Intro to DataFrame Merge

DataFrame Merge is an operation that is usually used to combine two or more types of DataFrames based on a certain criterion, such as various types of columns that have the same contents and values. This DataFrame operation is very similar to one of the operations in Structured query language (SQL), namely the JOIN operation in Structured query language (SQL) in database use (Database). In the Pandas Library, someone who frequently interacts with data such as Data Scientists and Data Analysts can use one of the methods in the Pandas Library namely the '.merge() method to perform DataFrame Merge or merge operations on DataFrames.

Types of Merge Methods in Pandas Library using Python Programming Language

To facilitate the use of the merge method in the Pandas Library, there are several types of merge that can be adjusted by someone who interacts with data (Data Scientist and Data Analyst) that can be done and used in the Pandas Library, here are the types of merge methods in the Pandas library using Python Programming Language:

1. Inner Merge: By using the Inner Merge type, only rows with the same and corresponding values in both DataFrames will be displayed in the resulting merge.
2. Outer Merge (Full Outer Join): By using the type of Outer Merge (Full Outer Join), all rows located in both DataFrames will be displayed in the results of the merged output, using the NaN value for rows that have no similarities in other forms of DataFrames.
3. Left Merge (Left Outer Join): By using the type of Left Merge (Left Outer Join), all rows from the first DataFrame (located on the left) will be displayed in the results of the merged output, using the NaN value for rows that have no similarities in the form of the second DataFrame (located on the right).
4. Right Merge (Right Outer Join): Using the Right Merge (Right Outer Join) type, it will result in all rows from the second DataFrame (located on the right) will be displayed in the resulting merge, using the NaN value for rows that have no similarities in the first DataFrame (located on the left).

NaN or the acronym for Not a Number is a form of default representation of the Pandas Library for any missing, invalid, or not found value in a given DataFrame. In the context of using DataFrame Merge, the NaN (Not a Number) value will appear or be displayed, when no similarities are found for a particular column in one of the two forms of a given DataFrame and a merge has been made between them (DataFrame located on the left (First Data Frame) and on the right (Second DataFrame) used).

Some Parameters that are often used by the Merge Method in the Pandas Library using Python Programming Language:

1. Parameter ' on ' : This parameter 'on' is usually used to select the key column to be used for merge as needed.
2. 'how' parameter: This 'how' parameter is used to determine or select the merge type (inner, outer, left, right) to be used as needed.
3. The 'left_on' and 'right_on' parameters: The 'left_on' and right_on' parameters are typically used when different key column names are found in the merged DataFrame that has been merged, someone using the Pandas Library (such as a Data Scientist and Data Analyst) can use the 'left_on' and 'right_on' parameters to select the key column names in the first DataFrame and the second DataFrame.
4. Parameter 'suffixes': The parameter 'suffixes' is usually used when if a column type with the same name is found in the first DataFrame and the second DataFrame, someone using the Pandas Library (such as Data Scientist and Data Analyst) can add a parameter 'suffixes' to distinguish between the two.

Append Method in DataFrame Merge in Pandas Library using Python Programming Language

The Append method or 'append' is one of the methods in the Pandas Library that is usually used to combine two types of DataFrame or Series objects that are combined into a single larger object form and includes both values from the DataFrame that is the element of the merge. This is one simple way to add new rows to an existing DataFrame.