Merging DataFrames is a common operation in data analysis, and it can be easily done using Dask's DataFrame methods.
Assuming you have two DataFrames df1 and df2 with columns in common, you can merge them using the merge() method as follows:
merged = df1.merge(df2, on='common_column') |
In this example, common_column is the column(s) that both df1 and df2 share in common, and the on parameter is used to specify the column(s) to merge on. By default, merge() performs an inner join, meaning that only rows that have matching values in both DataFrames are included in the result.
You can also use the how parameter to specify a different type of join, such as a left join or right join:
merged = df1.merge(df2, on='common_column', how='left') |
This would perform a left join, meaning that all the rows in df1 are included in the result, even if they don't have a matching value in df2.
You can also merge on multiple columns by passing a list of column names to the on parameter:
merged = df1.merge(df2, on=['common_column_1', 'common_column_2']) |
If the column names are different in the two DataFrames, you can use the left_on and right_on parameters to specify the corresponding column names:
merged = df1.merge(df2, left_on='df1_column', right_on='df2_column') |
Once you have merged the DataFrames, you can perform further analyses or computations using the resulting merged DataFrame.