] > “2023–01–08”]\n```\n\nThis checks for orders placed after January 8, 2023. And here’s the output.\n![](../images/body-546cb3c7c796080f216a.webp)\nThe cool thing about Pandas is that it converts string data types to dates automatically. In cases where you encounter an error. You might want to convert to a date before filtering using the `to_datetime()` function. Here’s an example\npython\n\n```\ndf[“OrderDate”] = pd.to_datetime(df[“OrderDate”])\n```\n\nThis converts our OrderDate column to a date data type. Let’s kick things up a notch.\n## Filtering by Multiple Conditions (AND, OR, NOT)\nPandas enables us to filter on multiple conditions using logical operators. However, these operators are different from Python's built-in operators like (`and`,` or`, `not`). Here are the logical operators you’ll be working with the most\n###  `&` (Logical AND)\nThe ampersand (`&)` symbol represents AND in pandas. We use this when we’re trying to fulfil two conditions. In this case, both conditions have to be true. For instance, let’s retrieve orders from the “Furniture” category where Price > 500.\npython\n\n```\n# Multiple conditions (AND)\n# Example: Orders from “Furniture” where Price > 500\ndf_sales[(df_sales[“Category”] == “Furniture”) & (df_sales[“Price”] > 500)]\n```\n\nLet’s break this down. Here, we have two conditions. One that retrieves orders in the Furniture category and another that filters for prices > 500. Using the &, we’re able to combine both conditions. \nHere’s the result.\n![](../images/body-8ffdef9749c5141c9eb8.webp)\nOne record was managed to be retrieved. Looking at it, it meets our condition. Let’s do the same for OR\n###  `|` (Logical OR)\nThe `|,`vertical bar symbol is used to represent OR in pandas. In this case, at least one of the corresponding elements should be True. For instance, let’s retrieve records with orders from the “North” region OR “East” region.\npython\n\n```\n# Multiple conditions (OR)\n# Example: Orders from “North” region OR “East” region.\ndf_sales[(df_sales[“Region”] == “North”) | (df_sales[“Region”] == “East”)]\n```\n\nHere’s the output\n![](../images/body-162c510f0d2148047951.webp)\n## Filter with isin()\nLet’s say I want to retrieve orders from multiple customers. I could always use the & operator. For instance\npython\n\n```\ndf_sales[(df_sales[‘Customer’] == ‘Alice’) | (df_sales[‘Customer’] == ‘Charlie’)]\n```\n\nOutput:\n![](../images/body-1f5b9e8aa6a137557089.webp)\nNothing wrong with that. But there’s a better and easier way to do this. That’s by using the `isin()` function. Here’s how it works\npython\n\n```\n# Orders from customers [\"Alice\", \"Diana\", \"James\"].\ndf_sales[df_sales[“Customer”].isin([“Alice”, “Diana”, “James”])]\n```\n\nOutput:\n![](../images/body-ec9e1ca5168c4d4eed8c.webp)\nThe code is much easier and cleaner. Using the `isin()` function, I can add as many parameters as I want. Let’s move on to some more **advanced filtering.**\n## Filter using string matching\nOne of Pandas' powerful but underused functions is string matching. It helps a ton in data cleaning tasks when you’re trying to search through patterns in the records in your DataFrame. Similar to the LIKE operator in SQL. For instance, let’s retrieve customers whose name starts with “A”.\npython\n\n```\n# Customers whose name starts with \"A\".\ndf_sales[df_sales[“Customer”].str.startswith(“A”)]\n```\n\nOutput:\n![](../images/body-835436f65b98a6ca79ec.webp)\nPandas gives you the `.str` accessor to use string functions. Here’s another example\npython\n\n```\n# Products ending with “top” (e.g., Laptop).\ndf_sales[df_sales[“Product”].str.endswith(“top”)]\n```\n\nOutput:\n![](../images/body-11ef91a5361fc7e42aa1.webp)\n## Filter using query() method\nIf you’re coming from a SQL background, this method would be so helpful for you. Let’s try to retrieve orders from the electronics category where the quantity > 2. It can always go like this.\npython\n\n```\ndf_sales[(df_sales[“Category”] == “Electronics”) & (df_sales[“Quantity”] >= 2)]\n```\n\nOutput:  \n  \n  \nBut if you’re someone trying to bring in your SQL sauce. This will work for you instead\npython\n\n```\ndf.query(“Category == ‘Electronics’ and Quantity >= 2”)\n```\n\nYou’ll get the same output above. Pretty similar to SQL if you ask me, and you’ll be able to ditch the `& `symbol. I’m gonna be using this method quite often.