I have a dataframe in pandas
val1 val2 val3 time
a b c 0
d e f 5
g h i 7
j k l 4
c a q 9
m e t 2
g n y 1
v k l 0
and timesteps = [0, 3, 8]
And I want to create a new column that is the the maximal value of an elemet from timesteps that is lower than row["time"]
For example, here the new column will be [0,3,3,3,8,0,0,0]
What is the best way to do so?
Using pd.cut():
timesteps = [0, 3, 8]
bins=timesteps+[df.time.max()]
#[0, 3, 8, 9]
pd.cut(df.time,bins=bins,labels=timesteps,include_lowest=True)
0 0
1 3
2 3
3 3
4 8
5 0
6 0
7 0
"Bin edges must be unique: array([3.11141521e+09, 3.11141521e+09, 3.11141521e+09, 3.11141521e+09]" When using duplicates="drop" I get the error: Bin labels must be one fewer than the number of bin edges What should I do?Try this method.
from pandas import Series,DataFrame
import pandas as pd
import random
df = DataFrame({
'val1': ['a','d','g','j','c','m','g','v'],
'val2':['b','e','h','k','a','e','n','k'],
'val3':['c','f','i','l','q','t','y','l'],
'time':[0,5,7,4,9,2,1,0]})
id = df['time'].idxmax()
max = df['time'][id]
df['timesteps'] = df.apply(lambda x: random.randint(0,max-1),axis = 1)
