I have these two initial tables: Table1: CustID StartTime EndTime Area 1 12/1/2022 4:00:00 PM 12/1/2022 4:05:00 PM ABC 2 12/1/2022 4:02:00 PM 12/1/2022 4:03:00 PM ABC Table2: Area StartTime EndTime ABC 12/1/2022 4:01:26 PM 12/1/2022 4:02:00 PM ABC 12/1/2022 4:02:05 PM 12/1/2022 4:02:55 PM ABC 12/1/2022 4:04:10 PM 12/1/2022 4:05:00 PM I need to end up with this: Table3:
Tag: join
How to perform split/merge/melt with Python and polars?
I have a data transformation problem where the original data consists of “blocks” of three rows of data, where the first row denotes a ‘parent’ and the two others are related children. A minimum working example looks like this: In reality, there are up to 15 Providers (so up to 30 columns), but they are not necessary for the example.
find missing datas between two tables with similar columns python
I have two dataframes with 2 similar columns “date” and “id_number” and I want to find all the id_number missing from the second table to compare. Here’s my code: import pandas as pd Answer If need compare per date and id_number use left join with indicator parameter: Or if need compare only by id_number use Series.isin:
How to do left join with larger table, keeping left tables size?
I have a dataframe1: and dataframe2: i want to join type column to dataframe1 by id to get: How could I do that? as you see output table is same shape as dataframe1? but when i use pd.merge output is larger Answer Try this: Output:
How to write a universal function to join two PySpark dataframes?
How to write a universal function to join two PySpark dataframes? I want to write a function that performs inner join on two dataframes and also eliminates the repeated common column after joining. As far as I’m aware there is no way to do that, as we always need to define common columns manually while joining. Or is there a
Django join tables with ForeignKey
I’m trying to join 3 tables with ForeignKey but it returns Null values. I’m using select related and also I tried Insight.objects.all() but both are not working. Here are my models: My View: Answer I solved my problem by the below, I could update the DB with the result of the below query
Using join to find similarities between two datasets containing strings in PySpark
I’m trying to match text records in two datasets, mostly via using PySpark (not using libraries such as BM25 or NLP techniques as much as I can for now -using Spark ML and SparkNLP libraries are fine). I’m towards finishing the pre-processing phase. I’ve cleaned the text in both datasets, tokenized it and created bi-Grams (stored in a column called
How does rsuffix and lsuffix work while joining multiple dataframes?
I have written the following code however I am unable to understand how to name the rsuffix and lsuffix parameters All my dfs have same column names example: When I am printing dfs_list[2].reset_index() I do get my expected output but I am unable to comprehend the suffix names. How do we define it? output: Can someone throw light on how
How to filter after two joins in flask SQL alchemy
I have the following 3 tables: Each sample has a chain of processes it goes through and I need to get a table which includes both Sample.id and the latest date it has encountered a process, so that I can filter by this date. Something like this What I tried so far was looking at this and similar solutions: https://blog.miguelgrinberg.com/post/nested-queries-with-sqlalchemy-orm
How to combine two nested dictionaries with same master keys
I have two nested dicts with same master keys: So I want to enrich dict 1 by the key value pairs from dict2. I’m able to do so with a for loop… Result: …but is there a more pythonic way to do so – e.g. with dict comprehension? Answer Yes: Firstly, use .items() to iterate over both keys and values