Untitled Python Project

1 + 4

variable = 'eres incfreible'

variable

!pvd !ls

/bin/bash: pvd: command not found
sample_data

!pip install session-info

Collecting session-info
  Downloading session_info-1.0.0.tar.gz (24 kB)
Collecting stdlib_list
  Downloading stdlib_list-0.8.0-py3-none-any.whl (63 kB)
     |████████████████████████████████| 63 kB 3.3 MB/s 
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=e190938a8d018c1350e766217d1649a1e19ee692e19c8bba9bf33de1c8412400
  Stored in directory: /root/.cache/pip/wheels/bd/ad/14/6a42359351a18337a8683854cfbba99dd782271f2d1767f87f
Successfully built session-info
Installing collected packages: stdlib-list, session-info
Successfully installed session-info-1.0.0 stdlib-list-0.8.0
WARNING: You are using pip version 20.1.1; however, version 22.0.3 is available.
You should consider upgrading via the '/root/venv/bin/python -m pip install --upgrade pip' command.

import session_info

session_info.show()

!cat drive/MyDrive/DM/iris.csv

cat: drive/MyDrive/DM/iris.csv: No such file or directory

!ls /datasets/mydrive

'22858 Formato_Presentación de DM_Obligatorio (0cb2c072).xlsx'
'22858 Formato_Presentación de DM_Obligatorio (0d9c03a1).xlsx'
'22858 Formato_Presentación de DM_Obligatorio.xlsx'
 Classroom
'Colab Notebooks'
 DM
'Formato_Presentación de DM_Obligatorio.xlsx'
 iris.csv
'Tutorial Placement.pdf'

!cat /datasets/mydrive/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 !pip install vega_datasets

Collecting vega_datasets
  Downloading vega_datasets-0.9.0-py3-none-any.whl (210 kB)
     |████████████████████████████████| 210 kB 19.7 MB/s 
Requirement already satisfied: pandas in /shared-libs/python3.7/py/lib/python3.7/site-packages (from vega_datasets) (1.2.5)
Requirement already satisfied: pytz>=2017.3 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from pandas->vega_datasets) (2021.3)
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: 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: 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)
Installing collected packages: vega-datasets
Successfully installed vega-datasets-0.9.0
WARNING: You are using pip version 20.1.1; however, version 22.0.3 is available.
You should consider upgrading via the '/root/venv/bin/python -m pip install --upgrade pip' command.

This chart is empty

Chart was probably not set up properly in the notebook

This chart is empty

Chart was probably not set up properly in the notebook

# 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()', )

# load an example dataset from vega_datasets import data cars = data.cars() import altair as alt interval = alt.selection_interval() base = alt.Chart(cars).mark_point().encode( y='Miles_per_Gallon', color=alt.condition(interval, 'Origin', alt.value('lightgray')) ).properties( selection=interval ) base.encode(x='Acceleration') | base.encode(x='Horsepower')

Execution error

ModuleNotFoundError: No module named 'vega_datasets'

This chart is empty

Chart was probably not set up properly in the notebook