spyder: Missing dependencies: rtree
Every time I start spyder oit tells me that there are mising dependencies. rtree 0.9.7 is missing.
conda install rtree says that it is installed
workarounds on this poage are either ineffectual or too complicated
PASTE TRACEBACK HERE
## Versions
Spyder 5.2.1
Python 3.8.8 64-bit |
Qt 5.12.9 |
PyQt5 5.12.3 |
Windows 10
### Dependencies
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Mandatory:
atomicwrites >=1.2.0 : 1.4.0 (OK) chardet >=2.0.0 : 4.0.0 (OK) cloudpickle >=0.5.0 : 2.0.0 (OK) cookiecutter >=1.6.0 : 1.6.0 (OK) diff_match_patch >=20181111 : 20200713 (OK) intervaltree >=3.0.2 : 3.0.2 (OK) IPython >=7.6.0 : 7.30.1 (OK) jedi >=0.17.2;<0.19.0 : 0.18.1 (OK) jellyfish >=0.7 : 0.8.9 (OK) jsonschema >=3.2.0 : 4.3.0 (OK) keyring >=17.0.0 : 23.4.0 (OK) nbconvert >=4.0 : 6.3.0 (OK) numpydoc >=0.6.0 : 1.1.0 (OK) paramiko >=2.4.0 : 2.8.1 (OK) parso >=0.7.0;<0.9.0 : 0.8.3 (OK) pexpect >=4.4.0 : 4.8.0 (OK) pickleshare >=0.4 : 0.7.5 (OK) psutil >=5.3 : 5.8.0 (OK) pygments >=2.0 : 2.10.0 (OK) pylint >=2.5.0 : 2.12.2 (OK) pyls_spyder >=0.4.0 : 0.4.0 (OK) pylsp >=1.3.2;<1.4.0 : 1.3.3 (OK) pylsp_black >=1.0.0 : 1.0.1 (OK) qdarkstyle =3.0.2 : 3.0.2 (OK) qstylizer >=0.1.10 : 0.2.1 (OK) qtawesome >=1.0.2 : 1.1.1 (OK) qtconsole >=5.2.1;<5.3.0 : 5.2.2 (OK) qtpy >=1.5.0 : 1.11.3 (OK) rtree >=0.9.7 : None (NOK) setuptools >=49.6.0 : 59.6.0 (OK) sphinx >=0.6.6 : 4.3.1 (OK) spyder_kernels >=2.2.0;<2.3.0 : 2.2.0 (OK) textdistance >=4.2.0 : 4.2.2 (OK) three_merge >=0.1.1 : 0.1.1 (OK) watchdog >=0.10.3 : 2.1.6 (OK) zmq >=17 : 22.3.0 (OK)
Optional:
cython >=0.21 : None (NOK) matplotlib >=2.0.0 : 3.4.3 (OK) numpy >=1.7 : 1.21.3 (OK) pandas >=1.1.1 : 1.3.4 (OK) scipy >=0.17.0 : None (NOK) sympy >=0.7.3 : None (NOK)
About this issue
- Original URL
- State: closed
- Created 3 years ago
- Comments: 15 (8 by maintainers)
Hi @Hawxxer thank you for your input here! I think the problem is that installing from different conda channels (defaults vs conda-forge for example) can lead to binary incompatibilities which then can lead then to problems like the one here. I would say that the best approach is to use always the conda-forge channel for everything (so you can get the up-to-date versions of the packages).
Also, just in case, @mczakk are you mixing in your setup packages channels (e.g defaults vs conda-forge)?
@mczakk, I also encountered a lot of problems with dependencies in Python when I was doing my PhD. Through constant troubleshooting, I finally gave up and switched to R and RStudio. In data analysis and visualization these languages are very similar, but however R is smoother and less problematic. Sorry for the offtopic, but maybe someone is also feeling overwhelmed and this tip might be helpful.