We Keep Coding

Joining a column from the same dataframe in multiindex dataframe

This is a follow up question from this question.
The data:
df1 = pd.DataFrame.from_dict({('group', ''): {0: 'A',
1: 'A',
2: 'A',
3: 'A',
4: 'A',
5: 'A',
6: 'A',
7: 'A',
8: 'B',
9: 'B',
10: 'B',
11: 'B',
12: 'B',
13: 'B',
14: 'B',
15: 'B',
16: 'C',
17: 'C',
18: 'C',
19: 'C',
20: 'C',
21: 'C',
22: 'C',
23: 'C',
24: 'D',
25: 'D',
26: 'D',
27: 'D',
28: 'D',
29: 'D',
30: 'D'},
('category', ''): {0: 'Apple',
1: 'Amazon',
2: 'Google',
3: 'Netflix',
4: 'Facebook',
5: 'Uber',
6: 'Tesla',
7: 'total',
8: 'Apple',
9: 'Amazon',
10: 'Google',
11: 'Netflix',
12: 'Facebook',
13: 'Uber',
14: 'Tesla',
15: 'total',
16: 'Apple',
17: 'Amazon',
18: 'Google',
19: 'Netflix',
20: 'Facebook',
21: 'Uber',
22: 'Tesla',
23: 'total',
24: 'Apple',
25: 'Amazon',
26: 'Google',
27: 'Netflix',
28: 'Uber',
29: 'Tesla',
30: 'total'},
(pd.Timestamp('2021-06-28 00:00:00'), 'total_orders'): {0: 88.0,
1: 66.0,
2: 191.0,
3: 558.0,
4: 12.0,
5: 4.0,
6: 56.0,
7: 975.0,
8: 90.0,
9: 26.0,
10: 49.0,
11: 250.0,
12: 7.0,
13: 2.0,
14: 44.0,
15: 468.0,
16: 36.0,
17: 52.0,
18: 94.0,
19: 750.0,
20: 10.0,
21: 0.0,
22: 52.0,
23: 994.0,
24: 16.0,
25: 22.0,
26: 5.0,
27: 57.0,
28: 3.0,
29: 33.0,
30: 136.0},
(pd.Timestamp('2021-06-28 00:00:00'), 'total_sales'): {0: 4603.209999999999,
1: 2485.059999999998,
2: 4919.39999999998,
3: 6097.77,
4: 31.22,
5: 155.71,
6: 3484.99,
7: 17237.35999999996,
8: 561.54,
9: 698.75,
10: 1290.13,
11: 4292.68000000001,
12: 947.65,
13: 329.0,
14: 2889.65,
15: 9989.4,
16: 330.8899999999994,
17: 2076.26,
18: 2982.270000000004,
19: 11978.62000000002,
20: 683.0,
21: 0.0,
22: 3812.16999999999,
23: 20963.21000000002,
24: 234.4900000000002,
25: 896.1,
26: 231.0,
27: 893.810000000001,
28: 129.0,
29: 1712.329999999998,
30: 4106.729999999996},
(pd.Timestamp('2021-07-05 00:00:00'), 'total_orders'): {0: 109.0,
1: 48.0,
2: 174.0,
3: 592.0,
4: 13.0,
5: 5.0,
6: 43.0,
7: 984.0,
8: 62.0,
9: 13.0,
10: 37.0,
11: 196.0,
12: 8.0,
13: 1.0,
14: 3.0,
15: 30.0,
16: 76.0,
17: 5.0,
18: 147.0,
19: 88.0,
20: 8.0,
21: 1.0,
22: 78.0,
23: 1248.0,
24: 1.0,
25: 18.0,
26: 23.0,
27: 83.0,
28: 0.0,
29: 29.0,
30: 154.0},
(pd.Timestamp('2021-07-05 00:00:00'), 'total_sales'): {0: 3453.02,
1: 17868.730000000003,
2: 44707.82999999999,
3: 61425.99,
4: 1261.0,
5: 1914.6000000000001,
6: 24146.09,
7: 154777.25999999998,
8: 6201.489999999999,
9: 5513.960000000001,
10: 9645.87,
11: 25086.785,
12: 663.0,
13: 448.61,
14: 26332.7,
15: 73892.415,
16: 556.749999999999,
17: 1746.859999999997,
18: 4103.219999999994,
19: 15571.52000000008,
20: 86.0,
21: 69.0,
22: 5882.759999999995,
23: 26476.11000000004,
24: 53.0,
25: 801.220000000001,
26: 684.56,
27: 1232.600000000002,
28: 0.0,
29: 15902.1,
30: 43943.48},
(pd.Timestamp('2021-07-12 00:00:00'), 'total_orders'): {0: 32.0,
1: 15.0,
2: 89.0,
3: 239.0,
4: 2.0,
5: 3.0,
6: 20.0,
7: 400.0,
8: 0.0,
9: 0.0,
10: 0.0,
11: 0.0,
12: 0.0,
13: 0.0,
14: 0.0,
15: 0.0,
16: 21.0,
17: 14.0,
18: 58.0,
19: 281.0,
20: 3.0,
21: 3.0,
22: 33.0,
23: 413.0,
24: 7.0,
25: 6.0,
