How to iterate through columns of the dataframe?

Question

I want go through the all the columns of the dataframe. so that I will get a particular data of the column, using these data I have to calculate for another dataframe. Here i have:

                         DP1         DP2         DP3         DP4         DP5         DP6         DP7         DP8        DP9       DP10       Total
OP1                  357848.0   1124788.0   1735330.0   2218270.0   2745596.0   3319994.0   3466336.0   3606286.0  3833515.0  3901463.0   3901463.0
OP2                  352118.0   1236139.0   2170033.0   3353322.0   3799067.0   4120063.0   4647867.0   4914039.0  5339085.0        NaN   5339085.0
OP3                  290507.0   1292306.0   2218525.0   3235179.0   3985995.0   4132918.0   4628910.0   4909315.0        NaN        NaN   4909315.0
OP4                  310608.0   1418858.0   2195047.0   3757447.0   4029929.0   4381982.0   4588268.0         NaN        NaN        NaN   4588268.0
OP5                  443160.0   1136350.0   2128333.0   2897821.0   3402672.0   3873311.0         NaN         NaN        NaN        NaN   3873311.0
OP6                  396132.0   1333217.0   2180715.0   2985752.0   3691712.0         NaN         NaN         NaN        NaN        NaN   3691712.0
OP7                  440832.0   1288463.0   2419861.0   3483130.0         NaN         NaN         NaN         NaN        NaN        NaN   3483130.0
OP8                  359480.0   1421128.0   2864498.0         NaN         NaN         NaN         NaN         NaN        NaN        NaN   2864498.0
OP9                  376686.0   1363294.0         NaN         NaN         NaN         NaN         NaN         NaN        NaN        NaN   1363294.0
OP10                 344014.0         NaN         NaN         NaN         NaN         NaN         NaN         NaN        NaN        NaN    344014.0
Total               3671385.0  11614543.0  17912342.0  21930921.0  21654971.0  19828268.0  17331381.0  13429640.0  9172600.0  3901463.0  34358090.0
Latest Observation   344014.0   1363294.0   2864498.0   3483130.0   3691712.0   3873311.0   4588268.0   4909315.0  5339085.0  3901463.0         NaN 

From this table I would like to calculate formula this formula:in column DP1,Total/Last observation and this answer is divides by DP2 columns total. Like this we have to calculate all the columns and save it in another dataframe.

we need row like this:

Weighted Average     3.491   1.747   1.457   1.174   1.104   1.086   1.054   1.077   1.018 

This code we tried:

LDFTriangledf['Weighted Average'] =CumulativePaidTriangledf.loc['Total','DP2']/(CumulativePaidTriangledf.loc['Total','DP1'] - CumulativePaidTriangledf.loc['Latest Observation','DP1'])

Answer 1

You can remove the column names from .loc and just shift(-1, axis=1) to get the next column's Total . This lets you apply the formula to all columns in a single operation:

CumulativePaidTriangledf.shift(-1, axis=1).loc['Total'] / (CumulativePaidTriangledf.loc['Total'] - CumulativePaidTriangledf.loc['Latest Observation'])

# DP1      3.490607
# DP2      1.747333
# DP3      1.457413
# DP4      1.173852
# DP5      1.103824
# DP6      1.086269
# DP7      1.053874
# DP8      1.076555
# DP9      1.017725
# DP10          inf
# Total         NaN
# dtype: float64

---

Here is a breakdown of what the three components are doing:

	DP1	DP2	DP3	DP4	DP5	DP6	DP7	DP8	DP9	DP10	Total
A: .shift(-1, axis=1).loc['Total'] -- We are shifting the whole Total row to the left, so every column now has the next Total value.	1.161454e+07	1.791234e+07	2.193092e+07	2.165497e+07	1.982827e+07	1.733138e+07	1.342964e+07	9.172600e+06	3.901463e+06	34358090.0	NaN
B: .loc['Total'] -- This is the normal Total row.	3.671385e+06	1.161454e+07	1.791234e+07	2.193092e+07	2.165497e+07	1.982827e+07	1.733138e+07	1.342964e+07	9.172600e+06	3901463.0	34358090.0
C: .loc['Latest Observation'] -- This is the normal Latest Observation .	3.440140e+05	1.363294e+06	2.864498e+06	3.483130e+06	3.691712e+06	3.873311e+06	4.588268e+06	4.909315e+06	5.339085e+06	3901463.0	NaN
A / (BC) -- This is what the code above does. It takes the shifted Total row (A) and divides it by the difference of the current Total row (B) and current Latest observation row (C).	3.490607	1.747333	1.457413	1.173852	1.103824	1.086269	1.053874	1.076555	1.017725	inf	NaN