Using /= and *= allows you to eliminate an intermediate temporary array, thus saving some memory. Multiplication is less expensive than division, so is marginally faster than Since we are using basic numpy methods here, I think this is about as efficient a solution in numpy as can be. In-place operations do not change the … Read more
You can use: Or with ax=0 the average is performed along the row, for each column, returning an array with ax=1 the average is performed along the column, for each row, returning an array with ax=None the average is performed element-wise along the array, returning a scalar value
One solution: The first part array < 25 gives you an array of the same shape that is 1 (True) where values are less than 25 and 0 (False) otherwise. Element-wise multiplication with the original array retains the values that are smaller than 25 and sets the rest to 0. This does not change the … Read more
@bogatron has it right, you can use where, it’s worth noting that you can do this natively in pandas: Note: this changes the dtype of all columns to object. Example: Note: what you cannot do recast the DataFrames dtype to allow all datatypes types, using astype, and then the DataFrame fillna method: Unfortunately neither this, nor using replace, works with None see this (closed) issue. As an aside, … Read more
Is there any function in numpy to group this array down below by the first column? I couldn’t find any good answer over the internet.. Wanted output:
I really can’t see any good reason not to just suppress the warning. The safest way would be to use the warnings.catch_warnings context manager to suppress the warning only where you anticipate it occurring – that way you won’t miss any additional RuntimeWarnings that might be unexpectedly raised in some other part of your code: … Read more
pd.concat requires that the indices be unique. To remove rows with duplicate indices, use Note there is also pd.join, which can join DataFrames based on their indices, and handle non-unique indices based on the how parameter. Rows with duplicate index are not removed.
For large arrays, a vectorised numpy operation is the fastest. If you must loop, prefer xrange/range and avoid using np.arange. In numpy you should use combinations of vectorized calculations, ufuncs and indexing to solve your problems as it runs at C speed. Looping over numpy arrays is inefficient compared to this. (Something like the worst … Read more
If you look at the line which is causing the error, you’ll see this: This line comment states the dependency as numpy+mkl (numpy with Intel Math Kernel Library). This means that you’ve installed the numpy by pip, but the scipy was installed by precompiled archive, which expects numpy+mkl. This problem can be easy solved by installation for numpy+mkl from whl file from here.
You have four options Finite Differences Automatic Derivatives Symbolic Differentiation Compute derivatives by hand. Finite differences require no external tools but are prone to numerical error and, if you’re in a multivariate situation, can take a while. Symbolic differentiation is ideal if your problem is simple enough. Symbolic methods are getting quite robust these days. SymPy is … Read more