PlanDF
DataFrame for computing Value-Over-Time for lists of tuples (Step.investables, Step.deliverables) of a Plan.
Depends on pandas.DataFrame and stepio.
pip install plandf
# curently only Python 2.7
More detailed example is here.
Examples
Using hour as currency. The time is a single reserved word, which is treated differently ONLY when it is added in the 'input' value. The time is also a required item in all the 'input' values.
Hello World (1 Step)
import plandf
p = plandf.Plan()
p.steps([
{'input': 'time: 1; carrots: 2; cucumbers: 3',
'output': 'liters of juice: 0.5; waste material grams: 20'}
])
p.info.dfHello Valued (1 Step)
p.steps([
{'input': 'time: 1@5h; carrots: 2@1usd; cucumbers: 3@1.5usd',
'output': 'liters of juice: 0.5@5usd; waste material grams: 20@-0.01usd'}
])
p.dfHello Loan (Multi-Step)
- The numbers in the brackets denote the most likely value.
p.steps(
[{'input': 'time: 0@1h; cash: 10000@1usd',
'output': 'loan note: 1'}] +
[{'input': 'time: 30@24h',
'output': 'loan payment: 1@320~375[325]usd'}] * 36
)
p.info.dfSimple Ranged (2 Steps)
- The numbers in the brackets denote the most likely value.
%matplotlib inline
p = plandf.Plan()
p.steps([
{'input': 'time: 1~10[2]@1h', 'output': 'relax: 1@25~50[30]usd'},
{'input': 'time: 4~5@1h', 'output': 'work: 1@200~320usd'},
])
p.plot()Comparison Example (2 Steps Each)
p1 = plandf.Plan()
p1.steps([
{'input': 'time: 1~10[2]@1h', 'output': 'relax: 1@25~50[30]usd'},
{'input': 'time: 4~5@1h', 'output': 'work: 1@200~320usd'},
])
p2 = plandf.Plan()
p2.steps([
{'input': 'time: 1~10[2]@1h', 'output': 'relax: 1@25~50[30]usd'},
{'input': 'time: 4~5@1h', 'output': 'work: 1@200~320usd'},
])
import pandas as pd
df = pd.concat({'plan1': p1.df,
'plan2': p2.df}, axis=1)
# Optional: humanize converts times from hours to days/minutes, and currency to USD.
plandf.utils.humanize(df)Visualizing Multiple Plans
All Scenarios
import pandas as pd
pd.concat({'plan1': p1.df,
'plan2': p2.df}, axis=1).fillna(method='bfill').plot()Only Chosen Scenarios
import pandas as pd
pd.concat({'plan1': p1.df['worst'],
'plan2': p2.df['best']}, axis=1).fillna(method='bfill').plot()Breakfast All in Hours, Without any National Currencies :)
Both elapsed time, and the values of commoditites in hours.
breakfast = plandf.Plan()
breakfast.steps([
{'input': 'time: 0.003~0.004@24h; loaf of black bread: 1@0.1h; butter grams: 15@0.01h; tomato: 0.5@0.2h',
'output': 'sandwitch: 1@0.12h'},
{'input': 'eggs: 2~5@0.012h; scrambling actions: 50~100; time: 0.003~0.005@24h',
'output': 'scrambled egg servings: 1@0.16h'},
{'input': 'coffee teaspoon: 1~2@0.00012h; liters of water: 0.2~0.3@0.0001h; time: 0.003~0.005@24h',
'output': 'cup of coffee: 1~1.5@0.08h'}
])
