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How to add column for every month and generate number i.e. 1,2,3..etc

I have a huge csv file of dataframe. However, I don't have the date column. I only have the sales for every month from Jan-2022 until Dec-2034. Below is the example of my dataframe:
import pandas as pd
data = [[6661, 'Mobile Phone', 43578, 5000, 78564, 52353, 67456, 86965, 43634, 32546, 56332, 58944, 98878, 68588, 43634, 3463, 74533, 73733, 64436, 45426, 57333, 89762, 4373, 75457, 74845, 86843, 59957, 74563, 745335, 46342, 463473, 52352, 23622],
[6672, 'Play Station', 4475, 2546, 5757, 2352, 57896, 98574, 53536, 56533, 88645, 44884, 76585, 43575, 74573, 75347, 57573, 5736, 53737, 35235, 5322, 54757, 74573, 75473, 77362, 21554, 73462, 74736, 1435, 4367, 63462, 32362, 56332],
[6631, 'Laptop', 35347, 36376, 164577, 94584, 78675, 76758, 75464, 56373, 56343, 54787, 7658, 76584, 47347, 5748, 8684, 75373, 57573, 26626, 25632, 73774, 847373, 736646, 847457, 57346, 43732, 347346, 75373, 6473, 85674, 35743, 45734],
[6600, 'Camera', 14365, 60785, 25436, 46747, 75456, 97644, 63573, 56433, 25646, 32548, 14325, 64748, 68458, 46537, 7537, 46266, 7457, 78235, 46223, 8747, 67453, 4636, 3425, 4636, 352236, 6622, 64625, 36346, 46346, 35225, 6436],
[6643, 'Lamp', 324355, 143255, 696954, 97823, 43657, 66686, 56346, 57563, 65734, 64484, 87685, 54748, 9868, 573, 73472, 5735, 73422, 86352, 5325, 84333, 7473, 35252, 7547, 73733, 7374, 32266, 654747, 85743, 57333, 46346, 46266]]
ds = pd.DataFrame(data, columns = ['ID', 'Product', 'SalesJan-22', 'SalesFeb-22', 'SalesMar-22', 'SalesApr-22', 'SalesMay-22', 'SalesJun-22', 'SalesJul-22', 'SalesAug-22', 'SalesSep-22', 'SalesOct-22', 'SalesNov-22', 'SalesDec-22', 'SalesJan-23', 'SalesFeb-23', 'SalesMar-23', 'SalesApr-23', 'SalesMay-23', 'SalesJun-23', 'SalesJul-23', 'SalesAug-23', 'SalesSep-23', 'SalesOct-23', 'SalesNov-23', 'SalesDec-23', 'SalesJan-24', 'SalesFeb-24', 'SalesMar-24', 'SalesApr-24', 'SalesMay-24', 'SalesJun-24', 'SalesJul-24']
Since I have more than 10 monthly sales column, I want to loop the date after each of the month sales column. Then, the first 6 months will generate number 1, while the next 12 months will generate number 2, then another 12 months will generate number 3, another subsequent 12 months will generate number 4 and so on.
Below shows the sample of result that I want:
Is there any way to perform the loop and adding the date column beside each of the sales month?

Here is the simplest approach I can think of:
for i, col in enumerate(ds.columns[2:]):
ds.insert(2 * i + 2, col.removeprefix("Sales"), (i - 6) // 12 + 2)

