Jupyter making 3D matplotlib graphs extremely small
up vote
0
down vote
favorite
Having read many of the posts on this site about resizing graphs and setting limits on graph sizes in Jupyter, I am virtually convinced there is something different when it comes to 3D plotting.
This is my 3D scatterplot that Jupyter keeps giving back to me, despite having tried many figsize
and dpi=
settings (either in plt.figure()
or within plt.rcParams()
),
This is my data and my current code,
%pylab inline
pylab.rcParams['figure.figsize'] = (20, 16)
pylab.rcParams['figure.dpi'] = 200
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.mplot3d import Axes3D
# data1
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(data1.a_close, data1.g_close, data1.m_close)
What am I doing wrong?
EDIT: I am using a Mac (10.11) and these are all my pip
installed packages, if this provides some detail. I also tried uninstalling and reinstalling jupyter
, but that has not helped
alabaster==0.7.12
anaconda-client==1.6.14
anaconda-navigator==1.8.7
anaconda-project==0.8.2
appnope==0.1.0
appscript==1.0.1
argh==0.26.2
asn1crypto==0.24.0
astroid==2.0.4
astropy==3.0.5
atomicwrites==1.2.1
attrs==18.2.0
Babel==2.6.0
backcall==0.1.0
backports.shutil-get-terminal-size==1.0.0
beautifulsoup4==4.6.3
bitarray==0.8.3
bkcharts==0.2
blaze==0.11.3
bleach==3.0.2
blist==1.3.6
bokeh==1.0.0
boto==2.48.0
Bottleneck==1.2.1
certifi==2018.4.16
cffi==1.11.5
chardet==3.0.4
Click==7.0
cloudpickle==0.6.1
clyent==1.2.2
colorama==0.4.0
conda==4.5.9
conda-build==3.0.27
conda-verify==2.0.0
contextlib2==0.5.5
cryptography==2.3.1
CVXcanon==0.1.1
cvxopt==1.2.2
cvxpy==1.0.10
cycler==0.10.0
Cython==0.29
cytoolz==0.9.0.1
dash==0.28.5
dash-core-components==0.35.2
dash-html-components==0.13.2
dash-renderer==0.14.3
dash-table-experiments==0.6.0
dask==0.19.4
datashape==0.5.4
decorator==4.3.0
defusedxml==0.5.0
dill==0.2.8.2
distcan==0.0.1
distributed==1.23.3
Django==2.1.2
docutils==0.14
ecos==2.0.5
entrypoints==0.2.3
et-xmlfile==1.0.1
eventsourcing==6.3.0
fastcache==1.0.2
fastnumbers==2.1.1
feather-format==0.4.0
filelock==3.0.9
fix-yahoo-finance==0.0.22
Flask==1.0.2
Flask-Caching==1.4.0
Flask-Compress==1.4.0
Flask-Cors==3.0.6
future==0.16.0
gevent==1.3.7
glmnet==2.0.0
glmnet-py==0.1.0b2
glob2==0.6
gmpy2==2.0.8
greenlet==0.4.15
h5py==2.8.0
heapdict==1.0.0
html5lib==1.0.1
hupper==1.3.1
idna==2.7
imageio==2.4.1
imagesize==1.1.0
importlib-metadata==0.6
inflection==0.3.1
ipykernel==5.1.0
ipython==7.0.1
ipython-genutils==0.2.0
ipywidgets==7.4.2
isort==4.3.4
ItsDangerous==1.0.0
jdcal==1.4
jedi==0.13.1
Jinja2==2.10
joblib==0.12.5
jsonschema==2.6.0
jupyter==1.0.0
jupyter-client==5.2.3
jupyter-console==6.0.0
jupyter-core==4.4.0
jupyterlab==0.35.2
jupyterlab-launcher==0.13.1
jupyterlab-server==0.2.0
keyring==15.1.0
kiwisolver==1.0.1
lazy-object-proxy==1.3.1
llvmlite==0.25.0
locket==0.2.0
lxml==4.2.5
Markdown==3.0.1
MarkupSafe==1.0
matplotlib==3.0.0
mccabe==0.6.1
mistune==0.8.4
mizani==0.5.2
mlxtend==0.13.0
mock==2.0.0
more-itertools==4.3.0
mpmath==1.0.0
msgpack==0.5.6
msgpack-python==0.5.6
multipledispatch==0.6.0
multiprocess==0.70.6.1
multitasking==0.0.7
natsort==5.4.1
navigator-updater==0.2.1
nbconvert==5.4.0
nbformat==4.4.0
ndg-httpsclient==0.5.1
networkx==2.2
nltk==3.3
nose==1.3.7
notebook==5.7.0
numba==0.40.1
numexpr==2.6.8
numpy==1.15.3
numpydoc==0.8.0
odo==0.5.1
olefile==0.46
openpyxl==2.5.9
osqp==0.4.1
packaging==18.0
palettable==3.1.1
pandas==0.23.4
pandas-datareader==0.7.0
pandocfilters==1.4.2
parso==0.3.1
partd==0.3.9
PasteDeploy==1.5.2
path.py==11.5.0
pathlib2==2.3.2
patsy==0.5.0
pbr==5.1.0
pep8==1.7.1
pexpect==4.6.0
pickleshare==0.7.5
Pillow==5.3.0
pkginfo==1.4.2
plaster==1.0
plaster-pastedeploy==0.6
plotly==3.3.0
pluggy==0.8.0
ply==3.11
prometheus-client==0.4.2
prompt-toolkit==2.0.6
psutil==5.4.7
ptyprocess==0.5.2
py==1.7.0
pyarrow==0.11.1
pyasn1==0.4.4
pycodestyle==2.4.0
pycosat==0.6.3
pycparser==2.19
pycrypto==2.6.1
pycryptodome==3.6.6
pycurl==7.43.0.2
pyflakes==2.0.0
Pygments==2.2.0
pylint==2.1.1
pymc3==3.5
pyodbc==4.0.24
pyOpenSSL==18.0.0
pyparsing==2.2.2
PyQt5==5.11.3
PyQt5-sip==4.19.13
pyramid-arima==0.8.1
PySocks==1.6.8
pystan==2.18.0.0
pytest==3.9.2
python-dateutil==2.7.3
pytz==2018.6
PyWavelets==1.0.1
PyYAML==3.12
pyzmq==17.1.2
qfrm==0.2.0.27
QtAwesome==0.5.1
qtconsole==4.3.1
QtPy==1.5.2
Quandl==3.4.3
redis==2.10.6
repoze.lru==0.7
requests==2.20.0
requests-file==1.4.3
requests-ftp==0.3.1
retrying==1.3.3
rope==0.11.0
rpy2==2.9.4
ruamel-yaml==0.11.14
scikit-image==0.14.1
scikit-learn==0.19.0
scipy==1.1.0
scs==2.0.2
seaborn==0.9.0
Send2Trash==1.5.0
simplegeneric==0.8.1
simplejson==3.16.0
singledispatch==3.4.0.3
sip==4.19.8
six==1.11.0
snowballstemmer==1.2.1
sortedcollections==1.0.1
sortedcontainers==2.0.5
Sphinx==1.8.1
sphinxcontrib-websupport==1.1.0
spyder==3.3.1
spyder-kernels==1.1.0
SQLAlchemy==1.2.12
statistics==1.0.3.5
statsmodels==0.9.0
sympy==1.1.1
tables==3.4.4
tblib==1.3.2
terminado==0.8.1
testpath==0.4.2
Theano==1.0.3
toolz==0.9.0
tornado==5.1.1
tqdm==4.28.1
traitlets==4.3.2
translationstring==1.3
typed-ast==1.1.0
typing==3.6.6
tzlocal==1.5.1
unicodecsv==0.14.1
urllib3==1.24
venusian==1.1.0
wcwidth==0.1.7
webencodings==0.5.1
WebOb==1.8.3
Werkzeug==0.14.1
widgetsnbextension==3.4.2
wrapt==1.10.11
xlrd==1.1.0
XlsxWriter==1.1.2
xlwings==0.13.0
xlwt==1.3.0
yahoo-finance==1.4.0
zict==0.1.3
zope.deprecation==4.3.0
zope.interface==4.6.0
python matplotlib 3d resize jupyter
add a comment |
up vote
0
down vote
favorite
Having read many of the posts on this site about resizing graphs and setting limits on graph sizes in Jupyter, I am virtually convinced there is something different when it comes to 3D plotting.
