I am getting an unexpected outcome in python. The output should be 440, but I get 370 so I am missing something in my loop I think.
The code is calculating fixed payments to pay off a balance (b) taking into account interest (mir) and increasing payment (mp) by 10 each loop if the balance isn't paid off.
My current code is
b = 4773
air = 0.2
mir = air/12.0
m = 1
mp = 10
while m in range(0,12):
ub = b - mp
b = ub + (mir * ub)
cb = b
m += 1
if cb> 0:
mp += 10
m = 0
print(str(mp))
I am not looking for so much a code fix, but an explanation of where I am going wrong and what I should look at to fix it. I can see the code running and it loops fine I am just ending up with an unexpected answer so I am missing something. I am trying to learn python and that would help me more than just a fix :)
You wrote in the comments:
The formula is calculating fixed payments to pay off a balance taking into account interest and increasing payment by 10 each loop if the balance isn't paid off.
I would do the following. (I tried to give your variables descriptive names.)
balance = 4773
annual_interest_rate = 0.2
monthly_interest_rate = annual_interest_rate / 12
number_of_payments = 0
payment = 10
while balance > 0:
number_of_payments += 1
balance -= payment
balance += balance * monthly_interest_rate
print(number_of_payments, payment, balance)
payment += 10
This outputs:
1 10 4842.38333333
2 20 4902.75638889
3 30 4953.96899537
4 40 4995.86847863
5 50 5028.29961994
6 60 5051.1046136
7 70 5064.12302383
8 80 5067.19174089
9 90 5060.14493657
10 100 5042.81401885
11 110 5015.02758583
12 120 4976.61137893
13 130 4927.38823524
14 140 4867.17803917
15 150 4795.79767315
16 160 4713.0609677
17 170 4618.7786505
18 180 4512.75829467
19 190 4394.80426625
20 200 4264.71767069
21 210 4122.29629853
22 220 3967.33457018
23 230 3799.62347968
24 240 3618.95053767
25 250 3425.0997133
26 260 3217.85137519
27 270 2996.98223144
28 280 2762.26526863
29 290 2513.46968978
30 300 2250.36085127
31 310 1972.7001988
32 320 1680.24520211
33 330 1372.74928881
34 340 1049.96177696
35 350 711.627806573
36 360 357.488270016
37 370 -12.7202588168
Related
I want to be able to calculate the average 'goal','shot',and 'miss' per shooterName to use for further analysis and visualization
The code below gives me the count of the 3 attributes(shot,goal,miss) in the 'event' column sorted by 'shooterName'
Dataframe columns:
season period time teamCode event goal xCord yCord xCordAdjusted yCordAdjusted ... playerPositionThatDidEvent timeSinceFaceoff playerNumThatDidEvent shooterPlayerId shooterName shooterLeftRight shooterTimeOnIce shooterTimeOnIceSinceFaceoff shotDistance
Corresponding data
2020 1 16 PHI SHOT 0 -74 29 74 -29 ... C 16 11 8478439.0 Travis Konecny R 16 16 32.649655
2020 1 34 PIT SHOT 0 49 -25 49 -25 ... C 34 9 8478542.0 Evan Rodrigues R 34 34 47.169906
2020 1 65 PHI SHOT 0 -52 -31 52 31 ... L 65 86 8480797.0 Joel Farabee L 31 31 48.270074
2020 1 171 PIT SHOT 0 43 39 43 39 ... C 42 9 8478542.0 Evan Rodrigues R 42 42 60.307545
2020 1 209 PHI MISS 0 -46 33 46 -33 ... D 38 5 8479026.0 Philippe Myers R 38 38 54.203321
Current code:
dft['count'] = df.groupby(['shooterName', 'event'])['event'].agg(['count'])
dft
Current Output:
shooterName event count
A.J. Greer GOAL 1
MISS 6
SHOT 29
Aaron Downey GOAL 1
MISS 4
SHOT 35
Zenon Konopka GOAL 8
MISS 57
SHOT 176
Desired Output:
shooterName event count %totalshooterNameevents
A.J. Greer GOAL 1 .0277
MISS 6 .1666
SHOT 29 .805