Here is a vectorial approach (using insert repeatedly is inefficient):
# convert (valid) columns to datetime
cols = pd.to_datetime(ds.columns, format='Sales%b-%y', errors='coerce')
# identify valid dates
m = cols.notna()
# get year
y = cols[m].year
# calculate number (1 for first 6 months, then +1 per 12 months)
num = ((cols[m].month+12*(y-y.min()))+5)//12+1
# slice dates columns, assign the number, rename
df2 = (ds.loc[:, m].assign(**dict(zip(ds.columns[m], num)))
.rename(columns=lambda x: x[5:])
)
# get new order of columns
idx = np.r_[np.zeros((~m).sum()), np.tile(np.arange(m.sum()), 2)+1]
# concat and reorder
out = pd.concat([ds, df2], axis=1).iloc[:, np.argsort(idx)]
print(out)
output:
ID Product SalesJan-22 Jan-22 SalesFeb-22 Feb-22 SalesMar-22 Mar-22 SalesApr-22 Apr-22 SalesMay-22 May-22 SalesJun-22 Jun-22 SalesJul-22 Jul-22 SalesAug-22 Aug-22 Sep-22 SalesSep-22 Oct-22 SalesOct-22 SalesNov-22 Nov-22 Dec-22 SalesDec-22 Jan-23 SalesJan-23 Feb-23 SalesFeb-23 SalesMar-23 Mar-23 Apr-23 SalesApr-23 SalesMay-23 May-23 SalesJun-23 Jun-23 Jul-23 SalesJul-23 SalesAug-23 Aug-23 Sep-23 SalesSep-23 SalesOct-23 Oct-23 Nov-23 SalesNov-23 Dec-23 SalesDec-23 Jan-24 SalesJan-24 Feb-24 SalesFeb-24 Mar-24 SalesMar-24 Apr-24 SalesApr-24 May-24 SalesMay-24 SalesJun-24 Jun-24 SalesJul-24 Jul-24
0 6661 Mobile Phone 43578 1 5000 1 78564 1 52353 1 67456 1 86965 1 43634 2 32546 2 2 56332 2 58944 98878 2 2 68588 2 43634 2 3463 74533 2 2 73733 64436 2 45426 2 3 57333 89762 3 3 4373 75457 3 3 74845 3 86843 3 59957 3 74563 3 745335 3 46342 3 463473 52352 3 23622 4
1 6672 Play Station 4475 1 2546 1 5757 1 2352 1 57896 1 98574 1 53536 2 56533 2 2 88645 2 44884 76585 2 2 43575 2 74573 2 75347 57573 2 2 5736 53737 2 35235 2 3 5322 54757 3 3 74573 75473 3 3 77362 3 21554 3 73462 3 74736 3 1435 3 4367 3 63462 32362 3 56332 4
2 6631 Laptop 35347 1 36376 1 164577 1 94584 1 78675 1 76758 1 75464 2 56373 2 2 56343 2 54787 7658 2 2 76584 2 47347 2 5748 8684 2 2 75373 57573 2 26626 2 3 25632 73774 3 3 847373 736646 3 3 847457 3 57346 3 43732 3 347346 3 75373 3 6473 3 85674 35743 3 45734 4
3 6600 Camera 14365 1 60785 1 25436 1 46747 1 75456 1 97644 1 63573 2 56433 2 2 25646 2 32548 14325 2 2 64748 2 68458 2 46537 7537 2 2 46266 7457 2 78235 2 3 46223 8747 3 3 67453 4636 3 3 3425 3 4636 3 352236 3 6622 3 64625 3 36346 3 46346 35225 3 6436 4
4 6643 Lamp 324355 1 143255 1 696954 1 97823 1 43657 1 66686 1 56346 2 57563 2 2 65734 2 64484 87685 2 2 54748 2 9868 2 573 73472 2 2 5735 73422 2 86352 2 3 5325 84333 3 3 7473 35252 3 3 7547 3 73733 3 7374 3 32266 3 654747 3 85743 3 57333 46346 3 46266 4

Here's a little solution : (I put the year unstead of your 1, 2, ... incrementation since i thought it is more representative, but you can change it easily)
idx_counter = 0
for idx, col in enumerate(ds.columns):
if col.startswith('Sales'):
date = col.replace('Sales', '')
year = col.split('-')[1]
ds.insert(loc=idx + 1 + idx_counter, column=date, value=[year] * ds.shape[0])
idx_counter += 1
output:
ID Product SalesJan-22 Jan-22 SalesFeb-22 Feb-22 SalesMar-22 Mar-22 SalesApr-22 Apr-22 ... SalesMar-24 Mar-24 SalesApr-24 Apr-24 SalesMay-24 May-24 SalesJun-24 Jun-24 SalesJul-24 Jul-24
0 6661 Mobile Phone 43578 22 5000 22 78564 22 52353 22 ... 745335 24 46342 24 463473 24 52352 24 23622 24
1 6672 Play Station 4475 22 2546 22 5757 22 2352 22 ... 1435 24 4367 24 63462 24 32362 24 56332 24
2 6631 Laptop 35347 22 36376 22 164577 22 94584 22 ... 75373 24 6473 24 85674 24 35743 24 45734 24
3 6600 Camera 14365 22 60785 22 25436 22 46747 22 ... 64625 24 36346 24 46346 24 35225 24 6436 24
4 6643 Lamp 324355 22 143255 22 696954 22 97823 22 ... 654747 24 85743 24 57333 24 46346 24 46266 24