26: 4.0,
27: 13.0,
28: 0.0,
29: 18.0,
30: 48.0},
(pd.Timestamp('2021-07-12 00:00:00'), 'total_sales'): {0: 2147.7000000000003,
1: 4767.3,
2: 2399.300000000003,
3: 3137.440000000002,
4: 178.0,
5: 866.61,
6: 10639.03,
7: 73235.38,
8: 0.0,
9: 0.0,
10: 0.0,
11: 0.0,
12: 0.0,
13: 0.0,
14: 0.0,
15: 0.0,
16: 220.94,
17: 727.5199999999995,
18: 2500.96999999999,
19: 4414.00999999998,
20: 15.0,
21: 196.71,
22: 2170.1,
23: 9745.24999999997,
24: 126.55,
25: 290.2,
26: 146.01,
27: 233.0,
28: 0.0,
29: 973.18,
30: 1658.940000000002}}).set_index(['group','category'])
df2 = pd.DataFrame.from_dict({'group': {0: 'total_full',
1: 'total_full',
2: 'A',
3: 'A',
4: 'B',
5: 'B',
6: 'C',
7: 'C',
8: 'D',
9: 'D',
10: 'Apple_total',
11: 'Apple_total',
12: 'A',
13: 'A',
14: 'B',
15: 'B',
16: 'C',
17: 'C',
18: 'D',
19: 'D',
20: 'Amazon_total',
21: 'Amazon_total',
22: 'A',
23: 'A',
24: 'B',
25: 'B',
26: 'C',
27: 'C',
28: 'D',
29: 'D',
30: 'Google_total',
31: 'Google_total',
32: 'A',
33: 'A',
34: 'B',
35: 'B',
36: 'C',
37: 'C',
38: 'D',
39: 'D',
40: 'Facebook_total',
41: 'Facebook_total',
42: 'A',
43: 'A',
44: 'B',
45: 'B',
46: 'C',
47: 'C',
48: 'D',
49: 'D',
50: 'Netflix_total',
51: 'Netflix_total',
52: 'A',
53: 'A',
54: 'B',
55: 'B',
56: 'C',
57: 'C',
58: 'D',
59: 'D',
60: 'Tesla_total',
61: 'Tesla_total',
62: 'A',
63: 'A',
64: 'B',
65: 'B',
66: 'C',
67: 'C',
68: 'D',
69: 'D',
70: 'Uber_total',
71: 'Uber_total',
72: 'A',
73: 'A',
74: 'B',
75: 'B',
76: 'C',
77: 'C',
78: 'D',
79: 'D'},
'category': {0: 'total_full',
1: 'total_full',
2: 'group_total',
3: 'group_total',
4: 'group_total',
5: 'group_total',
6: 'group_total',
7: 'group_total',
8: 'group_total',
9: 'group_total',
10: 'Apple_total',
11: 'Apple_total',
12: 'Apple',
13: 'Apple',
14: 'Apple',
15: 'Apple',
16: 'Apple',
17: 'Apple',
18: 'Apple',
19: 'Apple',
20: 'Amazon_total',
21: 'Amazon_total',
22: 'Amazon',
23: 'Amazon',
24: 'Amazon',
25: 'Amazon',
26: 'Amazon',
27: 'Amazon',
28: 'Amazon',
29: 'Amazon',
30: 'Google_total',
31: 'Google_total',
32: 'Google',
33: 'Google',
34: 'Google',
35: 'Google',
36: 'Google',
37: 'Google',
38: 'Google',
39: 'Google',
40: 'Facebook_total',
41: 'Facebook_total',
42: 'Facebook',
43: 'Facebook',
44: 'Facebook',
45: 'Facebook',
46: 'Facebook',
47: 'Facebook',
48: 'Facebook',
49: 'Facebook',
50: 'Netflix_total',
51: 'Netflix_total',
52: 'Netflix',
53: 'Netflix',
54: 'Netflix',
55: 'Netflix',
56: 'Netflix',
57: 'Netflix',
58: 'Netflix',
59: 'Netflix',
60: 'Tesla_total',
61: 'Tesla_total',
62: 'Tesla',
63: 'Tesla',
64: 'Tesla',
65: 'Tesla',
66: 'Tesla',
67: 'Tesla',
68: 'Tesla',
69: 'Tesla',
70: 'Uber_total',
71: 'Uber_total',
72: 'Uber',
73: 'Uber',
74: 'Uber',
75: 'Uber',
76: 'Uber',
77: 'Uber',
78: 'Uber',
79: 'Uber'},
'type': {0: 'Sales_1',
1: 'Sales_2',
2: 'Sales_1',
3: 'Sales_2',
4: 'Sales_1',
5: 'Sales_2',
6: 'Sales_1',
7: 'Sales_2',
8: 'Sales_1',
9: 'Sales_2',
10: 'Sales_1',
11: 'Sales_2',
12: 'Sales_1',
13: 'Sales_2',
14: 'Sales_1',
15: 'Sales_2',
16: 'Sales_1',
17: 'Sales_2',
18: 'Sales_1',
19: 'Sales_2',
20: 'Sales_1',
21: 'Sales_2',
