pandas provide various facilities for easily combining together Series, DataFrame, and Panel objects with various kinds of set logic to merge data frames for the indexes and relational algebra functionality in the case of join / merge-type operations.
We will create two data frame df1 and df2 to illustrate joins in R.
df1 = data.frame(CustomerId = c(1:6), Product=c(rep("Toaster”,3), rep("Radio",3)))
df2 = data.frame(CustomerId = c(2,4,6), State = c(rep("Alabama",2), rep("Ohio",1)))
By using the
merge function and its optional parameters:
merge(df1, df2) will work for these examples because R automatically joins the frames by common variable names, but you would most likely want to specify to
merge(df1, df2, by = "CustomerId")
make sure that you were matching on only the fields you desired. You can also use the
by.y if the matching variables have different names in the different data frames.
merge(x = df1, y = df2, by = "CustomerId", all = TRUE)
merge(x = df1, y = df2, by = "CustomerId", all.x = TRUE)
merge(x = df1, y = df2, by = "CustomerId", all.y = TRUE)
merge(x = df1, y = df2, by = NULL)
The same can also be achieved using the sqldf package in R, But using Merge is much easier and understandable.