This should do the trick.
import math
new_cols = []
old_cols = [x for x in df.columns if x.startswith('Sales')]
for i, col in enumerate(old_cols):
new_cols.append(col[5:])
if i < 6:
val = 1
else:
val = ((i+6)/12)+1
df[col[5:]] = math.floor(val)
df[['ID', 'Product'] + [x for y in zip(old_cols, new_cols) for x in y]]

df.apply(sorted, axis=1) removes column names?

Working through Pandas Cookbook. Counting the Total Number of Flights Between Cities.
import pandas as pd
import numpy as np
# import matplotlib.pyplot as plt
print('NumPy: {}'.format(np.__version__))
print('Pandas: {}'.format(pd.__version__))
print('-----')
desired_width = 320
pd.set_option('display.width', desired_width)
pd.options.display.max_rows = 50
pd.options.display.max_columns = 14
# pd.options.display.float_format = '{:,.2f}'.format
file = "e:\\packt\\data_analysis_and_exploration_with_pandas\\section07\\data\\flights.csv"
flights = pd.read_csv(file)
print(flights.head(10))
print()
# This returns the total number of rows for each group.
flights_ct = flights.groupby(['ORG_AIR', 'DEST_AIR']).size()
print(flights_ct.head(10))
print()
# Get the number of flights between Atlanta and Houston in both directions.
print(flights_ct.loc[[('ATL', 'IAH'), ('IAH', 'ATL')]])
print()
# Sort the origin and destination cities:
# flights_sort = flights.sort_values(by=['ORG_AIR', 'DEST_AIR'], axis=1)
flights_sort = flights[['ORG_AIR', 'DEST_AIR']].apply(sorted, axis=1)
print(flights_sort.head(10))
print()
# Passing just the first row.
print(sorted(flights.loc[0, ['ORG_AIR', 'DEST_AIR']]))
print()
# Once each row is independently sorted, the column name are no longer correct.
# We will rename them to something generic, then again find the total number of flights between all cities.
rename_dict = {'ORG_AIR': 'AIR1', 'DEST_AIR': 'AIR2'}
flights_sort = flights_sort.rename(columns=rename_dict)
flights_ct2 = flights_sort.groupby(['AIR1', 'AIR2']).size()
print(flights_ct2.head(10))
print()
When I get to this line of code my output differs from the authors:
```flights_sort = flights[['ORG_AIR', 'DEST_AIR']].apply(sorted, axis=1)```
My output does not contain any column names. As a result, when I get to:
```flights_ct2 = flights_sort.groupby(['AIR1', 'AIR2']).size()```
it throws a KeyError. This makes sense, as I am trying to rename columns when no column names exist.
My question is, why are the column names gone? All other output matches the authors output exactly:
Connected to pydev debugger (build 191.7141.48)
NumPy: 1.16.3
Pandas: 0.24.2
-----
MONTH DAY WEEKDAY AIRLINE ORG_AIR DEST_AIR SCHED_DEP DEP_DELAY AIR_TIME DIST SCHED_ARR ARR_DELAY DIVERTED CANCELLED
0 1 1 4 WN LAX SLC 1625 58.0 94.0 590 1905 65.0 0 0
1 1 1 4 UA DEN IAD 823 7.0 154.0 1452 1333 -13.0 0 0
2 1 1 4 MQ DFW VPS 1305 36.0 85.0 641 1453 35.0 0 0
3 1 1 4 AA DFW DCA 1555 7.0 126.0 1192 1935 -7.0 0 0
4 1 1 4 WN LAX MCI 1720 48.0 166.0 1363 2225 39.0 0 0
5 1 1 4 UA IAH SAN 1450 1.0 178.0 1303 1620 -14.0 0 0
6 1 1 4 AA DFW MSY 1250 84.0 64.0 447 1410 83.0 0 0
7 1 1 4 F9 SFO PHX 1020 -7.0 91.0 651 1315 -6.0 0 0
8 1 1 4 AA ORD STL 1845 -5.0 44.0 258 1950 -5.0 0 0
9 1 1 4 UA IAH SJC 925 3.0 215.0 1608 1136 -14.0 0 0
ORG_AIR DEST_AIR
ATL ABE 31
ABQ 16
ABY 19
ACY 6
AEX 40
AGS 83
ALB 33
ANC 2
ASE 1
ATW 10
dtype: int64
ORG_AIR DEST_AIR
ATL IAH 121
IAH ATL 148
dtype: int64
*** No columns names *** Why?
0 [LAX, SLC]
1 [DEN, IAD]
2 [DFW, VPS]
3 [DCA, DFW]
4 [LAX, MCI]
5 [IAH, SAN]
6 [DFW, MSY]
7 [PHX, SFO]
8 [ORD, STL]
9 [IAH, SJC]
dtype: object
The author's output. Note the columns names are present.

