I am learning Python, and have encountered numpy.sum. It has an optional parameter axis. This parameter is used to get either column-wise summation or row-wise summation. When axis = 0 we imply to sum it over columns only. For example, This snippet of code produces output: array([5, 7, 9]), fine. But if I do: I get result: 6, why is
Tag: numpy
Pandas: convert dtype ‘object’ to int
I’ve read an SQL query into Pandas and the values are coming in as dtype ‘object’, although they are strings, dates and integers. I am able to convert the date ‘object’ to a Pandas datetime dtype, but I’m getting an error when trying to convert the string and integers. Here is an example: Converting the df[‘date’] to a datetime works:
how to calculate accuracy based on two lists python?
I have two lists. How can I calculate accuracy based on these lists? Answer This will give you the percentage that were correct – that is, the number correct over the total number. It works by calculating the number that are equal between the two lists then dividing by the total number of labels. Also note that if you’re not
Converting dictionary with known indices to a multidimensional array
I have a dictionary with entries labelled as {(k,i): value, …}. I now want to convert this dictionary into a 2d array where the value given for an element of the array at position [k,i] is the value from the dictionary with label (k,i). The length of the rows will not necessarily be of the same size (e.g. row k
What is the inverse of the numpy cumsum function?
If I have z = cumsum( [ 0, 1, 2, 6, 9 ] ), which gives me z = [ 0, 1, 3, 9, 18 ], how can I get back to the original array [ 0, 1, 2, 6, 9 ] ? Answer Short and sweet, with no slow Python loops. We take views of all but the first
How can I get descriptive statistics of a NumPy array?
I use the following code to create a numpy-ndarray. The file has 9 columns. I explicitly type each column: Now I would like to get some descriptive statistics for each column (min, max, stdev, mean, median, etc.). Shouldn’t there be an easy way to do this? I tried this: but this returns an error: TypeError: cannot perform reduce with flexible
How to create a numpy array from a pydub AudioSegment?
I’m aware of the following question: How to create a pydub AudioSegment using an numpy array? My question is the right opposite. If I have a pydub AudioSegment how can I convert it to a numpy array? I would like to use scipy filters and so on. It is not very clear to me what is the internal structure of
In numpy, what does selection by [:,None] do?
I’m taking the Udacity course on deep learning and I came across the following code: What does labels[:,None] actually do here? Answer http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html numpy.newaxis The newaxis object can be used in all slicing operations to create an axis of length one. :const: newaxis is an alias for ‘None’, and ‘None’ can be used in place of this with the same
How can I read in a binary file from hdfs into a Spark dataframe?
I am trying to port some code from pandas to (py)Spark. Unfortunately I am already failing with the input part, where I want to read in binary data and put it in a Spark Dataframe. So far I am using fromfile from numpy: But for Spark I couldn’t find how to do it. My workaround so far was to use
Convert a 2d matrix to a 3d one hot matrix numpy
I have np matrix and I want to convert it to a 3d array with one hot encoding of the elements as third dimension. Is there a way to do with without looping over each row eg should be made into Answer Approach #1 Here’s a cheeky one-liner that abuses broadcasted comparison – Sample run – For 0-based indexing, it