Showing posts with the label PL/SQL error handling

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How to Check Column Nulls and Replace: Pandas

Here is a post that shows how to count Nulls and replace them with the value you want in the Pandas Dataframe. We have explained the process in two steps - Counting and Replacing the Null values. Count null values (column-wise) in Pandas ## count null values column-wise null_counts = df.isnull(). sum() print(null_counts) ``` Output: ``` Column1    1 Column2    1 Column3    5 dtype: int64 ``` In the above code, we first create a sample Pandas DataFrame `df` with some null values. Then, we use the `isnull()` function to create a DataFrame of the same shape as `df`, where each element is a boolean value indicating whether that element is null or not. Finally, we use the `sum()` function to count the number of null values in each column of the resulting DataFrame. The output shows the count of null values column-wise. to count null values column-wise: ``` df.isnull().sum() ``` ##Code snippet to count null values row-wise: ``` df.isnull().sum(axis=1) ``` In the above code, `df` is the Panda

PL/SQL Sample code and error handling mechanism

SAMPLE PL/SQL CREATE TABLE dummy ( dummy_value VARCHAR2(1)); DECLARE -- Define local variable. my_string VARCHAR2(1) := ' '; my_number NUMBER; BEGIN -- Select a white space into a local variable. SELECT ' ' INTO my_string FROM dummy; -- Attempt to assign a single white space to a number. my_number := TO_NUMBER(my_string); EXCEPTION WHEN no_data_found THEN dbms_output.put_line('SELECT-INTO'||CHR(10)||SQLERRM); END; / Output and Error: The program returns the following output, which illustrates formatting user- defined exceptions.  The CHR(10) inserts a line return and provides a clean break between the program's SQLCODE and SQLERRM messages: RAISE my_error SQLCODE [1]  SQLERRM [User-Defined Exception]