Unintentional loop about calc.pandas

[hori1537]

description

--
test.py
import pandas as pd
from mordred import descriptors, Calculator
from rdkit import Chem

i = 0
print('program start')
i += 1
print(i)

def fit_by_mordred():
print('start the def_xxxx')
df_mordred=pd.DataFrame({ 'smiles' : ['C', 'CC', 'CCC'],
'name' : ['a', 'b', 'C'],
'value' : [1,2,3,] })

df_mordred=pd.DataFrame({   'smiles' : ['C'],
                            'name'   : ['a'],
                            'value'  : [1] })

def apply_molfromsmiles(smiles_name):
    mols = Chem.MolFromSmiles(smiles_name)
    return mols

df_mordred['mols'] = df_mordred['smiles'].map(apply_molfromsmiles)
#print(df_mordred)


calc =Calculator(descriptors, ignore_3D = True)
print('before calc.pandas')
X = calc.pandas(df_mordred['mols']).astype('float').dropna(axis = 1)
print('after calc.pandas')

if name == "main":
fit_by_mordred()

--

--
results
program start
1
start the def_xxxx
before calc.pandas
program start
1
program start
1
program start
1
program start
1
0%| | 0/1 [00:00<?, ?it/s]program start
1
100%|██████████████████████████████████████████████| 1/1 [00:00<00:00, 4.10it/s]
after calc.pandas

--

environment

OS/distribution

Windows 10

conda or pip

pip

python version

Please execute the command and paste result.

python 3.7.4

library version

Please execute the command and paste result.

• pip

mordred 1.2.0
tensorflow 2.0.0

• conda
  absl-py 0.8.0
  astor 0.8.0
  backcall 0.1.0
  blas 1.0 mkl
  ca-certificates 2019.8.28 0
  cairo 1.14.12 hf171d8a_3
  category-encoders 2.1.0
  certifi 2019.9.11 py37_0
  chainer 6.4.0
  chainer-chemistry 0.6.0
  colorama 0.4.1
  cycler 0.10.0
  decorator 4.4.0
  filelock 3.0.12
  freetype 2.9.1 ha9979f8_1
  gast 0.2.2
  google-pasta 0.1.7
  grpcio 1.24.1
  h5py 2.10.0
  icc_rt 2019.0.0 h0cc432a_1
  icu 58.2 ha66f8fd_1
  intel-openmp 2019.4 245
  ipython 7.8.0
  ipython-genutils 0.2.0
  jedi 0.15.1
  joblib 0.14.0
  jpeg 9b hb83a4c4_2
  Keras 2.3.1
  Keras-Applications 1.0.8
  Keras-Preprocessing 1.1.0
  kiwisolver 1.1.0
  libboost 1.67.0 hd9e427e_4
  libpng 1.6.37 h2a8f88b_0
  libtiff 4.0.10 hb898794_2
  Markdown 3.1.1
  matplotlib 3.1.1
  mkl 2019.4 245
  mkl-service 2.3.0 py37hb782905_0
  mkl_fft 1.0.14 py37h14836fe_0
  mkl_random 1.1.0 py37h675688f_0
  mordred 1.2.0
  networkx 2.3
  numpy 1.16.5 py37h19fb1c0_0
  numpy-base 1.16.5 py37hc3f5095_0
  olefile 0.46 py37_0
  openssl 1.1.1d he774522_2
  opt-einsum 3.1.0
  pandas 0.25.1 py37ha925a31_0
  parso 0.5.1
  patsy 0.5.1
  pickleshare 0.7.5
  pillow 6.2.0 py37hdc69c19_0
  pip 19.2.3 py37_0
  pixman 0.38.0 he774522_0
  prompt-toolkit 2.0.10
  protobuf 3.7.1
  PubChemPy 1.0.4
  py-boost 1.67.0 py37h8300f20_4
  Pygments 2.4.2
  pyparsing 2.4.2
  python 3.7.4 h5263a28_0
  python-dateutil 2.8.0 py37_0
  pytz 2019.3 py_0
  PyYAML 5.1.2
  rdkit 2019.03.4.0 py37hc1e3ae0_1 rdkit
  scikit-learn 0.21.3
  scipy 1.3.1
  setuptools 41.4.0 py37_0
  six 1.12.0 py37_0
  sqlite 3.30.0 he774522_0
  statsmodels 0.10.1
  tensorboard 2.0.0
  tensorflow 2.0.0
  tensorflow-estimator 2.0.0
  termcolor 1.1.0
  tk 8.6.8 hfa6e2cd_0
  tqdm 4.36.1
  traitlets 4.3.3
  typing 3.7.4.1
  typing-extensions 3.6.6
  vc 14.1 h0510ff6_4
  vs2015_runtime 14.16.27012 hf0eaf9b_0
  wcwidth 0.1.7
  Werkzeug 0.16.0
  wheel 0.33.6 py37_0
  wincertstore 0.2 py37_0
  wrapt 1.11.2
  xz 5.2.4 h2fa13f4_4
  zlib 1.2.11 h62dcd97_3
  zstd 1.3.7 h508b16e_0