I wonder if it is possible to merge multiple rows from just one column in SQL. Here’s an example of my initial table: Here’s my expected output: I do not know if it’s possible to have a table like the later one. So, any advice would be much appreciated. Answer This type of task is usually done in the presentation
Tag: dataframe
How to merge rows in sql
I am trying to understand how to merge two rows (which are the same) into one. They both have data that needs to be in the same row but the raw data currently has split them into two rows. Essentially,…
How do I extract these SQL queries from these pandas dataframes?
SQL QUERIES Using SQL or Pandas, please tell me how to 1. Compare the monthly sales (GMV) trend in Q4 2019, across all countries (venture_code) 2. Show the top 10 brands for each product category, based on total sales (GMV) I wrote but got the query wrong! Answer Concerning the error, you have a space in the column name. In
Load the ResultSet of query in dataframe using Spark / java
I want to load the Result Set of a select query in dataframe Spark. I’m using the following code : public static void func (Dataset df){ df.repartition(20); //one connection per …
SQL to Dataframe not accounting for empty cells
I pull from a SQL database which sometimes looks like this. A B C D _ E F _ G So when I put it into a data frame and print it as a CSV I want it to look like A, B, C, D, , E, F, , G But instead it looks like A, B, C, D, E,
Importing JSON data from SQL DB to an R dataframe
I would like to know whether there is a way of importing JSON data from a MySQL DB to an R dataframe. I have a table like this: I would like to get the columns ‘id’ and ‘json’. I am using RMySQL package for getting the data from the db to an R dataframe but this gives me only the
Pandas dataframe combine unique row values
I have a dataframe like the following with over 90000 rows. As you can see, some origin and destination values repeat for example there are multiple rows where origin=101011001, destination=101011002. My goal is to group the repeating origin and destination values and sum the the people column, so the dataframe looks like this: I’ve tried jsondf.groupby([‘origin’, ‘destination’]).sum() which gives me
SQL & Pandas Efficiency [closed]
Closed. This question is opinion-based. It is not currently accepting answers. Want to improve this question? Update the question so it can be answered with facts and citations by editing this post. Closed 2 years ago. Improve this question Quick question. What is the rule of thumb when deciding where to begin manipulating data? Should I do it when I
how to pass value from one dataframe to another dataframe?
I have to pass the the C_ID value to where condition in below data frame as parameter. Any suggestions how I can do this ? i should not use subquery concept as data is in millions and multiple tables are there in joins,here i have mentioned sample query. Answer Store sql result into a variable using mkString and then use
Intersect two sf data.frames based on date and geometry using R
So, I have two R “sf” “data.frames”, one with millions of linestring geometries (vsr_segments: see below) and the other with 5 polygons (vsr_zones: see below). Each linestring has a datetime and each polygon has a unique date range. I’m trying to intersect the linestrings dataframe with the polygon data.frame based on whether the linestring datetime falls within a specific polygon’s