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Excellent video! What I don't understand is how does the map and reduce function in python have anything to do with Hadoop's map and reduce? how are they related?
Correction in a binary search tree (non-balanced) you say O(h) = O(log n) but worst case for non-balanced is a tree that is only going in one direction: 1,2,3,4,5,6. The worst case run time here is O(h) = O(n). Just for anyone who might end up confused. In a RB tree we leverage that we constantly rebalance and therefore achieve O(log n)
def fu(x): if x>0: result = x+fu(x-1) print(result) else: result = 0 return result fu(5) output: 1 3 6 10 15 could anybody please help me out with how this is working .. iv not been able to figure it out yet. thanks
This was infinitely helpful. I love how you gave so many examples. Thus, allowing me to understand and play around with these examples. Best stream tutorial and will come back to it later as refreshers.
thanks for the video, this is really good way to handle nested lists as compared to few other options available. However realized this cannot be used as a static methos or static class method.
hey stop doing the "inlike C and Java" thing. I don't care. I'm learning Python I don't need a comparison every step of the way. It's distracting and it's information overload
Thank you for very detailed explanation. I have got a task to analyse MODIS Fire dataset and as a one without previous backgroud in data analysis and python, your video helps me understand what should be my first steps and how to plot the results. Thank you!
def radix_sort(iterable): all_values = [] for i in iterable: for j in range(0, len(str(i))): digit = int(str(i)[j]) try: all_values[j][digit].append(i) except IndexError: all_values.append([[] if c != digit else [i] for c in range(10)]) return [numbers for obj in all_values for lists in obj for numbers in sorted(filter(None, lists))][:len(iterable)]
def merge_sort(iterable: list): cont = 0 if len(iterable) % 2 == 0: new_iterable = [[i] if not isinstance(i, list) else i for i in iterable] else: new_iterable = [[i] if not isinstance(i, list) else i for i in iterable] new_iterable[-2].append(new_iterable[-1][0]) del new_iterable[-1] for i in range(0, len(new_iterable), 2): for j in range(len(new_iterable[cont + 1])): new_iterable[cont].sort() new_iterable[cont + 1].sort() new_iterable[cont].append(new_iterable[cont + 1][j]) cont += 1 del new_iterable[cont] if len(new_iterable) == 1: return new_iterable[0] return merge_sort(new_iterable)
If you want to see the process: def merge_sort(iterable: list): cont = 0 if len(iterable) % 2 == 0: new_iterable = [[i] if not isinstance(i, list) else i for i in iterable] else: new_iterable = [[i] if not isinstance(i, list) else i for i in iterable] new_iterable[-2].append(new_iterable[-1][0]) del new_iterable[-1] print(new_iterable) for i in range(0, len(new_iterable), 2): for j in range(len(new_iterable[cont + 1])): new_iterable[cont].sort() new_iterable[cont + 1].sort() new_iterable[cont].append(new_iterable[cont + 1][j]) cont += 1 del new_iterable[cont] print(new_iterable) if len(new_iterable) == 1: return new_iterable[0] return merge_sort(new_iterable)
I appreciate your video! As someone diving into data analysis with pandas, your clear explanation and step-by-step breakdown are incredibly helpful for beginners like me. Your content has provided the perfect roadmap for understanding how to think about data and its approach. Consider me a new subscriber-I'm eagerly anticipating more of your insightful videos!
