My code for loading image dataset in colab don't work
df = pd.read_csv("/content/drive/MyDrive/kaggle/artist/images/images.csv")
Can somebody help me?
You need to mount Google Drive to get that data. It will look something like this:
from google.colab import drive
drive.mount('/gdrive')
with open('/gdrive/MyDrive/kaggle/artist/images/images.csv', 'r') as f:
df = pd.read_csv(f)
Colab provides example code snippets for you to use... Insert > Code Snippets > Search: "google drive" > Select "Mounting Google Drive in your VM". Good luck!
Related
I have an Excel file uploaded to my ML workspace.
I can access the file as an azure FileDataset object. However, I don't know how to get it into a pandas DataFrame since 'FileDataset' object has no attribute 'to_dataframe'.
Azure ML notebooks seem to make a point of avoiding pandas for some reason.
Does anyone know how to get blob files into pandas dataframes from within Azure ML notebooks?
To explore and manipulate a dataset, it must first be downloaded from the blob source to a local file, which can then be loaded in a pandas DataFrame.
Here are the steps to follow for this procedure:
Download the data from Azure blob with the following Python code sample using Blob service. Replace the variable in the following code with your specific values:
from azure.storage.blob import BlobServiceClient
import pandas as pd
STORAGEACCOUNTURL= <storage_account_url>
STORAGEACCOUNTKEY= <storage_account_key>
LOCALFILENAME= <local_file_name>
CONTAINERNAME= <container_name>
BLOBNAME= <blob_name>
#download from blob
t1=time.time()
blob_service_client_instance =
BlobServiceClient(account_url=STORAGEACCOUNTURL,
credential=STORAGEACCOUNTKEY)
blob_client_instance =
blob_service_client_instance.get_blob_client(CONTAINERNAME, BLOBNAME,
snapshot=None)
with open(LOCALFILENAME, "wb") as my_blob:
blob_data = blob_client_instance.download_blob()
blob_data.readinto(my_blob)
t2=time.time()
print(("It takes %s seconds to download "+BLOBNAME) % (t2 - t1))
Read the data into a pandas DataFrame from the downloaded file.
#LOCALFILE is the file path
dataframe_blobdata = pd.read_csv(LOCALFILENAME)
For more details you can follow this link
I cloned a repo from GitHub to the Google Cloud Workbench. I haven't been able to read in my data to the Jupyter notebook. It seems like it is unable to locate the file. I have checked the file spellings and location, it all seems to be in place. I also tried to read it in as
PATH = "data/countypres_2000-2020.csv"
df = pd.read_csv(PATH)
or as
PATH = "eco395m-homework-6/data/countypres_2000-2020.csv"
df = pd.read_csv(PATH)
Try this:
PATH = r"/data/countypres_2000-2020.csv"
df = pd.read_csv(PATH)
How can I change the dictionary to dataframe in colab?
I added two pictures. One from colab and the other from notebook.
https://i.stack.imgur.com/o9yMf.png
https://i.stack.imgur.com/DcY8T.png
Thanks!
Using your notebook you have read your data using Pandas library
data = pd.read_csv('data.csv')
And that's why you it was uploaded as dataframe. While the files.upload() funstion it uplodes your files as dictionary and you need to read it as dataframe. However, you just need to read your data again after it has been uploeded using
data = pd.read_csv('DailyDelhiClimateTest.csv.csv')
Best of luck :)
I'm trying to import a file to c-lab. I've tried various versions https://buomsoo-kim.github.io/colab/2018/04/15/Colab-Importing-CSV-and-JSON-files-in-Google-Colab.md/
#import packages
import pandas as pd
pd.plotting.register_matplotlib_converters()
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import io
print("Setup Complete")
from google.colab import files
uploaded = files.upload()
# Read the file into a variable power_data
#power_data = pd.read("DE_power prices historical.csv")
data = pd.read_csv('DE_power prices historical.csv', error_bad_lines=False)
Keep getting error:
enter image description here
Try using this method it works a bit easier:
Upload .csv files to your Google Drive
Run the following code in your Colab cell:
from google.colab import drive
drive.mount('/content/drive')
Follow the link the output cell gives you and verify your Gmail account
Import using Pandas like:
power_data = pd.read_csv('/content/drive/My Drive/*filename.csv*')
Mount google drive in google-colab
from google.colab import drive
drive.mount('/content/drive')
copy file path add into URL variable
import pandas as pd
url = 'add copy path your csv file'
df=pd.read_csv(url)
df.head()
The Google Colab output is being truncated. I've looked through the settings and I didn't see a limitation there. What is the best option to solve the problem?
I had the same problem and managed it by writing the output on a file on drive:
from google.colab import drive
drive.mount('/content/drive')
import os
os.chdir("/content/drive/")
with open('/content/drive/output.txt','w') as out:
out.write(' abcd \n')
I have the same issue currently, I found this link on medium, check the part "How do I use Colab for long training times/runs?"
So basically according to this article you need to store checkpoints on your drive and by using callbacks from Keras, you will be able to run it nonstop.
from keras.callbacks import *
filepath = "/content/gdrive/My Drive/MyCNN/epochs:{epoch:03d}-val_acc:{val_acc:.3f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]
Other solution to solve this problem is according to my researches, you should put this code to your console but make sure that you save your progress to drive, because it will be terminated in 12 hours.
function ClickConnect() {
console.log("Working");
document
.querySelector('#top-toolbar > colab-connect-button')
.shadowRoot.querySelector('#connect')
.click()
}
setInterval(ClickConnect, 60000)