Primer deepnote jeje

print("¡Hola, Platzinauta!")

¡Hola, Platzinauta!

mi_primera_variable = "tiene un contenido muy extenso" mi_segunda_variable = "es muy interesante"

print(f"¡Este curso {mi_primera_variable} y {mi_segunda_variable}!")

¡Este curso tiene un contenido muy extenso y es muy interesante!

!echo "Cada línea es un comando y tiene su función:" !ls # Listar archivos !pwd # Directorio actual !cd .. && pwd # Moverte un directorio atrás. !echo "¡Lo estás haciendo increíble!"

Cada línea es un comando y tiene su función:
0-0-jvs-introduction_deepnote.ipynb  introduccion_colab_live.ipynb
init.ipynb			     iris.csv
/work
/
¡Lo estás haciendo increíble!

!pip install vega-datasets session-info

!cat /datasets/drive/'Curso de Ciencia de Datos'/iris.csv

"sepal.length","sepal.width","petal.length","petal.width","variety"
5.1,3.5,1.4,.2,"Setosa"
4.9,3,1.4,.2,"Setosa"
4.7,3.2,1.3,.2,"Setosa"
4.6,3.1,1.5,.2,"Setosa"
5,3.6,1.4,.2,"Setosa"
5.4,3.9,1.7,.4,"Setosa"
4.6,3.4,1.4,.3,"Setosa"
5,3.4,1.5,.2,"Setosa"
4.4,2.9,1.4,.2,"Setosa"
4.9,3.1,1.5,.1,"Setosa"
5.4,3.7,1.5,.2,"Setosa"
4.8,3.4,1.6,.2,"Setosa"
4.8,3,1.4,.1,"Setosa"
4.3,3,1.1,.1,"Setosa"
5.8,4,1.2,.2,"Setosa"
5.7,4.4,1.5,.4,"Setosa"
5.4,3.9,1.3,.4,"Setosa"
5.1,3.5,1.4,.3,"Setosa"
5.7,3.8,1.7,.3,"Setosa"
5.1,3.8,1.5,.3,"Setosa"
5.4,3.4,1.7,.2,"Setosa"
5.1,3.7,1.5,.4,"Setosa"
4.6,3.6,1,.2,"Setosa"
5.1,3.3,1.7,.5,"Setosa"
4.8,3.4,1.9,.2,"Setosa"
5,3,1.6,.2,"Setosa"
5,3.4,1.6,.4,"Setosa"
5.2,3.5,1.5,.2,"Setosa"
5.2,3.4,1.4,.2,"Setosa"
4.7,3.2,1.6,.2,"Setosa"
4.8,3.1,1.6,.2,"Setosa"
5.4,3.4,1.5,.4,"Setosa"
5.2,4.1,1.5,.1,"Setosa"
5.5,4.2,1.4,.2,"Setosa"
4.9,3.1,1.5,.2,"Setosa"
5,3.2,1.2,.2,"Setosa"
5.5,3.5,1.3,.2,"Setosa"
4.9,3.6,1.4,.1,"Setosa"
4.4,3,1.3,.2,"Setosa"
5.1,3.4,1.5,.2,"Setosa"
5,3.5,1.3,.3,"Setosa"
4.5,2.3,1.3,.3,"Setosa"
4.4,3.2,1.3,.2,"Setosa"
5,3.5,1.6,.6,"Setosa"
5.1,3.8,1.9,.4,"Setosa"
4.8,3,1.4,.3,"Setosa"
5.1,3.8,1.6,.2,"Setosa"
4.6,3.2,1.4,.2,"Setosa"
5.3,3.7,1.5,.2,"Setosa"
5,3.3,1.4,.2,"Setosa"
7,3.2,4.7,1.4,"Versicolor"
6.4,3.2,4.5,1.5,"Versicolor"
6.9,3.1,4.9,1.5,"Versicolor"
5.5,2.3,4,1.3,"Versicolor"
6.5,2.8,4.6,1.5,"Versicolor"
5.7,2.8,4.5,1.3,"Versicolor"
6.3,3.3,4.7,1.6,"Versicolor"
4.9,2.4,3.3,1,"Versicolor"
6.6,2.9,4.6,1.3,"Versicolor"
5.2,2.7,3.9,1.4,"Versicolor"
5,2,3.5,1,"Versicolor"
5.9,3,4.2,1.5,"Versicolor"
6,2.2,4,1,"Versicolor"
6.1,2.9,4.7,1.4,"Versicolor"
5.6,2.9,3.6,1.3,"Versicolor"
6.7,3.1,4.4,1.4,"Versicolor"
5.6,3,4.5,1.5,"Versicolor"
5.8,2.7,4.1,1,"Versicolor"
6.2,2.2,4.5,1.5,"Versicolor"
5.6,2.5,3.9,1.1,"Versicolor"
5.9,3.2,4.8,1.8,"Versicolor"
6.1,2.8,4,1.3,"Versicolor"