sorted returns a list object and obliterates the columns:
In [11]: df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
In [12]: df.apply(sorted, axis=1)
Out[12]:
0 [1, 2]
1 [3, 4]
dtype: object
In [13]: type(df.apply(sorted, axis=1).iloc[0])
Out[13]: list
It's possible that this wouldn't have been the case in earlier pandas... but it would still be bad code.
You can do this by passing the columns explicitly:
In [14]: df.apply(lambda x: pd.Series(sorted(x), df.columns), axis=1)
Out[14]:
A B
0 1 2
1 3 4
A more efficient way to do this is to sort the sort the underlying numpy array:
In [21]: df = pd.DataFrame([[1, 2], [3, 1]], columns=["A", "B"])
In [22]: df
Out[22]:
A B
0 1 2
1 3 1
In [23]: arr = df[["A", "B"]].values
In [24]: arr.sort(axis=1)
In [25]: df[["A", "B"]] = arr
In [26]: df
Out[26]:
A B
0 1 2
1 1 3
As you can see this sorts each row.

A final note. I just applied #AndyHayden numpy based solution from above.
flights_sort = flights[["ORG_AIR", "DEST_AIR"]].values
flights_sort.sort(axis=1)
flights[["ORG_AIR", "DEST_AIR"]] = flights_sort
All I can say is … Wow. What an enormous performance difference. I get the exact same
correct answer and I get it as soon as I click the mouse as compared to the pandas lambda solution also provided by #AndyHayden which takes about 20 seconds to perform the sort. That dataset is 58,000+ rows. The numpy solution returns the sort instantly.

How to create Traingular moving average in python using for loop

I use python pandas to caculate the following formula
(https://i.stack.imgur.com/XIKBz.png)
I do it in python like this :
EURUSD['SMA2']= EURUSD['Close']. rolling (2).mean()
EURUSD['TMA2']= ( EURUSD['Close'] + EURUSD[SMA2']) / 2
The proplem is long coding when i calculated TMA 100 , so i need to use " for loop " to easy change TMA period .
Thanks in advance
Edited :
I had found the code but there is an error :
values = []
for i in range(1,201): values.append(eurusd['Close']).rolling(window=i).mean() values.mean()

