Using Google Drive as a filesystem in Google Colab¶

Mount Google Drive¶

In [ ]:

from google.colab import drive
drive.mount('/content/gdrive')

from google.colab import drive drive.mount('/content/gdrive')

Mounted at /content/gdrive

Basic Linux commands¶

Prefixing a command with "!" will allow for commands to be sent directly to the operating system underneath. Which is a linux machine.

In [1]:

!pwd

!pwd

/content

In [ ]:

!ls

!ls

gdrive	sample_data

In [ ]:

!ls '/content/gdrive/MyDrive/Marquins Colab Notebooks/Intro to Google Colab training'

!ls '/content/gdrive/MyDrive/Marquins Colab Notebooks/Intro to Google Colab training'

 00_intro_to_colab.ipynb
 01_using_google_drive_as_a_filesystem.ipynb
 02_reading_and_saving_to_google_sheets.ipynb
 03_pulling_data_from_Adobe_analytics.ipynb
'04_querying BigQuery.ipynb'
 iris.csv
 Misc.ipynb
 prem_data.gsheet
'Welcome To Colaboratory [MS copy]'

Load a csv file that is saved on Google Drive¶

In [ ]:

import pandas as pd

import pandas as pd

In [ ]:

data = pd.read_csv('/content/gdrive/MyDrive/Marquins Colab Notebooks/Intro to Google Colab training/iris.csv')

data = pd.read_csv('/content/gdrive/MyDrive/Marquins Colab Notebooks/Intro to Google Colab training/iris.csv')

In [ ]:

data.head()

data.head()

Out[ ]:

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	Iris-setosa
1	4.9	3.0	1.4	0.2	Iris-setosa
2	4.7	3.2	1.3	0.2	Iris-setosa
3	4.6	3.1	1.5	0.2	Iris-setosa
4	5.0	3.6	1.4	0.2	Iris-setosa

Basic interactive filtering¶

In [ ]:

%load_ext google.colab.data_table

%load_ext google.colab.data_table

In [ ]:

data

data

Out[ ]:

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	Iris-setosa
1	4.9	3.0	1.4	0.2	Iris-setosa
2	4.7	3.2	1.3	0.2	Iris-setosa
3	4.6	3.1	1.5	0.2	Iris-setosa
4	5.0	3.6	1.4	0.2	Iris-setosa
...	...	...	...	...	...
145	6.7	3.0	5.2	2.3	Iris-virginica
146	6.3	2.5	5.0	1.9	Iris-virginica
147	6.5	3.0	5.2	2.0	Iris-virginica
148	6.2	3.4	5.4	2.3	Iris-virginica
149	5.9	3.0	5.1	1.8	Iris-virginica

150 rows × 5 columns

In [ ]: