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How to create multiple triangle using another dataframe? [closed]

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Below is my code:
triangle = cl.load_sample('genins')
# Use bootstrap sampler to get resampled triangles
bootstrapdataframe = cl.BootstrapODPSample(n_sims=4, random_state=42).fit(triangle).resampled_triangles_
#converting to dataframe
resampledtriangledf = bootstrapdataframe.to_frame()
print(resampledtriangledf)
In above code i mentioned n_sims(number of simulation)=4. So it generates below datafame:
0 2001 12 254,926
0 2001 24 535,877
0 2001 36 1,355,613
0 2001 48 2,034,557
0 2001 60 2,311,789
0 2001 72 2,539,807
0 2001 84 2,724,773
0 2001 96 3,187,095
0 2001 108 3,498,646
0 2001 120 3,586,037
0 2002 12 542,369
0 2002 24 1,016,927
0 2002 36 2,201,329
0 2002 48 2,923,381
0 2002 60 3,711,305
0 2002 72 3,914,829
0 2002 84 4,385,757
0 2002 96 4,596,072
0 2002 108 5,047,861
0 2003 12 235,361
0 2003 24 960,355
0 2003 36 1,661,972
0 2003 48 2,643,370
0 2003 60 3,372,684
0 2003 72 3,642,605
0 2003 84 4,160,583
0 2003 96 4,480,332
0 2004 12 764,553
0 2004 24 1,703,557
0 2004 36 2,498,418
0 2004 48 3,198,358
0 2004 60 3,524,562
0 2004 72 3,884,971
0 2004 84 4,268,241
0 2005 12 381,670
0 2005 24 1,124,054
0 2005 36 2,026,434
0 2005 48 2,863,902
0 2005 60 3,039,322
0 2005 72 3,288,253
0 2006 12 320,332
0 2006 24 1,022,323
0 2006 36 1,830,842
0 2006 48 2,676,710
0 2006 60 3,375,172
0 2007 12 330,361
0 2007 24 1,463,348
0 2007 36 2,771,839
0 2007 48 4,003,745
0 2008 12 282,143
0 2008 24 1,782,267
0 2008 36 2,898,699
0 2009 12 362,726
0 2009 24 1,277,750
0 2010 12 321,247
1 2001 12 219,021
1 2001 24 755,975
1 2001 36 1,360,298
1 2001 48 2,062,947
1 2001 60 2,356,983
1 2001 72 2,781,187
1 2001 84 2,987,837
1 2001 96 3,118,952
1 2001 108 3,307,522
1 2001 120 3,455,107
1 2002 12 302,932
1 2002 24 1,022,459
1 2002 36 1,634,938
1 2002 48 2,538,708
1 2002 60 3,005,695
1 2002 72 3,274,719
1 2002 84 3,356,499
1 2002 96 3,595,361
1 2002 108 4,100,065
1 2003 12 489,934
1 2003 24 1,233,438
1 2003 36 2,471,849
1 2003 48 3,672,629
1 2003 60 4,157,489
1 2003 72 4,498,470
1 2003 84 4,587,579
1 2003 96 4,816,232
1 2004 12 518,680
1 2004 24 1,209,705
1 2004 36 2,019,757
1 2004 48 2,997,820
1 2004 60 3,630,442
1 2004 72 3,881,093
1 2004 84 4,080,322
1 2005 12 453,963
1 2005 24 1,458,504
1 2005 36 2,036,506
1 2005 48 2,846,464
1 2005 60 3,280,124
1 2005 72 3,544,597
1 2006 12 369,755
1 2006 24 1,209,117
1 2006 36 1,973,136
1 2006 48 3,034,294
1 2006 60 3,537,784
1 2007 12 477,788
1 2007 24 1,524,537
1 2007 36 2,170,391
1 2007 48 3,355,093
1 2008 12 250,690
1 2008 24 1,546,986
1 2008 36 2,996,737
1 2009 12 271,270
1 2009 24 1,446,353
1 2010 12 510,114
2 2001 12 170,866
2 2001 24 797,338
2 2001 36 1,663,610
2 2001 48 2,293,697
2 2001 60 2,607,067
2 2001 72 2,979,479
2 2001 84 3,127,308
2 2001 96 3,285,338
2 2001 108 3,574,272
2 2001 120 3,630,610
2 2002 12 259,060
2 2002 24 1,011,092
2 2002 36 1,851,504
2 2002 48 2,705,313
