Have you ever wished your SAS code would run faster? Then this is the class for you! Take a deep dive into identifying processing bottlenecks, then practice tuning SAS programs for faster execution. Only at WUSS 2024. Learn more at wuss.org. #wuss2024 #sas #r #python
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#DAY20 #python **FUNCTIONS Vs LAMBDA FUNCTIONS** SYNTAX: # Regular function def square(x): return x ** 2 # Lambda function square_lambda = lambda x: x ** 2 print(square(5)) # Output: 25 print(square_lambda(5)) # Output: 25 -> Regular fn(function) has "def" keyword with a name of fn while the lambda is without name and so is called anonymous fn. -> Lambda fn has implicit return while in Regular fn we have to use "return" keyword in order to return something. -> Lambda fns are used for short operations/expressions while we can use multiple expressions/statements in Regular fn. #ContinuousLearning #AchievementUnlocked #SQLCertification #DataAnalysisSkills #DatabaseManagement #HappyCoding #PowerBIDeveloper #DataAnalystJourney #HackerRankCertified #SQL #DataAnalytics
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The question of "Should I use Python or R for data analysis?" has long been debated by analysts. In general, I would say you should use whichever tools your data team uses. But if you still aren’t sure, or just want to learn more about the use of both languages in analytics, check out this new series! https://lnkd.in/gQbx4FHD
Python vs. R: Reading CSV & Excel Files
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#python3🐍🐍🐍 Hi all, ----lambda functions lambda functions are very much useful to manipulate your data making your code precise #veryimportant question: write python code whether a number is even or odd code: even_or_odd = lambda x: 'even' if x%2==0 else 'odd' even_or_odd(2) even_or_odd(3) first define your variables, then desired result if condition exists, else only desired result It's like defining a function and then calling it to get your desired output Thanks #dataanalysts #datascientists #dataengineers #pythondevelopers
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Check out the new series! PYTHON VS R
The question of "Should I use Python or R for data analysis?" has long been debated by analysts. In general, I would say you should use whichever tools your data team uses. But if you still aren’t sure, or just want to learn more about the use of both languages in analytics, check out this new series! https://lnkd.in/gQbx4FHD
Python vs. R: Reading CSV & Excel Files
https://www.youtube.com/
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