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SQL Query: 3 Methods for Calculating Cumulative SUM

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SQL provides various constructs for calculating cumulative sums, offering flexibility and efficiency in data analysis. In this article, we explore three distinct SQL queries that facilitate the computation of cumulative sums. Each query leverages different SQL constructs to achieve the desired outcome, catering to diverse analytical needs and preferences. Using Window Functions (e.g., PostgreSQL, SQL Server, Oracle) SELECT id, value, SUM(value) OVER (ORDER BY id) AS cumulative_sum  FROM your_table; This query uses the SUM() window function with the OVER clause to calculate the cumulative sum of the value column ordered by the id column. Using Subqueries (e.g., MySQL, SQLite): SELECT t1.id, t1.value, SUM(t2.value) AS cumulative_sum FROM your_table t1 JOIN your_table t2 ON t1.id >= t2.id GROUP BY t1.id, t1.value ORDER BY t1.id; This query uses a self-join to calculate the cumulative sum. It joins the table with itself, matching rows where the id in the first table is greater than or

Best Practices for Handling Duplicate Elements in Python Lists

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Here are three awesome ways that you can use to remove duplicates in a list. These are helpful in resolving your data analytics solutions.  01. Using a Set Convert the list into a set , which automatically removes duplicates due to its unique element nature, and then convert the set back to a list. Solution: original_list = [2, 4, 6, 2, 8, 6, 10] unique_list = list(set(original_list)) 02. Using a Loop Iterate through the original list and append elements to a new list only if they haven't been added before. Solution: original_list = [2, 4, 6, 2, 8, 6, 10] unique_list = [] for item in original_list:     if item not in unique_list:         unique_list.append(item) 03. Using List Comprehension Create a new list using a list comprehension that includes only the elements not already present in the new list. Solution: original_list = [2, 4, 6, 2, 8, 6, 10] unique_list = [] [unique_list.append(item) for item in original_list if item not in unique_list] All three methods will result in uni

Numpy Array Vs. List: What's the Difference

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Here are the differences between List and NumPy Array. Both store data, but technically these are not the same. You'll find here where they differ from each other. Python Lists Here is all about Python lists: Lists can have data of different data types. For instance, data = [3, 3.2, 4.6, 6, 6.8, 9, “hello”, ‘a’] Operations such as subtraction, multiplying, and division allow doing through loops Storage space required is more, as each element is considered an object in Python Execution time is high for large datasets Lists are inbuilt data types How to create array types in Python NumPy Arrays Here is all about NumPy Arrays: Numpy arrays are containers for storing only homogeneous data types. For example: data= [3.2, 4.6, 6.8]; data=[3, 6, 9]; data=[‘hello’, ‘a’] Numpy is designed to do all mathematical operations in parallel and is also simpler than Python Numpy storage space is very much less compared to the list due to the practice of homogeneous data type Execution time is

Sets Vs Lists Python Programmer Tips

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Sets are only useful when trying to ensure unique items are preserved. Before sets were available, it was common to process items and check if they exist in a list (or dictionary) before adding them. List example Here unique is an empty list. Every time I compare with this list, and if it is not duplicated then the input item will append to the unique list.  >>> unique = []  >>> for name in ['srini', 'srini', 'rao', 'srini']:  ... if name not in unique:  ... unique.append(name)  ... >>> unique ['srini', 'rao'] There is no need to do this when using sets. Instead of appending you add to a set: Set example >>> for name in ['srini', 'srini', 'rao', 'srini']: ... unique.add(name)  ...  >>> unique {'srini', 'rao'} Just like tuples and lists, interacting with sets have some differences on how to access their items. You can't index them like lists an