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4 min readTo count columns by row in Python Pandas, you can use the count method along the rows axis. This method will return the number of non-null values in each row of the dataframe, effectively counting the number of columns that have a value for that specific row. You can use the count method like this: df.count(axis=1), where df is your pandas dataframe. This will return a pandas series with the count of columns for each row.
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5 min readTo print a __int128 in g++, you can use the printf function with the format specifier "%lld" for signed __int128 values. Alternatively, you can use the __int128's built-in conversion to a string and then print the string using cout or printf. Another option is to create a custom function to handle the printing of __int128 values. Just be aware that __int128 is not a standard data type in C++ and may not be supported on all compilers.
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5 min readTo make a for loop run faster on a large pandas dataframe, you can optimize your code by avoiding iterative operations on the dataframe and instead using vectorized operations wherever possible. Vectorized operations are more efficient because they apply operations to entire columns or rows at once, rather than processing each element individually.
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4 min readTo use C++ 20 in g++, you need to make sure you have the latest version of g++ installed on your system. You can check the version of g++ by running the command g++ --version in your terminal.Once you have confirmed that you have the latest version of g++, you can start writing code using C++ 20 features. You can specify the C++ standard you want to use by adding the flag -std=c++20 to your g++ compilation command.For example, if you have a file named example.
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4 min readTo change the pandas dataframe style back to default, simply reset the style using the reset_index() method. This will remove any custom styling that has been applied to the dataframe and revert it back to the default styling.[rating:c36a0b44-a88a-44f5-99fb-b0a6f274c6bc]How to create a new style template for pandas dataframes?To create a new custom style template for pandas dataframes, you can follow these steps:Create a new Python file or script where you define your custom style template.
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8 min readTo use modules in C++ 20 using g++, you'll need to first enable the experimental module feature by using the following compiler flags: -std=c++20 -fmodules-ts Next, you'll need to write your module interface file (.ixx) and module implementation file (.cxx). The module interface file will declare the module and its dependencies, while the module implementation file will define the module.
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4 min readIn Python pandas, to divide numbers more accurately, you can utilize the div() function. This function allows you to perform division on two dataframes or series while handling cases such as division by zero or missing values more accurately. Additionally, you can use the numeric_only=True parameter to only perform division on numeric data within the dataframes or series, ensuring more precise results. By employing these methods, you can divide numbers more accurately in Python pandas.
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5 min readTo pass Unicode arguments to g++ in a Linux environment, you can use the -Wl,-rpath,@loader_path flag along with the correct encoding for the Unicode characters you want to pass. For example, if you want to pass the Unicode character 'รก' as an argument, you can encode it in UTF-8 as '\xc3\xa1' and pass it to g++ using the -Wl,-rpath,@loader_path flag. This flag allows you to pass arbitrary arguments to the linker, including Unicode characters encoded in the proper format.
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4 min readTo convert a nested dictionary to a pandas dataframe, you can use the pandas DataFrame constructor. First, flatten the nested dictionary to a dictionary with a single level of keys by recursively iterating through the nested dictionary. Then, pass the flattened dictionary to the DataFrame constructor to create a pandas dataframe. This will create a tabular structure where each key in the dictionary will become a column in the dataframe.
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3 min readTo add a name to a grouped column in pandas, you can use the "rename" method along with the "groupby" method. First, group the DataFrame by the desired column(s) using the groupby method. Then, use the "agg" method to specify the function(s) you want to apply to the grouped data.After grouping the data, you can use the "rename" method to add a name to the grouped column.
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3 min readTo unset the -fstack-protector flag with g++, you can use the -fno-stack-protector flag instead. This flag tells the compiler to not use stack protection measures for your code. You can simply add -fno-stack-protector to your g++ command when compiling your code to disable the stack protector feature.[rating:f57ed76a-ab98-4054-8d2c-1baca0521009]How to undo the effects of the -fstack-protector flag in g++.