2 2002 60 3,195,774
2 2002 72 3,766,008
2 2002 84 3,944,417
2 2002 96 4,234,043
2 2002 108 4,763,664
2 2003 12 239,981
2 2003 24 983,484
2 2003 36 1,929,785
2 2003 48 2,497,929
2 2003 60 2,972,887
2 2003 72 3,313,868
2 2003 84 3,727,432
2 2003 96 4,024,122
2 2004 12 77,522
2 2004 24 729,401
2 2004 36 1,473,914
2 2004 48 2,376,313
2 2004 60 2,999,197
2 2004 72 3,372,020
2 2004 84 3,887,883
2 2005 12 321,598
2 2005 24 1,132,502
2 2005 36 1,710,504
2 2005 48 2,438,620
2 2005 60 2,801,957
2 2005 72 3,182,466
2 2006 12 255,407
2 2006 24 1,275,141
2 2006 36 2,083,421
2 2006 48 3,144,579
2 2006 60 3,891,772
2 2007 12 338,120
2 2007 24 1,275,697
2 2007 36 2,238,715
2 2007 48 3,615,323
2 2008 12 310,214
2 2008 24 1,237,156
2 2008 36 2,563,326
2 2009 12 271,093
2 2009 24 1,523,131
2 2010 12 430,591
3 2001 12 330,887
3 2001 24 831,193
3 2001 36 1,601,374
3 2001 48 2,188,879
3 2001 60 2,662,773
3 2001 72 3,086,976
3 2001 84 3,332,247
3 2001 96 3,317,279
3 2001 108 3,576,659
3 2001 120 3,613,563
3 2002 12 358,263
3 2002 24 1,139,259
3 2002 36 2,236,375
3 2002 48 3,163,464
3 2002 60 3,715,130
3 2002 72 4,295,638
3 2002 84 4,502,105
3 2002 96 4,769,139
3 2002 108 5,323,304
3 2003 12 489,934
3 2003 24 1,570,352
3 2003 36 3,123,215
3 2003 48 4,189,299
3 2003 60 4,819,070
3 2003 72 5,306,689
3 2003 84 5,560,371
3 2003 96 5,827,003
3 2004 12 419,727
3 2004 24 1,308,884
3 2004 36 2,118,936
3 2004 48 2,906,732
3 2004 60 3,561,577
3 2004 72 3,934,400
3 2004 84 4,010,511
3 2005 12 389,217
3 2005 24 1,173,226
3 2005 36 1,794,216
3 2005 48 2,528,910
3 2005 60 3,474,035
3 2005 72 3,908,999
3 2006 12 291,940
3 2006 24 1,136,674
3 2006 36 1,915,614
3 2006 48 2,693,930
3 2006 60 3,375,601
3 2007 12 506,055
3 2007 24 1,684,660
3 2007 36 2,678,739
3 2007 48 3,545,156
3 2008 12 282,143
3 2008 24 1,536,490
3 2008 36 2,458,789
3 2009 12 271,093
3 2009 24 1,199,897
3 2010 12 266,359
Using above dataframe I have to create 4 triangles based on Toatal column:
For example:
Row Labels 12 24 36 48 60 72 84 96 108 120 Grand Total
2001 254,926 535,877 1,355,613 2,034,557 2,311,789 2,539,807 2,724,773 3,187,095 3,498,646 3,586,037 22,029,119
2002 542,369 1,016,927 2,201,329 2,923,381 3,711,305 3,914,829 4,385,757 4,596,072 5,047,861 28,339,832
2003 235,361 960,355 1,661,972 2,643,370 3,372,684 3,642,605 4,160,583 4,480,332 21,157,261
2004 764,553 1,703,557 2,498,418 3,198,358 3,524,562 3,884,971 4,268,241 19,842,659
2005 381,670 1,124,054 2,026,434 2,863,902 3,039,322 3,288,253 12,723,635
2006 320,332 1,022,323 1,830,842 2,676,710 3,375,172 9,225,377
2007 330,361 1,463,348 2,771,839 4,003,745 8,569,294
2008 282,143 1,782,267 2,898,699 4,963,110
2009 362,726 1,277,750 1,640,475
2010 321,247 321,247
Grand Total 3,795,687 10,886,456 17,245,147 20,344,022 19,334,833 17,270,466 15,539,355 12,263,499 8,546,507 3,586,037 128,812,009
.
.
.
Like this i need 4 triangles (4 is number of simulation) using 1st dataframe.
If user gives s_sims=900 then it creates 900 totals values based on this we have to create 900 triangles.

