Lucas Humbert
02/21/2024, 1:42 PMScout
02/21/2024, 1:42 PMop_returning_list
.
2. Second Op: This op returns a pandas DataFrame. Let's call it op_returning_dataframe
.
3. Third Op: This op will take the outputs of the first two ops, convert the DataFrame to a list, and then perform the comparison. Let's call it compare_lists_op
.
The third op would look something like this, based on the functions described in the documentation:
python
from dagster import op, Out
import pandas as pd
@op
def op_returning_list():
# Your logic here
return some_list
@op
def op_returning_dataframe():
# Your logic here
return some_dataframe
@op
def compare_lists_op(list_result, dataframe_result):
# Convert DataFrame to list
dataframe_as_list = dataframe_result.values.tolist()
# Perform comparison
comparison_result = list(set(list_result) & set(dataframe_as_list))
# You can now work with the comparison_result
# For example, return it or print it
return comparison_result
In the above code, dataframe_result.values.tolist()
is used to convert the DataFrame to a list of lists. If you want to flatten this list of lists into a single list (assuming the DataFrame is one-dimensional), you could use a list comprehension or another method to do so.
Please note that the actual implementation of the comparison logic will depend on the specific details of your comparison (e.g., whether you're comparing values directly, whether you need to handle nested lists, etc.).
To wire these ops together in a job, you would use a graph:
python
from dagster import graph, execute_graph
@graph
def my_comparison_graph():
list_result = op_returning_list()
dataframe_result = op_returning_dataframe()
compare_lists_op(list_result, dataframe_result)
# Convert the graph to a job to execute it
my_comparison_job = my_comparison_graph.to_job()
Remember to replace the placeholder logic in op_returning_list
and op_returning_dataframe
with your actual logic for generating the list and DataFrame, respectively.
To execute the job, you would call execute_graph
with the appropriate parameters:
python
if __name__ == "__main__":
result = execute_graph(my_comparison_job)
This is a high-level guide based on the functions and concepts provided in the documentation. For a more detailed implementatScout
02/21/2024, 1:42 PMLucas Humbert
02/21/2024, 1:46 PMLucas Humbert
02/21/2024, 1:48 PMScout
02/21/2024, 1:48 PMcompare_lists_op
, you can create an intermediate op that performs the conversion. This op will take the DataFrame as input, convert it to a list, and then output that list. Here's how you can define this intermediate op: python from dagster import op @op def dataframe_to_list(dataframe_result): # Assuming you want to convert the entire DataFrame into a list of values # This will create a list of lists if the DataFrame has multiple columns list_from_dataframe = dataframe_result.values.tolist() # If you want to flatten the list of lists into a single list (for a single column DataFrame) # You can use a list comprehension or itertools.chain # flattened_list = [item for sublist in list_from_dataframe for item in sublist] return list_from_dataframe
Now, you can use this intermediate op in your graph to convert the DataFrame to a list before passing both lists to the `compare_lists_op`: python from dagster import graph @graph def my_comparison_graph(): list_result = op_returning_list() dataframe_result = op_returning_dataframe() list_from_dataframe = dataframe_to_list(dataframe_result) compare_lists_op(list_result, list_from_dataframe) # Convert the graph to a job to execute it my_comparison_job = my_comparison_graph.to_job()
The compare_lists_op
will now receive two lists as its inputs: python @op def compare_lists_op(list1, list2): # Perform comparison comparison_result = list(set(list1) & set(list2)) # You can now work with the comparison_result # For example, return it or print it return comparison_result
This setup ensures that the DataFrame is converted to a list before the comparison takes place in compare_lists_op
. Remember to adjust the conversion logic in dataframe_to_list
based on the structure of your DataFrame and how you want the data to be represented in the list format.Lucas Humbert
02/21/2024, 1:56 PMScout
02/21/2024, 1:56 PMpython def difference_between_lists(list1, list2): # Convert lists to sets to perform set operations set1 = set(list1) set2 = set(list2) # Find the difference between the two sets # This will return items that are in set1 but not in set2 difference = set1 - set2 # Convert the set back to a list of strings difference_list = list(difference) return difference_list # Example usage: list_a = ["apple", "banana", "cherry"] list_b = ["banana", "kiwi", "apple"] # Find items in list_a that are not in list_b result = difference_between_lists(list_a, list_b) print(result) # Output: ['cherry']
In this example, difference_between_lists
function takes two lists of strings, converts them to sets, and then calculates the difference using the -
operator. The result is a set containing elements that are in list1
but not in list2
. Finally, the set is converted back to a list of strings and returned. If you want to find items that are unique to each list (i.e., in list1
but not in list2
, and vice versa), you can use the symmetric difference operation: python def symmetric_difference_between_lists(list1, list2): # Convert lists to sets to perform set operations set1 = set(list1) set2 = set(list2) # Find the symmetric difference between the two sets # This will return items that are unique to each set symmetric_difference = set1 ^ set2 # Convert the set back to a list of strings symmetric_difference_list = list(symmetric_difference) return symmetric_difference_list # Example usage: result = symmetric_difference_between_lists(list_a, list_b) print(result) # Output may vary in order, e.g., ['cherry', 'kiwi']
The symmetric_difference_between_lists
function uses the ^
operator to find the symmetric difference between the two sets, which includes elements that are in either set1
or set2
but not in both. The result is then converted back to a list of strings. Note that the order of items in the output list may vary because sets do not maintain order.