Posts (page 63)
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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.
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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.