2. Importing data in colab notebook

There are several ways to import time series data into Google Colab, depending on the format and location of the data. Here are some common methods:

2.1 Importing data from local storage

You can upload time series data directly from your local machine to Google Colab. To do this, click on the file icon on the left sidebar of the Colab interface, then click “Upload” and select the file(s) containing your time series data.

from google.colab import files

# Upload the file
uploaded = files.upload()

# Read the uploaded file
for filename in uploaded.keys():
  print('Uploaded file:', filename)
  with open(filename, 'r') as file:
      data = file.read()
          # Process the data as needed

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

2.2 Importing data from Google Drive

If your time series data is stored in Google Drive, you can mount your Google Drive in Colab to access the data.Use the following code snippet to mount Google Drive:

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

You will be prompted to authenticate and provide an authorization code. After successful authentication, you can access your Google Drive files and load your time series data.

from google.colab import drive

# Mount Google Drive
drive.mount('/content/drive')

# Read a file from Google Drive
file_path = '/content/drive/MyDrive/book writing/AirPassengers.csv'
with open(file_path, 'r') as file:
    data = file.read()
    # Process the data as needed
print(data[:101])

Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
Month,#Passengers
1949-01,112
1949-02,118
1949-03,132
1949-04,129
1949-05,121
1949-06,135
1949-07,148

2.3 Importing data from a remote URL

If your time series data is available as a direct download link, you can use the wget or curl command in Colab to download the data.

import requests

url = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/airline-passengers.csv'
response = requests.get(url)
data = response.text

print(data[:96])
# Process the data as needed

"Month","Passengers"
"1949-01",112
"1949-02",118
"1949-03",132
"1949-04",129
"1949-05",121

2.4 Importing data from a GitHub repository

import requests

url = 'https://raw.githubusercontent.com/ejgao/Time-Series-Datasets/master/Electric_Production.csv'
response = requests.get(url)
data = response.text

print(data[:100])
# Process the data as needed

DATE,IPG2211A2N
1/1/1985,72.5052
2/1/1985,70.672
3/1/1985,62.4502
4/1/1985,57.4714
5/1/1985,55.3151

2.5 Google Cloud Storage (GCS)

If your data is stored in Google Cloud Storage, you can use the google-cloud-storage library to access it. First, you’ll need to authenticate your Google Cloud account:

   from google.colab import auth
   auth.authenticate_user()

Then, you can use the gsutil command to copy data from GCS to your Colab environment.

These are some common methods to import time series data into Google Colab, depending on the data’s location and format. Choose the method that suits your specific use case and data source.

Explorering the sheets with the same name as excel file

excelfile = pd.ExcelFile(filepath)
excelfile.sheet_names

['2017', '2018']

excelfile.parse('2017')[:5]

	Line_Item_ID	Date	Credit_Card_Number	Quantity	Menu_Item
0	1	2017-01-01	7437926611570790	1	spicy miso ramen
1	2	2017-01-01	7437926611570790	1	spicy miso ramen
2	3	2017-01-01	8421920068932810	3	tori paitan ramen
3	4	2017-01-01	8421920068932810	3	tori paitan ramen
4	5	2017-01-01	4787310681569640	1	truffle butter ramen

excelfile.parse('2018')[:5]

	Line_Item_ID	Date	Credit_Card_Number	Quantity	Menu_Item
0	36765	2018-01-01	4178504356986060	1	burnt garlic tonkotsu ramen
1	36766	2018-01-01	4178504356986060	1	burnt garlic tonkotsu ramen
2	36767	2018-01-01	9385429783634070	2	vegetarian spicy miso
3	36768	2018-01-01	9385429783634070	2	vegetarian spicy miso
4	36769	2018-01-01	2528867357453560	1	tori paitan ramen