22: 'Sales_1',
23: 'Sales_2',
24: 'Sales_1',
25: 'Sales_2',
26: 'Sales_1',
27: 'Sales_2',
28: 'Sales_1',
29: 'Sales_2',
30: 'Sales_1',
31: 'Sales_2',
32: 'Sales_1',
33: 'Sales_2',
34: 'Sales_1',
35: 'Sales_2',
36: 'Sales_1',
37: 'Sales_2',
38: 'Sales_1',
39: 'Sales_2',
40: 'Sales_1',
41: 'Sales_2',
42: 'Sales_1',
43: 'Sales_2',
44: 'Sales_1',
45: 'Sales_2',
46: 'Sales_1',
47: 'Sales_2',
48: 'Sales_1',
49: 'Sales_2',
50: 'Sales_1',
51: 'Sales_2',
52: 'Sales_1',
53: 'Sales_2',
54: 'Sales_1',
55: 'Sales_2',
56: 'Sales_1',
57: 'Sales_2',
58: 'Sales_1',
59: 'Sales_2',
60: 'Sales_1',
61: 'Sales_2',
62: 'Sales_1',
63: 'Sales_2',
64: 'Sales_1',
65: 'Sales_2',
66: 'Sales_1',
67: 'Sales_2',
68: 'Sales_1',
69: 'Sales_2',
70: 'Sales_1',
71: 'Sales_2',
72: 'Sales_1',
73: 'Sales_2',
74: 'Sales_1',
75: 'Sales_2',
76: 'Sales_1',
77: 'Sales_2',
78: 'Sales_1',
79: 'Sales_2'},
'2021-06-28': {0: 67.5277641202152,
1: 82.7854700135998,
2: 21.50082266792856,
3: 22.03644997199996,
4: 64.460440147,
5: 10.1060499896,
6: 65.1530371974946,
7: 50.6429700519999,
8: 56.413464107792045,
9: 0,
10: 17.48074540313092,
11: 26.8376199976,
12: 52.172,
13: 61.16600000040001,
14: 20.9447844,
15: 40.69122000000001,
16: 83.55718929717925,
17: 14.98039999719995,
18: 20.806771705951697,
19: np.nan,
20: 18.3766353690825,
21: 12.82565001479992,
22: 52.425508769690694,
23: 25.661999978399994,
24: 17.88071596,
25: 24.384659998799997,
26: 91.10086982794643,
27: 12.77899003759993,
28: 16.969540811445366,
29: np.nan,
30: 18.8795397517309,
31: 26.73017999840005,
32: 53.52039700062155,
33: 58.81199999639999,
34: 12.1243325,
35: 24.0544100028,
36: 55.94068246571674,
37: 133.86376999920006,
38: 7.294127785392621,
39: np.nan,
40: 6.07807089184563,
41: 7.27483001599998,
42: 2.300470581874837,
43: 30.71300000639998,
44: 5.810764652,
45: 12.333119997600003,
46: 25.475930745418292,
47: 64.228710012,
48: 9.490904912552498,
49: np.nan,
50: 8.184780211399392,
51: 24.59321999400001,
52: 6.807138946302334,
53: 12.0879999972,
54: 0.869207661,
55: 0.324,
56: 0.5084336040970575,
57: 12.181219996800007,
58: 0,
59: np.nan,
60: 9.293956915067886,
61: 11.171379993599999,
62: 6.384936971649232,
63: 3.657999996,
64: 0.913782413,
65: 1.9992000012000002,
66: 1.5322078073061867,
67: 5.514179996399999,
68: 0.4630297231124678,
69: np.nan,
70: 36.23403557795798,
71: 53.35258999919999,
72: 21.890370397789923,
73: 9.937449997200002,
74: 5.916852561,
75: 6.319439989199998,
76: 7.03772344983066,
77: 37.095700012799995,
78: 1.3890891693374032,
79: np.nan},
'2021-07-05': {0: 65.4690491915759,
1: 98.5235100112003,
2: 21.4573181155924,
3: 241.06741999679997,
4: 67.481716829,
5: 11.60325000040002,
6: 27.5807099999998,
7: 65.8528400140003,
8: 58.949304246983736,
9: 0.0,
10: 185.65887577993723,
11: 318.9965699964001,
12: 54.517,
13: 66.55265999039996,
14: 21.92632044,
15: 43.67116000320002,
16: 87.47349898707688,
17: 208.7727500028001,
18: 21.742056352860352,
19: np.nan,
20: 16.6038963173654,
21: 25.28952001920013,
22: 54.7820864335212,
23: 36.75802000560001,
24: 18.71872129,
