When Two Dataframes Become One: Pandas Merging and Joining

In Pandas, the merge function allows you to merge two dataframes using a common field. In addition, pandas provide several functions for connecting and joining DataFrames, including merge(), join(), and concat().

Merge () is used to combine DataFrames based on one or more common columns or keys. It supports various types of joins, such as inner, outer, left, and right joins.

Join () is similar to merge(), but it is used specifically to combine DataFrames that have a common index.

Both merging and joining can be done using different types of join operations:

• inner join: only returns rows with matching keys in both DataFrames.
• outer join: returns all rows from both DataFrames
• left join: delivers all rows from the left DataFrame and any matching rows from the right DataFrame
• right join: returns all rows from the right DataFrame and any matching rows from the left DataFrame.

Pandas also provide merge_asof() function which is similar to an ordered left-join, but where the join is done on nearest key(s) less than the given key.

concat() is used to stack DataFrames vertically or horizontally. It can be used to append one DataFrame to another, or to concatenate multiple DataFrames together.

When using these functions, it is important to consider the index and column labels of the DataFrames being merged or joined and the desired behavior when there are duplicate values or missing data.

For example:

import pandas as pd

df1 = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
                    'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3']})
df2 = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
                    'C': ['C0', 'C1', 'C2', 'C3'],
                    'D': ['D0', 'D1', 'D2', 'D3']})
result = pd.merge(df1, df2, on='key')

The on parameter specifies the field that should be used to join the two dataframes. The resulting dataframe will contain the rows from both dataframes that have the same value in the key column. The resulting dataframe will look like this:

     key   A   B   C   D
0   K0  A0  B0  C0  D0
1   K1  A1  B1  C1  D1
2   K2  A2  B2  C2  D2
3   K3  A3  B3  C3  D3

You can use a few other parameters to customize the merge operation. For example, you can use the how parameter to specify the type of join to perform. The possible values are ‘inner’, ‘outer’, ‘left’, and ‘right’. The default value is ‘inner’, meaning that only the rows present in both dataframes will be included in the result.

The left_on and right_on parameters can specify the fields to use for the join when the field names are different in the two dataframes.

The left and right parameters can be used to specify the dataframes to join.

The suffixes parameter can be used to specify the suffixes for the column names in the resulting dataframe when a column name is present in both dataframes.

The join function is similar to merge, but it is more flexible in terms of the input data types.

For example, it can be used to join two dataframes or two series, or a dataframe and a series. The join function uses the index of the objects to be joined, rather than a common field, as the join key.

For example:

import pandas as pd

df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2'], 'B': ['B0', 'B1', 'B2']}, index=['K0', 'K1', 'K2'])
df2 = pd.DataFrame({'C': ['C0', 'C1'], 'D': ['D0', 'D1']}, index=['K0', 'K1'])
result = df1.join(df2)