This is my 3D scatterplot that Jupyter keeps giving back to me, despite having tried many figsize
and dpi=
settings (either in plt.figure()
or within plt.rcParams()
),
This is my data and my current code,
%pylab inline
pylab.rcParams['figure.figsize'] = (20, 16)
pylab.rcParams['figure.dpi'] = 200
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.mplot3d import Axes3D
# data1
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(data1.a_close, data1.g_close, data1.m_close)
What am I doing wrong?
EDIT: I am using a Mac (10.11) and these are all my pip
installed packages, if this provides some detail. I also tried uninstalling and reinstalling jupyter
, but that has not helped
alabaster==0.7.12
anaconda-client==1.6.14
anaconda-navigator==1.8.7
anaconda-project==0.8.2
appnope==0.1.0
appscript==1.0.1
argh==0.26.2
asn1crypto==0.24.0
astroid==2.0.4
astropy==3.0.5
atomicwrites==1.2.1
attrs==18.2.0
Babel==2.6.0
backcall==0.1.0
backports.shutil-get-terminal-size==1.0.0
beautifulsoup4==4.6.3
bitarray==0.8.3
bkcharts==0.2
blaze==0.11.3
bleach==3.0.2
blist==1.3.6
bokeh==1.0.0
boto==2.48.0
Bottleneck==1.2.1
certifi==2018.4.16
cffi==1.11.5
chardet==3.0.4
Click==7.0
cloudpickle==0.6.1
clyent==1.2.2
colorama==0.4.0
conda==4.5.9
conda-build==3.0.27
conda-verify==2.0.0
contextlib2==0.5.5
cryptography==2.3.1
CVXcanon==0.1.1
cvxopt==1.2.2
cvxpy==1.0.10
cycler==0.10.0
Cython==0.29
cytoolz==0.9.0.1
dash==0.28.5
dash-core-components==0.35.2
dash-html-components==0.13.2
dash-renderer==0.14.3
dash-table-experiments==0.6.0
dask==0.19.4
datashape==0.5.4
decorator==4.3.0
defusedxml==0.5.0
dill==0.2.8.2
distcan==0.0.1
distributed==1.23.3
Django==2.1.2
docutils==0.14
ecos==2.0.5
entrypoints==0.2.3
et-xmlfile==1.0.1
eventsourcing==6.3.0
fastcache==1.0.2
fastnumbers==2.1.1
feather-format==0.4.0
filelock==3.0.9
fix-yahoo-finance==0.0.22
Flask==1.0.2
Flask-Caching==1.4.0
Flask-Compress==1.4.0
Flask-Cors==3.0.6
future==0.16.0
gevent==1.3.7
glmnet==2.0.0
glmnet-py==0.1.0b2
glob2==0.6
gmpy2==2.0.8
greenlet==0.4.15
h5py==2.8.0
heapdict==1.0.0
html5lib==1.0.1
hupper==1.3.1
idna==2.7
imageio==2.4.1
imagesize==1.1.0
importlib-metadata==0.6
inflection==0.3.1
ipykernel==5.1.0
ipython==7.0.1
ipython-genutils==0.2.0
ipywidgets==7.4.2
isort==4.3.4
ItsDangerous==1.0.0
jdcal==1.4
jedi==0.13.1
Jinja2==2.10
joblib==0.12.5
jsonschema==2.6.0
jupyter==1.0.0
jupyter-client==5.2.3
jupyter-console==6.0.0
jupyter-core==4.4.0
jupyterlab==0.35.2
jupyterlab-launcher==0.13.1
jupyterlab-server==0.2.0
keyring==15.1.0
kiwisolver==1.0.1
lazy-object-proxy==1.3.1
llvmlite==0.25.0
locket==0.2.0
lxml==4.2.5
Markdown==3.0.1
MarkupSafe==1.0
matplotlib==3.0.0
mccabe==0.6.1
mistune==0.8.4
mizani==0.5.2
mlxtend==0.13.0
mock==2.0.0
more-itertools==4.3.0
mpmath==1.0.0
msgpack==0.5.6
msgpack-python==0.5.6
multipledispatch==0.6.0
multiprocess==0.70.6.1
multitasking==0.0.7
natsort==5.4.1
navigator-updater==0.2.1
nbconvert==5.4.0
nbformat==4.4.0
ndg-httpsclient==0.5.1
networkx==2.2
nltk==3.3
nose==1.3.7
notebook==5.7.0
numba==0.40.1
numexpr==2.6.8
numpy==1.15.3
numpydoc==0.8.0
odo==0.5.1
olefile==0.46
openpyxl==2.5.9
osqp==0.4.1
packaging==18.0
palettable==3.1.1
pandas==0.23.4
pandas-datareader==0.7.0
pandocfilters==1.4.2
parso==0.3.1
partd==0.3.9
PasteDeploy==1.5.2
path.py==11.5.0
pathlib2==2.3.2
patsy==0.5.0
pbr==5.1.0
pep8==1.7.1
pexpect==4.6.0
pickleshare==0.7.5
Pillow==5.3.0