\n## Filter by column values in a range\nPandas allows you to retrieve a range of values. For instance, Orders where the Price is between 50 and 500 would go like this\npython\n\n```\n# Orders where the Price is between 50 and 500\ndf_sales[df_sales[“Price”].between(50, 500)]\n```\n\nOutput:\n![](../images/body-ec0f9a1121cf8344e096.webp)\nPretty straightforward.\n## Filter missing values (NaN)\nThis is probably the most helpful function because, as a data analyst, one of the data cleaning tasks you’ll be working on the most is filtering out missing values. To do this in Pandas is straightforward. That’s by using the `notna()` function. Let’s filter rows where Price is not null.\npython\n\n```\n# filter rows where Price is not null.\ndf_sales[df_sales[“Price”].notna()]\n```\n\nOutput:\n![](../images/body-f0664519a5bca8272cc6.webp)\nAnd there you go. I don’t really notice the difference, though, but I’m gonna trust it’s done.\n## Conclusion\nThe next time you open a messy CSV and wonder  _“Where do I even start?”_ , try filtering first. It’s the quickest way to cut through the noise and find the story hidden in your data.\nThe transition to Python for data analysis used to feel like a huge step, coming from a SQL background. But for some reason, Pandas seems way easier and less time-consuming for me for filtering data  \nThe cool part about this is that these same techniques work no matter the dataset — sales numbers, survey responses, web analytics, you name it.\nI hope you found this article helpful.\n_I write these articles as a way to test and strengthen my own understanding of technical concepts — and to share what I’m learning with others who might be on the same path._  _Feel free to share with others. Let’s learn and grow together. Cheers!_\nFeel free to say hi on any of these platforms\n[Medium](https://medium.com/@ibbysalam)\n[LinkedIn](https://www.linkedin.com/in/ibrahim-salami-059863228/)\n[Twitter](https://x.com/IbbySalam)\n[YouTube](https://www.youtube.com/@ibbysalam)\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[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/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# Stop Writing Messy Boolean Masks: 10 Elegant Ways to Filter Pandas DataFrames\nMaster the art of readable, high-performance data selection using .query(), .isin(), and advanced vectorized logic.\n[Ibrahim Salami](https://towardsdatascience.com/author/ibrahim.salami/)\nJanuary 22, 20267 min read\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/01/filter-with-pandas.jpg)\nImage by Author. Created with Gemini\nIn a recent article, I discussed [how to create your first DataFrame](https://towardsdatascience.com/the-absolute-beginners-guide-to-pandas-dataframes/) using Pandas. I mentioned that the first thing you need to master is Data structures and arrays before moving on to data analysis with Python.\nPandas is an excellent library for data manipulation and retrieval. Combine it with Numpy and Seaborne, and you’ve got yourself a powerhouse for data analysis.\nIn this article, I’ll be walking you through **practical ways to filter data in pandas** , starting with simple conditions and moving on to powerful methods like `.isin()`, `.str.startswith()`, and `.query()`. By the end, you’ll have a toolkit of filtering techniques you can apply to any dataset.\nWithout further ado, let’s get into it!\n## Importing our data\nOk, to start, I’ll import our pandas library\npython\n\n```\n# importing the pandas library\nimport pandas as pd\n```\n\nThat’s the only library I’ll need for this use case\nNext, I’ll import the dataset. The dataset comes from ChatGPT, btw. It consists of basic sales transaction records. Let’s take a look at our dataset.\npython\n\n```\n# checking out our data\ndf_sales = pd.read_csv('sales_data.csv')\ndf_sales\n```\n\nHere’s a preview of the data\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/01/1-1.webp)\nIt consists of basic sales records with columns OrderId, Customer, Product, Category, Quantity, Price, OrderDate and Region.\nAlright, let’s begin our filtering!\n## Filtering by a single condition\nLet’s try to select all records from a particular category. For instance, I want to know how many unique orders were made in the Electronics category. To do that, it’s pretty straightforward\npython\n\n```\n# Filter by a single condition\n# Example: All orders from the “Electronics” category.