Use pivot_table and choose the aggregation function (sum here but you can use mean or whatever):
df = df.pivot_table(index="origin", columns="development",
values="values", aggfunc="sum")
df = df.set_index(df.index.year)
df.loc["Grand Total"] = df.sum()
df.loc[:, "Grand Total"] = df.sum(axis=1)
>>> df
development 12 24 36 48 60 72 84 96 108 120 Grand Total
origin
2001 1.356449e+09 4.695043e+09 8.226504e+09 1.200121e+10 1.408404e+10 1.555555e+10 1.690673e+10 1.781579e+10 1.917689e+10 1.951240e+10 1.293306e+11
2002 1.887634e+09 6.573443e+09 1.150100e+10 1.671772e+10 1.960781e+10 2.164808e+10 2.352267e+10 2.480478e+10 2.671911e+10 NaN 1.529823e+11
2003 1.866031e+09 6.531145e+09 1.137408e+10 1.657377e+10 1.945944e+10 2.148353e+10 2.334087e+10 2.459720e+10 NaN NaN 1.252261e+11
2004 1.842447e+09 6.411653e+09 1.120732e+10 1.633725e+10 1.917381e+10 2.117893e+10 2.301072e+10 NaN NaN NaN 9.916214e+10
2005 1.688064e+09 5.876106e+09 1.027445e+10 1.496756e+10 1.757424e+10 1.939891e+10 NaN NaN NaN NaN 6.977932e+10
2006 1.762834e+09 6.154760e+09 1.076776e+10 1.569864e+10 1.843549e+10 NaN NaN NaN NaN NaN 5.281948e+10
2007 1.968264e+09 6.855178e+09 1.195292e+10 1.741326e+10 NaN NaN NaN NaN NaN NaN 3.818962e+10
2008 2.344669e+09 8.218527e+09 1.433187e+10 NaN NaN NaN NaN NaN NaN NaN 2.489507e+10
2009 1.955145e+09 6.813284e+09 NaN NaN NaN NaN NaN NaN NaN NaN 8.768429e+09
2010 1.716057e+09 NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.716057e+09
Grand Total 1.838759e+10 5.812914e+10 8.963591e+10 1.097094e+11 1.083348e+11 9.926499e+10 8.678100e+10 6.721778e+10 4.589601e+10 1.951240e+10 7.028691e+11
The code above works for the following input data:
>>> df
origin development values
Total
0 2001-01-01 12 3.766810e+05
0 2001-01-01 24 1.025411e+06
0 2001-01-01 36 1.541503e+06
0 2001-01-01 48 2.155232e+06
0 2001-01-01 60 2.422287e+06
... ... ... ...
4999 2008-01-01 24 2.403488e+06
4999 2008-01-01 36 3.100034e+06
4999 2009-01-01 12 3.747304e+05
4999 2009-01-01 24 1.262821e+06
4999 2010-01-01 12 2.469928e+05
[275000 rows x 3 columns]