pkginfo==1.4.2
plaster==1.0
plaster-pastedeploy==0.6
plotly==3.3.0
pluggy==0.8.0
ply==3.11
prometheus-client==0.4.2
prompt-toolkit==2.0.6
psutil==5.4.7
ptyprocess==0.5.2
py==1.7.0
pyarrow==0.11.1
pyasn1==0.4.4
pycodestyle==2.4.0
pycosat==0.6.3
pycparser==2.19
pycrypto==2.6.1
pycryptodome==3.6.6
pycurl==7.43.0.2
pyflakes==2.0.0
Pygments==2.2.0
pylint==2.1.1
pymc3==3.5
pyodbc==4.0.24
pyOpenSSL==18.0.0
pyparsing==2.2.2
PyQt5==5.11.3
PyQt5-sip==4.19.13
pyramid-arima==0.8.1
PySocks==1.6.8
pystan==2.18.0.0
pytest==3.9.2
python-dateutil==2.7.3
pytz==2018.6
PyWavelets==1.0.1
PyYAML==3.12
pyzmq==17.1.2
qfrm==0.2.0.27
QtAwesome==0.5.1
qtconsole==4.3.1
QtPy==1.5.2
Quandl==3.4.3
redis==2.10.6
repoze.lru==0.7
requests==2.20.0
requests-file==1.4.3
requests-ftp==0.3.1
retrying==1.3.3
rope==0.11.0
rpy2==2.9.4
ruamel-yaml==0.11.14
scikit-image==0.14.1
scikit-learn==0.19.0
scipy==1.1.0
scs==2.0.2
seaborn==0.9.0
Send2Trash==1.5.0
simplegeneric==0.8.1
simplejson==3.16.0
singledispatch==3.4.0.3
sip==4.19.8
six==1.11.0
snowballstemmer==1.2.1
sortedcollections==1.0.1
sortedcontainers==2.0.5
Sphinx==1.8.1
sphinxcontrib-websupport==1.1.0
spyder==3.3.1
spyder-kernels==1.1.0
SQLAlchemy==1.2.12
statistics==1.0.3.5
statsmodels==0.9.0
sympy==1.1.1
tables==3.4.4
tblib==1.3.2
terminado==0.8.1
testpath==0.4.2
Theano==1.0.3
toolz==0.9.0
tornado==5.1.1
tqdm==4.28.1
traitlets==4.3.2
translationstring==1.3
typed-ast==1.1.0
typing==3.6.6
tzlocal==1.5.1
unicodecsv==0.14.1
urllib3==1.24
venusian==1.1.0
wcwidth==0.1.7
webencodings==0.5.1
WebOb==1.8.3
Werkzeug==0.14.1
widgetsnbextension==3.4.2
wrapt==1.10.11
xlrd==1.1.0
XlsxWriter==1.1.2
xlwings==0.13.0
xlwt==1.3.0
yahoo-finance==1.4.0
zict==0.1.3
zope.deprecation==4.3.0
zope.interface==4.6.0
python matplotlib 3d resize jupyter
I tried to reproduce this with random data, and the resulting graph was huge - could you share some sample data and more information about your environment?
– Charles Landau
Nov 8 at 23:55
I added an EDIT to my question with more information.
– Coolio2654
Nov 9 at 0:04
Does changing the figure size e.g. from(20, 16)
to(40, 32)
not change the output at all?
– ImportanceOfBeingErnest
Nov 9 at 0:49
There is a difference, just not in size. See here, imgur.com/a/4wyN9pI The data in the graph ends up looking sparser, and the graph calculation takes a lot longer.
– Coolio2654
Nov 9 at 1:16
That seems logical at this point. I just tested out the jupyter notebook on two other browsers I have (1 completely vanilla with no addons/custom settings), and the same problem is there. Could this be an OS thing?
– Coolio2654
Nov 9 at 18:28
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
Having read many of the posts on this site about resizing graphs and setting limits on graph sizes in Jupyter, I am virtually convinced there is something different when it comes to 3D plotting.
This is my 3D scatterplot that Jupyter keeps giving back to me, despite having tried many figsize
and dpi=
settings (either in plt.figure()
or within plt.rcParams()
),
This is my data and my current code,
%pylab inline
pylab.rcParams['figure.figsize'] = (20, 16)
pylab.rcParams['figure.dpi'] = 200
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.mplot3d import Axes3D
# data1
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(data1.a_close, data1.g_close, data1.m_close)
What am I doing wrong?
