All Questions
127 questions
0
votes
1
answer
169
views
Get Dataframe from pandas groupby (pd.grouper)
From the below data, i need to create daywise chunk through iteration
data:
lable datetime value daytime
0 tag1 5/30/2022 0:00 361.378 2022-05-30 00:00:00
1 ...
- 35
1
vote
2
answers
61
views
Give unique identifiers to clusters containing the same value
Say I had a dataframe column of ones and zeros, and I wanted to group by clusters of where the value is 1. Using groupby would ordinarily render 2 groups, a single group of zeros, and a single group ...
- 2,670
0
votes
1
answer
848
views
How to count the values of multiple '0' and '1' columns and group by another binary column ('Male' and Female')?
I'd like to group the binary information by 'Gender' and count the values of the other/ following fields 'Married', 'Citizen' and 'License'
The below code was my attempt, but it was unsucessful.
...
- 29
0
votes
1
answer
314
views
python pandas. Sum of a column based on a match of string column in a comma separated values of another column
I have 2 data frames sample, with names changed:
df1 =
Comp_code
DepartmentListA
DepartmentListB
Code_1
"Dept1"
"Dept3"
Code_2
"Dept2"
"Dept4"
Code_3
"...
- 17
1
vote
2
answers
58
views
Python pandas. Need to aggregate if column value is in another comma separated value column of second dataframe
I have 2 data frames sample, with names changed
df1 =
| Comp_code | DepartmentListA | DepartmentListB |
| -------- | --------------- | --------------- |
| Code_1 | "Dept1" | &...
- 17
0
votes
1
answer
77
views
pandas how to split one row to multiple row for variable product?
I have multiple variable product in my csv. Assume I have an product which title "Car model145" and this "Car model145" have three different price and size. Now I want to expand ...
- 1,637
1
vote
2
answers
90
views
Conditional groupping in Python using groupby function on Dataframe
I was coding for a project and I got stuck here. I have used groupby function before but not like this. My doubt is-
I have a dataframe as belows:
| ID | Side | Price |
| 1 | left | 100 |
| 2 | ...
0
votes
1
answer
211
views
Split daily data into Weekly data
I am using the nsepy module to get the data for 2 week for SBIN share.
Code :
from nsepy import get_history
from datetime import date
import pandas as pd
today = date.today()
# print(today)
sbin = ...
- 33
0
votes
1
answer
99
views
Filter maximum value and corresponding date based on the country time series data using group by?
I'm trying to find which day has the maximum number of new cases and group-by country. The dataset contains the list of country time-series data with the number of COVID total cases, new cases, new ...
- 378
0
votes
1
answer
1k
views
Split a Dataframe per time interval
I have a dataframe in the following format:
timestamp,name,age
2020-03-01 00:00:01,nick
2020-03-01 00:00:01,john
2020-03-01 00:00:02,nick
2020-03-01 00:00:02,john
2020-03-01 00:00:04,peter
2020-03-01 ...
- 1,961
3
votes
2
answers
953
views
Pandas groubpy on substring or regex
Given a pandas dataframe.
How can i do a groupby on the column 'a' such all rows get grouped together that start with the same string. The end of the comparison part is the _. (So here y_s1 and y_s2 ...
- 277
0
votes
1
answer
62
views
How to apply a function fastly on the list of DataFrame in Python?
I have a list of DataFrames with equal length of columns and rows but different values, such as
data = [df1, df2,df3.... dfn] .
How can I apply a function function on each dataframe in the list ...
- 45
3
votes
2
answers
60
views
Mutliple sums on the same dataframe
I'm trying to perform multiple sums on the same dataframe and then concatenate the new dataframes into one final dataframe. Is there a concise way of doing this, or do I need use iteration ?
I have a ...
- 373
1
vote
1
answer
135
views
Add missing datetime rows and check duplicates
I'm relatively new to pandas and I hope you could give me a hand ;-)
I want to group a pandas dataframe by name_id and datetime, adding also missing datetime values (NaN).
Input example:
name_id ...
- 13