Python: Merge on 2 columns

I'm working with a large dataset. The following is an example, calculated with a smaller dataset.
In this example i got the measurements of the pollution of 3 rivers for different timespans. Each year, the amount pollution of a river is measured at a measuring station downstream ("pollution"). It has already been calculated, in which year the river water was polluted upstream ("year_of_upstream_pollution"). My goal ist to create a new column ["result_of_upstream_pollution"], which contains the amount of pollution connected to the "year_of_upstream_pollution". For this, the data from the "pollution"-column has to be reassigned.
ids = [1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3]
year = [2000,2001,2002,2003,2004,2005,1990,1991,1992,1993,1994,1995,2000,2001,2002,2003,2004,2005]
y1 = [2002,2002,2003,2005,2005,np.NaN,1991,1992,1993,1994,np.NaN,np.NaN,2012,2012,2013,2014,2015,np.NaN]
poll = [10,14,20,11,8,11,
20,22,20,25,18,21,
30,19,15,10,26,28]
dictr1 ={"river_id":ids,"year":year,"pollution": poll,"year_of_upstream_pollution":y1}
dfr1 = pd.DataFrame(dictr1)
print(dfr1)
river_id year pollution year_of_upstream_pollution
0 1 2000 10 2002.0
1 1 2001 14 2002.0
2 1 2002 20 2003.0
3 1 2003 11 2005.0
4 1 2004 8 2005.0
5 1 2005 11 NaN
6 2 1990 20 1991.0
7 2 1991 22 1992.0
8 2 1992 20 1993.0
9 2 1993 25 1994.0
10 2 1994 18 NaN
11 2 1995 21 NaN
12 3 2000 30 2002.0
13 3 2001 19 2002.0
14 3 2002 15 2003.0
15 3 2003 10 2004.0
16 3 2004 26 2005.0
17 3 2005 28 NaN
Example: river_id = 1, year = 2000, year_of_upstream_pollution = 2002
value of the pollution-column in year 2002 = 20
Therefore: result_of_upstream_pollution = 20
The resulting column should look like this:
result_of_upstream_pollution
0 20.0
1 20.0
2 11.0
3 11.0
4 11.0
5 NaN
6 22.0
7 20.0
8 25.0
9 18.0
10 NaN
11 NaN
12 15.0
13 15.0
14 10.0
15 26.0
16 28.0
17 NaN
My own approach:
### My approach
# Split dfr1 in two
dfr3 = pd.DataFrame(dfr1, columns = ["river_id","year","pollution"])
dfr4 = pd.DataFrame(dfr1, columns = ["river_id","year_of_upstream_pollution"])
# Merge the two dataframes on the "year" and "year_of_upstream_pollution"-column
arrayr= dfr4.merge(dfr3, left_on = "year_of_upstream_pollution", right_on = "year", how = "left").pollution.values
listr = arrayr.tolist()
dfr1["result_of_upstream_pollution"] = listr
print(dfr1)
len(listr) # = 28
This results in the following ValueError:
"Length of values does not match length of index"
My explanation for this is, that the values in the "year"-column of "dfr3" are not unique, which leads to several numbers being assigned to each year and explains why: len(listr) = 28
I haven't been able to find a way around this error yet. Please keep in mind that the real dataset is much larger than this one. Any help would be much appreciated!

As you said in the title, this is merge on two column:
dfr1['result_of_upstream_pollution'] = dfr1.merge(dfr1, left_on=['river_id','year'],
right_on=['river_id','year_of_upstream_pollution'],
how='right')['pollution_x']
print(df)
Output:
result_of_upstream_pollution
0 20.0
1 20.0
2 11.0
3 11.0
4 11.0
5 NaN
6 22.0
7 20.0
8 25.0
9 18.0
10 NaN
11 NaN
12 15.0
13 15.0
14 10.0
15 26.0
16 28.0
17 NaN

I just realized that this solution doesn't seem to be working for me.
When i execute the code, this is what happens:
dfr1['result_of_upstream_pollution'] = dfr1.merge(dfr1, left_on=['river_id','year'],
right_on=['river_id','year_of_upstream_pollution'],
how='right')['pollution_x']
print(dfr1)
river_id year pollution year_of_upstream_pollution \
0 1 2000 10 2002.0
1 1 2001 14 2002.0
2 1 2002 20 2003.0
3 1 2003 11 2005.0
4 1 2004 8 2005.0
5 1 2005 11 NaN
6 2 1990 20 1991.0
7 2 1991 22 1992.0
8 2 1992 20 1993.0
9 2 1993 25 1994.0
10 2 1994 18 NaN
11 2 1995 21 NaN
12 3 2000 30 2002.0
13 3 2001 19 2002.0
14 3 2002 15 2003.0
15 3 2003 10 2004.0
16 3 2004 26 2005.0
17 3 2005 28 NaN
result_of_upstream_pollution
0 20.0
1 20.0
2 11.0
3 11.0
4 11.0
5 22.0
6 20.0
7 25.0
8 18.0
9 15.0
10 15.0
11 10.0
12 26.0
13 28.0
14 NaN
15 NaN
16 NaN
17 NaN
For some reason, this code doesn't seem to be handling the "NaN" values in the right way.
If there is an "NaN"-value (in the column: "year_of_upstream_pollution"), there shouldnt be a value in "result_of_upstream_pollution".
Equally, the ids 14,15 and 16 all have values for the "year_of_upstream_pollution" which has matching data in the "pollution-column" and therefore should also have values in the result-column.
On top of that, it seems that all values after the first "NaN" (at id = 5) are assigned the wrong values.
#Quang Hoang Thank you very much for trying to solve my problem! Could you maybe explain why my results differ from yours?
Does anyone know how i can get this code to work?

