Jupyter making 3D matplotlib graphs extremely small











up vote
0
down vote

favorite
1












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()),



enter image description here



This is my data and my current code,



enter image description here



%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









share|improve this question
























  • 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

















up vote
0
down vote

favorite
1












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()),



enter image description here



This is my data and my current code,



enter image description here



%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









share|improve this question
























  • 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















up vote
0
down vote

favorite
1









up vote
0
down vote

favorite
1






1





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()),



enter image description here



This is my data and my current code,



enter image description here



%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









share|improve this question















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()),



enter image description here



This is my data and my current code,



enter image description here



%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






share|improve this question















share|improve this question













share|improve this question




share|improve this question








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




















  • 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














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.







share|improve this answer





















  • 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


















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





share|improve this answer





















  • That did not work for me.
    – Coolio2654
    Nov 9 at 1:15


















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.






share|improve this answer



















  • 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











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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.







share|improve this answer





















  • 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















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.







share|improve this answer





















  • 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













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.







share|improve this answer












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.








share|improve this answer












share|improve this answer



share|improve this answer










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


















  • 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












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





share|improve this answer





















  • That did not work for me.
    – Coolio2654
    Nov 9 at 1:15















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





share|improve this answer





















  • That did not work for me.
    – Coolio2654
    Nov 9 at 1:15













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





share|improve this answer












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






share|improve this answer












share|improve this answer



share|improve this answer










answered Nov 9 at 0:19









HUSMEN

8011




8011












  • 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




That did not work for me.
– Coolio2654
Nov 9 at 1:15










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.






share|improve this answer



















  • 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















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.






share|improve this answer



















  • 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













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.






share|improve this answer














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.







share|improve this answer














share|improve this answer



share|improve this answer








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














  • 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


















 

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