#JOINING DICTIONARIES PYTHON CODE#

Finally, the nesting behavior of Table.NestedJoin isn't the most efficient method of joining two tables. Additionally, a record 'table' might be more memory efficient than a regular table (I don't know, but it is a possible reason). Since keys must be unique, once a match is found on the key, PQ stops scanning the mapping table for matches. The difference is likely caused by the restrictions on the record type.

Conclusionįor merges/joins with a 'one' on either side, the record lookup method with Record.FieldOrDefault is the more efficient method by far and that difference seems to scale. For everyone else, the distribution of times over the 500 runs for each method aren't even close. After adding the additional logic for multi-key lookups/ merges/ joins, Record.FieldOrDefault is still 5.3x faster with 15% of the variability on 500 runs.įor those interested, the p-value on a Welch's t-test is pretty much zero (queue the arguing over p-values). In the previous posts, we found that Record.FieldOrDefault was 4.5x faster for single-key many-to-one lookups and 6x faster for single-key one-to-many lookups. Do we get a speed boost that is worth the effort? You betcha! Now the ice cream! Overall, the coding for multi-key merges/joins via record lookup is more complicated than in the previous post's examples. NOTE: you have to concatenate the keys for the lookup I've included Text.From functions to ensure that we get text values (a bit of error-proofing). This you'll do in the second argument to Record.FieldOrDefault. On the timed code, you'll have to concatenate the keys when doing the lookup. RecordVals = Record.FromTable(#'Removed Other Columns') make sure we only have the columns we need this will concatenate the multi-keys into a single one Second, you'll have to concatenate some values to use it for multi-key queries.

#JOINING DICTIONARIES PYTHON CODE#

First, there is no UI button for it, so we'll have to open the 'Advanced Editor' and code it ourselves. Using Record.FieldOrDefault is not a simple. Merging/joining and expanding the nested column results in the following code: // Timed code As with all UI operations, however, M code is always produced on the back end. Using Table.NestedJoin for this operation can be done entirely from the UI, which is a significant benefit when testing processes and working up a prototype. That is to say it does not include loading time for the data or any other calculation-set. The timing methodology only includes runtime for the calculation-set of interest.

With this data, I run a simulation (usually 500 runs) of each methodology in series.

It is a 105 MB csv file with over 500,000 rows and 20 columns of various data types. For these tests, I'm using one portion of the publicly available NYC taxi cab data. I didn't cover this significantly in the last post, but thought it would be good to add for interested parties. Are they possible? Are they still faster? Background Information: The data and timing methodology A question arose from the first post about the efficacy of Record.FieldOrDefault for more traditional multi-key joins. A more friendly version for those not as familiar with Power Query will follow shortly.ĭue to some commitments elsewhere, this week's post will continue from last week. The first post has some background information, so if you haven't read it, it's a good place to start. Then, we updated dict_3 with the values of dict_1 using the dictionary update() method.Note 1: This is the second post on this topic. Here, we have first copied the elements of dict_2 to dict_3 using the dictionary copy() method. Example 1: Using the | Operator dict_1 =

Joining dictionaries python