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GitHub / Biel-Altimira / used_cars

Projecte amb el dataset de Kaggle de Used Cars Dataset per la segona part pràctica del TDR

JSON API: https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Biel-Altimira%2Fused_cars

Stars: 0
Forks: 0
Open Issues: 0

License: None
Language: Jupyter Notebook
Repo Size: 5.58 MB
Dependencies: 196

Created: over 2 years ago
Updated: over 2 years ago
Last pushed: about 2 years ago
Last synced: about 1 year ago

Files

Readme

Dependencies

requirements.txt pypi

affine =2.3.0=py_0
argon2-cffi =20.1.0=py38h27cfd23_1
async_generator =1.10=pyhd3eb1b0_0
attrs =21.2.0=pyhd3eb1b0_0
aws-c-common =0.4.57=he6710b0_1
aws-c-event-stream =0.1.6=h2531618_5
aws-checksums =0.1.9=he6710b0_0
aws-sdk-cpp =1.8.185=hce553d0_0
backcall =0.2.0=pyhd3eb1b0_0
blas =1.0=mkl
bleach =4.0.0=pyhd3eb1b0_0
bottleneck =1.3.2=py38heb32a55_1
brotlipy =0.7.0=py38h8df0ef7_1001
bzip2 =1.0.8=h7b6447c_0
c-ares =1.17.1=h27cfd23_0
ca-certificates =2021.7.5=h06a4308_1
cairo =1.16.0=hf32fb01_1
certifi =2021.5.30=py38h06a4308_0
cffi =1.14.6=py38h400218f_0
cfitsio =3.470=hf0d0db6_6
chardet =4.0.0=py38h578d9bd_1
click =7.1.2=pyhd3eb1b0_0
click-plugins =1.1.1=pyhd3eb1b0_0
cligj =0.7.2=pyhd3eb1b0_0
contextily =1.1.0=pyhd8ed1ab_0
cryptography =3.4.7=py38ha5dfef3_0
curl =7.78.0=h1ccaba5_0
cycler =0.10.0=py38_0
dbus =1.13.18=hb2f20db_0
debugpy =1.4.1=py38h295c915_0
decorator =5.0.9=pyhd3eb1b0_0
defusedxml =0.7.1=pyhd3eb1b0_0
entrypoints =0.3=py38_0
expat =2.4.1=h2531618_2
fiona =1.8.13.post1=py38hc820daa_0
fontconfig =2.13.1=h6c09931_0
freetype =2.10.4=h5ab3b9f_0
freexl =1.0.6=h27cfd23_0
gdal =3.0.2=py38h4694593_1
geographiclib =1.52=pyhd8ed1ab_0
geopandas =0.9.0=py_1
geopandas-base =0.9.0=py_1
geopy =2.2.0=pyhd8ed1ab_0
geos =3.8.0=he6710b0_0
geotiff =1.6.0=h21e8280_0
giflib =5.2.1=h7b6447c_0
glib =2.69.1=h5202010_0
gst-plugins-base =1.14.0=h8213a91_2
gstreamer =1.14.0=h28cd5cc_2
hdf4 =4.2.13=h3ca952b_2
hdf5 =1.10.6=hb1b8bf9_0
icu =58.2=he6710b0_3
idna =2.10=pyh9f0ad1d_0
importlib-metadata =3.10.0=py38h06a4308_0
importlib_metadata =3.10.0=hd3eb1b0_0
intel-openmp =2021.3.0=h06a4308_3350
ipykernel =6.2.0=py38h06a4308_1
ipython =7.26.0=py38hb070fc8_0
ipython_genutils =0.2.0=pyhd3eb1b0_1
jedi =0.18.0=py38h06a4308_1
jinja2 =3.0.1=pyhd3eb1b0_0
joblib =1.0.1=pyhd3eb1b0_0
jpeg =9b=h024ee3a_2
json-c =0.13.1=h1bed415_0
jsonschema =3.2.0=pyhd3eb1b0_2
jupyter_client =7.0.1=pyhd3eb1b0_0
jupyter_core =4.7.1=py38h06a4308_0
jupyterlab_pygments =0.1.2=py_0
kealib =1.4.14=h54c064f_0
kiwisolver =1.3.1=py38h2531618_0
krb5 =1.19.2=hac12032_0
lcms2 =2.12=h3be6417_0
ld_impl_linux-64 =2.35.1=h7274673_9
libboost =1.73.0=h3ff78a5_11
libcurl =7.78.0=h0b77cf5_0
libdap4 =3.19.1=h6ec2957_0
libedit =3.1.20210714=h7f8727e_0
libev =4.33=h7b6447c_0
libffi =3.3=he6710b0_2
libgcc-ng =11.2.0=h1d223b6_9
libgdal =3.0.2=h7c14f60_1
libgfortran-ng =7.3.0=hdf63c60_0
libgfortran4 =7.5.0=ha8ba4b0_17
libgomp =11.2.0=h1d223b6_9
libkml =1.3.0=h7ecb851_5
libnetcdf =4.6.1=h2053bdc_4
libnghttp2 =1.41.0=hf8bcb03_2
libpng =1.6.37=hbc83047_0
libpq =12.2=h553bfba_1
