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3 min readTo read data with a text line as a column in pandas, you can use the read_csv function and pass the parameter sep='\t' if the text line is delimited by tabs. If the text line is enclosed in quotes, you can use the quoting parameter with a value of 3. This will help pandas properly read the text line as a column in the DataFrame. Additionally, you may need to specify the header=None parameter if the text line does not contain column names.
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3 min readTo get a specific string of a pandas column value, you can use string methods such as str.contains(), str.extract(), or regular expressions. These methods allow you to filter and extract specific strings from a pandas column based on certain criteria. By using these methods, you can easily access and manipulate the strings in a pandas column to get the desired values.[rating:c36a0b44-a88a-44f5-99fb-b0a6f274c6bc]What is the fastest way to search for a specific string in a pandas column value.
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5 min readWhen encountering an "undefined reference" error with OpenCV and g++, it usually means that the compiler is unable to find the necessary definitions for the functions or libraries being used in the code.One common solution is to make sure that you are linking the necessary OpenCV libraries when compiling the code. This can be done by adding the appropriate flags or options to the g++ command: g++ -o outputfile inputfile.
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3 min readTo merge cells in pandas using Python, you can use the groupby() and agg() functions. First, you need to group the rows based on the columns you want to merge. Then, you can use the agg() function to apply an aggregation function (e.g., join()) to merge the cells in each group. Finally, you can reset the index to flatten the dataframe and display the merged cells as a single row. This allows you to combine multiple rows into a single row based on a common value or values in the grouped columns.
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3 min readTo statically link an external library using g++, you need to specify the path to the library when compiling your program. This can be done by using the -L flag followed by the directory containing the library, and then using the -l flag followed by the name of the library (without the "lib" prefix and the file extension).For example, if you have a library called libfoo.a located in /path/to/lib, you would compile your program like this: g++ -o my_program my_program.
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4 min readTo display a pandas dataframe in tkinter, you can create a tkinter widget such as a Text or Label widget and then insert the dataframe into it as a string. You can convert the dataframe to a string using the to_string() method in pandas. Alternatively, you can convert the dataframe to a numpy array and then use the insert() method in the Text widget to display the data. Make sure to organize the data in a format that is easy to read and visually appealing for the user.
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3 min readTo link GTKmm with g++ on Linux, you first need to install the gtkmm development package using your package manager. This package includes all the necessary header files and libraries required to compile GTKmm programs.Once you have the gtkmm development package installed, you can compile your GTKmm program using the g++ compiler with the appropriate flags.
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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.