Filtering and Grouping

df = Frame(data_dict.get('df1')[['col1', 'col2']])[0, :, by('col1')]

Frame(...)[0, :, by('col1')]: The resulting data table from step 2 is wrapped in the Frame() function to convert it back to a datatable.Frame object. Then, the following operations are applied to this frame:

[0, :] selects the first row (index 0) and all columns from the frame. This operation filters the data table to only include the first row.

by('col1') groups the data by the values in the 'col1' column. This operation groups the datatable based on unique values in the 'col1' column.

df = df[:, :, join(df1)] [:, :, join(df1)]: This part specifies the join operation between df (left table) and df1 (right table). Here's what each component means:

• [:, :]: The first colon (:) represents all rows, and the second colon (:) represents all columns. This selection specifies that you want to include all rows and columns from the left table (df).

• join(df1): The join() function is used to perform a join operation between two tables. In this case, df1 is the right table that you want to join with df. By specifying join(df1), you are instructing the join() function to perform a join between the left table (df) and the right table (df1).

df = another_df[:, {'col1': f.col1}]

[:, {'col1': f.col1}]: This part specifies the selection of columns from another_df. Here's what each component means:

• [:, ...]: The first colon (:) represents all rows, and the ellipsis (...) indicates that all rows are selected. This selection specifies that you want to include all rows from another_df.

• {'col1': f.col1}: This dictionary specifies the columns you want to select. In this case, you are selecting the 'col1' column from another_df. The key 'col1' represents the name of the column in the new datatable (df), and f.col1 refers to the column named 'col1' in another_df. The f.col1 expression is used to reference the column 'col1' from the original datatable.