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How to Check Column Nulls and Replace: Pandas

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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
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Oracle 12C 'Bitmap Index' benefits over B-tree Index

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Oracle 12C 'Bitmap Index' benefits over B-tree Index
#Oracle 12C 'Bitmap Index' benefits over B-tree Index:
A bitmap index has a significantly different structure from a B-tree index in the leaf node of the index. It stores one string of bits for each possible value (the cardinality) of the column being indexed.

Note: One string of BITs means -Each tupple of possible value it assigns '1' bit in a string.So, all the BITs become a string ( This is an example, on which column you created BIT map index)

The length of the string of bits is the same as the number of rows in the table being indexed.

In addition to saving a tremendous amount of space compared to traditional indexes, a bitmap index can provide dramatic improvements in response time because Oracle can quickly remove potential rows from a query containing multiple WHERE clauses long before the table itself needs to be accessed.

Multiple bitmaps can use logical AND and OR operations to determine which rows to access from the table.

Although you can use a bitmap index on any column in a table, it is most efficient when the column being indexed has a low cardinality, or number of distinct values.

For example, the GENDER column in the PERS table will be either NULL, M, or F. The bitmap index on the GENDER column will have only three bitmaps stored in the index. On the other hand, a bitmap index on the LAST_NAME column will have close to the same number of bitmap strings as rows in the table itself. The queries looking for a particular last name will most likely take less time if a full table scan is performed instead of using an index. In this case, a traditional B-tree non-unique index makes more sense.

A variation of bitmap indexes called bitmap join indexes creates a bitmap index on a table column that is frequently joined with one or more other tables on the same column. This provides tremendous benefits in a data warehouse environment where a bitmap join index is created on a fact table and one or more dimension tables, essentially pre-joining those tables and saving CPU and I/O resources when an actual join is performed.

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