EDIT: I am using a Mac (10.11) and these are all my pip
installed packages, if this provides some detail. I also tried uninstalling and reinstalling jupyter
, but that has not helped
alabaster==0.7.12
anaconda-client==1.6.14
anaconda-navigator==1.8.7
anaconda-project==0.8.2
appnope==0.1.0
appscript==1.0.1
argh==0.26.2
asn1crypto==0.24.0
astroid==2.0.4
astropy==3.0.5
atomicwrites==1.2.1
attrs==18.2.0
Babel==2.6.0
backcall==0.1.0
backports.shutil-get-terminal-size==1.0.0
beautifulsoup4==4.6.3
bitarray==0.8.3
bkcharts==0.2
blaze==0.11.3
bleach==3.0.2
blist==1.3.6
bokeh==1.0.0
boto==2.48.0
Bottleneck==1.2.1
certifi==2018.4.16
cffi==1.11.5
chardet==3.0.4
Click==7.0
cloudpickle==0.6.1
clyent==1.2.2
colorama==0.4.0
conda==4.5.9
conda-build==3.0.27
conda-verify==2.0.0
contextlib2==0.5.5
cryptography==2.3.1
CVXcanon==0.1.1
cvxopt==1.2.2
cvxpy==1.0.10
cycler==0.10.0
Cython==0.29
cytoolz==0.9.0.1
dash==0.28.5
dash-core-components==0.35.2
dash-html-components==0.13.2
dash-renderer==0.14.3
dash-table-experiments==0.6.0
dask==0.19.4
datashape==0.5.4
decorator==4.3.0
defusedxml==0.5.0
dill==0.2.8.2
distcan==0.0.1
distributed==1.23.3
Django==2.1.2
docutils==0.14
ecos==2.0.5
entrypoints==0.2.3
et-xmlfile==1.0.1
eventsourcing==6.3.0
fastcache==1.0.2
fastnumbers==2.1.1
feather-format==0.4.0
filelock==3.0.9
fix-yahoo-finance==0.0.22
Flask==1.0.2
Flask-Caching==1.4.0
Flask-Compress==1.4.0
Flask-Cors==3.0.6
future==0.16.0
gevent==1.3.7
glmnet==2.0.0
glmnet-py==0.1.0b2
glob2==0.6
gmpy2==2.0.8
greenlet==0.4.15
h5py==2.8.0
heapdict==1.0.0
html5lib==1.0.1
hupper==1.3.1
idna==2.7
imageio==2.4.1
imagesize==1.1.0
importlib-metadata==0.6
inflection==0.3.1
ipykernel==5.1.0
ipython==7.0.1
ipython-genutils==0.2.0
ipywidgets==7.4.2
isort==4.3.4
ItsDangerous==1.0.0
jdcal==1.4
jedi==0.13.1
Jinja2==2.10
joblib==0.12.5
jsonschema==2.6.0
jupyter==1.0.0
jupyter-client==5.2.3
jupyter-console==6.0.0
jupyter-core==4.4.0
jupyterlab==0.35.2
jupyterlab-launcher==0.13.1
jupyterlab-server==0.2.0
keyring==15.1.0
kiwisolver==1.0.1
lazy-object-proxy==1.3.1
llvmlite==0.25.0
locket==0.2.0
lxml==4.2.5
Markdown==3.0.1
MarkupSafe==1.0
matplotlib==3.0.0
mccabe==0.6.1
mistune==0.8.4
mizani==0.5.2
mlxtend==0.13.0
mock==2.0.0
more-itertools==4.3.0
mpmath==1.0.0
msgpack==0.5.6
msgpack-python==0.5.6
multipledispatch==0.6.0
multiprocess==0.70.6.1
multitasking==0.0.7
natsort==5.4.1
navigator-updater==0.2.1
nbconvert==5.4.0
nbformat==4.4.0
ndg-httpsclient==0.5.1
networkx==2.2
nltk==3.3
nose==1.3.7
notebook==5.7.0
numba==0.40.1
numexpr==2.6.8
numpy==1.15.3
numpydoc==0.8.0
odo==0.5.1
olefile==0.46
openpyxl==2.5.9
osqp==0.4.1
packaging==18.0
palettable==3.1.1
pandas==0.23.4
pandas-datareader==0.7.0
pandocfilters==1.4.2
parso==0.3.1
partd==0.3.9
PasteDeploy==1.5.2
path.py==11.5.0
pathlib2==2.3.2
patsy==0.5.0
pbr==5.1.0
pep8==1.7.1
pexpect==4.6.0
pickleshare==0.7.5
Pillow==5.3.0
pkginfo==1.4.2
plaster==1.0
plaster-pastedeploy==0.6
plotly==3.3.0
pluggy==0.8.0
ply==3.11
prometheus-client==0.4.2
prompt-toolkit==2.0.6
psutil==5.4.7
ptyprocess==0.5.2
py==1.7.0
pyarrow==0.11.1
pyasn1==0.4.4
pycodestyle==2.4.0
pycosat==0.6.3
pycparser==2.19
pycrypto==2.6.1
pycryptodome==3.6.6
pycurl==7.43.0.2
pyflakes==2.0.0
Pygments==2.2.0
pylint==2.1.1
pymc3==3.5
pyodbc==4.0.24
pyOpenSSL==18.0.0
pyparsing==2.2.2
PyQt5==5.11.3
PyQt5-sip==4.19.13
pyramid-arima==0.8.1
PySocks==1.6.8
pystan==2.18.0.0
pytest==3.9.2
python-dateutil==2.7.3
pytz==2018.6
PyWavelets==1.0.1
PyYAML==3.12
pyzmq==17.1.2
qfrm==0.2.0.27
QtAwesome==0.5.1
qtconsole==4.3.1
QtPy==1.5.2
Quandl==3.4.3
redis==2.10.6
repoze.lru==0.7
requests==2.20.0
requests-file==1.4.3
requests-ftp==0.3.1
retrying==1.3.3
rope==0.11.0
rpy2==2.9.4
ruamel-yaml==0.11.14
scikit-image==0.14.1
scikit-learn==0.19.0
scipy==1.1.0
scs==2.0.2
seaborn==0.9.0
Send2Trash==1.5.0
simplegeneric==0.8.1
simplejson==3.16.0
singledispatch==3.4.0.3
sip==4.19.8
six==1.11.0
snowballstemmer==1.2.1
sortedcollections==1.0.1
sortedcontainers==2.0.5
Sphinx==1.8.1
sphinxcontrib-websupport==1.1.0
spyder==3.3.1
spyder-kernels==1.1.0
SQLAlchemy==1.2.12
statistics==1.0.3.5
statsmodels==0.9.0
sympy==1.1.1
tables==3.4.4
tblib==1.3.2
terminado==0.8.1
testpath==0.4.2
Theano==1.0.3
toolz==0.9.0
tornado==5.1.1
tqdm==4.28.1
traitlets==4.3.2
translationstring==1.3
typed-ast==1.1.0
typing==3.6.6
tzlocal==1.5.1
unicodecsv==0.14.1
urllib3==1.24
venusian==1.1.0
wcwidth==0.1.7
webencodings==0.5.1
WebOb==1.8.3
Werkzeug==0.14.1
widgetsnbextension==3.4.2
wrapt==1.10.11
xlrd==1.1.0
XlsxWriter==1.1.2
xlwings==0.13.0
xlwt==1.3.0
yahoo-finance==1.4.0
zict==0.1.3
zope.deprecation==4.3.0
zope.interface==4.6.0
python matplotlib 3d resize jupyter
Having read many of the posts on this site about resizing graphs and setting limits on graph sizes in Jupyter, I am virtually convinced there is something different when it comes to 3D plotting.
This is my 3D scatterplot that Jupyter keeps giving back to me, despite having tried many figsize
and dpi=
settings (either in plt.figure()
or within plt.rcParams()
),
This is my data and my current code,
%pylab inline
pylab.rcParams['figure.figsize'] = (20, 16)
pylab.rcParams['figure.dpi'] = 200
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.mplot3d import Axes3D
# data1
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(data1.a_close, data1.g_close, data1.m_close)
What am I doing wrong?
