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Google offers two libraries for generative AI: google-generativeai for complex, direct work with Gemini models, and google-genai for simplified access and easier integration in applications.
This tutorial explains creating an In-App Update feature for apps, utilizing Google Play’s AppUpdateManager to check for updates, and initiating updates if available upon app launch.
Debugging code generated by ChatGPT by pasting the error into ChatGPT for it to resolve its own problem, most of…
This content explains how to link a Google Form to a Google Sheet and submit user email data using a C# script in Unity.
Sometimes, even though you already installed torchtext, but can’t import it, or when you import it, you receive this error:…
In NumPy, you can perform element-wise operations on matrices using vectorized operations, numpy.vectorize for custom functions, and numpy.apply_along_axis for applying functions along specific axes.
The content outlines various PySpark functions used for data manipulation in DataFrames. Key functions include filtering with where(), limiting rows with limit(), returning distinct rows, dropping columns, and grouping by criteria. Each function includes a brief example, illustrating how to access, modify, and aggregate data effectively within PySpark.
This song and example code help remember PySpark data frame creation functions easier. Key functions include creating Data Frames, displaying data, printing schemas, and filtering. The document facilitates understanding how to manipulate data effectively in PySpark, making it a useful reference for users working with large datasets.
This song and code examples help us understand and remember various Pandas functions for data manipulation, including grouping, aggregating, and transforming data. Key functions include groupby(), pivot_table(), resample(), rolling(), expanding(), cumsum(), cumprod(), cut(), qcut(), aggregate(), and transform().
A cute, catchy song on various Pandas functions applied to DataFrames. Key functions include sorting values, resetting the index, dropping columns and duplicates, sampling data, and handling missing values. Example codes illustrate each function’s output, demonstrating how to manipulate and visualize data effectively with Pandas.
