Develop Reference

Concat and Merge columns with another in Pandas Dataframe

This what look like my dataframe:
Type
Cat
tax
Car_tax
Type_tax
0
Car_1
Car_1_Rent
NW;TAX1/Car_1
NW;TAX1/Car_1_Rent
1
Car_2
Car_2_Rent
NW;TAX1/Car_2
NW;TAX1/Car_2_Rent
2
Car_3
Car_3_Rent
NW;TAX1/Car_3
NW;TAX1/Car_3_Rent
3
Car_4
Car_4_Rent
NW;TAX1/Car_4
NW;TAX1/Car_4_Rent
4
Car_5
Car_5_Rent
NW;TAX1/Car_5
NW;TAX1/Car_5_Rent
5
Car_6
Car_6_Rent
NW;TAX1/Car_6
NW;TAX1/Car_6_Rent
6
Car_7
Car_7_Rent
NW;TAX1/Car_7
NW;TAX1/Car_7_Rent
23
Moto_1
Moto_1_Rent
NW;TAX1/Moto_1
NW;TAX1/Moto_1_Rent
24
Moto_2
Moto_2_Rent
NW;TAX1/Moto_2
NW;TAX1/Moto_2_Rent
25
Moto_3
Moto_3_Rent
NW;TAX1/Moto_3
NW;TAX1/Moto_3_Rent
26
Moto_4
Moto_4_Rent
NW;TAX1/Moto_4
NW;TAX1/Moto_4_Rent
27
Moto_5
Moto_5_Rent
NW;TAX1/Moto_5
NW;TAX1/Moto_5_Rent
28
Moto_6
Moto_6_Rent
NW;TAX1/Moto_6
NW;TAX1/Moto_6_Rent
29
Moto_7
Moto_7_Rent
NW;TAX1/Moto_7
NW;TAX1/Moto_7_Rent
And this is what I want to achieve :
index
Cat
Type
0
Car_1
NW;TAX1/Car_1
1
Car_2
NW;TAX1/Car_2
2
Car_3
NW;TAX1/Car_3
3
Car_4
NW;TAX1/Car_4
4
Car_5
NW;TAX1/Car_5
5
Car_6
NW;TAX1/Car_6
6
Car_7
NW;TAX1/Car_7
7
Moto_1
NW;TAX1/Moto_1
8
Moto_2
NW;TAX1/Moto_2
9
Moto_3
NW;TAX1/Moto_3
10
Moto_4
NW;TAX1/Moto_4
11
Moto_5
NW;TAX1/Moto_5
12
Moto_6
NW;TAX1/Moto_6
13
Moto_7
NW;TAX1/Moto_7
14
Car_1_Rent
NW;TAX1/Car_1_Rent
15
Car_2_Rent
NW;TAX1/Car_2_Rent
16
Car_3_Rent
NW;TAX1/Car_3_Rent
17
Car_4_Rent
NW;TAX1/Car_4_Rent
18
Car_5_Rent
NW;TAX1/Car_5_Rent
19
Car_6_Rent
NW;TAX1/Car_6_Rent
20
Car_7_Rent
NW;TAX1/Car_7_Rent
21
Moto_1_Rent
NW;TAX1/Moto_1_Rent
22
Moto_2_Rent
NW;TAX1/Moto_2_Rent
23
Moto_3_Rent
NW;TAX1/Moto_3_Rent
24
Moto_4_Rent
NW;TAX1/Moto_4_Rent
25
Moto_5_Rent
NW;TAX1/Moto_5_Rent
26
Moto_6_Rent
NW;TAX1/Moto_6_Rent
27
Moto_7_Rent
NW;TAX1/Moto_7_Rent
I have tried many things but with no luck.

Assuming your dataframe is named 'd', have you tried:
a = d[['Cat', 'Car_tax']].rename(columns={'Car_tax':'Type'})
b = d[['tax','Type_tax']]].rename(columns={'Type_tax':'Type', 'tax':'Cat' })
pd.concat([a,b]).reset_index(drop=True)

new_cols = ['Cat', 'Type']
sub_1 = df[['Cat', 'Car_tax']]
sub_2 = df[['tax', 'Type_tax']]
sub_1.columns = sub_2.columns = new_cols
result = pd.concat([sub_1, sub_2]).reset_index(drop=True)

You can just append the columns to each other, there is no merge/concat involved:
import pandas as pd
data = {'car1' : ['car10', 'car11'], 'car2': ['car20', 'car21'], 'car3': ['car30', 'car31'], 'car4': ['car40', 'car41']}
df = pd.DataFrame(data)
car1 = df['car1'].append(df['car2'])
car2 = df['car3'].append(df['car4'])
df2 = pd.DataFrame({'car1': car1, 'car2': car2})
df2 = df2.reset_index(drop=True)

pd.concat keys to separate column

I have several tables imported from an Excel file:
df = pd.read_excel(ffile, 'Constraints', header = None, names = range(13))
table_names = ['A', ...., 'W']
groups = df[0].isin(table_names).cumsum()
tables = {g.iloc[0,0]: g.iloc[1:] for k,g in df.groupby(groups)}
This is the first time I've tried to read multiple tables from a single sheet, so I'm not sure if this is the best manner. If printed like this:
for k,v in tables.items():
print("table:", k)
print(v)
print()
The output is:
table: A
0 1 2 ... 10 11 12
2 Sxxxxxx Dxxx 21 20 ... 22 19 22
3 Rxxx Sxxxx / Lxxx Cxxxxxxxxxxx 7 7 ... 7 7 7
4 AVG Sxxxx per xxx # xx% Pxxxxxxxxxxxx 5 X 5.95 5.95 ... 5.95 5.95 5.95
...
...
...
table: W
0 1 2 ... 10 11 12
6 Sxxxxxx Dxxx 21 20 ... 22 19 22
7 Rxxx Sxxxx / Lxxx Cxxxxxxxxxxx 30 30 ... 30 30 30
8 AVG Sxxxx per xxx # xx% Pxxxxxxxxxxxx 5 x 28.5 28.5 ... 28.5 28.5 28.5
I tried to combine them all into one DataFrame using dfa = pd.DataFrame(tables['A'])
for each table, and then using fdf = pd.concat([dfa,...,dwf], keys =['A', ... 'W']).
The keys are hierarchically placed, but the autonumbered index column inserts itself after the keys and before the first column:
0 1 2 ... 10 11 12
A 2 Sxxxxxx Dxxx 21 20 ... 22 19 22
3 Rxxx Sxxxx / Lxxx Cxxxxxxxxxxx 7 7 ... 7 7 7
4 AVG Sxxxx per xxx # xx% Pxxxxxxxxxxxx 5 X 5.95 5.95 ... 5.95 5.95 5.95
I would like to convert the keys to an actual column and switch places with the pandas numbered index, but I'm not sure how to do that. I've tried pd.reset_index() in various configurations, but am wondering if I maybe constructed the tables wrong in the first place?
If any of this information is not necessary, please let me know and I will remove it. I'm trying to follow the MCV guidelines and am not sure how much people need to know.

After you get the your tables, Just do
pd.concat(tables)

Scale values of a particular column of python dataframe between 1-10

I have a dataframe which contains youtube videos views, I want to scale these values in the range of 1-10.
Below is the sample of how values look like? How do i normalize it in the range of 1-10 or is there any more efficient way to do this thing?
rating
4394029
274358
473691
282858
703750
255967
3298456
136643
796896
2932
220661
48688
4661584
2526119
332176
7189818
322896
188162
157437
1153128
788310
1307902

One possibility is performing a scaling with max.
1 + df / df.max() * 9
rating
0 6.500315
1 1.343433
2 1.592952
3 1.354073
4 1.880933
5 1.320412
6 5.128909
7 1.171046
8 1.997531
9 1.003670
10 1.276217
11 1.060946
12 6.835232
13 4.162121
14 1.415808
15 10.000000
16 1.404192
17 1.235536
18 1.197075
19 2.443451
20 1.986783
21 2.637193
Similar solution by Wen (now deleted):
1 + (df - df.min()) * 9 / (df.max() - df.min())
rating
0 6.498887
1 1.339902
2 1.589522
3 1.350546
4 1.877621
5 1.316871
6 5.126922
7 1.167444
8 1.994266
9 1.000000
10 1.272658
11 1.057299
12 6.833941
13 4.159739
14 1.412306
15 10.000000
16 1.400685
17 1.231960
18 1.193484
19 2.440368
20 1.983514
21 2.634189

Pandas - formatting a NxN matrix

I have to deal with a square matrix (N x N) (N will change depending on the system, but the matrix will always be a square matrix).
Here is an example:
0 1 2 3 4
0 5.1677124550E-001 5.4962112499E-005 3.2484393256E-002 -1.8901697652E-001 -6.7156804753E-003
1 5.5380106796E-005 5.6159927753E-001 -1.9000545049E-003 -1.4737748792E-002 -7.2598453774E-002
2 3.2486915835E-002 -1.8996351539E-003 5.6791783316E-001 7.2316374186E-002 1.5013066446E-003
3 -1.8901411495E-001 -1.4737367075E-002 7.2315825338E-002 6.2721160365E-001 3.1553528602E-002
4 -6.7136454124E-003 -7.2597907350E-002 1.5007743348E-003 3.1554372311E-002 2.7318109331E-001
5 6.6738948243E-002 1.4102132238E-003 -1.2689244944E-001 4.7666038803E-002 1.8559074897E-002
6 -2.5293332676E-002 3.7536452002E-002 -1.3453018251E-002 -1.3177136905E-001 6.8262612506E-002
7 5.0951492945E-003 2.1082303893E-005 2.2599127408E-004 1.0287898189E-001 -1.1117916184E-001
8 1.0818230191E-003 -1.2435319909E-002 8.1008075834E-003 -4.2864102001E-002 4.2865913226E-002
9 -1.8399671295E-002 -2.1579653853E-002 -8.3073582356E-003 -2.1848513510E-001 -7.3408914625E-002
10 3.4566032399E-003 -4.0687639382E-003 1.3769999130E-003 -1.1873434189E-001 -3.3274201039E-002
11 6.6093238125E-003 1.7153435473E-002 4.9392012712E-003 -8.4590814134E-002 -4.3601041176E-002
12 -1.1418316960E-001 -1.1241625427E-001 -3.2263873516E-002 -1.9323129435E-002 -2.6233049625E-002
13 -1.1352899039E-001 -2.2898299860E-001 -5.3035072561E-002 7.4480651562E-004 6.3778892206E-004
14 -3.2197359289E-002 -5.3404040557E-002 -6.2530142462E-002 9.6648204015E-003 1.5382174347E-002
15 -1.2210509335E-001 1.1380412205E-001 -3.8374895516E-002 -1.2823165326E-002 2.3865200517E-002
16 1.1478157080E-001 -2.1487971631E-001 5.9955334103E-002 -1.2803721235E-003 -2.2477259002E-004
17 -3.9162044498E-002 6.0167325377E-002 -6.7692892326E-002 6.3814569032E-003 -1.3309923267E-002
18 -5.1386866211E-002 -1.1483215267E-003 -3.8482481829E-002 2.2227734790E-003 2.4860195290E-004
19 -1.8287048910E-003 -4.5442287955E-002 -7.6787332291E-003 7.6970470456E-004 -1.8456603178E-003
20 -3.4812676792E-002 -7.8376169613E-003 -3.1205975353E-001 -2.8005140005E-003 3.9792109835E-004
21 2.6908361866E-003 3.7102890907E-004 2.8494060446E-002 -4.8904422930E-002 -5.8840348122E-004
22 -1.6354677061E-003 2.2592828188E-003 1.6591434361E-004 -4.9992263663E-003 -4.3243295112E-002
23 -1.4297833794E-003 -1.7830154308E-003 -1.1426700328E-002 1.7125095395E-003 -1.2016863398E-002
24 1.6271802154E-003 1.6383303957E-003 -7.8049656555E-004 3.7177399735E-003 -1.0472268655E-002
25 -4.1949740427E-004 1.5301971185E-004 -9.8681335931E-004 -2.2257204483E-004 -5.1722898203E-003
26 1.0290471110E-003 9.3255502541E-004 7.7166886713E-004 4.5630851485E-003 -4.3761358485E-003
27 -7.0031784470E-004 -3.5205332654E-003 -1.6311730073E-003 -1.2805479632E-002 -6.5565487971E-003
28 7.4046927792E-004 1.9332629981E-003 3.7374682636E-004 3.9965654817E-003 -6.2275912806E-003
29 -3.4680278867E-004 -2.3027344089E-003 -1.1338817043E-003 -1.2023581780E-002 -5.4242202971E-003
5 6 7 8 9
0 6.6743285428E-002 -2.5292337123E-002 5.0949675928E-003 1.0817408844E-003 -1.8399704662E-002
1 1.4100215877E-003 3.7536256943E-002 2.1212526899E-005 -1.2435482773E-002 -2.1579384876E-002
2 -1.2689432485E-001 -1.3453164785E-002 2.2618690004E-004 8.1008703937E-003 -8.3084039605E-003
3 4.7663851818E-002 -1.3181118094E-001 1.0290976691E-001 -4.2887391630E-002 -2.1847562123E-001
4 1.8558453001E-002 6.8311145594E-002 -1.1122358467E-001 4.2891711956E-002 -7.3413776745E-002
5 6.5246209445E-001 -3.7960754525E-002 5.8439215647E-002 -9.0620367134E-002 -8.4164313206E-002
6 -3.7935271881E-002 1.9415336793E-001 -6.8115262349E-002 5.0899890760E-002 -3.3687874555E-002
7 5.8422477033E-002 -6.8128901087E-002 3.9950499633E-001 -4.4336879147E-002 -4.0665928103E-002
8 -9.0612201567E-002 5.0902528870E-002 -4.4330072001E-002 1.2680415316E-001 1.7096405711E-002
9 -8.4167028549E-002 -3.3690056890E-002 -4.0677875424E-002 1.7097273427E-002 5.2579065978E-001
10 -6.4841142152E-002 -5.4453858464E-003 -2.4697277476E-001 8.5069643903E-005 1.8744016178E-001
11 -1.0367060076E-001 1.5864203200E-002 -1.6074822795E-002 -5.5265410413E-002 -7.3152548403E-002
12 -9.0665723957E-003 3.3027526012E-003 1.8484849938E-003 -7.5841163489E-004 -3.3700244298E-003
13 4.7717318460E-004 -1.8118719766E-003 1.6014630540E-003 -2.3830908057E-004 2.1049292570E-003
14 4.3836856576E-003 -1.7242302777E-003 -1.2023546553E-003 4.0533783460E-004 1.4850814596E-003
15 -1.2402059167E-002 -7.4793143461E-003 -3.8769252328E-004 3.9551076185E-003 1.0737706641E-003
16 -9.3076805579E-005 -1.6074185601E-003 1.7551579833E-003 -5.1663470094E-004 1.1072804383E-003
17 4.6817349747E-003 3.6900011954E-003 -8.6155331565E-004 -9.1007768778E-005 -7.3899260162E-004
18 3.2959550689E-002 3.0400921147E-003 3.9724187499E-004 -1.9220339108E-003 1.8075790317E-003
19 7.0905456379E-004 -5.0949208181E-004 -4.6021500516E-004 -7.9847500945E-004 1.4079850530E-004
20 -1.8687467448E-002 -6.3913023759E-004 -7.3566296037E-004 2.3726543730E-003 -1.0663719038E-003
21 3.6598966411E-003 -8.2335128379E-003 7.5645765132E-004 -2.1824880567E-002 -3.5125687811E-003
22 -1.6198130808E-002 8.4576317115E-003 -6.2045498682E-004 3.3460766491E-002 3.2638760335E-003
23 -3.2057393808E-001 -1.1315081941E-002 3.4822885510E-003 -5.8263446092E-003 2.9508421818E-004
24 -2.6366856593E-002 -5.8331954255E-004 1.1995976399E-003 3.4813904521E-003 -5.0942740761E-002
25 6.5474742063E-003 -5.7681583908E-003 -2.2680039574E-002 -3.3264360995E-002 4.8925407218E-003
26 -1.1288074542E-002 -4.5938216710E-003 -1.9339903561E-003 1.0812058656E-002 2.3005958417E-002
27 1.8937006089E-002 6.5590668002E-003 -2.9973042787E-003 -9.1466195902E-003 -2.0027029530E-001
28 -5.0006834397E-003 -3.1011487603E-002 -2.1071980031E-002 1.5171078954E-002 -6.3286786806E-002
29 1.0199591553E-002 -7.9372677248E-004 3.0157129340E-003 3.3043947441E-003 1.2554933598E-001
10 11 12 13 14
0 3.4566170422E-003 6.6091516193E-003 -1.1418209846E-001 -1.1352717720E-001 -3.2196213169E-002
1 -4.0687114857E-003 1.7153538295E-002 -1.1241515840E-001 -2.2897846552E-001 -5.3401852861E-002
2 1.3767476381E-003 4.9395834885E-003 -3.2262805417E-002 -5.3032729716E-002 -6.2527093260E-002
3 -1.1874067860E-001 -8.4586993618E-002 -1.9322697616E-002 7.4504831410E-004 9.6646936748E-003
4 -3.3280804952E-002 -4.3604931512E-002 -2.6232842935E-002 6.3789697287E-004 1.5382093474E-002
5 -6.4845769217E-002 -1.0366990398E-001 -9.0664935892E-003 4.7719667654E-004 4.3835884630E-003
6 -5.4306282394E-003 1.5863464756E-002 3.3027917727E-003 -1.8118646089E-003 -1.7242102753E-003
7 -2.4687457565E-001 -1.6075394559E-002 1.8484728466E-003 1.6014634135E-003 -1.2023496466E-003
8 8.5962912652E-005 -5.5265657567E-002 -7.5843145596E-004 -2.3831274033E-004 4.0533385644E-004
9 1.8744386918E-001 -7.3152643002E-002 -3.3700964189E-003 2.1048865009E-003 1.4850822567E-003
10 4.2975054072E-001 1.0364270794E-001 -1.5875283846E-003 6.7147216913E-004 1.2875627684E-004
11 1.0364402707E-001 6.0712435750E-001 5.1492123223E-003 8.2705404716E-004 -1.8653698814E-003
12 -1.5875318643E-003 5.1492269487E-003 1.2662026379E-001 1.2488481495E-001 3.3008712754E-002
13 6.7147489686E-004 8.2705994225E-004 1.2488477299E-001 2.4603749137E-001 5.7666439818E-002
14 1.2875157882E-004 -1.8653719810E-003 3.3008614344E-002 5.7666322609E-002 6.3196096154E-002
15 1.1375173141E-003 -1.2188735107E-003 9.5708352328E-003 -1.3282223067E-002 5.3571128896E-003
16 2.1319373893E-004 -2.6367828437E-004 1.4833724552E-002 -2.0115235494E-002 7.8461850894E-003
17 2.3051283757E-004 3.4044831571E-004 4.9262824289E-003 -6.6151918659E-003 1.1684894610E-003
18 -5.6658408835E-004 1.5710333316E-003 -2.6543076573E-003 1.0490950154E-003 -1.5676208892E-002
19 1.0005496308E-003 1.0400419914E-003 -2.7122935995E-003 -5.3716049248E-005 -2.6747366947E-002
20 3.1068907684E-004 5.3348953665E-004 -4.7934824223E-004 4.4853558686E-004 -6.0300656596E-003
21 2.7080517882E-003 -1.9033626829E-002 8.8615570289E-004 -3.7735646663E-004 -7.4101143501E-004
22 -2.9622921796E-003 -2.4159082408E-002 6.6943323966E-004 1.1154593780E-004 1.5914682394E-004
23 3.2842560830E-003 -6.2612752482E-003 1.5738434272E-004 4.6284599959E-004 4.0588132107E-004
24 1.6971737369E-003 2.4217812563E-002 4.3246402884E-004 9.5059931011E-005 3.5484698283E-004
25 -7.4868993750E-002 -8.7332668698E-002 -6.0147742690E-005 -4.8099146029E-005 1.1509155506E-004
26 -9.3177706949E-002 -2.9315061874E-001 2.1287190612E-004 5.0813661565E-005 2.6955715462E-004
27 -7.0097859908E-002 1.2458191360E-001 -1.2846480258E-003 1.2192486380E-004 4.6853704861E-004
28 -6.9485493530E-002 4.8763866344E-002 7.7223643475E-004 1.3853535883E-004 5.4636752811E-005
29 4.8961381968E-002 -1.5272337445E-001 -8.8648769643E-004 -4.4975303480E-005 5.9586006091E-004
15 16 17 18 19
0 -1.2210501176E-001 1.1478027359E-001 -3.9162145749E-002 -5.1389252158E-002 -1.8288904037E-003
1 1.1380272374E-001 -2.1487588526E-001 6.0165774430E-002 -1.1487007778E-003 -4.5441546655E-002
2 -3.8374694597E-002 5.9953296524E-002 -6.7691825286E-002 -3.8484030260E-002 -7.6800715249E-003
3 -1.2822729286E-002 -1.2805898275E-003 6.3813065178E-003 2.2220841872E-003 7.6991955181E-004
4 2.3864994996E-002 -2.2470892452E-004 -1.3309838494E-002 2.4851560674E-004 -1.8460620529E-003
5 -1.2402212045E-002 -9.2994801153E-005 4.6817064931E-003 3.2958166488E-002 7.0866732024E-004
6 -7.4793278406E-003 -1.6074103229E-003 3.6899979002E-003 3.0392561951E-003 -5.0946020505E-004
7 -3.8770026733E-004 1.7551659565E-003 -8.6155605026E-004 3.9692465089E-004 -4.6038088334E-004
8 3.9551171890E-003 -5.1663991899E-004 -9.1008948343E-005 -1.9220277566E-003 -7.9837924658E-004
9 1.0738350084E-003 1.1072790098E-003 -7.3897453645E-004 1.8057852560E-003 1.4013275714E-004
10 1.1375075076E-003 2.1317640112E-004 2.3050639764E-004 -5.6673414945E-004 1.0005316579E-003
11 -1.2189105982E-003 -2.6367792495E-004 3.4043235164E-004 1.5732522246E-003 1.0407973658E-003
12 9.5708232459E-003 1.4833737759E-002 4.9262816092E-003 -2.6542614308E-003 -2.7122986789E-003
13 -1.3282260152E-002 -2.0115238348E-002 -6.6152067653E-003 1.0491248568E-003 -5.3705750675E-005
14 5.3571028398E-003 7.8462085672E-003 1.1684872139E-003 -1.5676176683E-002 -2.6747374282E-002
15 1.3378635756E-001 -1.2613361119E-001 4.2401828623E-002 -2.6595403473E-003 1.9873360401E-003
16 -1.2613349126E-001 2.3154756121E-001 -6.5778628114E-002 -2.2828335280E-003 1.4601821131E-003
17 4.2401749392E-002 -6.5778591727E-002 6.8187241643E-002 -1.6653902450E-002 2.5505038138E-002
18 -2.6595920073E-003 -2.2828074980E-003 -1.6653942562E-002 5.4855247002E-002 2.4729783529E-003
19 1.9873415121E-003 1.4601899329E-003 2.5505058190E-002 2.4729967206E-003 4.4724663284E-002
20 -3.8366743828E-004 -8.8746730931E-004 -6.4420927497E-003 3.6656962180E-002 8.1224860664E-003
21 9.2845385141E-004 3.6802433505E-004 -9.5040708316E-004 -5.1941208846E-003 -1.2444625713E-004
22 -5.0318487549E-004 1.4342911215E-004 2.8985859503E-004 2.0416113478E-004 9.1951318240E-004
23 7.4036073171E-004 -3.4730013615E-004 -1.3351566400E-004 2.3474188588E-003 1.3102362758E-005
24 -2.7749145090E-004 4.7724454321E-005 5.5527644806E-005 -1.7302886151E-004 -1.7726879169E-004
25 -2.5090250470E-004 2.1741519930E-005 2.7208805916E-004 -2.5982303487E-004 -1.9668228900E-004
26 -1.4489113997E-004 -3.0397727583E-005 2.7239543481E-005 -6.0050637375E-004 -2.9892198193E-005
27 -1.6519482597E-005 1.6435294198E-004 5.0961893634E-005 1.4077278097E-004 -1.9027010603E-005
28 -2.3547595249E-004 7.6124571826E-005 1.0117983985E-004 -1.1534040559E-004 -1.0579685787E-004
29 7.0507166233E-005 1.1552377841E-004 -4.5931305760E-005 -2.0007797315E-004 -1.3505340062E-004
20 21 22 23 24
0 -3.4812101478E-002 2.6911592086E-003 -1.6354152863E-003 -1.4301333227E-003 1.6249964844E-003
1 -7.8382610347E-003 3.7103408229E-004 2.2593110441E-003 -1.7829862164E-003 1.6374435740E-003
2 -3.1205423941E-001 2.8493671639E-002 1.6587990556E-004 -1.1426237591E-002 -7.8189111866E-004
3 -2.8004725758E-003 -4.8903739721E-002 -4.9988134121E-003 1.7100983514E-003 3.7179545055E-003
4 3.9806443322E-004 -5.8790208912E-004 -4.3242458298E-002 -1.2016207108E-002 -1.0472139534E-002
5 -1.8686790048E-002 3.6592865292E-003 -1.6198931842E-002 -3.2057224847E-001 -2.6367531700E-002
6 -6.3919412091E-004 -8.2335246704E-003 8.4576155591E-003 -1.1315054733E-002 -5.8369163532E-004
7 -7.3581915791E-004 7.5646519519E-004 -6.2047477465E-004 3.4823216513E-003 1.1991380964E-003
8 2.3726528036E-003 -2.1824763131E-002 3.3460717579E-002 -5.8262172949E-003 3.4812921433E-003
9 -1.0665296285E-003 -3.5124206435E-003 3.2639684654E-003 2.9530797749E-004 -5.0943824872E-002
10 3.1067613876E-004 2.7079189356E-003 -2.9623459983E-003 3.2841200274E-003 1.6984442797E-003
11 5.3351732140E-004 -1.9033427571E-002 -2.4158940046E-002 -6.2609613281E-003 2.4221378111E-002
12 -4.7937892256E-004 8.8611314755E-004 6.6939922854E-004 1.5740024716E-004 4.3249394082E-004
13 4.4851926804E-004 -3.7736678097E-004 1.1153694999E-004 4.6284806253E-004 9.5077824774E-005
14 -6.0300787410E-003 -7.4096053004E-004 1.5918637627E-004 4.0586523098E-004 3.5485782222E-004
15 -3.8368712363E-004 9.2843754228E-004 -5.0316845184E-004 7.4036906127E-004 -2.7745851356E-004
16 -8.8745240886E-004 3.6801936222E-004 1.4342995270E-004 -3.4729860789E-004 4.7711904531E-005
17 -6.4420819427E-003 -9.5038506002E-004 2.8983698019E-004 -1.3352326563E-004 5.5544671478E-005
18 3.6656852373E-002 -5.1941195232E-003 2.0415783452E-004 2.3474119607E-003 -1.7153048632E-004
19 8.1224361521E-003 -1.2444681834E-004 9.1951236579E-004 1.3097434442E-005 -1.7668019335E-004
20 3.3911554853E-001 2.8652507893E-003 -6.8339696880E-005 3.7476484447E-004 8.3606654277E-004
21 2.8652527558E-003 6.1967615286E-002 -3.2455918220E-003 7.8074203872E-003 -1.5351890960E-003
22 -6.8340068690E-005 -3.2455946984E-003 4.1826230856E-002 6.5337193429E-003 -3.1932674182E-003
23 3.7476336333E-004 7.8073802579E-003 6.5336763366E-003 3.4246747567E-001 -2.2590437719E-005
24 8.3515185725E-004 -1.5351889308E-003 -3.1932682244E-003 -2.2585651674E-005 4.7006835231E-002
25 5.3158843621E-007 1.0652535047E-003 1.4954902777E-003 2.4073368793E-004 1.1954474977E-003
26 5.5963948637E-004 -4.4872582333E-004 -1.4772351943E-003 6.3199701928E-004 -2.1389718034E-002
27 -1.7619372799E-004 9.0741766644E-004 9.8175835796E-004 -2.9459682310E-004 7.2835611826E-004
28 2.5127782091E-004 -9.3298199434E-004 6.8787235133E-005 1.2732690365E-004 7.9688727422E-003
29 2.6201943695E-004 1.7128017387E-004 1.2934748675E-003 3.4008367645E-004 1.9615268308E-002
25 26 27 28 29
0 -4.2035299977E-004 1.0294528397E-003 -7.0032537135E-004 7.4047266192E-004 -3.4678947810E-004
1 1.5264932827E-004 9.3263518942E-004 -3.5205362458E-003 1.9332600101E-003 -2.3027335108E-003
2 -9.8735571502E-004 7.7177183895E-004 -1.6311830663E-003 3.7374078263E-004 -1.1338849320E-003
3 -2.2267753982E-004 4.5631164845E-003 -1.2805227755E-002 3.9967067646E-003 -1.2023590679E-002
4 -5.1722782688E-003 -4.3757731112E-003 -6.5561880794E-003 -6.2274289617E-003 -5.4242286711E-003
5 6.5472637324E-003 -1.1287788747E-002 1.8937046693E-002 -5.0006811267E-003 1.0199602824E-002
6 -5.7685226078E-003 -4.5935456207E-003 6.5591405092E-003 -3.1011377655E-002 -7.9382348181E-004
7 -2.2680665405E-002 -1.9338350120E-003 -2.9972765688E-003 -2.1071947728E-002 3.0156847654E-003
8 -3.3264515239E-002 1.0812126530E-002 -9.1466888768E-003 1.5170890552E-002 3.3044094214E-003
9 4.8928775025E-003 2.3007654009E-002 -2.0026482543E-001 -6.3285758846E-002 1.2554808336E-001
10 -7.4869041758E-002 -9.3178724533E-002 -7.0098856149E-002 -6.9485640501E-002 4.8962839723E-002
11 -8.7330564494E-002 -2.9314613543E-001 1.2458021507E-001 4.8763534298E-002 -1.5272144228E-001
12 -6.0132426168E-005 2.1286995818E-004 -1.2846479090E-003 7.7223667108E-004 -8.8648784383E-004
13 -4.8090893023E-005 5.0813447259E-005 1.2192474211E-004 1.3853537972E-004 -4.4975512069E-005
14 1.1509828375E-004 2.6955725919E-004 4.6853708025E-004 5.4636589826E-005 5.9585997916E-004
15 -2.5088560837E-004 -1.4490239429E-004 -1.6517113547E-005 -2.3547725232E-004 7.0506301073E-005
16 2.1741623849E-005 -3.0396484786E-005 1.6435437640E-004 7.6123660238E-005 1.1552303684E-004
17 2.7209709129E-004 2.7234932342E-005 5.0963084246E-005 1.0117936124E-004 -4.5931984725E-005
18 -2.5882735848E-004 -6.0031848430E-004 1.4070861538E-004 -1.1535910049E-004 -2.0001808065E-004
19 -1.9638025822E-004 -2.9919459983E-005 -1.9047914816E-005 -1.0580143635E-004 -1.3503643634E-004
20 8.4829116415E-007 5.5948891149E-004 -1.7619563318E-004 2.5127749619E-004 2.6202088722E-004
21 1.0652521780E-003 -4.4872868033E-004 9.0739586785E-004 -9.3299673048E-004 1.7126146660E-004
22 1.4954902653E-003 -1.4772362211E-003 9.8175151528E-004 6.8801505444E-005 1.2934673074E-003
23 2.4072903510E-004 6.3199689136E-004 -2.9460500091E-004 1.2731327319E-004 3.4007600115E-004
24 1.1952923145E-003 -2.1389995888E-002 7.2832026293E-004 7.9688600183E-003 1.9615297182E-002
25 9.4289717269E-002 1.0562741426E-001 -1.7552990896E-004 7.0060843371E-003 8.7782610441E-003
26 1.0562750999E-001 3.0308674016E-001 -1.6382699707E-003 -5.5832273099E-003 -1.1726448645E-002
27 -1.7551353029E-004 -1.6382784849E-003 2.0673701256E-001 8.2101212014E-002 -1.3115219203E-001
28 7.0060896795E-003 -5.5832572276E-003 8.2101377926E-002 8.7668224780E-002 -5.4259499038E-002
29 8.7782416309E-003 -1.1726450275E-002 -1.3115216547E-001 -5.4259354736E-002 1.5092602943E-001
This should be a 30x30 matrix and I'm trying:
data = pd.read_fwf('C:/Users/henri/Documents/Projects/Python-Lessons/ORCA/orca.hess',
widths=[9, 19, 19, 19, 19, 19])
But it reads as 185x6. I'd like to ignore the first column (numbering the lines) from 0-29 and I'm not using the columns indexes (from 0-29 too) to perform any mathematical operation. Also, Pandas is rounding my numbers and I'd like to keep the original format.
Here is a snip of my output:
Unnamed: 0 0 1 2 3 4
0 0.0 5.167712e-01 0.000055 0.032484 -0.189017 -0.006716
1 1.0 5.538011e-05 0.561599 -0.001900 -0.014738 -0.072598
2 2.0 3.248692e-02 -0.001900 0.567918 0.072316 0.001501
Any help is much appreciated, guys.