How to unpack a list of tuple in various length in a panda dataframe?

ID LIST_OF_TUPLE (2col)
1 [('2012','12'), ('2012','33'), ('2014', '82')]
2 NA
3 [('2012','12')]
4 [('2012','12'), ('2012','33'), ('2014', '82'), ('2022', '67')]
Result:
ID TUP_1 TUP_2(3col)
1 2012 12
1 2012 33
1 2014 82
3 2012 12
4 2012 12
4 2012 33
4 2014 82
4 2022 67
Thanks in advance.

This is explode then create a dataframe and then join:
s = df['LIST_OF_TUPLE'].explode()
out = (df[['ID']].join(pd.DataFrame(s.tolist(),index=s.index)
.add_prefix("TUP_")).reset_index(drop=True)) #you can chain a dropna if reqd
print(out)
ID TUP_0 TUP_1
0 1 2012 12
1 1 2012 33
2 1 2014 82
3 2 NaN None
4 3 2012 12
5 4 2012 12
6 4 2012 33
7 4 2014 82
8 4 2022 67

how to apply unique function and transform and keep the complete columns in the data frame pandas

My goal here is to extract the count of rows in the data frame in which for each PatienNumber and year and month show the count of them and keep all the columns in the data frame.
This is the original data frame:
PatientNumber QT Answer Answerdate year month dayofyear count formula
1 1 transferring No 2017-03-03 2017 3 62 2.0 (1/3)
2 1 preparing food No 2017-03-03 2017 3 62 2.0 (1/3)
3 1 medications Yes 2017-03-03 2017 3 62 1.0 (1/3)
4 2 transferring No 2006-10-05 2006 10 275 3.0 0
5 2 preparing food No 2006-10-05 2006 10 275 3.0 0
6 2 medications No 2006-10-05 2006 10 275 3.0 0
7 2 transferring Yes 2007-4-15 2007 4 105 2.0 2/3
8 2 preparing food Yes 2007-4-15 2007 4 105 2.0 2/3
9 2 medications No 2007-4-15 2007 4 105 1.0 2/3
10 2 transferring Yes 2007-12-15 2007 12 345 1.0 1/3
11 2 preparing food No 2007-12-15 2007 12 345 2.0 1/3
12 2 medications No 2007-12-15 2007 12 345 2.0 1/3
13 2 transferring Yes 2008-10-10 2008 10 280 1.0 (1/3)
14 2 preparing food No 2008-10-10 2008 10 280 2.0 (1/3)
15 2 medications No 2008-10-10 2008 10 280 2.0 (1/3)
16 3 medications No 2008-10-10 2008 12 280 …… ………..
so the desired output should be the same as this with one more column which shows the unique rows of [patientNumber, year, month]. for patient number=1 shows 1 for the PatientNumber= 2 shows 1 in year 2006, shows 2 in year 2007
I applied this code:
data=data.groupby(['Clinic Number','year'])["month"].nunique().reset_index(name='counts')
the output of this code look like:
Clinic Number year **counts**
0 494383 1999 1
1 494383 2000 2
2 494383 2001 1
3 494383 2002 1
4 494383 2003 1
the output counts is correct, except it does not keep the whole fields. I want the complete columns because later I have to do some calculation on them.
then I tried this code:
data['counts'] = data.groupby(['Clinic Number','year','month'])['month'].transform('count')
Again its not good because it does not show correct count. the output of this code is like this:
Clinic Number Question Text Answer Text ... year month counts
1 3529933 bathing No ... 2011 1 10
2 3529933 dressing No ... 2011 1 10
3 3529933 feeding No ... 2011 1 10
4 3529933 housekeeping No ... 2011 1 10
5 3529933 medications No ... 2011 1 10
here counts should be 1 because for that patient and that year there is just one month.

Use, the following modification to your code.
df['counts'] = df.groupby(['PatientNumber','year'])["month"].transform('nunique')
transform returns a series equal length to your original dataframe, therefore you can add this series into your dataframe as a column.

