I am new to data structures and I would like to make my code faster (this is just part of a bigger code). Using dataframes while looking up variables is slowing
So I have a data set with about 70,000 data points, and I'm trying to test out some code on a sample data set to make sure it will work on the large one. The sa
I was wondering if it were possible to merge two datasets if the values were in a certain range of each other. For example, If I want to join on zip codes, then
I am very new to R and I was exploring a function in a library that download data from a server and leaves the data as dataframe. The data are stored in a varia
I am trying to train the model for recommendation for movie. I have a dataset which has list of all the casts, movie details with description. based on the occu
Can anyone explain what is the use of 100 * in the following line of code: 100 * df.isna().mean() Is it intended to get the percentage of the average value?
I am trying to train the model for recommendation for movie. I have a dataset which has list of all the casts, movie details with description. based on the occu
At first, I wrote: import numpy as np import pandas as pd import glob all_data = pd.DataFrame() for f in glob.glob("*.xlsx"): df = pd.read_excel(f) all_
I have the following code for an LME: IDRTlme <- lme(Score ~ Group*Condition, random = ~1|ID, data=IDRT) I want to check the normality assumption, and so I h
I am trying to do something which is very simple in excel, but I cant seem to find the way the way to do it in python. I want to calculate the next value in a d
Objective: To view .fcs data as a dataframe using R language. Flow Cytometry data comes in .fcs file format. The file is read in the flowFrame structure produce
I have a dataframe that I performed a filter on and then added some virtual columns. I wish to add those columns back in with the original data frame. Here is m
I'm working on pulling data from a public API and converting the response JSON file to a Pandas Dataframe. I've written the code to pull the data and gotten a s
Given: A pandas dataframe that contains a user_url column among other columns. Expectation: New columns added to the original dataframe where the columns are co
I have the following code df.groupby('AccountNumber')[['TotalStake','TotalPayout']].sum() which displays as I would like it to in pandas The issue is when I ou
I'm using the world bank API to analyze data and I want to create multiple data frames with the same indicators for different countries. import wbgapi as wb imp
I have a dataframe that looks something like this: df <- data.frame(gvkey = c(1,1,1,2,2,2,3,3,3,4,4,4,5,5,5,6,6,6), date = c(01,02,03,01,02,03,01,02,03,01,0
I'm trying to put some sort of length validation for columns using Pandas. For example, let's say I have a csv named test.csv that has the following data within
I am trying to divide the entire dataframe by a fix number but I want to keep the 'Year' column as is. I tried dividing the entire df with 100 and then multiply
I have data of many companies by month (End of Month). I want to create a new columns with groupby for each company where: new_col from Jul of this year to Jun