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5 min readYou can count the number of changes in a pandas dataframe by using the groupby function along with the diff function. First, group the dataframe by the desired columns using the groupby function. Then, apply the diff function to calculate the difference between consecutive rows. Finally, count the number of non-zero values in the resulting dataframe to get the total number of changes in each group.
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5 min readTo compile an independent executable file using g++, you will need to have the g++ compiler installed on your system. Once you have the compiler installed, you can create a new source code file with a .cpp extension that contains the C++ code you want to compile.Open a terminal window and navigate to the directory where your source code file is located. Then, use the g++ command to compile the source code file into an executable file.
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4 min readTo change cell values to list format in a pandas dataframe, you can use the apply method along with a lambda function. You can create a lambda function that converts the cell value to a list and then use the apply method to apply this lambda function to each cell in the dataframe. This will transform the cell values into list format.[rating:c36a0b44-a88a-44f5-99fb-b0a6f274c6bc]How to transform pandas dataframe columns to list.
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3 min readTo disable a specific warning in g++, you can use the -Wno-<warning-name> flag when compiling your code. Replace <warning-name> with the specific warning you want to disable. This will suppress that particular warning during compilation. For example, if you want to disable the warning about unused variables, you can use the -Wno-unused-variable flag.
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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.