6.3,2.5,4.9,1.5,"Versicolor"
6.1,2.8,4.7,1.2,"Versicolor"
6.4,2.9,4.3,1.3,"Versicolor"
6.6,3,4.4,1.4,"Versicolor"
6.8,2.8,4.8,1.4,"Versicolor"
6.7,3,5,1.7,"Versicolor"
6,2.9,4.5,1.5,"Versicolor"
5.7,2.6,3.5,1,"Versicolor"
5.5,2.4,3.8,1.1,"Versicolor"
5.5,2.4,3.7,1,"Versicolor"
5.8,2.7,3.9,1.2,"Versicolor"
6,2.7,5.1,1.6,"Versicolor"
5.4,3,4.5,1.5,"Versicolor"
6,3.4,4.5,1.6,"Versicolor"
6.7,3.1,4.7,1.5,"Versicolor"
6.3,2.3,4.4,1.3,"Versicolor"
5.6,3,4.1,1.3,"Versicolor"
5.5,2.5,4,1.3,"Versicolor"
5.5,2.6,4.4,1.2,"Versicolor"
6.1,3,4.6,1.4,"Versicolor"
5.8,2.6,4,1.2,"Versicolor"
5,2.3,3.3,1,"Versicolor"
5.6,2.7,4.2,1.3,"Versicolor"
5.7,3,4.2,1.2,"Versicolor"
5.7,2.9,4.2,1.3,"Versicolor"
6.2,2.9,4.3,1.3,"Versicolor"
5.1,2.5,3,1.1,"Versicolor"
5.7,2.8,4.1,1.3,"Versicolor"
6.3,3.3,6,2.5,"Virginica"
5.8,2.7,5.1,1.9,"Virginica"
7.1,3,5.9,2.1,"Virginica"
6.3,2.9,5.6,1.8,"Virginica"
6.5,3,5.8,2.2,"Virginica"
7.6,3,6.6,2.1,"Virginica"
4.9,2.5,4.5,1.7,"Virginica"
7.3,2.9,6.3,1.8,"Virginica"
6.7,2.5,5.8,1.8,"Virginica"
7.2,3.6,6.1,2.5,"Virginica"
6.5,3.2,5.1,2,"Virginica"
6.4,2.7,5.3,1.9,"Virginica"
6.8,3,5.5,2.1,"Virginica"
5.7,2.5,5,2,"Virginica"
5.8,2.8,5.1,2.4,"Virginica"
6.4,3.2,5.3,2.3,"Virginica"
6.5,3,5.5,1.8,"Virginica"
7.7,3.8,6.7,2.2,"Virginica"
7.7,2.6,6.9,2.3,"Virginica"
6,2.2,5,1.5,"Virginica"
6.9,3.2,5.7,2.3,"Virginica"
5.6,2.8,4.9,2,"Virginica"
7.7,2.8,6.7,2,"Virginica"
6.3,2.7,4.9,1.8,"Virginica"
6.7,3.3,5.7,2.1,"Virginica"
7.2,3.2,6,1.8,"Virginica"
6.2,2.8,4.8,1.8,"Virginica"
6.1,3,4.9,1.8,"Virginica"
6.4,2.8,5.6,2.1,"Virginica"
7.2,3,5.8,1.6,"Virginica"
7.4,2.8,6.1,1.9,"Virginica"
7.9,3.8,6.4,2,"Virginica"
6.4,2.8,5.6,2.2,"Virginica"
6.3,2.8,5.1,1.5,"Virginica"
6.1,2.6,5.6,1.4,"Virginica"
7.7,3,6.1,2.3,"Virginica"
6.3,3.4,5.6,2.4,"Virginica"
6.4,3.1,5.5,1.8,"Virginica"
6,3,4.8,1.8,"Virginica"
6.9,3.1,5.4,2.1,"Virginica"
6.7,3.1,5.6,2.4,"Virginica"
6.9,3.1,5.1,2.3,"Virginica"
5.8,2.7,5.1,1.9,"Virginica"
6.8,3.2,5.9,2.3,"Virginica"
6.7,3.3,5.7,2.5,"Virginica"
6.7,3,5.2,2.3,"Virginica"
6.3,2.5,5,1.9,"Virginica"
6.5,3,5.2,2,"Virginica"
6.2,3.4,5.4,2.3,"Virginica"
5.9,3,5.1,1.8,"Virginica"

from vega_datasets import data cars = data.cars()

cars

Nameobject

ford pinto1.5%

ford maverick1.2%

309 others97.3%

Miles_per_Gallonfloat64

9.0 - 46.6

chevrolet chevelle malibu

buick skylark 320

plymouth satellite

amc rebel sst

ford torino

ford galaxie 500

chevrolet impala

plymouth fury iii

pontiac catalina

amc ambassador dpl

import matplotlib.pyplot as plt import numpy as np import pandas as pd import scipy as sc import seaborn as sns

import session_info session_info.show()