Given the following: import pandas as pd import numpy as np df = pd.DataFrame({‘a’:[‘a’,’a’,’b’,’b’,’c’,’c’],:[1,1,np.nan,np.nan,1,1]}) df a b 0 a 1.0 1 a 1.0 2 b NaN 3 b NaN 4 c 1.0 5 c 1.0 I need to create a new column ("c") by grouping by "a" and shifting the values of "b" down from… Read More Pandas Groupby Get Values from Previous Group
I have the following dataframe df (dput below): group date1 date2 value 1 A 2022-01-01 2022-01-07 NA 2 A 2022-01-02 2022-01-07 1 3 A 2022-01-04 2022-01-07 NA 4 A 2022-01-10 2022-01-07 NA 5 B 2022-01-01 2022-01-06 3 6 B 2022-01-03 2022-01-06 NA 7 B 2022-01-04 2022-01-06 NA 8 B 2022-01-06 2022-01-06 NA 9 C 2022-01-01… Read More Fill NA until certain date based on different column per group
I have a pandas dataframe with an id column called doc_ID and a boolean column that reports if a certain value is below a threshold, like so: df = pd.DataFrame({‘doc_ID’: [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3], ‘below_threshold’: [False, False, False, False,… Read More Fill grouping varible pandas dataframe
I have a time shift column in my tblCalendar table called TimeShift and need to fill two other columns in the same table, both columns have Null values and their names are StartTime and EndTime. This serves as an example: TimeShift StartTime EndTime 8:00AM – 4:00PM Null Null 10:00AM – 6:00PM Null Null 11:00AM –… Read More Fill two columns in SQL table as a result of an operation done on another (third) column
I´ve got this dataset (something like below) db <- as.data.frame(cbind(c(1,2,2,3,3,4,4,4,5,5),c(‘a’,’b’,NA,NA,’i’,NA,’d’,NA, NA, NA))) I´d like to fill the V2 NA with same correpondance from ID column V1. At the end I expect this result db <- as.data.frame(cbind(c(1,2,2,3,3,4,4,4,5,5),c(‘a’,’b’,’b’,’i’,’i’,’d’,’d’,’d’, NA, NA))) I´ve tried to make a list with unique ID db_aux <- as.data.frame(cbind(c(1,2,3,4),c(‘a’,’b’,’i’,’d’))) but I guess it´s necessary… Read More Fill NA in R: imput NA in a column X with values from same ID (column Y ) correspondance
Tables and code at the bottom will probs help much more than description. I have a solution that works but think its very inefficient see bottom. Problem: I have two data frames df_1 and df_2 — these dataframes have a match column – match_id df_2 has a date column that I am trying to write… Read More Python: Filling a dataframe sequentially based on matching ids (inefficient code)
this is my first question on StackOverflow ! 🙂 To be honest I’m about to destroy my whole setup. My code is making me crazy. My problem is, I am not able to fill a dynamic array with the return of a function. My goal here is, for each array box, fill it with a… Read More Attribute return of a function to an array
What I want here is for the Nans to ultimately be integer values. Since my dataset is 1000s of columns, I can’t just change a couple of columns to make them integer and when I tried df = df.astype(‘int’) in Dask, after changing the values to 0 floats, for whatever reason, it didn’t work. `… Read More Filling NAs to string 0 and then to int reverts to float
Suppose I have a groupby object (grouped on Col1) like below: Col1 Col2 Col3 Col4 Col5 —————————————- AAA 001 456 846 239 row1 002 374 238 904 row2 003 456 846 239 row3 BBB 001 923 222 398 row1 002 923 222 398 row2 003 755 656 949 row3 CCC 001 324 454 565 row1… Read More Check for duplicate rows for a subset of columns in a Pandas DataFrameGroupBy object
I am working with a svg element as following const src = [{ “Name”: “Australia”, “Year”: 1997, “Value”: 15.540540540540499 }, { “Name”: “Australia”, “Year”: 1998, “Value”: 15.540540540540499 }, { “Name”: “Australia”, “Year”: 1999, “Value”: 22.4489795918367 }, { “Name”: “Brunei”, “Year”: 1998, “Value”: 6.4516129032258096 }, { “Name”: “Brunei”, “Year”: 2017, “Value”: 9.0909090909090899 }, { “Name”: “Brunei”,… Read More Javascript to dynamically select based on another query