import pandas as pd
filename = 'data'
df = pd.read_fwf(filename, widths=[9, 19, 19, 19, 19, 19])
df = df.rename(columns={'Unnamed: 0':'row'})
df = df.dropna(subset=['row'], how='any')
df['col'] = df.groupby('row').cumcount()
df = df.pivot(index='row', columns='col')
df = df.dropna(how='any', axis=1)
df.columns = range(len(df.columns))
print(df.head())
yields
0 1 2 3 4 5 6 \
row
0.0 0.516771 0.066743 0.003457 -0.122105 -0.034812 -0.000420 0.000055
1.0 0.000055 0.001410 -0.004069 0.113803 -0.007838 0.000153 0.561599
2.0 0.032487 -0.126894 0.001377 -0.038375 -0.312054 -0.000987 -0.001900
3.0 -0.189014 0.047664 -0.118741 -0.012823 -0.002800 -0.000223 -0.014737
4.0 -0.006714 0.018558 -0.033281 0.023865 0.000398 -0.005172 -0.072598
7 8 9 ... 20 21 22 \
row ...
0.0 -0.025292 0.006609 0.114780 ... -0.113527 -0.051389 -0.001430
1.0 0.037536 0.017154 -0.214876 ... -0.228978 -0.001149 -0.001783
2.0 -0.013453 0.004940 0.059953 ... -0.053033 -0.038484 -0.011426
3.0 -0.131811 -0.084587 -0.001281 ... 0.000745 0.002222 0.001710
4.0 0.068311 -0.043605 -0.000225 ... 0.000638 0.000249 -0.012016
23 24 25 26 27 28 29
row
0.0 0.000740 -0.006716 -0.018400 -0.032196 -0.001829 0.001625 -0.000347
1.0 0.001933 -0.072598 -0.021579 -0.053402 -0.045442 0.001637 -0.002303
2.0 0.000374 0.001501 -0.008308 -0.062527 -0.007680 -0.000782 -0.001134
3.0 0.003997 0.031554 -0.218476 0.009665 0.000770 0.003718 -0.012024
4.0 -0.006227 0.273181 -0.073414 0.015382 -0.001846 -0.010472 -0.005424
[5 rows x 30 columns]
After parsing the file with
df = pd.read_fwf(filename, widths=[9, 19, 19, 19, 19, 19])
df = df.rename(columns={'Unnamed: 0':'row'})
the column headers can be identified by have a df['row'] value of NaN.
So they can be removed with
df = df.dropna(subset=['row'], how='any')
Now the row numbers keep repeating from 0 to 29. If we group by the row
value, then we can assign an intra-group "cumulative count" to the rows within
each group. That is, the first row of the group gets assigned the value 0, the
next row 1, etc. -- within that group -- and the process is repeated for each
group.
df['col'] = df.groupby('row').cumcount()
# row 0 1 2 3 4 col
# 0 0.0 5.167712e-01 0.000055 0.032484 -0.189017 -0.006716 0
# 1 1.0 5.538011e-05 0.561599 -0.001900 -0.014738 -0.072598 0
# 2 2.0 3.248692e-02 -0.001900 0.567918 0.072316 0.001501 0
# ...
# 182 27.0 -1.755135e-04 -0.001638 0.206737 0.082101 -0.131152 5
# 183 28.0 7.006090e-03 -0.005583 0.082101 0.087668 -0.054259 5
# 184 29.0 8.778242e-03 -0.011726 -0.131152 -0.054259 0.150926 5
Now the desired DataFrame can be obtained by pivoting:
df = df.pivot(index='row', columns='col')
and relabeling the columns:
df.columns = range(len(df.columns))
A more NumPy-based approach might look like this:
import numpy as np
import pandas as pd
filename = 'data'
df = pd.read_csv(filename, delim_whitespace=True)
arr = df.values
N = df.index.max()+1
arr = np.delete(arr, np.arange(N, len(arr), N+1), axis=0)
chunks = np.split(arr, np.arange(N, len(arr), N))
result = pd.DataFrame(np.hstack(chunks)).dropna(axis=1)
print(result)
This will also work for any sized matrix.