25: 30.1634600016,
26: 95.37075040035738,
27: 138.3680400120001,
28: 17.73233819348684,
29: np.nan,
30: 14.80302342121337,
31: 251.83851001200003,
32: 55.926190956481534,
33: 72.4443400032,
34: 12.69221484,
35: 26.032340003999998,
36: 58.56261169338368,
37: 153.36183000480003,
38: 7.622005931348156,
39: np.nan,
40: 72.24367956241771,
41: 14.83083001279999,
42: 29.5726042895728,
43: 38.723000005199985,
44: 6.083562133,
45: 12.845630001599998,
46: 26.66998281055652,
47: 63.26220000600001,
48: 9.917530329288393,
49: np.nan,
50: 8.555606693927,
51: 23.802009994800002,
52: 7.113126469779095,
53: 7.206999998399999,
54: 0.910216433,
55: 1.4089999991999997,
56: 0.5322637911479053,
57: 15.186009997200001,
58: 0.0,
59: np.nan,
60: 9.716385738295367,
61: 14.7327399948,
62: 6.671946105284065,
63: 5.691999996,
64: 0.956574175,
65: 1.0203399996,
66: 1.6040220980113027,
67: 8.020399999199999,
68: 0.4838433599999999,
69: np.nan,
70: 37.88758167841983,
71: 59.03332998119994,
72: 22.874363860953647,
73: 13.690399997999998,
74: 6.194107518,
75: 6.4613199911999954,
76: 7.367580219466185,
77: 38.881609991999944,
78: 1.4515300799999995,
79: np.nan},
'2021-07-12': {0: 607.2971827405001,
1: 88.9671100664001,
2: 21.26749278974862,
3: 17.1524199804,
4: 64.471138092,
5: 89.84481002279999,
6: 26.2044999999998,
7: 51.9698800632001,
8: 5.354051858751745,
9: 0.0,
10: 177.42361595891452,
11: 287.5395700032,
12: 52.117,
13: 47.388199995600004,
14: 20.94835038,
15: 41.4250800048,
16: 83.57340667555117,
17: 198.72629000280003,
18: 20.784858903363354,
19: np.nan,
20: 178.323907459086,
21: 185.83897002839998,
22: 52.37029646474982,
23: 27.87144997800001,
24: 17.88339044,
25: 23.645340010799984,
26: 91.11855133792106,
27: 134.3221800396,
28: 16.95166921641509,
29: np.nan,
30: 128.82813286243115,
31: 192.6867300156,
32: 53.46403160619618,
33: 41.412320006399995,
34: 12.1261155,
35: 11.840830002000002,
36: 55.95153983444301,
37: 139.43358000720002,
38: 7.286445921791947,
39: np.nan,
40: 69.04410667683521,
41: 93.877410018,
42: 28.270665735943805,
43: 27.512680004399986,
44: 5.811656147,
45: 5.2319800032,
46: 25.480875296710053,
47: 61.132750010400024,
48: 9.480909497181356,
49: np.nan,
50: 8.178601399067174,
51: 17.6743199976,
52: 6.7999699585309585,
53: 6.131999998799999,
54: 0.870099156,
55: 0.6185600004,
56: 0.5085322845362154,
57: 10.923759998400003,
58: 0.0,
59: np.nan,
60: 9.287042311133577,
61: 19.966500000000007,
62: 6.378212628950804,
63: 6.524999997600001,
64: 0.913782413,
65: 1.9303400016,
66: 1.5325051891827732,
67: 11.511160000800006,
68: 0.4625420799999998,
69: np.nan,
70: 36.21177607303267,
71: 51.3836100036,
72: 21.86731639537707,
73: 10.310769999600003,
74: 5.917744056,
75: 5.152679999999999,
76: 7.039089381655591,
77: 35.920160003999996,
78: 1.3876262399999995,
79: np.nan}}).set_index(['group','category','type'])
f = lambda x: pd.to_datetime(x)
df = (df1.merge(df2.rename(columns=f).unstack(), left_index=True, right_index=True)
.sort_index(axis=1))
I am trying to add a column last_week_sales to each multiindex column.