EDIT: I am using a Mac (10.11) and these are all my pip
installed packages, if this provides some detail. I also tried uninstalling and reinstalling jupyter
, but that has not helped
alabaster==0.7.12
anaconda-client==1.6.14
anaconda-navigator==1.8.7
anaconda-project==0.8.2
appnope==0.1.0
appscript==1.0.1
argh==0.26.2
asn1crypto==0.24.0
astroid==2.0.4
astropy==3.0.5
atomicwrites==1.2.1
attrs==18.2.0
Babel==2.6.0
backcall==0.1.0
backports.shutil-get-terminal-size==1.0.0
beautifulsoup4==4.6.3
bitarray==0.8.3
bkcharts==0.2
blaze==0.11.3
bleach==3.0.2
blist==1.3.6
bokeh==1.0.0
boto==2.48.0
Bottleneck==1.2.1
certifi==2018.4.16
cffi==1.11.5
chardet==3.0.4
Click==7.0
cloudpickle==0.6.1
clyent==1.2.2
colorama==0.4.0
conda==4.5.9
conda-build==3.0.27
conda-verify==2.0.0
contextlib2==0.5.5
cryptography==2.3.1
CVXcanon==0.1.1
cvxopt==1.2.2
cvxpy==1.0.10
cycler==0.10.0
Cython==0.29
cytoolz==0.9.0.1
dash==0.28.5
dash-core-components==0.35.2
dash-html-components==0.13.2
dash-renderer==0.14.3
dash-table-experiments==0.6.0
dask==0.19.4
datashape==0.5.4
decorator==4.3.0
defusedxml==0.5.0
dill==0.2.8.2
distcan==0.0.1
distributed==1.23.3
Django==2.1.2
docutils==0.14
ecos==2.0.5
entrypoints==0.2.3
et-xmlfile==1.0.1
eventsourcing==6.3.0
fastcache==1.0.2
fastnumbers==2.1.1
feather-format==0.4.0
filelock==3.0.9
fix-yahoo-finance==0.0.22
Flask==1.0.2
Flask-Caching==1.4.0
Flask-Compress==1.4.0
Flask-Cors==3.0.6
future==0.16.0
gevent==1.3.7
glmnet==2.0.0
glmnet-py==0.1.0b2
glob2==0.6
gmpy2==2.0.8
greenlet==0.4.15
h5py==2.8.0
heapdict==1.0.0
html5lib==1.0.1
hupper==1.3.1
idna==2.7
imageio==2.4.1
imagesize==1.1.0
importlib-metadata==0.6
inflection==0.3.1
ipykernel==5.1.0
ipython==7.0.1
ipython-genutils==0.2.0
ipywidgets==7.4.2
isort==4.3.4
ItsDangerous==1.0.0
jdcal==1.4
jedi==0.13.1
Jinja2==2.10
joblib==0.12.5
jsonschema==2.6.0
jupyter==1.0.0
jupyter-client==5.2.3
jupyter-console==6.0.0
jupyter-core==4.4.0
jupyterlab==0.35.2
jupyterlab-launcher==0.13.1
jupyterlab-server==0.2.0
keyring==15.1.0
kiwisolver==1.0.1
lazy-object-proxy==1.3.1
llvmlite==0.25.0
locket==0.2.0
lxml==4.2.5
Markdown==3.0.1
MarkupSafe==1.0
matplotlib==3.0.0
mccabe==0.6.1
mistune==0.8.4
mizani==0.5.2
mlxtend==0.13.0
mock==2.0.0
more-itertools==4.3.0
mpmath==1.0.0
msgpack==0.5.6
msgpack-python==0.5.6
multipledispatch==0.6.0
multiprocess==0.70.6.1
multitasking==0.0.7
natsort==5.4.1
navigator-updater==0.2.1
nbconvert==5.4.0
nbformat==4.4.0
ndg-httpsclient==0.5.1
networkx==2.2
nltk==3.3
nose==1.3.7
notebook==5.7.0
numba==0.40.1
numexpr==2.6.8
numpy==1.15.3
numpydoc==0.8.0
odo==0.5.1
olefile==0.46
openpyxl==2.5.9
osqp==0.4.1
packaging==18.0
palettable==3.1.1
pandas==0.23.4
pandas-datareader==0.7.0
pandocfilters==1.4.2
parso==0.3.1
partd==0.3.9
PasteDeploy==1.5.2
path.py==11.5.0
pathlib2==2.3.2
patsy==0.5.0
pbr==5.1.0
pep8==1.7.1
pexpect==4.6.0
pickleshare==0.7.5
Pillow==5.3.0
pkginfo==1.4.2
plaster==1.0
plaster-pastedeploy==0.6
plotly==3.3.0
pluggy==0.8.0
ply==3.11
prometheus-client==0.4.2
prompt-toolkit==2.0.6
psutil==5.4.7
ptyprocess==0.5.2
py==1.7.0
pyarrow==0.11.1
pyasn1==0.4.4
pycodestyle==2.4.0
pycosat==0.6.3
pycparser==2.19
pycrypto==2.6.1
pycryptodome==3.6.6
pycurl==7.43.0.2
pyflakes==2.0.0
Pygments==2.2.0
pylint==2.1.1
pymc3==3.5
pyodbc==4.0.24
pyOpenSSL==18.0.0
pyparsing==2.2.2
PyQt5==5.11.3
PyQt5-sip==4.19.13
pyramid-arima==0.8.1
PySocks==1.6.8
pystan==2.18.0.0
pytest==3.9.2
python-dateutil==2.7.3
pytz==2018.6
PyWavelets==1.0.1
PyYAML==3.12
pyzmq==17.1.2
qfrm==0.2.0.27
QtAwesome==0.5.1
qtconsole==4.3.1
QtPy==1.5.2
Quandl==3.4.3
redis==2.10.6
repoze.lru==0.7
requests==2.20.0
requests-file==1.4.3
requests-ftp==0.3.1
retrying==1.3.3
rope==0.11.0
rpy2==2.9.4
ruamel-yaml==0.11.14
scikit-image==0.14.1
scikit-learn==0.19.0
scipy==1.1.0
scs==2.0.2
seaborn==0.9.0
Send2Trash==1.5.0
simplegeneric==0.8.1
simplejson==3.16.0
singledispatch==3.4.0.3
sip==4.19.8
six==1.11.0
snowballstemmer==1.2.1
sortedcollections==1.0.1
sortedcontainers==2.0.5
Sphinx==1.8.1
sphinxcontrib-websupport==1.1.0
spyder==3.3.1
spyder-kernels==1.1.0
SQLAlchemy==1.2.12
statistics==1.0.3.5
statsmodels==0.9.0
sympy==1.1.1
tables==3.4.4
tblib==1.3.2
terminado==0.8.1
testpath==0.4.2
Theano==1.0.3
toolz==0.9.0
tornado==5.1.1
tqdm==4.28.1
traitlets==4.3.2
translationstring==1.3
typed-ast==1.1.0
typing==3.6.6
tzlocal==1.5.1
unicodecsv==0.14.1
urllib3==1.24
venusian==1.1.0
wcwidth==0.1.7
webencodings==0.5.1
WebOb==1.8.3
Werkzeug==0.14.1
widgetsnbextension==3.4.2
wrapt==1.10.11
xlrd==1.1.0
XlsxWriter==1.1.2
xlwings==0.13.0
xlwt==1.3.0
yahoo-finance==1.4.0
zict==0.1.3
zope.deprecation==4.3.0
zope.interface==4.6.0
python matplotlib 3d resize jupyter
python matplotlib 3d resize jupyter
edited Nov 9 at 0:01
asked Nov 8 at 23:38
Coolio2654
17319
17319
I tried to reproduce this with random data, and the resulting graph was huge - could you share some sample data and more information about your environment?