\ndf_sales[‘Category’] == ‘Electronics’\n```\n\nIn Python, you need to distinguish between the `=` operator and the `==` operator.\n`=` is used to assign a value to a variable.\nFor instance\npython\n\n```\nx = 10 # Assigns the value 10 to the variable x\n```\n\n`==` on the other hand is used to compare two values together. For instance\npython\n\n```\na = 3\nb = 3\nprint(a == b) # Output: True\n\nc = 5\nd = 10\nprint(c == d) # Output: False\n```\n\nWith that said, let’s apply the same notion to the filtering I did above\npython\n\n```\n# Filter by a single condition\n# Example: All orders from the “Electronics” category.\ndf_sales[‘Category’] == ‘Electronics’\n```\n\nHere, I’m basically telling Python to search through our entire record to find a category named Electronics. When it finds a match, it displays a Boolean result, True or False. Here’s the result\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/01/2-1.webp)\nAs you can see. We are getting a Boolean output. True means Electronics exists, while False means the latter. This is okay and all, but it can become confusing if you’re dealing with a large number of records. Let’s fix that.\npython\n\n```\n# Filter by a single condition\n# Example: All orders from the “Electronics” category.\ndf_sales[df_sales[‘Category’] == ‘Electronics’]\n```\n\nHere, I just wrapped the condition in the DataFrame. And with that, we get this output\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/01/3-1.webp)\nMuch better, right? Let’s move on\n## Filter rows by numeric condition\nLet’s try to retrieve records where the order quantity is greater than 2. It’s pretty straightforward.\npython\n\n```\n# Filter rows by numeric condition\n# Example: Orders where Quantity > 2\ndf_sales[‘Quantity’] > 2\n```\n\nHere, I’m using the greater than > operator. Similar to our output above, we’re gonna get a Boolean result with True and False values. Let’s fix it up real quick.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/01/4-1.webp)\nAnd there we go!\n## Filter by date condition\nFiltering by date is straightforward. For instance.\npython\n\n```\n# Filter by date condition\n# Example: Orders placed after “2023–01–08”\ndf_sales[df_sales[“OrderDate”] > “2023–01–08”]\n```\n\nThis checks for orders placed after January 8, 2023. And here’s the output.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/01/5-1.webp)\nThe cool thing about Pandas is that it converts string data types to dates automatically. In cases where you encounter an error. You might want to convert to a date before filtering using the `to_datetime()` function. Here’s an example\npython\n\n```\ndf[“OrderDate”] = pd.to_datetime(df[“OrderDate”])\n```\n\nThis converts our OrderDate column to a date data type. Let’s kick things up a notch.\n## Filtering by Multiple Conditions (AND, OR, NOT)\nPandas enables us to filter on multiple conditions using logical operators. However, these operators are different from Python's built-in operators like (`and`,` or`, `not`). Here are the logical operators you’ll be working with the most\n###  `&` (Logical AND)\nThe ampersand (`&)` symbol represents AND in pandas. We use this when we’re trying to fulfil two conditions. In this case, both conditions have to be true. For instance, let’s retrieve orders from the “Furniture” category where Price > 500.\npython\n\n```\n# Multiple conditions (AND)\n# Example: Orders from “Furniture” where Price > 500\ndf_sales[(df_sales[“Category”] == “Furniture”) & (df_sales[“Price”] > 500)]\n```\n\nLet’s break this down. Here, we have two conditions. One that retrieves orders in the Furniture category and another that filters for prices > 500. Using the &, we’re able to combine both conditions. \nHere’s the result.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/01/6-2.webp)\nOne record was managed to be retrieved. Looking at it, it meets our condition. Let’s do the same for OR\n###  `|` (Logical OR)\nThe `|,`vertical bar symbol is used to represent OR in pandas. In this case, at least one of the corresponding elements should be True. For instance, let’s retrieve records with orders from the “North” region OR “East” region.\npython\n\n```\n# Multiple conditions (OR)\n# Example: Orders from “North” region OR “East” region.