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_bfe32adb-b4d0-4e06-acc9-f8842ac6090d.ipynb
init.ipynb
notebook.ipynb
/work
/
¡Lo estás haciendo increíble!
!pip install vega-datasets session-info
!cat /datasets/drive/Colab/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
0
chevrolet chevelle malibu
18
1
buick skylark 320
15
2
plymouth satellite
18
3
amc rebel sst
16
4
ford torino
17
5
ford galaxie 500
15
6
chevrolet impala
14
7
plymouth fury iii
14
8
pontiac catalina
14
9
amc ambassador dpl
15
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()