– Charles Landau
Nov 8 at 23:55
I added an EDIT to my question with more information.
– Coolio2654
Nov 9 at 0:04
Does changing the figure size e.g. from(20, 16)
to(40, 32)
not change the output at all?
– ImportanceOfBeingErnest
Nov 9 at 0:49
There is a difference, just not in size. See here, imgur.com/a/4wyN9pI The data in the graph ends up looking sparser, and the graph calculation takes a lot longer.
– Coolio2654
Nov 9 at 1:16
That seems logical at this point. I just tested out the jupyter notebook on two other browsers I have (1 completely vanilla with no addons/custom settings), and the same problem is there. Could this be an OS thing?
– Coolio2654
Nov 9 at 18:28
add a comment |
I tried to reproduce this with random data, and the resulting graph was huge - could you share some sample data and more information about your environment?
– Charles Landau
Nov 8 at 23:55
I added an EDIT to my question with more information.
– Coolio2654
Nov 9 at 0:04
Does changing the figure size e.g. from(20, 16)
to(40, 32)
not change the output at all?
– ImportanceOfBeingErnest
Nov 9 at 0:49
There is a difference, just not in size. See here, imgur.com/a/4wyN9pI The data in the graph ends up looking sparser, and the graph calculation takes a lot longer.
– Coolio2654
Nov 9 at 1:16
That seems logical at this point. I just tested out the jupyter notebook on two other browsers I have (1 completely vanilla with no addons/custom settings), and the same problem is there. Could this be an OS thing?
– Coolio2654
Nov 9 at 18:28
I tried to reproduce this with random data, and the resulting graph was huge - could you share some sample data and more information about your environment?
– Charles Landau
Nov 8 at 23:55
I tried to reproduce this with random data, and the resulting graph was huge - could you share some sample data and more information about your environment?
– Charles Landau
Nov 8 at 23:55
I added an EDIT to my question with more information.
– Coolio2654
Nov 9 at 0:04
I added an EDIT to my question with more information.
– Coolio2654
Nov 9 at 0:04
Does changing the figure size e.g. from
(20, 16)
to (40, 32)
not change the output at all?– ImportanceOfBeingErnest
Nov 9 at 0:49
Does changing the figure size e.g. from
(20, 16)
to (40, 32)
not change the output at all?– ImportanceOfBeingErnest
Nov 9 at 0:49
There is a difference, just not in size. See here, imgur.com/a/4wyN9pI The data in the graph ends up looking sparser, and the graph calculation takes a lot longer.
– Coolio2654
Nov 9 at 1:16
There is a difference, just not in size. See here, imgur.com/a/4wyN9pI The data in the graph ends up looking sparser, and the graph calculation takes a lot longer.
– Coolio2654
Nov 9 at 1:16
That seems logical at this point. I just tested out the jupyter notebook on two other browsers I have (1 completely vanilla with no addons/custom settings), and the same problem is there. Could this be an OS thing?
– Coolio2654
Nov 9 at 18:28
That seems logical at this point. I just tested out the jupyter notebook on two other browsers I have (1 completely vanilla with no addons/custom settings), and the same problem is there. Could this be an OS thing?
– Coolio2654
Nov 9 at 18:28
add a comment |
3 Answers
3
active
oldest
votes
up vote
1
down vote
accepted
This is due to a bug in matplotlib 3.0.0. It should not occur in matplotlib 3.0.1.
Options you have:
- Update to matplotlib 3.0.1
Set the following option in your jupyter notebook before plotting
%config InlineBackend.print_figure_kwargs = {'bbox_inches':None}
Use the
%matplotlib notebook
backend instead of the%matplotlib inline
one.
The update seems to have done the trick. What threw me off guard is that my version was not that old in the first place (a few months at best, and did not use to exhibit this error). Thanks for tracking it down for me! And also for letting me know of%matplotlib notebook
for the added graph interaction (assuming that is why you recommended that over%matplotlib inline
)!
– Coolio2654
Nov 12 at 21:57
No, the bug should only be present in inline plotting, so using%matplotlib notebook
would be a third way to get rid of the bug.
– ImportanceOfBeingErnest
Nov 13 at 11:30
add a comment |
up vote
-1
down vote
Try replacing
%pylab inline
pylab.rcParams['figure.figsize'] = (20, 16)
pylab.rcParams['figure.dpi'] = 200
import matplotlib.pyplot as plt
import matplotlib
with
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams['figure.figsize'] = (20, 16)
matplotlib.rcParams['figure.dpi'] = 200
That did not work for me.
– Coolio2654
Nov 9 at 1:15
add a comment |
up vote
-1
down vote
Sometimes that happens to me as well on my Mac.
First use this:
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
matplotlib.rcParams['figure.figsize'] = (20, 16)
matplotlib.rcParams['figure.dpi'] = 200
The trick for my case: First import and then use the %matplotlib inline
command. However, seems like a bug.
1
Why do you say "First import and then use the %matplotlib inline command."? Do you have a reference for that? I'm pretty sure this can cause problems and one should rather first define the backend and then import pyplot. Apart, this is pretty unlikely to solve the OP's problem, see my last comment below the question. Because the figure size itself is actually correct, it's just the dispayed image which gets scaled wrongly.
– ImportanceOfBeingErnest
Nov 9 at 15:20
I was really hoping this would work, but it didn't :(. Thanks for the answer either way, though.
