psicobloc

{
    "git.autofetch": true,
    "editor.suggestSelection": "first",
    "vsintellicode.modify.editor.suggestSelection": "automaticallyOverrodeDefaultValue",
    "C_Cpp.default.compilerPath": "/usr/bin/gcc",
    "terminal.integrated.enableMultiLinePasteWarning": false,
    "jupyter.askForKernelRestart": false,
    "editor.fontSize": 15,
    "editor.renderWhitespace": "boundary",
    "editor.bracketPairColorization.enabled": true,

psicobloc

/* QWERTY
 * ,-----------------------------------------.                    ,-----------------------------------------.
 * | ESC  |   1  |   2  |   3  |   4  |   5  |                    |   6  |   7  |   8  |   9  |   0  |  `   |
 * |------+------+------+------+------+------|                    |------+------+------+------+------+------|
 * | Tab  |   Q  |   W  |   E  |   R  |   T  |                    |   Y  |   U  |   I  |   O  |   P  |  -   |
 * |------+------+------+------+------+------|                    |------+------+------+------+------+------|
 * |LCTRL |   A  |   S  |   D  |   F  |   G  |-------.    ,-------|   H  |   J  |   K  |   L  |   ;  |  '   |
 * |------+------+------+------+------+------|   [   |    |    ]  |------+------+------+------+------+------|
 * |LShift|   Z  |   X  |   C  |   V  |   B  |-------|    |-------|   N  |   M  |   ,  |   .  |   /  |RShift|
 * `-----------------------------------------/       /     \      \-----------------------------------------'

psicobloc

{
"Theme stuff": {
    "workbench.colorCustomizations": {
        "[Tokyo Night Storm]": {
            "foreground": "#959cbd",
            "editorLineNumber.foreground": "#5a3e85",
            "editorLineNumber.activeForeground": "#d0d560",
            "editor.findMatchHighlightBackground": "#f10bbb66",
        },
    },

psicobloc

/* QWERTY
 * ,-----------------------------------------.                    ,-----------------------------------------.
 * | ESC  |   1  |   2  |   3  |   4  |   5  |                    |   6  |   7  |   8  |   9  |   0  |  `   |
 * |------+------+------+------+------+------|                    |------+------+------+------+------+------|
 * | Tab  |   Q  |   W  |   E  |   R  |   T  |                    |   Y  |   U  |   I  |   O  |   P  |  -   |
 * |------+------+------+------+------+------|                    |------+------+------+------+------+------|
 * |LCTRL |   A  |   S  |   D  |   F  |   G  |-------.    ,-------|   H  |   J  |   K  |   L  |   ;  |  '   |
 * |------+------+------+------+------+------|   [   |    |    ]  |------+------+------+------+------+------|
 * |LShift|   Z  |   X  |   C  |   V  |   B  |-------|    |-------|   N  |   M  |   ,  |   .  |   /  |RShift|

psicobloc

ej_uno = pd.DataFrame({ 'atributo1': [2,5,3,67,8,9],'atributo2': [5,8,79,21,6,5],'atributo3': [3,6,89,4,1,6],
'atributo4': [5,7,5,3,65,4],'atributo5': ['uno','dos','tres','cuatro','cinco','seis']}, index=['inst1','inst2','inst3','inst4','inst5','inst6'])

ej_uno.atributo1.describe()
ej_uno.atributo5.describe()
"""To interpret the min, 25%, 50%, 75% and max values, imagine sorting each column from
lowest to highest value. The first (smallest) value is the min. If you go a quarter
way through the list, you'll find a number that is bigger than 25% of the values and
smaller than 75% of the values. That is the 25% value (pronounced "25th percentile").
The 50th and 75th percentiles are defined analogously, and the max is the largest number. """

psicobloc

"""iloc uses the Python stdlib indexing scheme, where the first element of the range is included and the last one excluded.
So 0:10 will select entries 0,...,9. loc, meanwhile, indexes inclusively. So 0:10 will select entries 0,...,10.
If we have a DataFrame with index values Apples, ..., Potatoes, ..., and we want to select
"all the alphabetical fruit choices between Apples and Potatoes", then it's a lot more convenient to index df.loc['Apples':'Potatoes']
than it is to index something like df.loc['Apples', 'Potatoet] (t coming after s in the alphabet).
when the DataFrame index is a simple numerical list, e.g. 0,...,1000.
In this case df.iloc[0:1000] will return 1000 entries, while df.loc[0:1000] return 1001 of them!"""
import pandas as pd

ej_uno = pd.DataFrame({ 'Yes':[50,21,89,15] , 'No':[131,2,54,69] })