Remember after you run the script, you'll be prompted in your default browser to select your Google account and permit your application for the scopes you specified earlier, don't worry, this will only happen the first time you run it, and then token.
Note: Sometimes you'll encounter a "This application is not validated" warning since Google didn't verify your app after you choose your Google account, It's okay to go "Advanced" section and permit the application to your account. Different scope means different privileges, you need to delete token. We used service. Next, we used MediaFileUpload class to upload the sample file and pass it to the same service. Let's run it:. After I ran the code, a new folder was created in my Google Drive:.
And indeed, after I enter that folder, I see the file we just uploaded:. We used a text file for demonstration, but you can upload any type of file you want. Check the full code of uploading files to Google Drive. Google Drive enables us to search for files and directories using the previously used list method just by passing the 'q' parameter, the below function takes the Drive API service and query, and returns filtered items:. Let's see how to use this function:. Prints the names and ids of the first 10 files the user has access to.
If this is your first time running the sample, the sample opens a new window prompting you to authorize access to your data:. This section describes some common issues that you may encounter while attempting to run this quickstart and suggests possible solutions. This error can occur in Mac OSX where the default installation of the six module a dependency of the Python library is loaded before the one that pip installed.
This error is due to a bug in httplib2. To resolve this problem, upgrade to the latest version of httplib2 using this command:. When running the pip install command you may receive the following error:. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.
Estimating churners before they discontinue using a product or service is extremely important. In this ML project, you will develop a churn prediction model in telecom to predict customers who are most likely subject to churn. In this time series project, you will learn how to build an autoregressive model in Python from Scratch for forecasting time series data.
In this deep learning project, you will build a convolutional neural network using MNIST dataset for handwritten digit recognition. In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.
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