Aaron Downey GOAL 1 .025
MISS 4 .1
SHOT 35 .875
Zenon Konopka GOAL 8 .0331
MISS 57 .236
SHOT 176 .7302
Something similar to this. My end goal is to be able to calculate each 'event' attribute as a percentage of the total 'event' by 'shooterName'. Below I added a column '%totalshooterNameevents' which is 'simply goal', 'shot', and 'miss' calculated by the sum of the 'goal, shot, and miss' per each 'shooterName'
Update
Try:
dft = df.groupby(['shooterName', 'event'])['event'].agg(['count']).reset_index()
dft['%total'] = dft.groupby('shooterName')['count'].apply(lambda x: x / sum(x))
print(dft)
# Output
shooterName event count %total
0 A.J. Greer GOAL 1 0.027778
1 A.J. Greer MISS 6 0.166667
2 A.J. Greer SHOT 29 0.805556
3 Aaron Downey GOAL 1 0.025000
4 Aaron Downey MISS 4 0.100000
5 Aaron Downey SHOT 35 0.875000
6 Zenon Konopka GOAL 8 0.033195
7 Zenon Konopka MISS 57 0.236515
8 Zenon Konopka SHOT 176 0.730290
Without sample, it's difficult to guess what you want. Try:
import pandas as pd
import numpy as np
# Setup a Minimal Reproducible Example
np.random.seed(2021)
df = pd.DataFrame({'shooterName': np.random.choice(list('AB'), 20),
'event': np.random.choice(['shot', 'goal', 'miss'], 20)})
# Create an empty dataframe?
dft = pd.DataFrame(index=df['shooterName'].unique())
# Do stuff
grp = df.groupby('shooterName')
dft['count'] = grp.count()
dft = dft.join(grp['event'].value_counts().unstack('event')
.div(dft['count'], axis=0))
Output:
>>> dft
count goal miss shot
A 12 0.416667 0.250 0.333333
B 8 0.500000 0.375 0.125000
This is a simple math question that i will be using excel formula for. I have 5 columns. broken up like this
Total Rentals| Out | New rented|Returned|Available|rate of retun
200 40 40 0 160
200 60 20 0 140 x
200 90 30 4 114 x
200 150 60 20 70 x
How do i find the rate of return?
As homework for IT lessons I need to write a script which will check for the highest power of 4 which is in modified input number, but I can use only 8MB of RAM. I used for this logarithmic function, so my code looks like this:
from math import log, floor
n = int(input())
numbers = []
for i in range (0, n):
numbers.append(floor(int(input()) / 10))
for i in numbers:
print(4 ** floor(log(i, 4)))
But I checked this script on my PC and it uses more than 8MB!
Partition of a set of 74690 objects. Total size = 8423721 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 23305 31 2100404 25 2100404 25 str
1 19322 26 1450248 17 3550652 42 tuple
2 5017 7 724648 9 4275300 51 types.CodeType
3 9953 13 716915 9 4992215 59 bytes
4 742 1 632536 8 5624751 67 type
5 4618 6 628048 7 6252799 74 function
6 742 1 405720 5 6658519 79 dict of type
7 187 0 323112 4 6981631 83 dict of module
8 612 1 278720 3 7260351 86 dict (no owner)
9 63 0 107296 1 7367647 87 set
<197 more rows. Type e.g. '_.more' to view.>
On my phone, however, this script uses only 2.5MB:
Partition of a set of 35586 objects. Total size = 2435735 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 9831 28 649462 27 649462 27 str
1 9014 25 365572 15 1015034 42 tuple
2 4669 13 261232 11 1276266 52 bytes
3 2357 7 198684 8 1474950 61 types.CodeType
4 436 1 166276 7 1641226 67 type
5 2156 6 155232 6 1796458 74 function
6 436 1 130836 5 1927294 79 dict of type
7 93 0 87384 4 2014678 83 dict of module
8 237 1 62280 3 2076958 85 dict (no owner) 9 1091 3 48004 2 2124962 87 types.WrapperDescriptorType
<115 more rows. Type e.g. '_.more' to view.>
I tried changing list to tuple, but it didn't make any difference.
