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3 min readTo convert a column with a list to different rows in pandas, you can use the explode function. This function will expand the list items into separate rows, while duplicating the values in the other columns.
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4 min readTo use strcmp in g++, you need to include the header file in your program. Then, you can use strcmp to compare two strings by passing them as arguments to the function. strcmp returns an integer value that indicates the result of the comparison: 0 if the strings are equal, a negative value if the first string is less than the second, and a positive value if the first string is greater than the second.
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5 min readIn Python pandas, you can combine a start date and end date by using the pd.date_range() function. This function allows you to create a range of dates between a start and end date.To do this, you can specify the start date, end date, and frequency of the dates you want to generate as parameters in the pd.date_range() function.
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4 min readTo remove a path from a linked library in g++, you can use the -L flag followed by the directory path you want to remove. This flag is used to specify additional directories to search for libraries. By not including the directory you want to remove in your g++ command, you effectively prevent g++ from searching for libraries in that directory. Remember to also remove any references to the specific library you want to exclude from your code.
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3 min readTo convert a string column to a dictionary type in a pandas dataframe, you can use the apply function along with the json.loads method. First, make sure that the strings in the column are in valid dictionary format. Then, apply the json.loads method to each value in the column using the apply function. This will convert each string in the column to a dictionary type. Finally, assign the column back to the dataframe to update it with the new dictionary values.
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3 min readTo get g++ to find import files, you need to make sure that the directory containing the header files is included in the search path. This can be done by using the -I option followed by the directory path when compiling the code. For example, if the header files are in a directory called "include", you would use the command g++ -I include -o output_filename source_filename.cpp. This way, g++ will be able to locate and include the necessary header files during the compilation process.
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5 min readTo apply a custom function to grouped pandas data, you can use the groupby() function in pandas to create groups of data based on a specific column. Once you have grouped the data, you can apply a custom function to each group using the apply() function. This allows you to perform custom calculations or transformations on each group of data separately. The custom function that you apply can be defined using a lambda function or by creating a separate function outside of the apply() call.
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5 min readTo put an execution time limit on g++, you can use the command "ulimit" to set a maximum CPU time limit for the process. This can be done by running the following command before compiling and running your program:ulimit -t <time_limit>For example, to set a time limit of 5 seconds, you can run:ulimit -t 5This will restrict the execution time of the g++ compiled program to 5 seconds. If the program exceeds this time limit, it will be terminated automatically by the operating system.
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6 min readTo use the Unicode character set in g++, you need to ensure that your source code files are saved in a Unicode encoding such as UTF-8. This can typically be done using a text editor that supports Unicode encoding.When compiling your code with g++, make sure to specify the appropriate flags to indicate that the source files are in UTF-8 encoding. You can do this by adding the "-finput-charset=UTF-8" flag to your g++ command.
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4 min readYou can use the loc method in pandas to select rows or columns based on a condition. For example, to get the values of a specific column (column_name) where a condition is met (e.g. where another column (condition_column) is equal to a certain value), you can use the following code: selected_values = df.loc[df['condition_column'] == 'condition_value', 'column_name'] This will return a series of values from column_name where the condition is true.
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3 min readTo run C++ files using g++ and cmake, you first need to create a CMakeLists.txt file in the root directory of your project. This file will contain the necessary instructions for cmake to build your project using g++.In the CMakeLists.txt file, you need to specify the minimum required version of cmake, the project name, and the source files that need to be built. You will also need to specify any additional libraries or dependencies that your project relies on.After creating the CMakeLists.