jkitchin
commented
1 year ago I would switch to emacs-jupyter:
#+begin_src jupyter-python
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
titanic = pd.read_csv("titanic.csv")
titanic
#+end_src
#+RESULTS:
:RESULTS:
| | PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked |
|-----+-------------+----------+--------+---------------------------------------------------+--------+------+-------+-------+------------------+---------+-------+----------|
| 0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S |
| 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
| 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S |
| 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S |
| 4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 886 | 887 | 0 | 2 | Montvila, Rev. Juozas | male | 27.0 | 0 | 0 | 211536 | 13.0000 | NaN | S |
| 887 | 888 | 1 | 1 | Graham, Miss. Margaret Edith | female | 19.0 | 0 | 0 | 112053 | 30.0000 | B42 | S |
| 888 | 889 | 0 | 3 | Johnston, Miss. Catherine Helen "Carrie" | female | NaN | 1 | 2 | W./C. 6607 | 23.4500 | NaN | S |
| 889 | 890 | 1 | 1 | Behr, Mr. Karl Howell | male | 26.0 | 0 | 0 | 111369 | 30.0000 | C148 | C |
| 890 | 891 | 0 | 3 | Dooley, Mr. Patrick | male | 32.0 | 0 | 0 | 370376 | 7.7500 | NaN | Q |
891 rows × 12 columns
:END:You could also look into tabulate like this:
#+BEGIN_SRC python :results output org
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from tabulate import tabulate
titanic = pd.read_csv("titanic.csv")
print(tabulate(titanic.head(), headers=titanic.columns, tablefmt='orgtbl'))
#+END_SRC
#+RESULTS:
#+begin_src org
| | PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked |
|---+-------------+----------+--------+-----------------------------------------------------+--------+-----+-------+-------+------------------+---------+-------+----------|
| 0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22 | 1 | 0 | A/5 21171 | 7.25 | nan | S |
| 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Thayer) | female | 38 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
| 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26 | 0 | 0 | STON/O2. 3101282 | 7.925 | nan | S |
| 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35 | 1 | 0 | 113803 | 53.1 | C123 | S |
| 4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35 | 0 | 0 | 373450 | 8.05 | nan | S |
#+end_src
OrionRandD
How to prettify this org mode block analysis of titanic.csv?
If i use jupyter-notebook in a browser, it will render beautifully.
https://yewtu.be/watch?v=I_oq-9OJkfc
But, I think, with Emacs/scimax I can do better. Or, Am I dreaming that Emacs is not the right tool for the job?
As a suggestion, if you have time, perhaps you could reproduce this girl tuto using scimax... It would be a big add to us who love scimax for science...
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
Titanic machine learning with scimax
+begin_src python :results output
import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt
titanic = pd.read_csv("titanic.csv")
print()
print(titanic[titanic["Age"]==30])
print(titanic[titanic["Age"]!=30])
+end_src
+RESULTS:
: PassengerId Survived Pclass ... Fare Cabin Embarked : 0 1 0 3 ... 7.2500 NaN S : 1 2 1 1 ... 71.2833 C85 C : 2 3 1 3 ... 7.9250 NaN S : 3 4 1 1 ... 53.1000 C123 S : 4 5 0 3 ... 8.0500 NaN S : : [5 rows x 12 columns]
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Dataset is here: ;; https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;