Is there any possibility to decrease/limit RAM usage?
I have following dataframe in pandas
data = {'call_put':['C', 'C', 'P','C', 'P'],'price':[10,20,30,40,50], 'qty':[11,12,11,14,9]}
df['amt']=df.price*df.qty
df=pd.DataFrame(data)
call_put price qty amt
0 C 10 11 110
1 C 20 12 240
2 P 30 11 330
3 C 40 14 560
4 P 50 9 450
I want output something like following based on call_put value is 'C' or 'P' count, median and calculation as follows
call_put price qty amt cummcount cummmedian cummsum
C 10 11 110 1 110 110
C 20 12 240 2 175 ((110+240)/2 ) 350
P 30 11 330 1 330 680
C 40 14 560 3 303.33 (110+240+560)/3 1240
P 50 9 450 2 390 ((330+450)/2) 1690
Can it be done in some easy way without creating additional dataframes and functions?
create a grouped element named g and use df.assign to assign values:
g=df.groupby('call_put')
final=df.assign(cum_count=g.cumcount().add(1),
cummedian=g['amt'].expanding().mean().reset_index(drop=True), cum_sum=df.amt.cumsum())
call_put price qty amt cum_count cummedian cum_sum
0 C 10 11 110 1 110.000000 110
1 C 20 12 240 2 175.000000 350
2 P 30 11 330 1 303.333333 680
3 C 40 14 560 3 330.000000 1240
4 P 50 9 450 2 390.000000 1690
Note: for P , the cummedian should be 390 since (330+450)/2 = 390
For cum_count look at df.groupby.cumcount()
for cummedian check how expanding() works ,
for cumsum check df.cumsum()
IIUC, this should work
df['cumcount']=df.groupby('call_put').cumcount()
df['cummidean']=df.groupby('call_put')['amt'].cumsum()
df['cumsum']=df.groupby('call_put').cumsum()
Thanks following solution is fine
g=df.groupby('call_put')
final=df.assign(cum_count=g.cumcount().add(1),
cummedian=g['amt'].expanding().mean().reset_index(drop=True), cum_sum=df.amt.cumsum())
if I run following without drop=True
g['amt'].expanding().mean().reset_index()
why output is showing level_1
call_put level_1 amt
0 C 0 110.000000
1 C 1 175.000000
2 C 3 303.333333
3 P 2 330.000000
4 P 4 390.000000
g['amt'].expanding().mean().reset_index(drop=True)
0 110.000000
1 175.000000
2 303.333333
3 330.000000
4 390.000000
Name: amt, dtype: float64
Can you pl explain in more detail ?
How do you add one more condition in groupby clause
g=df.groupby('call_put', 'price' < 50)
TypeError: '<' not supported between instances of 'str' and 'int'
I'm working on spreadsheet with logged flows that are not at uniform periods.
Looking for formula for Col G that will average values in Col A for logged values for previous 10 minutes.
Here's the spreadsheet data:
Flow Time min sec sec 10_min Average
187.29 06:10:09 10 9 609
202.90 06:11:21 11 21 681
280.94 06:12:37 12 37 757
218.51 06:13:43 13 43 823
187.29 06:15:13 15 13 913
124.86 06:16:26 16 26 986
109.25 06:18:52 18 52 1132
109.25 06:20:00 20 0 1200 1 177.54
202.90 06:22:30 22 30 1350
265.33 06:23:36 23 36 1416
280.94 06:24:42 24 42 1482
249.73 06:25:58 25 58 1558
218.51 06:27:39 27 39 1659
421.41 06:28:47 28 47 1727
421.41 06:30:00 30 0 1800 1 294.32
Use an AVERAGEIFS and construct the criteria with the TEXT function while modifying one criteria by ten minutes.
=AVERAGEIFS(A:A,B:B, TEXT(B9-TIME(0, 10, 0), "\>0.0###############"),B:B, TEXT(B9, "\<\=0.0###############"))
Note that times can also be resolved as decimal numbers which I have used here. My second average came up slightly different from yours. You may wish to change the \>\= to \> .