– Coolio2654
Nov 9 at 18:29
According to documentation > before any plotting or import of matplotlib is performed you must execute the%matplotlib
magic command
– HUSMEN
Nov 10 at 11:56
add a comment |
3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
1
down vote
accepted
This is due to a bug in matplotlib 3.0.0. It should not occur in matplotlib 3.0.1.
Options you have:
- Update to matplotlib 3.0.1
Set the following option in your jupyter notebook before plotting
%config InlineBackend.print_figure_kwargs = {'bbox_inches':None}
Use the
%matplotlib notebook
backend instead of the%matplotlib inline
one.
The update seems to have done the trick. What threw me off guard is that my version was not that old in the first place (a few months at best, and did not use to exhibit this error). Thanks for tracking it down for me! And also for letting me know of%matplotlib notebook
for the added graph interaction (assuming that is why you recommended that over%matplotlib inline
)!
– Coolio2654
Nov 12 at 21:57
No, the bug should only be present in inline plotting, so using%matplotlib notebook
would be a third way to get rid of the bug.
– ImportanceOfBeingErnest
Nov 13 at 11:30
add a comment |
up vote
1
down vote
accepted
This is due to a bug in matplotlib 3.0.0. It should not occur in matplotlib 3.0.1.
Options you have:
- Update to matplotlib 3.0.1
Set the following option in your jupyter notebook before plotting
%config InlineBackend.print_figure_kwargs = {'bbox_inches':None}
Use the
%matplotlib notebook
backend instead of the%matplotlib inline
one.
The update seems to have done the trick. What threw me off guard is that my version was not that old in the first place (a few months at best, and did not use to exhibit this error). Thanks for tracking it down for me! And also for letting me know of%matplotlib notebook
for the added graph interaction (assuming that is why you recommended that over%matplotlib inline
)!
– Coolio2654
Nov 12 at 21:57
No, the bug should only be present in inline plotting, so using%matplotlib notebook
would be a third way to get rid of the bug.
– ImportanceOfBeingErnest
Nov 13 at 11:30
add a comment |
up vote
1
down vote
accepted
up vote
1
down vote
accepted
This is due to a bug in matplotlib 3.0.0. It should not occur in matplotlib 3.0.1.
Options you have:
- Update to matplotlib 3.0.1
Set the following option in your jupyter notebook before plotting
%config InlineBackend.print_figure_kwargs = {'bbox_inches':None}
Use the
%matplotlib notebook
backend instead of the%matplotlib inline
one.
This is due to a bug in matplotlib 3.0.0. It should not occur in matplotlib 3.0.1.
Options you have:
- Update to matplotlib 3.0.1
Set the following option in your jupyter notebook before plotting
%config InlineBackend.print_figure_kwargs = {'bbox_inches':None}
Use the
%matplotlib notebook
backend instead of the%matplotlib inline
one.
answered Nov 9 at 20:25
ImportanceOfBeingErnest
119k10117190
119k10117190
The update seems to have done the trick. What threw me off guard is that my version was not that old in the first place (a few months at best, and did not use to exhibit this error). Thanks for tracking it down for me! And also for letting me know of%matplotlib notebook
for the added graph interaction (assuming that is why you recommended that over%matplotlib inline
)!
– Coolio2654
Nov 12 at 21:57
No, the bug should only be present in inline plotting, so using%matplotlib notebook
would be a third way to get rid of the bug.
– ImportanceOfBeingErnest
Nov 13 at 11:30
add a comment |
The update seems to have done the trick. What threw me off guard is that my version was not that old in the first place (a few months at best, and did not use to exhibit this error). Thanks for tracking it down for me! And also for letting me know of%matplotlib notebook
for the added graph interaction (assuming that is why you recommended that over%matplotlib inline
)!
– Coolio2654
Nov 12 at 21:57
No, the bug should only be present in inline plotting, so using%matplotlib notebook
would be a third way to get rid of the bug.
– ImportanceOfBeingErnest
Nov 13 at 11:30
The update seems to have done the trick. What threw me off guard is that my version was not that old in the first place (a few months at best, and did not use to exhibit this error). Thanks for tracking it down for me! And also for letting me know of
%matplotlib notebook
for the added graph interaction (assuming that is why you recommended that over %matplotlib inline
)!– Coolio2654
Nov 12 at 21:57
The update seems to have done the trick. What threw me off guard is that my version was not that old in the first place (a few months at best, and did not use to exhibit this error). Thanks for tracking it down for me! And also for letting me know of
%matplotlib notebook
for the added graph interaction (assuming that is why you recommended that over %matplotlib inline
)!– Coolio2654
Nov 12 at 21:57
No, the bug should only be present in inline plotting, so using
%matplotlib notebook
would be a third way to get rid of the bug.– ImportanceOfBeingErnest
Nov 13 at 11:30
No, the bug should only be present in inline plotting, so using
%matplotlib notebook
would be a third way to get rid of the bug.– ImportanceOfBeingErnest
Nov 13 at 11:30
add a comment |
up vote
-1
down vote
Try replacing
%pylab inline
pylab.rcParams['figure.figsize'] = (20, 16)
pylab.rcParams['figure.dpi'] = 200
import matplotlib.pyplot as plt
import matplotlib
with
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams['figure.figsize'] = (20, 16)
matplotlib.rcParams['figure.dpi'] = 200
That did not work for me.
– Coolio2654
Nov 9 at 1:15
add a comment |
up vote
-1
down vote
Try replacing
%pylab inline
pylab.rcParams['figure.figsize'] = (20, 16)
pylab.rcParams['figure.dpi'] = 200
import matplotlib.pyplot as plt
import matplotlib
with
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams['figure.figsize'] = (20, 16)
matplotlib.rcParams['figure.dpi'] = 200
That did not work for me.
– Coolio2654
Nov 9 at 1:15
add a comment |
up vote
-1
down vote
up vote
-1
down vote
Try replacing
%pylab inline
pylab.rcParams['figure.figsize'] = (20, 16)
pylab.rcParams['figure.dpi'] = 200
import matplotlib.pyplot as plt
import matplotlib
with
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams['figure.figsize'] = (20, 16)
matplotlib.rcParams['figure.dpi'] = 200
Try replacing
%pylab inline
pylab.rcParams['figure.figsize'] = (20, 16)
pylab.rcParams['figure.dpi'] = 200
import matplotlib.pyplot as plt
import matplotlib
with
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams['figure.figsize'] = (20, 16)
matplotlib.rcParams['figure.dpi'] = 200
answered Nov 9 at 0:19
HUSMEN
8011
8011
That did not work for me.
