Set maximum value (upper bound) in pandas DataFrame
You can use clip. Apply to all columns of the data frame: df.clip(upper=15) Otherwise apply to selected columns as seen here: df.clip(upper=pd.Series({‘a’: 15}), axis=1)
max
max date record in LINQ
Starting from .NET 6 MaxBy LINQ method is available. var result = items.MaxBy(i => i.Date); Prior to .NET 6: O(n): var result = items.Aggregate((x, y) => x.Date > y.Date ? x : y); O(n log n): var result = items.OrderByDescending(i => i.Date).First(); O(n) – but iterates over the sequence twice: var max = items.Max(i => … Read more
Element-wise array maximum function in NumPy (more than two arrays)
With this setup: >>> A = np.array([0,1,2]) >>> B = np.array([1,0,3]) >>> C = np.array([3,0,4]) You can either do: >>> np.maximum.reduce([A,B,C]) array([3, 1, 4]) Or: >>> np.vstack([A,B,C]).max(axis=0) array([3, 1, 4]) I would go with the first option.
How to calculate the maximum of two numbers in Oracle SQL select?
It looks like you’re using Oracle so you can use the greatest function for this in place of max select total/greatest(1,number_of_items) from xxx;
get min and max from a specific column scala spark dataframe
How about getting the column name from the metadata: val selectedColumnName = df.columns(q) //pull the (q + 1)th column from the columns array df.agg(min(selectedColumnName), max(selectedColumnName))
Why does Math.min() return positive infinity, while Math.max() returns negative infinity?
Of course it would, because the start number should be Infinity for Math.min. All number that are lower than positive infinity should be the smallest from a list, if there are no smaller. And for Math.max it’s the same; all numbers that are larger than negative infinity should be the biggest if there are no … Read more
Python: take max N elements from some list
heapq.nlargest: >>> import heapq, random >>> heapq.nlargest(3, (random.gauss(0, 1) for _ in xrange(100))) [1.9730767232998481, 1.9326532289091407, 1.7762926716966254]
Pandas max value index
Use argmax() idxmax() to get the index of the max value. Then you can use loc df.loc[df[‘favcount’].idxmax(), ‘sn’] Edit: argmax() is now deprecated, switching for idxmax()
why is c++ std::max_element so slow?
Before voting on this answer, please test (and verify) this on your machine and comment/add the results. Note that I used a vector size of 1000*1000*1000 for my tests. Currently, this answer has 19 upvotes but only one posted results, and these results did not show the effect described below (though obtained with a different … Read more