Break Existing Dataframe Apart Based on Multi Index

I have an existing dataframe that is sorted like this:
In [3]: result_GB_daily_average
Out[3]:
NREL Avert
Month Day
1 1 14.718417 37.250000
2 40.381167 45.250000
3 42.512646 40.666667
4 12.166896 31.583333
5 14.583208 50.416667
6 34.238000 45.333333
7 45.581229 29.125000
8 60.548479 27.916667
9 48.061583 34.041667
10 20.606958 37.583333
11 5.418833 70.833333
12 51.261375 43.208333
13 21.796771 42.541667
14 27.118979 41.958333
15 8.230542 43.625000
16 14.233958 48.708333
17 28.345875 51.125000
18 43.896375 55.500000
19 95.800542 44.500000
20 53.763104 39.958333
21 26.171437 50.958333
22 20.372688 66.916667
23 20.594042 42.541667
24 16.889083 48.083333
25 16.416479 42.125000
26 28.459625 40.125000
27 1.055229 49.833333
28 36.798792 42.791667
29 27.260083 47.041667
30 23.584917 55.750000
... ... ...
12 2 34.491604 55.916667
3 26.444333 53.458333
4 15.088333 45.000000
5 10.213500 32.083333
6 19.087688 17.000000
7 23.078292 17.375000
8 41.523667 29.458333
9 17.173854 37.833333
10 11.488687 52.541667
11 15.203479 30.000000
12 8.390917 37.666667
13 70.067062 23.458333
14 24.281729 25.583333
15 31.826104 33.458333
16 5.085271 42.916667
17 3.778229 46.916667
18 31.276958 57.625000
19 7.399458 46.916667
20 18.531958 39.291667
21 26.831937 35.958333
22 55.514000 32.375000
23 24.018875 34.041667
24 54.454125 43.083333
25 57.379812 25.250000
26 94.520833 33.958333
27 49.693854 27.500000
28 2.406438 46.916667
29 7.133833 53.916667
30 7.829167 51.500000
31 5.584646 55.791667
I would like to split this dataframe apart into 12 different data frames, one for each month, but the problem is they are all slightly different lengths because the amount of days in a month vary, meaning that attempts at using np.array_split have failed. How can I split this based on the Month index?

One solution :
df=result_GB_daily_average
[df.iloc[df.index.get_level_values('Month')==i+1] for i in range(12)]
or, shorter:
[df.ix[i] for i in range(12)]