– Coolio2654
Nov 9 at 1:15
add a comment |
That did not work for me.
– Coolio2654
Nov 9 at 1:15
That did not work for me.
– Coolio2654
Nov 9 at 1:15
That did not work for me.
– Coolio2654
Nov 9 at 1:15
add a comment |
up vote
-1
down vote
Sometimes that happens to me as well on my Mac.
First use this:
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
matplotlib.rcParams['figure.figsize'] = (20, 16)
matplotlib.rcParams['figure.dpi'] = 200
The trick for my case: First import and then use the %matplotlib inline
command. However, seems like a bug.
1
Why do you say "First import and then use the %matplotlib inline command."? Do you have a reference for that? I'm pretty sure this can cause problems and one should rather first define the backend and then import pyplot. Apart, this is pretty unlikely to solve the OP's problem, see my last comment below the question. Because the figure size itself is actually correct, it's just the dispayed image which gets scaled wrongly.
– ImportanceOfBeingErnest
Nov 9 at 15:20
I was really hoping this would work, but it didn't :(. Thanks for the answer either way, though.
– Coolio2654
Nov 9 at 18:29
According to documentation > before any plotting or import of matplotlib is performed you must execute the%matplotlib
magic command
– HUSMEN
Nov 10 at 11:56
add a comment |
up vote
-1
down vote
Sometimes that happens to me as well on my Mac.
First use this:
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
matplotlib.rcParams['figure.figsize'] = (20, 16)
matplotlib.rcParams['figure.dpi'] = 200
The trick for my case: First import and then use the %matplotlib inline
command. However, seems like a bug.
1
Why do you say "First import and then use the %matplotlib inline command."? Do you have a reference for that? I'm pretty sure this can cause problems and one should rather first define the backend and then import pyplot. Apart, this is pretty unlikely to solve the OP's problem, see my last comment below the question. Because the figure size itself is actually correct, it's just the dispayed image which gets scaled wrongly.
– ImportanceOfBeingErnest
Nov 9 at 15:20
I was really hoping this would work, but it didn't :(. Thanks for the answer either way, though.
– Coolio2654
Nov 9 at 18:29
According to documentation > before any plotting or import of matplotlib is performed you must execute the%matplotlib
magic command
– HUSMEN
Nov 10 at 11:56
add a comment |
up vote
-1
down vote
up vote
-1
down vote
Sometimes that happens to me as well on my Mac.
First use this:
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
matplotlib.rcParams['figure.figsize'] = (20, 16)
matplotlib.rcParams['figure.dpi'] = 200
The trick for my case: First import and then use the %matplotlib inline
command. However, seems like a bug.
Sometimes that happens to me as well on my Mac.
First use this:
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
matplotlib.rcParams['figure.figsize'] = (20, 16)
matplotlib.rcParams['figure.dpi'] = 200
The trick for my case: First import and then use the %matplotlib inline
command. However, seems like a bug.
edited Nov 12 at 9:54
answered Nov 9 at 8:15
seralouk
5,26322238
5,26322238
1
Why do you say "First import and then use the %matplotlib inline command."? Do you have a reference for that? I'm pretty sure this can cause problems and one should rather first define the backend and then import pyplot. Apart, this is pretty unlikely to solve the OP's problem, see my last comment below the question. Because the figure size itself is actually correct, it's just the dispayed image which gets scaled wrongly.
– ImportanceOfBeingErnest
Nov 9 at 15:20
I was really hoping this would work, but it didn't :(. Thanks for the answer either way, though.
– Coolio2654
Nov 9 at 18:29
According to documentation > before any plotting or import of matplotlib is performed you must execute the%matplotlib
magic command
– HUSMEN
Nov 10 at 11:56
add a comment |
1
Why do you say "First import and then use the %matplotlib inline command."? Do you have a reference for that? I'm pretty sure this can cause problems and one should rather first define the backend and then import pyplot. Apart, this is pretty unlikely to solve the OP's problem, see my last comment below the question. Because the figure size itself is actually correct, it's just the dispayed image which gets scaled wrongly.
– ImportanceOfBeingErnest
Nov 9 at 15:20
I was really hoping this would work, but it didn't :(. Thanks for the answer either way, though.
– Coolio2654
Nov 9 at 18:29
According to documentation > before any plotting or import of matplotlib is performed you must execute the%matplotlib
magic command
– HUSMEN
Nov 10 at 11:56
1
1
Why do you say "First import and then use the %matplotlib inline command."? Do you have a reference for that? I'm pretty sure this can cause problems and one should rather first define the backend and then import pyplot. Apart, this is pretty unlikely to solve the OP's problem, see my last comment below the question. Because the figure size itself is actually correct, it's just the dispayed image which gets scaled wrongly.
– ImportanceOfBeingErnest
Nov 9 at 15:20
Why do you say "First import and then use the %matplotlib inline command."? Do you have a reference for that? I'm pretty sure this can cause problems and one should rather first define the backend and then import pyplot. Apart, this is pretty unlikely to solve the OP's problem, see my last comment below the question. Because the figure size itself is actually correct, it's just the dispayed image which gets scaled wrongly.
– ImportanceOfBeingErnest
Nov 9 at 15:20
I was really hoping this would work, but it didn't :(. Thanks for the answer either way, though.
– Coolio2654
Nov 9 at 18:29
I was really hoping this would work, but it didn't :(. Thanks for the answer either way, though.
– Coolio2654
Nov 9 at 18:29
According to documentation > before any plotting or import of matplotlib is performed you must execute the
%matplotlib
magic command– HUSMEN
Nov 10 at 11:56
According to documentation > before any plotting or import of matplotlib is performed you must execute the
%matplotlib
magic command– HUSMEN
Nov 10 at 11:56
add a comment |
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I tried to reproduce this with random data, and the resulting graph was huge - could you share some sample data and more information about your environment?
– Charles Landau
Nov 8 at 23:55
I added an EDIT to my question with more information.
– Coolio2654
Nov 9 at 0:04
Does changing the figure size e.g. from
(20, 16)
to(40, 32)
not change the output at all?– ImportanceOfBeingErnest
Nov 9 at 0:49
There is a difference, just not in size. See here, imgur.com/a/4wyN9pI The data in the graph ends up looking sparser, and the graph calculation takes a lot longer.
– Coolio2654
Nov 9 at 1:16
That seems logical at this point. I just tested out the jupyter notebook on two other browsers I have (1 completely vanilla with no addons/custom settings), and the same problem is there. Could this be an OS thing?
– Coolio2654
Nov 9 at 18:28