\ndf_sales[(df_sales[“Region”] == “North”) | (df_sales[“Region”] == “East”)]\n```\n\nHere’s the output\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/01/7-2.webp)\n## Filter with isin()\nLet’s say I want to retrieve orders from multiple customers. I could always use the & operator. For instance\npython\n\n```\ndf_sales[(df_sales[‘Customer’] == ‘Alice’) | (df_sales[‘Customer’] == ‘Charlie’)]\n```\n\nOutput:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/01/8.webp)\nNothing wrong with that. But there’s a better and easier way to do this. That’s by using the `isin()` function. Here’s how it works\npython\n\n```\n# Orders from customers [\"Alice\", \"Diana\", \"James\"].\ndf_sales[df_sales[“Customer”].isin([“Alice”, “Diana”, “James”])]\n```\n\nOutput:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/01/9.webp)\nThe code is much easier and cleaner. Using the `isin()` function, I can add as many parameters as I want. Let’s move on to some more **advanced filtering.**\n## Filter using string matching\nOne of Pandas' powerful but underused functions is string matching. It helps a ton in data cleaning tasks when you’re trying to search through patterns in the records in your DataFrame. Similar to the LIKE operator in SQL. For instance, let’s retrieve customers whose name starts with “A”.\npython\n\n```\n# Customers whose name starts with \"A\".\ndf_sales[df_sales[“Customer”].str.startswith(“A”)]\n```\n\nOutput:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/01/10.webp)\nPandas gives you the `.str` accessor to use string functions. Here’s another example\npython\n\n```\n# Products ending with “top” (e.g., Laptop).\ndf_sales[df_sales[“Product”].str.endswith(“top”)]\n```\n\nOutput:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/01/11.webp)\n## Filter using query() method\nIf you’re coming from a SQL background, this method would be so helpful for you. Let’s try to retrieve orders from the electronics category where the quantity > 2. It can always go like this.\npython\n\n```\ndf_sales[(df_sales[“Category”] == “Electronics”) & (df_sales[“Quantity”] >= 2)]\n```\n\nOutput:  \n  \n  \nBut if you’re someone trying to bring in your SQL sauce. This will work for you instead\npython\n\n```\ndf.query(“Category == ‘Electronics’ and Quantity >= 2”)\n```\n\nYou’ll get the same output above. Pretty similar to SQL if you ask me, and you’ll be able to ditch the `& `symbol. I’m gonna be using this method quite often.\n## Filter by column values in a range\nPandas allows you to retrieve a range of values. For instance, Orders where the Price is between 50 and 500 would go like this\npython\n\n```\n# Orders where the Price is between 50 and 500\ndf_sales[df_sales[“Price”].between(50, 500)]\n```\n\nOutput:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/01/13-1.webp)\nPretty straightforward.\n## Filter missing values (NaN)\nThis is probably the most helpful function because, as a data analyst, one of the data cleaning tasks you’ll be working on the most is filtering out missing values. To do this in Pandas is straightforward. That’s by using the `notna()` function. Let’s filter rows where Price is not null.\npython\n\n```\n# filter rows where Price is not null.\ndf_sales[df_sales[“Price”].notna()]\n```\n\nOutput:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/01/14.webp)\nAnd there you go. I don’t really notice the difference, though, but I’m gonna trust it’s done.\n## Conclusion\nThe next time you open a messy CSV and wonder  _“Where do I even start?”_ , try filtering first. It’s the quickest way to cut through the noise and find the story hidden in your data.\nThe transition to Python for data analysis used to feel like a huge step, coming from a SQL background. But for some reason, Pandas seems way easier and less time-consuming for me for filtering data  \nThe cool part about this is that these same techniques work no matter the dataset — sales numbers, survey responses, web analytics, you name it.\nI hope you found this article helpful.\n_I write these articles as a way to test and strengthen my own understanding of technical concepts — and to share what I’m learning with others who might be on the same path._  _Feel free to share with others. Let’s learn and grow together. Cheers!_\nFeel free to say hi on any of these platforms\n[Medium](https://medium.com/@ibbysalam)\n[LinkedIn](https://www.linkedin.com/in/ibrahim-salami-059863228/)\n[Twitter](https://x.com/IbbySalam)\n[YouTube](https://www.youtube.com/@ibbysalam)\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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