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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++.
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4 min readTo split a string using multiple characters in pandas, you can use the str.split() method with a regular expression pattern as the separator. For example, if you want to split a string based on both commas and spaces, you can pass a regex pattern such as '[,\s]+' to the str.split() method. This will split the string whenever it encounters either a comma or a space.
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5 min readTo create heatmaps for different rows in pandas, you can use the seaborn library in conjunction with pandas. First, you need to import both libraries. Then, you can select the rows you want to visualize from your pandas DataFrame and pass them to the seaborn heatmap function. Make sure to set the appropriate parameters such as the colormap and the size of the heatmap. Finally, display the heatmap to visualize the data distribution across the selected rows.
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4 min readTo add a column based on a boolean list in pandas, you can use the loc function to insert values based on the condition provided by the boolean list. By selecting the rows where the boolean list evaluates to True, you can assign a value to a new column in the DataFrame. This allows you to dynamically create a new column based on the conditions specified by the boolean list.[rating:c36a0b44-a88a-44f5-99fb-b0a6f274c6bc]How to combine multiple boolean lists in pandas.
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4 min readTo extract the number before specific strings in pandas, you can use regular expressions in combination with the str.extract function. First, you need to define a regular expression pattern that matches the number before the specific string you are looking for. Then, you can use the str.extract function to extract the matched number from the target column in your pandas DataFrame. This approach allows you to extract the desired number before the specific string efficiently and accurately.