Python Function to Plot Missing Values Percentage in DataFrame

This code defines a function called 'plot_missing' which takes a pandas DataFrame and a string representing its name as input. The function aims to generate a bar plot visualizing the percentage of missing values in each column of the DataFrame.

1. Data Preparation:

   • It calculates the number of rows and columns in the input DataFrame.
   • It creates a new DataFrame 'notna_df' by converting each value in the original DataFrame to True if it's not NaN and False if it's NaN using the 'notna()' method. The 'sum()' method counts the number of True values in each column, representing the non-NaN values.
   • The result is then transformed into a transposed DataFrame so that columns become rows.

2. Calculating Percentages:

   • The 'percent_notna_df' DataFrame is created by dividing each value in 'notna_df' by the total number of rows, effectively calculating the percentage of non-NaN values in each column.

3. Visualization with Seaborn:

   • Using the seaborn library, it creates a horizontal bar plot with 'barplot()' where the x-axis represents the percentage of non-NaN values and the y-axis represents the column names.
   • It sets the x-axis limit to 110 for a better visual representation.
   • Each bar is labeled with its corresponding percentage using 'bar_label()'.

4. Adding Labels and Title:

   • It sets the title of the plot using the provided name and indicates the plot represents the percentage of non-null values.
   • It labels the x-axis as '%' and the y-axis as 'Column'.

This function provides a straightforward way to understand the distribution of missing values within a DataFrame, facilitating data analysis and decision-making.