Introducción a Deepnote (Miguel Curso Platzi)

1 + 2

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:
init.ipynb  intro-deepnote-guia-completa.ipynb	intro-deepnote.ipynb
/work
/
¡Lo estás haciendo increíble!

!pip install vega-datasets session-info

Collecting vega-datasets
  Downloading vega_datasets-0.9.0-py3-none-any.whl (210 kB)
     |████████████████████████████████| 210 kB 31.9 MB/s 
Collecting session-info
  Downloading session_info-1.0.0.tar.gz (24 kB)
Requirement already satisfied: pandas in /shared-libs/python3.7/py/lib/python3.7/site-packages (from vega-datasets) (1.2.5)
Collecting stdlib_list
  Downloading stdlib_list-0.8.0-py3-none-any.whl (63 kB)
     |████████████████████████████████| 63 kB 1.8 MB/s 
Requirement already satisfied: python-dateutil>=2.7.3 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from pandas->vega-datasets) (2.8.2)
Requirement already satisfied: numpy>=1.16.5 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from pandas->vega-datasets) (1.19.5)
Requirement already satisfied: pytz>=2017.3 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from pandas->vega-datasets) (2021.1)
Requirement already satisfied: six>=1.5 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas->vega-datasets) (1.16.0)
Building wheels for collected packages: session-info
  Building wheel for session-info (setup.py) ... done
  Created wheel for session-info: filename=session_info-1.0.0-py3-none-any.whl size=8045 sha256=1621fcef0ed6c796819fee8fc01c2ac03915008eedca3515fda3a1b4389f6f6d
  Stored in directory: /root/.cache/pip/wheels/bd/ad/14/6a42359351a18337a8683854cfbba99dd782271f2d1767f87f
Successfully built session-info
Installing collected packages: stdlib-list, vega-datasets, session-info
Successfully installed session-info-1.0.0 stdlib-list-0.8.0 vega-datasets-0.9.0

!ls /datasets/personal-google-drive/Cursos-Platzi/'Curso de Entorno de Trabajo para Ciencia de Datos con Jupyter Notebooks y Anaconda'

introduccion_colab-guia_completa.ipynb
introduccion_colab_live_vacia.ipynb
iris.csv
slides_curso_jupyter_notebooks_c4ee4183-f83b-4b15-ba5d-ef42cc8b93a8.pdf

!cat /datasets/personal-google-drive/Cursos-Platzi/'Curso de Entorno de Trabajo para Ciencia de Datos con Jupyter Notebooks y Anaconda'/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"

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

# load an example dataset from vega_datasets import data cars = data.cars() # plot the dataset, referencing dataframe column names import altair as alt alt.Chart(cars).mark_bar().encode( x=alt.X('Miles_per_Gallon', bin=True), y='count()', )