Please, I have a dataframe that is listed in ascending order. My goal is to average similar numbers (numbers that are within 10% of each other in ‘both directions’) and concate their ‘Bell’ name together. For example, the image shows the input and output dataframe. I tried coding it but I stuck on how to progress. Answer Assuming you really
Tag: numpy
How to remove numpy columns based on condition?
I have a numpy array which contains the correlation between a label column And also a numpy array containing Can I use a function to determine which columns to keep? Such as It will yield then the resulting numpy array will becomes May I know how to achieve this? Answer You can do boolean indexing along values with something like
How do I append a repeating list to a dataframe?
I have a list sub = [“A”,”B”,”C”,”D”,”E”,”F”] and a dataframe of the following format: I need to write a code for my dataframe to finally look like the following format: Answer You can create a cycle using itertools.cycle, and cut it to the appropriate length using itertools.islice. So, in your case, you can just cut it to the length of
Strictly positive values in normal distribution in Python
Is it possible to define a strictly positive range for normal distribution like I want to have a distribution in the range (0,10) with a certain mu, sigma? Using np.random.normal, sometimes I get negative values which I don’t want. Answer You should try ‘scipy.stats.truncnorm’ – quote The standard form of this distribution is a standard normal truncated to the range
Create NumPy array from list of tuples
I have data in the following format: And I want use this information to create a NumPy array that has the value 1.0 in position 2, value 2.5 in position 6, etc. All positions not listed in the above should be zeroes. Like this: Answer First reformat the data: And then create the array: Note that you need to convert
Initialize deque efficiently
I am using a deque to store data that is going to be processed. The processing only starts when the deque is full so in a first step I fill my buffer the following way: However, when I do this and I modify one of the elements of my_deque, all elements of my_deque are modified. One alternative I found to
Hiding axes values in Matplotlib
I want to hide the x,y axes values as highlighted in the figure. Is it possible to do it? I also attach the expected representation. The expected representation is Answer You need to empty x and y tick labels from ax variable:
Reshape 3-d array to 2-d
I want to change my array type as pd.DataFrame but its shape is: I’ve tried to reshape by the following code, but it didn’t work: How can I change its shape? Answer NumPy array dimensions can be reduced using various ways; some are: using np.squeeze: using np.reshape: or with using -1 in np.reshape: using indexing as Szczesny’s comment:
Get the index of the largest two values per row
For each row, I would like to get the index of the largest two values. Here’s the result I would like to obtain: Answer Use np.argsort along the second axis and take last two values reversed.
Difference of Numpy Dimension Expand code
I got confuse between two Numpy dimension expand code. First code is X[:, np.newaxis]. Second code is X[:, np.newaxis, :]. I ran this code on Jupyter, But it returns same shape and result. What is the difference? Answer Refer numpy documentation for newaxis. https://numpy.org/doc/stable/reference/constants.html#numpy.newaxis x[newaxis, :] is equivalent to x[newaxis] and x[None] Any dimension after np.newaxis is still present in