TMA is average of averages.
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.rand(10, 5))
print(df)
# df['mean0']=df.mean(0)
df['mean1']=df.mean(1)
print(df)
df['TMA'] = df['mean1'].rolling(window=10,center=False).mean()
print(df)
Or you can easily print it.
print(df["mean1"].mean())
Here is how it looks:
0 1 2 3 4
0 0.643560 0.412046 0.072525 0.618968 0.080146
1 0.018226 0.222212 0.077592 0.125714 0.595707
2 0.652139 0.907341 0.581802 0.021503 0.849562
3 0.129509 0.315618 0.711265 0.812318 0.757575
4 0.881567 0.455848 0.470282 0.367477 0.326812
5 0.102455 0.156075 0.272582 0.719158 0.266293
6 0.412049 0.527936 0.054381 0.587994 0.442144
7 0.063904 0.635857 0.244050 0.002459 0.423960
8 0.446264 0.116646 0.990394 0.678823 0.027085
9 0.951547 0.947705 0.080846 0.848772 0.699036
0 1 2 3 4 mean1
0 0.643560 0.412046 0.072525 0.618968 0.080146 0.365449
1 0.018226 0.222212 0.077592 0.125714 0.595707 0.207890
2 0.652139 0.907341 0.581802 0.021503 0.849562 0.602470
3 0.129509 0.315618 0.711265 0.812318 0.757575 0.545257
4 0.881567 0.455848 0.470282 0.367477 0.326812 0.500397
5 0.102455 0.156075 0.272582 0.719158 0.266293 0.303313
6 0.412049 0.527936 0.054381 0.587994 0.442144 0.404901
7 0.063904 0.635857 0.244050 0.002459 0.423960 0.274046
8 0.446264 0.116646 0.990394 0.678823 0.027085 0.451842
9 0.951547 0.947705 0.080846 0.848772 0.699036 0.705581
0 1 2 3 4 mean1 TMA
0 0.643560 0.412046 0.072525 0.618968 0.080146 0.365449 NaN
1 0.018226 0.222212 0.077592 0.125714 0.595707 0.207890 NaN
2 0.652139 0.907341 0.581802 0.021503 0.849562 0.602470 NaN
3 0.129509 0.315618 0.711265 0.812318 0.757575 0.545257 NaN
4 0.881567 0.455848 0.470282 0.367477 0.326812 0.500397 NaN
5 0.102455 0.156075 0.272582 0.719158 0.266293 0.303313 NaN
6 0.412049 0.527936 0.054381 0.587994 0.442144 0.404901 NaN
7 0.063904 0.635857 0.244050 0.002459 0.423960 0.274046 NaN
8 0.446264 0.116646 0.990394 0.678823 0.027085 0.451842 NaN
9 0.951547 0.947705 0.080846 0.848772 0.699036 0.705581 0.436115

Transform dataframe value to range value in Python 3

I have a dataframe with the values:
3.05
35.97
49.11
48.80
48.02
10.61
25.69
6.02
55.36
0.42
47.87
2.26
54.43
8.85
8.75
14.29
41.29
35.69
44.27
1.08
I want transform the value into range and give new value to each value.
From the df we know the min value is 0.42 and the max value is 55.36.
From range min to max, I want divide to 4 group which is:
0.42 - 14.15 transform to 1
14.16 - 27.88 transform to 2
27.89 - 41.61 transform to 3
41.62 - 55.36 transform to 4
so the result I expected is
1
3
4
4
4
1
2
1
4
1
4
1
4
1
1
2
3
3
4
1

This is normally called binning, but pandas calls it cut. Sample code is below:
import pandas as pd
# Create a list of numbers, with a header called "nums"
data_list = [('nums', [3.05, 35.97, 49.11, 48.80, 48.02, 10.61, 25.69, 6.02, 55.36, 0.42, 47.87, 2.26, 54.43, 8.85, 8.75, 14.29, 41.29, 35.69, 44.27, 1.08])]
# Create the labels for the bin
bin_labels = [1,2,3,4]
# Create the dataframe object using the data_list
df = pd.DataFrame.from_items(data_list)
# Define the scope of the bins
bins = [0.41, 14.16, 27.89, 41.62, 55.37]
# Create the "bins" column using the cut function using the bins and labels
df['bins'] = pd.cut(df['nums'], bins=bins, labels=bin_labels)
This creates a dataframe which has the following structure:
print(df)
nums bins
0 3.05 1
1 35.97 3
2 49.11 4
3 48.80 4
4 48.02 4
5 10.61 1
6 25.69 2
7 6.02 1
8 55.36 4
9 0.42 1
10 47.87 4
11 2.26 1
12 54.43 4
13 8.85 1
14 8.75 1
15 14.29 2
16 41.29 3
17 35.69 3
18 44.27 4
19 1.08 1

You could construct a function like the following to have full control over the process:
def transform(l):
l2 = []
for i in l:
if 0.42 <= i <= 14.15:
l2.append(1)
elif i <= 27.8:
l2.append(2)
elif i <= 41.61:
l2.append(3)
elif i <= 55.36:
l2.append(4)
return(l2)
df['nums'] = transform(df['nums'])

