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I have some large matrices and vectors calculated in R. I want to transfer this data to Python (2.7) in order to do some further data analysis.
What is a recommended way to do this?
I am very familiar with R, but a beginner in Python.
Use write.csv(matrix, "~/filename.csv) in R and then in Python either (if you want to use pandas)
import pandas as pd
new_matrix = pd.read_csv("~/filename.csv")
or (if you want to use numpy)
import numpy as np
new_matrix = np.genfromtxt("~/filename.csv", delimiter = ",")
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I am solving an issue in Python and Pygame (of which I have little knowledge). I am trying to find the way for finding the column x in a 2d rectangular board. I know that to find row y, I would go by:
ind = y * self._num_cols
return self._grid[ind : ind + self._num_cols]
How do I find column x then, without using numPy?
Assuming that it is 1 dimensional, you can do:
return [self._grid[x + i*self._num_cols] for i in range(self._num_cols)]
I know it seems long, but it is just iteratively getting the one in that column for each row.
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I am working on a machine learning task and trying to convert all strings in a set of data to floats using hash() to do this I need to iterate over all the elements of a numpy array whilst not knowing if it is a 2D 3D or 4D array and then change each element. Is there any way to do this without using nested loops?
You can try numpu.vectorize, already mentioned here
Note:
The vectorize function is provided primarily for convenience, not for performance. The implementation is essentially a for loop.here
arr = np.array([['aba', 'baa', 'bbb'],
['xxy', 'xyy', 'yyy']])
v_hash = np.vectorize(hash)
v_hash(arr)
array([[-1538054455328520296, -1528482088733019667, -7962229468338304433],
[ 5621962119614158870, 1918700875003591346, -3216770211373729154]])
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My data was modelled with a Cox-regression, using R, however I would like to use this model into a python GUI. As my knowledge of R is very limited. This way non-coders would be able to 'predict' survival rates based on our model.
What is the best way that I could use this model (combination of 3 different regressions) in python?
Do you want to predict values based on your estimates?
In this case you can just copy the R outputs into python and apply to
respective procedures.
Do you want the user to be able to run "your R regression pipeline" from within Python?
There are python libraries that help with that. I find this
source a useful start.
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I want to create a heating degree days (HDD) column in a Pandas dataframe, using another column (df.temp) for the temperature.
Here's the formula I'd like to replicate in a Pandas dataframe:
df['hdd'] = max(0, (15 - df.temp))
Try this:
import numpy as np
df['hdd'] = np.maximum(0, (15 - df['temp']))
numpy vectorises calculations, so it applies across the series.
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How can I generate random data in google maps like format i.e. 29.299332, 52.892959?
Do you prefer any package for this purpose?
If you just want a pair of random numbers between 0 and 90 degrees, why not just use the random package?
import random
print([random.random()*90, random.random()*90]) #[34.050498339418986, 5.622759330528135]