I am working on a small library and I need to know can I import modules like numpy, sklearn and etc. Using functions. For example:
def ml():
import numpy as np
import pandas as pd
x = np.array([1,2,647,345,3,7,3,8,36,64])
Is this possible ?
Simply can I import a module using a function and then use that later outside the function
The main idea is when the user calls the function ml he has all the modules related to machine learning imported and then he can use them. X = np.array was just kind of an example.
UPDATED
This should work
import importlib
def importmd(modulex):
return importlib.import_module(modulex) #Returning the module
np = importmd("numpy") #Same as import numpy as np
Related
In the following code I'm getting errors when trying to call librosa.grifflim, telling me the attribute does not exist.
import os
from matplotlib import pyplot as plt
import librosa
import librosa.display
import IPython.display as ipd
import numpy as np
import cv2
S = cv2.imread('spectrograms/CantinaBand60.wav10.jpg')
D = librosa.amplitude_to_db(np.abs(S), ref=np.max)
signal = librosa.griffinlim(D)
sf.write('test.wav', signal, 352000)
I've upgraded librosa, and I still encounter the error. The documentation page for this function no longer seems to exist either. I've also tried import just that module using librosa.griffinlim but it continues to tell me this module doesn't exist. Was this function removed during a recent version? If so, is there another function I can use to apply the griffin lim algorithm?
librosa.griffinlim was introduced in librosa 0.7.0. So you need to have that version or later. You can check this using the following code.
import librosa; print(librosa.__version__)
The following code works-
import sklearn.linear_model
clf= sklearn.linear_model.LogisticRegressionCV()
The following code does not work-
import sklearn
clf= sklearn.linear_model.LogisticRegressionCV()
whereas in case of Numpy, the following also works
import numpy as np
np.random.randint()
Why is that? Please elaborate.
I'm writing code in a Jupyter Notebook that involves cleaning and analyzing a large amount of consumer data. I'm trying to use dill to save the dataframes with thousands of rows so I don't have to run the code every time I want to make an adjustment, so dill seems like the perfect package to do so... Except I'm getting this error when attempting to pickle the notebook:
AttributeError: module 'dill' has no attribute 'dump_session'
Let me know if the program code is necessary - I don't think it should make a difference. The imports are:
import numpy as np
import pandas as pd
import dill
import scipy
from matplotlib import pyplot as plt
from __future__ import division
from collections import OrderedDict
from sklearn.cluster import KMeans
pd.options.display.max_columns = None
and when I run this code I get the error from above:
dill.dump_session('recengine.db')
Is there another package that's interfering with dill's use of pickle vs. cpickle?
This question already has answers here:
Python import functions from module twice with different internal imports
(3 answers)
Closed 4 years ago.
I am using numpy in one of my libraries. No surprise there.
One user would essentially like a copy of my project where I don't use the default numpy, but the one bundled with autograd. For instance, let's say I have a dumb function:
import numpy
def doSomething(x):
return numpy.sin(x)
They would like a copy of the library where all of these import numpy are replaced by from autograd import numpy:
from autograd import numpy
def doSomething(x):
return numpy.sin(x)
This would allow them to easily compute gradients and jacobians of my functions.
I would like to know what the easiest way to handle this is without copying the whole codebase and replacing all of these lines.
Options I am aware of:
I could make a copy of the codebase (lib and lib_autograd) where the first uses import numpy, and the second uses from autograd import numpy. This is bad because then I have to maintain two codebases.
I could automatically import from autograd if it is available:
try:
from autograd import numpy
except ImportError:
import numpy
The reason I do not want to do this is that many people have highly optimized numpy installs, whereas autograd might not. So I want to give the user an option which version to import. Forcing the user to use the autograd version if they have it seems bad since it would not be apparent to the user what is going on, and would require the user to uninstall autograd if they want to use the library with their default numpy installation.
So what are my options?
Ideally there would be a way of doing something like passing a parameter to the import statement (I do realize that you can't do this):
useAutograd = False
from lib(useAutograd) import doSomething
You can have 'conditional' import with:
try:
from autograd import numpy
except ImportError:
import numpy
One of other options is to have environment variable that switches whether you want to use numpy from autograd or regular one, because here you either use autograd.numpy (if it exists) or numpy. You don't have an option to use numpy if there is autograd module/package.
To elaborate on giving user an option to switch, here is one possibility:
import os
if os.environ.get('AUTOGRADNUMPY'):
try:
from autograd import numpy
except ImportError:
import numpy
else:
import numpy
Having environment variable AUTOGRADNUMPY set to True (or anything else that is not empty string) when you want to load numpy from autograd package. If it is not set or doesn't exist, regular numpy is imported.
All of this stands if user has at least numpy installed.
This might help:
try:
from autograd import numpy as np
except ImportError:
import numpy as np
...
...
np.sum(..)
I am new to Python programming, so please bear with my nascent question.
If we want to use certain function inside numpy, say func, do we need to just import numpy once and then call the function as following:
import numpy
np.func
Or, do we further need to import specific sub modules of numpy before calling any function? Thanks.
Almost, if you want to use numpy as np you have to import it like this:
import numpy as np
Other than that you can use the functions like that.
As with every other fricking module in existence, you use the name you import it as.
import numpy
numpy.func
...
import numpy as np
np.func
If you want to use linalg you have to do:
numpy.linalg
For example, if you want to calculate determinant of x, you would do
import numpy
x = numpy.array([[1,2],[5,7]])
det_x = numpy.linalg.det(x)
#or
import numpy as np
x = np.array([[1,2],[5,7]])
det_x = np.linalg.det(x)