I have a table in the form: ID DATE_ENCOUNTER LOAD 151336 2017-08-22 40 151336 2017-08-23 40 151336 2017-08-24 40 151336 2017-08-25 40 151336 2017-09-05 50 151336 2017-09-06 50 151336 2017-10-16 51 151336 2017-10-17 51 151336 2017-10-18 51 151336 2017-10-30 50 151336 2017-10-31 50 151336 2017-11-01 50 151336 2017-12-13 62 151336 2018-01-03 65 151336 2018-02-09 60 Although the dates are not
Tag: duplicates
Summing duplicates rows
I have a database with more than 300 duplicates that look like this: I want that for each duplicate shipment_id only original_cost gets added together and rates remain as they are. like for these duplicates: it should look something like this: is there any way to do this? Answer Group by the duplicate values ([‘shipment_id’, ‘rate’]) and use transform on
Replace a series of repeated occurrences in a list?
I would like to replace the consecutive occurrences with the first appearance. For example if I currently have a list the desired output will be I know that I can definitely do this by using a for loop to iterate through the list but is there a more pythonic way of doing it? Answer Without import anything:
Remove duplicated considering one element from a list of lists python
I basically have a list of list like this: I would like to remove the lists which contains duplicated values in index [0]. Also, i always need to have the list which its index [1] = ’20’, in this case [‘1348562275′, ’20’]. So my wanted list would be: Does anyone have any idea how could i do it? Answer You
How to drop rows in one DataFrame based on one similar column in another Dataframe that has a different number of rows
I have two DataFrames that are completely dissimilar except for certain values in one particular column: How would I go about finding the matching values in the Email column of df and the Contact column of df2, and then dropping the whole row in df based on that match? Output I’m looking for (index numbering doesn’t matter): I’ve been able
python – find duplicates in a column, replace values in another column for that duplicate
I have a dataframe that consists of of video game titles on various platforms. it contains, among other values the name, critic’s average score and user’s average score. Many of them are missing scores for the user, critic and/or ESRB rating. What i’d like to do is replace the missing rating, critic and user scores with those for the same
replace/change duplicate columns values where column name is same but values are different, then drop duplicate columns
Is there any way to drop duplicate columns, but replacing their values depending upon conditions like in table below, I would like to remove duplicate/second A and B columns, but want to replace the value of primary A and B (1st and 2nd column) where value is 0 but 1 in duplicate columns. Ex – In 3rd row, where A,
Python : Dropping specific rows in a dataframe and keep a specif one
Let’s say that I have this dataframe I want to reduce this dataframe ! I want to reduce only the rows that contains the string “info” by keeping the ones that have the highest level in the column “Group”. So in this dataframe, it would mean that I keep the row “ID_info_1” in the group 4, and “ID_info_1” in the
pandas, merge duplicates if row contains wildcard text
I have a dataset of duplicates (ID). Dataset contains both information and emails. I’m trying to concatenate the emails (if row have character @) and then remove the duplicates. My original dataset: What I wish to accomplish: My current code is a modification of Eric Ed Lohmar code and give the following output. My issue is that I’m not able
Filter non-duplicated records in Python-pandas, based on group-by column and row-level comparison
This is a complicated issue and I am not able to figure this out, and I really appreciate your help in this. The below dataframe is generated from a pandas function DataFrame.duplicated(), based on ‘Loc'(groupby) and ‘Category’ repeated records are marked as True/False accordingly. My Expectation is to create another column based on ‘Loc'(groupby), ‘Category’ and ‘IsDuplicate’ to represent only