OS:Ubuntu 18.04LTS
bash
$ conda create -n pycaret python=3.6.10
bash
$ conda activate pycaret
(pycaret)$ pip install pycaret
(pycaret)$ python -m ipykernel install --user --name pycaret --display-name "display-name-here"
However, after installing it recently, when I execute the following command with jupyter notebook, it starts to throw an error.
python
from pycaret.datasets import get_data
dataset = get_data('credit', profile=True)
This is a command to download from PyCaret's data respository with get_data, and the original tutorial didn't give the argument profile = True. In other words, it is executed with the default argument profile = False. * In this case, only the first 5 lines of data are displayed *.
On the other hand, if you give the argument profile = True, it will be output in the format of pandas profiling report. You can check the basic statistics and correlation coefficient of DataFrame all at once, but you don't have to bother with ʻimport pandas_profiling`.
However, if I installed using pip install pycaret at different times, I got an error with profile = True, probably because the subversions of some packages were different, so requirements.txt I'm installing using.
bash
$ conda activate pycaret
(pycaret)$ pip install -r requirements.txt
(pycaret)$ python -m ipykernel install --user --name pycaret --display-name "display-name-here"
Describe the following in requirements.txt.
astropy==4.0.1.post1
attrs==19.3.0
awscli==1.18.64
backcall==0.1.0
bleach==3.1.5
blis==0.4.1
boto==2.49.0
boto3==1.13.14
botocore==1.16.14
catalogue==1.0.0
catboost==0.20.2
certifi==2020.4.5.1
chardet==3.0.4
chart-studio==1.1.0
click==7.1.2
colorama==0.4.3
colorlover==0.3.0
combo==0.1.0
confuse==1.1.0
cufflinks==0.17.0
cycler==0.10.0
cymem==2.0.3
datefinder==0.7.0
DateTime==4.3
decorator==4.4.2
defusedxml==0.6.0
docutils==0.15.2
entrypoints==0.3
funcy==1.14
future==0.18.2
gensim==3.8.3
graphviz==0.14
htmlmin==0.1.12
idna==2.9
importlib-metadata==1.6.0
ipykernel==5.3.0
ipython==7.14.0
ipython-genutils==0.2.0
ipywidgets==7.5.1
jedi==0.17.0
Jinja2==2.11.2
jmespath==0.10.0
joblib==0.15.1
jsonschema==3.2.0
jupyter-client==6.1.3
jupyter-core==4.6.3
kiwisolver==1.2.0
kmodes==0.10.1
lightgbm==2.3.1
llvmlite==0.32.1
MarkupSafe==1.1.1
matplotlib==3.2.1
missingno==0.4.2
mistune==0.8.4
mlxtend==0.17.2
more-itertools==8.3.0
murmurhash==1.0.2
nbconvert==5.6.1
nbformat==5.0.6
nltk==3.5
notebook==6.0.3
numba==0.49.1
numexpr==2.7.1
numpy==1.18.4
packaging==20.4
pandas==1.0.3
pandas-profiling==2.3.0
pandocfilters==1.4.2
parso==0.7.0
pexpect==4.8.0
phik==0.9.12
pickleshare==0.7.5
Pillow==7.1.2
plac==1.1.3
plotly==4.4.1
pluggy==0.13.1
preshed==3.0.2
prometheus-client==0.7.1
prompt-toolkit==3.0.5
ptyprocess==0.6.0
py==1.8.1
pyasn1==0.4.8
pycaret==1.0.0
Pygments==2.6.1
pyLDAvis==2.1.2
pyod==0.7.9
pyparsing==2.4.7
pyrsistent==0.16.0
pytest==5.4.2
python-dateutil==2.8.1
pytz==2020.1
PyYAML==5.3.1
pyzmq==19.0.1
regex==2020.5.14
requests==2.23.0
retrying==1.3.3
rsa==3.4.2
s3transfer==0.3.3
scikit-learn==0.22
scipy==1.4.1
seaborn==0.10.1
Send2Trash==1.5.0
shap==0.32.1
six==1.14.0
smart-open==2.0.0
spacy==2.2.4
srsly==1.0.2
suod==0.0.4
tbb==2020.0.133
terminado==0.8.3
testpath==0.4.4
textblob==0.15.3
thinc==7.4.0
tornado==6.0.4
tqdm==4.46.0
traitlets==4.3.3
umap-learn==0.4.3
urllib3==1.25.9
wasabi==0.6.0
wcwidth==0.1.9
webencodings==0.5.1
widgetsnbextension==3.5.1
wordcloud==1.7.0
xgboost==0.90
yellowbrick==1.0.1
zipp==3.1.0
zope.interface==5.1.0
python
import pandas as pd
import numpy as np
df = pd.read_csv('/path/to/data.csv',sep=",", encoding="utf-8")
import pandas_profiling
pandas_profiling.ProfileReport(df)
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