Multiplying data within columns python

I've been working on this all morning and for the life of me cannot figure it out. I'm sure this is very basic, but I've become so frustrated my mind is being clouded. I'm attempting to calculate the total return of a portfolio of securities at each date (monthly).
The formula is (1 + r1) * (1+r2) * (1+ r(t))..... - 1
Here is what I'm working with:
Adj_Returns = Adj_Close/Adj_Close.shift(1)-1
Adj_Returns['Risk Parity Portfolio'] = (Adj_Returns.loc['2003-01-31':]*Weights.shift(1)).sum(axis = 1)
Adj_Returns
SPY IYR LQD Risk Parity Portfolio
Date
2002-12-31 NaN NaN NaN 0.000000
2003-01-31 -0.019802 -0.014723 0.000774 -0.006840
2003-02-28 -0.013479 0.019342 0.015533 0.011701
2003-03-31 -0.001885 0.010015 0.001564 0.003556
2003-04-30 0.088985 0.045647 0.020696 0.036997
For example, with 2002-12-31 being base 100 for risk parity, I want 2003-01-31 to be 99.316 (100 * (1-0.006840)), 2003-02-28 to be 100.478 (99.316 * (1+ 0.011701)) so on and so forth.
Thanks!!

You want to use pd.DataFrame.cumprod
df.add(1).cumprod().sub(1).sum(1)
Consider the dataframe of returns df
np.random.seed([3,1415])
df = pd.DataFrame(np.random.normal(.025, .03, (10, 5)), columns=list('ABCDE'))
df
A B C D E
0 -0.038892 -0.013054 -0.034115 -0.042772 0.014521
1 0.024191 0.034487 0.035463 0.046461 0.048123
2 0.006754 0.035572 0.014424 0.012524 -0.002347
3 0.020724 0.047405 -0.020125 0.043341 0.037007
4 -0.003783 0.069827 0.014605 -0.019147 0.056897
5 0.056890 0.042756 0.033886 0.001758 0.049944
6 0.069609 0.032687 -0.001997 0.036253 0.009415
7 0.026503 0.053499 -0.006013 0.053447 0.047013
8 0.062084 0.029664 -0.015238 0.029886 0.062748
9 0.048341 0.065248 -0.024081 0.019139 0.028955
We can see the cumulative return or total return is
df.add(1).cumprod().sub(1)
A B C D E
0 -0.038892 -0.013054 -0.034115 -0.042772 0.014521
1 -0.015641 0.020983 0.000139 0.001702 0.063343
2 -0.008993 0.057301 0.014565 0.014247 0.060847
3 0.011544 0.107423 -0.005853 0.058206 0.100105
4 0.007717 0.184750 0.008666 0.037944 0.162699
5 0.065046 0.235405 0.042847 0.039769 0.220768
6 0.139183 0.275786 0.040764 0.077464 0.232261
7 0.169375 0.344039 0.034505 0.135051 0.290194
8 0.241974 0.383909 0.018742 0.168973 0.371151
9 0.302013 0.474207 -0.005791 0.191346 0.410852
Plot it
df.add(1).cumprod().sub(1).plot()
Add sum of returns to new column
df.assign(Portfolio=df.add(1).cumprod().sub(1).sum(1))
A B C D E Portfolio
0 -0.038892 -0.013054 -0.034115 -0.042772 0.014521 -0.114311
1 0.024191 0.034487 0.035463 0.046461 0.048123 0.070526
2 0.006754 0.035572 0.014424 0.012524 -0.002347 0.137967
3 0.020724 0.047405 -0.020125 0.043341 0.037007 0.271425
4 -0.003783 0.069827 0.014605 -0.019147 0.056897 0.401777
5 0.056890 0.042756 0.033886 0.001758 0.049944 0.603835
6 0.069609 0.032687 -0.001997 0.036253 0.009415 0.765459
7 0.026503 0.053499 -0.006013 0.053447 0.047013 0.973165
8 0.062084 0.029664 -0.015238 0.029886 0.062748 1.184749
9 0.048341 0.065248 -0.024081 0.019139 0.028955 1.372626