Panel data pandas, variation according to a certain condition

i am a stata user and i trying to switch to python and i having problem with some codes. If i have the following panel data
id year quarter fecha jobs
1 2007 1 220 10
1 2007 2 221 12
1 2007 3 222 12
1 2007 4 223 12
1 2008 1 224 12
1 2008 2 225 13
1 2008 3 226 14
1 2008 4 227 9
1 2009 1 228 12
1 2009 2 229 15
1 2009 3 230 18
1 2009 4 231 15
1 2010 1 232 15
1 2010 2 233 16
1 2010 3 234 17
1 2010 4 235 18
2 2007 1 220 10
2 2007 2 221 12
2 2007 3 222 12
2 2007 4 223 12
2 2008 1 224 12
2 2008 2 225 13
2 2008 3 226 14
2 2008 4 227 9
2 2009 1 228 12
2 2009 2 229 15
2 2009 3 230 18
2 2009 4 231 15
2 2010 1 232 15
2 2010 2 233 16
2 2010 4 235 18
(My panel data is much bigger than the example, is just to illustrate my problem). I want to calculate the variation of jobs of the same quarter and three year before
So result should look like these
id year quarter fecha jobs jobs_variation
1 2007 1 220 10 Nan
1 2007 2 221 12 Nan
1 2007 3 222 12 Nan
1 2007 4 223 12 Nan
1 2008 1 224 12 Nan
1 2008 2 225 13 Nan
1 2008 3 226 14 Nan
1 2008 4 227 9 Nan
1 2009 1 228 12 Nan
1 2009 2 229 15 Nan
1 2009 3 230 18 Nan
1 2009 4 231 15 Nan
1 2010 1 232 15 0.5
1 2010 2 233 16 0.33
1 2010 3 234 17 0.30769
1 2010 4 235 18 0.5
2 2007 1 220 10 Nan
2 2007 4 223 12 Nan
2 2008 1 224 12 Nan
2 2008 2 225 13 Nan
2 2008 3 226 14 Nan
2 2008 4 227 9 Nan
2 2009 1 228 12 Nan
2 2009 2 229 15 Nan
2 2009 3 230 18 Nan
2 2009 4 231 15 Nan
2 2010 1 232 15 0.5
2 2010 2 233 16 Nan
2 2010 3 234 20 Nan
2 2010 4 235 18 0.5
Check that in the second id year 2010 in the second and thir quarter calculation must not be me made because the id was not present at 2007Q2 and 2007Q3.
In stata the code would be,
bys id: gen jobs_variation=jobs/jobs[_n-12]-1 if fecha[_n-12]==fecha-12

IIUC, you need a groupby on id and quarter followed by apply:
df['jobs_variation'] = df.groupby(['id', 'quarter']).jobs\
.apply(lambda x: x / x.shift(3) - 1)
df
id year quarter fecha jobs jobs_variation
0 1 2007 1 220 10 NaN
1 1 2007 2 221 12 NaN
2 1 2007 3 222 12 NaN
3 1 2007 4 223 12 NaN
4 1 2008 1 224 12 NaN
5 1 2008 2 225 13 NaN
6 1 2008 3 226 14 NaN
7 1 2008 4 227 9 NaN
8 1 2009 1 228 12 NaN
9 1 2009 2 229 15 NaN
10 1 2009 3 230 18 NaN
11 1 2009 4 231 15 NaN
12 1 2010 1 232 15 0.500000
13 1 2010 2 233 16 0.333333
14 1 2010 3 234 17 0.416667
15 1 2010 4 235 18 0.500000
16 2 2007 1 220 10 NaN
17 2 2007 4 223 12 NaN
18 2 2008 1 224 12 NaN
19 2 2008 2 225 13 NaN
20 2 2008 3 226 14 NaN
21 2 2008 4 227 9 NaN
22 2 2009 1 228 12 NaN
23 2 2009 2 229 15 NaN
24 2 2009 3 230 18 NaN
25 2 2009 4 231 15 NaN
26 2 2010 1 232 15 0.500000
27 2 2010 2 233 16 NaN
28 2 2010 3 234 20 NaN
29 2 2010 4 235 18 0.500000
x / x.shift(3) will divide the current year's job count (for that quarter) by the corresponding value from 3 years ago.