libsodium =1.0.18=h7b6447c_0
libspatialindex =1.9.3=h2531618_0
libspatialite =4.3.0a=h793db0d_0
libssh2 =1.9.0=h1ba5d50_1
libstdcxx-ng =11.2.0=he4da1e4_9
libtiff =4.2.0=h85742a9_0
libuuid =1.0.3=h1bed415_2
libwebp =1.2.0=h89dd481_0
libwebp-base =1.2.0=h27cfd23_0
libxcb =1.14=h7b6447c_0
libxgboost =1.4.2=h9c3ff4c_0
libxml2 =2.9.10=hb55368b_3
lz4-c =1.9.3=h295c915_1
mapclassify =2.4.3=pyhd3eb1b0_0
markupsafe =2.0.1=py38h27cfd23_0
matplotlib =3.3.4=py38h06a4308_0
matplotlib-base =3.3.4=py38h62a2d02_0
matplotlib-inline =0.1.2=pyhd3eb1b0_2
mercantile =1.2.1=pyhd8ed1ab_0
missingno =0.4.2=py_1
mistune =0.8.4=py38h7b6447c_1000
mkl =2021.3.0=h06a4308_520
mkl-service =2.4.0=py38h7f8727e_0
mkl_fft =1.3.0=py38h42c9631_2
mkl_random =1.2.2=py38h51133e4_0
munch =2.5.0=pyhd3eb1b0_0
nb_conda =2.2.1=py38_1
nb_conda_kernels =2.3.1=py38h06a4308_0
nbclient =0.5.3=pyhd3eb1b0_0
nbconvert =6.1.0=py38h06a4308_0
nbformat =5.1.3=pyhd3eb1b0_0
ncurses =6.2=he6710b0_1
nest-asyncio =1.5.1=pyhd3eb1b0_0
networkx =2.6.2=pyhd3eb1b0_0
notebook =6.4.3=py38h06a4308_0
numexpr =2.7.3=py38h22e1b3c_1
numpy =1.20.3=py38hf144106_0
numpy-base =1.20.3=py38h74d4b33_0
olefile =0.46=pyhd3eb1b0_0
openjpeg =2.4.0=h3ad879b_0
openssl =1.1.1k=h27cfd23_0
packaging =21.0=pyhd3eb1b0_0
pandas =1.3.2=py38h8c16a72_0
pandocfilters =1.4.3=py38h06a4308_1
parso =0.8.2=pyhd3eb1b0_0
pcre =8.45=h295c915_0
pexpect =4.8.0=pyhd3eb1b0_3
pickleshare =0.7.5=pyhd3eb1b0_1003
pillow =8.3.1=py38h5aabda8_0
pip =21.2.2=py38h06a4308_0
pixman =0.40.0=h7b6447c_0
plotly =4.11.0=py_0
poppler =0.81.0=h01f5e8b_2
poppler-data =0.4.10=h06a4308_0
postgresql =12.2=h553bfba_1
proj =6.2.1=haa6030c_0
prometheus_client =0.11.0=pyhd3eb1b0_0
prompt-toolkit =3.0.17=pyhca03da5_0
ptyprocess =0.7.0=pyhd3eb1b0_2
py-xgboost =1.4.2=py38h578d9bd_0
pycparser =2.20=py_2
pygments =2.10.0=pyhd3eb1b0_0
pyopenssl =20.0.1=pyhd8ed1ab_0
pyparsing =2.4.7=pyhd3eb1b0_0
pyproj =2.6.1.post1=py38hb3025e9_1
pyqt =5.9.2=py38h05f1152_4
pyrsistent =0.17.3=py38h7b6447c_0
pysocks =1.7.1=py38h578d9bd_3
python =3.8.11=h12debd9_0_cpython
python-dateutil =2.8.2=pyhd3eb1b0_0
python_abi =3.8=2_cp38
pytz =2021.1=pyhd3eb1b0_0
pyzmq =22.2.1=py38h295c915_1
qt =5.9.7=h5867ecd_1
rasterio =1.1.2=py38h900e953_0
readline =8.1=h27cfd23_0
requests =2.25.1=pyhd3deb0d_0
retrying =1.3.3=py_2
rtree =0.9.7=py38h06a4308_1
scikit-learn =0.24.2=py38ha9443f7_0
scipy =1.7.1=py38h292c36d_2
seaborn =0.11.2=pyhd3eb1b0_0
send2trash =1.5.0=pyhd3eb1b0_1
setuptools =52.0.0=py38h06a4308_0
shapely =1.7.1=py38h98ec03d_0
sip =4.19.13=py38he6710b0_0
six =1.16.0=pyhd3eb1b0_0
snuggs =1.4.7=py_0
sqlite =3.36.0=hc218d9a_0
terminado =0.9.4=py38h06a4308_0
testpath =0.5.0=pyhd3eb1b0_0
threadpoolctl =2.2.0=pyh0d69192_0
tiledb =2.2.9=hffe1d7a_0
tk =8.6.10=hbc83047_0
tornado =6.1=py38h27cfd23_0
traitlets =5.0.5=pyhd3eb1b0_0
urllib3 =1.26.7=pyhd8ed1ab_0
wcwidth =0.2.5=pyhd3eb1b0_0
webencodings =0.5.1=py38_1
wheel =0.36.2=pyhd3eb1b0_0
xerces-c =3.2.3=h780794e_0
xgboost =1.4.2=py38h578d9bd_0
xz =5.2.5=h7b6447c_0
zeromq =4.3.4=h2531618_0
zipp =3.5.0=pyhd3eb1b0_0
zlib =1.2.11=h7b6447c_3
zstd =1.4.9=haebb681_0