Colab and Jupyter Notebooks Quick Tour

Welcome

First we are going to visit a notebbok from .

Loading and saving data

There are several options for Google external sources:

Local files
Google Drive
Google Sheets
Google Cloud Storage

Upload local files

With this we can choose a file to upload from our local system:



In [1]:

    
from google.colab import files

uploaded = files.upload()

for fn in uploaded.keys():
  print('User uploaded file "{name}" with length {length} bytes'.format(
      name=fn, length=len(uploaded[fn])))









    





     
     
      Upload widget is only available when the cell has been executed in the
      current browser session. Please rerun this cell to enable.
      
       






    



Saving auto-mpg.data to auto-mpg.data
User uploaded file "auto-mpg.data" with length 30286 bytes

We can check to see if it was uploaded correctly by using a system call:



In [10]:

    
!ls









    



auto-mpg.data  datalab

Upload a file from Google Drive using PyDrive

We must install the PyDrive library first:



In [1]:

    
!pip install -U -q PyDrive

We need to import the libraries we need



In [2]:

    
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials

We must first authenticate with google and create the GoogleDrive client.



In [3]:

    
auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)

Now we can download the file and print its contents.
If an output is to long we can delete it with the corresponding option in the code cell menu.



In [0]:

    
data = drive.CreateFile({'id': '1pi40wcRtqCdqwbBx9ISrAZVLHF-MMQox'})
print('Downloaded content "{}"'.format(data.GetContentString()))

Upload a file from the web

Another option is to download the file from the UCI ML Repository. We can use the requests library.



In [0]:

    
import requests
r = requests.get('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data')
print(r.text)

Python + Numpy + Matplotlib

We are going to use python to read the file. We need to split the file into lines first:



In [11]:

    
iris_rows = r.text.split('\n')
print (iris_rows[:5])









    



['5.1,3.5,1.4,0.2,Iris-setosa', '4.9,3.0,1.4,0.2,Iris-setosa', '4.7,3.2,1.3,0.2,Iris-setosa', '4.6,3.1,1.5,0.2,Iris-setosa', '5.0,3.6,1.4,0.2,Iris-setosa']

We use slicing to get fragments from the list.



In [13]:

    
iris_rows[-4:]









    Out[13]:





['6.2,3.4,5.4,2.3,Iris-virginica', '5.9,3.0,5.1,1.8,Iris-virginica', '', '']

We use list comprehensions to get only what we need:



In [0]:

    
[row.split(',')[:-1] for row in iris_rows[:-2]]

Finally by using the function map we can get the array we need.



In [0]:

    
[map(float, row.split(',')[:-1]) for row in iris_rows[:-2]]

In Python 3, map is a generator object, but we can extract a list.



In [0]:

    
[list(map(float, row.split(',')[:-1])) for row in iris_rows[:-2]]

Let's create a NumPy array:



In [21]:

    
import numpy
iris_data = [list(map(float, row.split(',')[:-1])) for row in iris_rows[:-2]]
iris = numpy.array(iris_data)
iris[:5]









    Out[21]:





array([[5.1, 3.5, 1.4, 0.2],
       [4.9, 3. , 1.4, 0.2],
       [4.7, 3.2, 1.3, 0.2],
       [4.6, 3.1, 1.5, 0.2],
       [5. , 3.6, 1.4, 0.2]])

We can explore the data with matplotlib:



In [23]:

    
import matplotlib.pyplot as plt
x = iris[:50,0]
y = iris[:50,1]
plt.plot(x, y, 'r.')
plt.show()

We can plot two types of flowers:



In [24]:

    
plt.plot( iris[:50,0], iris[:50,1], 'r.')
plt.plot( iris[51:100,0], iris[51:100,1], 'b.')









    Out[24]:





[<matplotlib.lines.Line2D at 0x7f70c375d748>]

Let's do three



In [26]:

    
plt.plot( iris[:50,0], iris[:50,1], 'r.')
plt.plot( iris[51:100,0], iris[51:100,1], 'b.')
plt.plot( iris[101:,0], iris[101:,1], 'g.')









    Out[26]:





[<matplotlib.lines.Line2D at 0x7f70c37ef128>]

DEAP notebook

Here we have an example DEAP notebook from Github. We can visit or we can invite. If we invite we need to !pip install deap