4 ms·

Curious what other approach you would take to do exploratory data analysis? It's so natural to me I can't think of another way that would be practical to achiev

by zmmmmm 3y ago

Curious what other approach you would take to do exploratory data analysis? It's so natural to me I can't think of another way that would be practical to achieve the same workflow.

akasakahakada 3y ago
Handcrafted machine code in punch hole cards. Interactive environment without compile nonsense is just too new for folks.
toolslive 3y ago
emacs org mode can do this but is not tied to just python. Anyway, something like this works: #+BEGIN_SRC python :results file import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt fn = 'my_fig.png' plt.plot([1, 2, 3, 2.5, 2.8]) plt.savefig('my_fig.png', dpi=50) return fn #+END_SRC #+RESULTS: [[file:my_fig.png]]
- bjornasm 3y ago
  In a true notebook you would maybe want to do the following: import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt plt.plot([1, 2, 3, 2.5, 2.8]) Alright, saving the figure at 50 dpi first plt.savefig('my_fig.png', dpi=50) Trying a bit more DPI to see if that makes a difference plt.savefig('my_fig2.png', dpi=150) Oh, wrong numbers, forgot that the fourth datapoint was going to signify 100, going back to 50 dpi as well plt.plot([1, 2, 3, 100, 2.3]) plt.savefig('my_fig4.png', dpi=50) It seems like your example misses the interactivity.
acomjean 3y ago
We have a lot of scientists using Rstudio. It’s not quite the same but you can do it. It lets you view your data frames like a spread sheet and generate graphs. It’s R and I get that Jupiter supports R but it’s always has some issue with some dependency.
- chaxor 3y ago
  Ew. R. No thank you.
  - acomjean 3y ago
    I used to think like that. Programmers hate R. But I took a biostatistics class and it really is the best tool for that job. Plus the graphic output can't be beat (ggplot2) and fairly easy to install packages make it quite valuable tool.
    - drBonkers 3y ago
      > it really is the best tool for that job. Besides the ecosystem, what makes R better than Python or Julia for biostats?
      - acomjean 3y ago
        Can't speak to julia.. The statistics built in are great. They're just there, less need to find a package (general stats, ttest, chi_squared test...). We tend to use the "tidyverse" packages [1] https://r4ds.hadley.nz/ https://r4ds.hadley.nz/. Bio-python is amazing for manipulating biodata, but once the data is extracted and you need statistics, our scientist seem to use R. I really don't love R's syntax, but I get why they use it. I use python all the time for data wrangling (right now I'm pulling sequences from a fasta file to inject into a table). Rstudio is like an IDE for your data. You can view the data tables, graph different things etc. If you try the first chapter of the R4data Science book, you can see how get up and graphing and analyzing quite quickly. https://r4ds.hadley.nz/data-visualize.html https://r4ds.hadley.nz/data-visualize.html Though at this point Python and R are necessary depending on what package/ algorithm you want to use. There are some good packages for single cell analysis: We use "Seurat". https://satijalab.org/seurat/articles/get_started_v5.html https://satijalab.org/seurat/articles/get_started_v5.html Jupyter supports R now with an add in, so its less of an issue.

by zmmmmm 3y ago

Curious what other approach you would take to do exploratory data analysis? It's so natural to me I can't think of another way that would be practical to achieve the same workflow.

akasakahakada 3y ago

Handcrafted machine code in punch hole cards. Interactive environment without compile nonsense is just too new for folks.

toolslive 3y ago

emacs org mode can do this but is not tied to just python. Anyway, something like this works: #+BEGIN_SRC python :results file import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt fn = 'my_fig.png' plt.plot([1, 2, 3, 2.5, 2.8]) plt.savefig('my_fig.png', dpi=50) return fn #+END_SRC #+RESULTS: [[file:my_fig.png]]

bjornasm 3y ago
In a true notebook you would maybe want to do the following: import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt plt.plot([1, 2, 3, 2.5, 2.8]) Alright, saving the figure at 50 dpi first plt.savefig('my_fig.png', dpi=50) Trying a bit more DPI to see if that makes a difference plt.savefig('my_fig2.png', dpi=150) Oh, wrong numbers, forgot that the fourth datapoint was going to signify 100, going back to 50 dpi as well plt.plot([1, 2, 3, 100, 2.3]) plt.savefig('my_fig4.png', dpi=50) It seems like your example misses the interactivity.

acomjean 3y ago

We have a lot of scientists using Rstudio. It’s not quite the same but you can do it. It lets you view your data frames like a spread sheet and generate graphs. It’s R and I get that Jupiter supports R but it’s always has some issue with some dependency.

chaxor 3y ago
Ew. R. No thank you.
- acomjean 3y ago
  I used to think like that. Programmers hate R. But I took a biostatistics class and it really is the best tool for that job. Plus the graphic output can't be beat (ggplot2) and fairly easy to install packages make it quite valuable tool.
  - drBonkers 3y ago
    > it really is the best tool for that job. Besides the ecosystem, what makes R better than Python or Julia for biostats?
    - acomjean 3y ago
      Can't speak to julia.. The statistics built in are great. They're just there, less need to find a package (general stats, ttest, chi_squared test...). We tend to use the "tidyverse" packages [1] https://r4ds.hadley.nz/ https://r4ds.hadley.nz/. Bio-python is amazing for manipulating biodata, but once the data is extracted and you need statistics, our scientist seem to use R. I really don't love R's syntax, but I get why they use it. I use python all the time for data wrangling (right now I'm pulling sequences from a fasta file to inject into a table). Rstudio is like an IDE for your data. You can view the data tables, graph different things etc. If you try the first chapter of the R4data Science book, you can see how get up and graphing and analyzing quite quickly. https://r4ds.hadley.nz/data-visualize.html https://r4ds.hadley.nz/data-visualize.html Though at this point Python and R are necessary depending on what package/ algorithm you want to use. There are some good packages for single cell analysis: We use "Seurat". https://satijalab.org/seurat/articles/get_started_v5.html https://satijalab.org/seurat/articles/get_started_v5.html Jupyter supports R now with an add in, so its less of an issue.