Pandas Cumsum in expanding rows - pandas

Question

Looking to learn how to code this solution in a more elegant way. Need to split a set of rows into a smaller pieces and control the utilization as well as calculate the balance. Current solution is not generating the balance properly
import pandas as pd
import numpy as np
box_list = [['Box0', 0.2],
['Box1', 1.0],
['Box2', 1.8],
['Box4', 2.0],
['Box8', 4.01],]
sdf = pd.DataFrame(box_list, columns = ['Name', 'Size'])
print(sdf)
Name
Size
1
Box1
1.00
2
Box2
1.80
3
Box4
2.00
4
Box8
4.01
df = pd.DataFrame({'Name': np.repeat(sdf['Name'], sdf['Size'].apply(np.ceil)),
'Size': np.repeat(sdf['Size'], sdf['Size'].apply(np.ceil)),})
df['Max_Units']=df['Size'].apply(lambda x: np.ceil(x) if x>1.0 else 1.0)
df = df.reset_index()
df['Utilization'] =df['Size'].apply(lambda x: x-int(x) if x>1.0 else (x if x<1.0 else 1.0))
df['Balance'] =df['Max_Units']
g = df.groupby(['index'], as_index=0, group_keys=0)
df['Utilization'] = g.apply(lambda x:
pd.Series(np.where((x.Balance.shift(1) >= 1.0),
1.0,
x.Utilization))).values
df.loc[(df.Utilization == 0.0), ['Utilization']] = 1.0
df['Balance'] = g.apply(lambda x:
pd.Series(np.where((x.Balance.shift(1) >= 1.0),
x.Max_Units-x.Utilization,
0))).values
print(df)
index
Name
Size
Max_Units
Utilization
Balance
0
0
Box0
0.20
1.0
0.20
0.0
1
1
Box1
1.00
1.0
1.00
0.0
2
2
Box2
1.80
2.0
0.80
0.0
3
2
Box2
1.80
2.0
1.00
1.0
4
3
Box4
2.00
2.0
1.00
0.0
5
3
Box4
2.00
2.0
1.00
1.0
6
4
Box8
4.01
5.0
0.01
0.0
7
4
Box8
4.01
5.0
1.00
4.0
8
4
Box8
4.01
5.0
1.00
4.0
9
4
Box8
4.01
5.0
1.00
4.0
10
4
Box8
4.01
5.0
1.00
4.0

Answer 1

I'm not sure if I completely understand what all of these values are supposed to be representing.
However, I've achieved the correct desired output for your sample set in more direct ways:
import pandas as pd
import numpy as np
box_list = [['Box0', 0.2],
['Box1', 1.0],
['Box2', 1.8],
['Box4', 2.0],
['Box8', 4.01], ]
df = pd.DataFrame(box_list, columns=['Name', 'Size'])
# Set ceil column to ceil of size since it's used more than once
df['ceil'] = df['Size'].apply(np.ceil)
# Duplicate Rows based on Ceil of Size
df = df.loc[df.index.repeat(df['ceil'])]
# Get Max Units by comparing it to the ceil column
df['Max_Units'] = df.apply(lambda s: max(s['ceil'], 1), axis=1)
# Extract Decimal Portion By Using % 1 (Catch Special Case of x == 1)
df['Utilization'] = df['Size'].apply(lambda x: 1 if x == 1 else x % 1)
# Everywhere Max_Units cumcount is not 0 set Utilization to 1
df.loc[df.groupby(df['Max_Units']).cumcount().ne(0), 'Utilization'] = 1
# Set Balance to index cumcount as float
df['Balance'] = df.groupby(df.index).cumcount().astype(float)
# Drop Unnecessary Column and reset index for output
df = df.drop(columns=['ceil']).reset_index()
# For Display
print(df)
Output:
index
Name
Size
Max_Units
Utilization
Balance
0
0
Box0
0.20
1.0
0.20
0.0
1
1
Box1
1.00
1.0
1.00
0.0
2
2
Box2
1.80
2.0
0.80
0.0
3
2
Box2
1.80
2.0
1.00
1.0
4
3
Box4
2.00
2.0
1.00
0.0
5
3
Box4
2.00
2.0
1.00
1.0
6
4
Box8
4.01
5.0
0.01
0.0
7
4
Box8
4.01
5.0
1.00
1.0
8
4
Box8
4.01
5.0
1.00
2.0
9
4
Box8
4.01
5.0
1.00
3.0
10
4
Box8
4.01
5.0
1.00
4.0