You should be able to install the python 3 version of PyQt4 in ubuntu. Open a terminal and type: sudo apt-get install python3-pyqt4 This way you don’t have to compile Qt4 from source. I have also tested this and pyqt4 works with python3. Source: A quick search in the ubuntu repositories.
I had this problem running numpy on an ubuntu server. I got all of the following errors, depending on whether I tried to import numpy in a shell or running my django app: PyCapsule_Import could not import module “datetime” from numpy.core._multiarray_umath import ( OpenBLAS blas_thread_init: pthread_create failed for thread 25 of 32: Resource temporarily unavailable … Read more
I have tried several solutions, none worked except simplest solution: pip3 install psycopg2==2.7.5 this command worked perfectly (seems like a problem with a version)
The python 3 library includes 3 modules for parsing the command line thus nothing extra to add to your setup. The one you should use is argparse import argparse parser = argparse.ArgumentParser() #-db DATABASE -u USERNAME -p PASSWORD -size 20 parser.add_argument(“-db”, “–hostname”, help=”Database name”) parser.add_argument(“-u”, “–username”, help=”User name”) parser.add_argument(“-p”, “–password”, help=”Password”) parser.add_argument(“-size”, “–size”, help=”Size”, type=int) … Read more
You should never need to manually set the quantization stats. Have you tried the post-training-quantization tutorials? https://www.tensorflow.org/lite/performance/post_training_integer_quant Basically they set the quantization options: converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] converter.inference_input_type = tf.uint8 converter.inference_output_type = tf.uint8 Then they pass a “representative dataset” to the converter, so that the converter can run the model a few batches to gather the … Read more
You could use a function like this: def nan_ints(df, convert_strings=False, subset=None): types = [“int64”, “float64”] if subset is None: subset = list(df) if convert_strings: types.append(“object”) for col in subset: if df[col].dtype in types: df[col] = ( df[col].astype(float, errors=”ignore”).astype(“Int64″, errors=”ignore”) ) return df It iterates through each column and coverts it to an Int64 if it … Read more
This is happening because Google Colab and Jupyter run on virtual environments which do not support GUI outputs as you cannot open new windows through a browser. Running it locally on a code editor(Spyder, or even IDLE) ensures that it can open a new window for the GUI to initialize. For coding the GUI it … Read more
I’m finding local library names must begin with a capital letter. If I keep all my local files in a folder called Code, I can import them; if it’s called code, I cannot. (The names of subfolders and subfiles don’t seem to suffer that restriction.) This appears to be a Jupyter restriction, not a Python … Read more
The simplest solution is to consider your argument as a string and split. #!/usr/bin/env python3 import argparse parser = argparse.ArgumentParser() parser.add_argument(“–myarg”, type=str) args = parser.parse_args() if args.myarg is not None: args.myarg = [s.strip() for s in args.myarg.split(“,”)] print(args) Result: $ ./toto.py –myarg=abcd,e,fg Namespace(myarg=[‘abcd’, ‘e’, ‘fg’]) $ ./toto.py –myarg=”abcd, e, fg” Namespace(myarg=[‘abcd’, ‘e’, ‘fg’])
You can use apply to index into leader and exchange values with DatasetLabel, although it’s not very pretty. One issue is that Pandas won’t let us index with NaN. Converting to str provides a workaround. But that creates a second issue, namely, column 9 is of type float (because NaN is float), so 5 becomes … Read more