So the total_sales from the previous week becomes last_week_sales in the following week in every multiindex column except for the first week as there are no data of the previous week.
What I've tried:
pd.merge(df.shift(-4, axis = 1).xs('total_sales', level=1,drop_level=False))
I am not sure how I can select only the second index, few answers suggested using .xs() but I am not sure how I would merge it on the date column, I tried shifting everything by -4 as then I get the desired week shift, but maybe there are better ways of achieving this.
Right now df.columns return
MultiIndex([('2021-06-28', 'Sales_1'),
('2021-06-28', 'Sales_2'),
('2021-06-28', 'total_orders'),
('2021-06-28', 'total_sales'),
('2021-07-05', 'Sales_1'),
('2021-07-05', 'Sales_2'),
('2021-07-05', 'total_orders'),
('2021-07-05', 'total_sales'),
('2021-07-12', 'Sales_1'),
('2021-07-12', 'Sales_2'),
('2021-07-12', 'total_orders'),
('2021-07-12', 'total_sales')])
And I would like to have:
MultiIndex([('2021-06-28', 'Sales_1'),
('2021-06-28', 'Sales_2'),
('2021-06-28', 'total_orders'),
('2021-06-28', 'total_sales'),
('2021-06-28', 'last_week_sales')
('2021-07-05', 'Sales_1'),
('2021-07-05', 'Sales_2'),
('2021-07-05', 'total_orders'),
('2021-07-05', 'total_sales'),
('2021-07-05', 'last_week_sales')
('2021-07-12', 'Sales_1'),
('2021-07-12', 'Sales_2'),
('2021-07-12', 'total_orders'),
('2021-07-12', 'total_sales'),
('2021-07-12', 'last_week_sales')])

Let us do in steps
Select the cross section of dataframe using loc
Shift the cross section along the columns axis
Rename total_sales to last_week_sales and combine the cross section with df
s = df.loc[:, (slice(None), 'total_sales')].shift(axis=1)
s = s.rename({'total_sales': 'last_week_sales'}, axis=1, level=1)
df.combine_first(s)

Plotting respecting the x-axis values scale

I generate the following plot:
By the following code:
data = {'BestFit_rej_ratio': {0: 0.1975987994, 1: 0.2006003002, 2: 0.1790895448, 3: 0.2216108054, 4: 0.1785892946, 5: 0.1890945473, 6: 0.1780890445, 7: 0.1780890445, 8: 0.2016008004, 9: 0.1900950475, 10: 0.1985992996, 11: 0.2031015508, 12: 0.2046023012, 13: 0.2071035518, 14: 0.1750875438, 15: 0.2166083042, 16: 0.1725862931, 17: 0.188094047, 18: 0.1870935468, 19: 0.1895947974, 20: 0.004502251126, 21: 0.006503251626, 22: 0.005002501251, 23: 0.006503251626, 24: 0.008004002001, 25: 0.006003001501, 26: 0.00300150075, 27: 0.005502751376, 28: 0.0100050025, 29: 0.005002501251, 30: 0.006003001501, 31: 0.005502751376, 32: 0.007503751876, 33: 0.005502751376, 34: 0.005502751376, 35: 0.005502751376, 36: 0.007503751876, 37: 0.005002501251, 38: 0.004002001001, 39: 0.009004502251, 40: 0.4172086043, 41: 0.4322161081, 42: 0.4017008504, 43: 0.4247123562, 44: 0.4292146073, 45: 0.4077038519, 46: 0.4282141071, 47: 0.4637318659, 48: 0.4392196098, 49: 0.4172086043, 50: 0.4187093547, 51: 0.4057028514, 52: 0.4287143572, 53: 0.4242121061, 54: 0.4347173587, 55: 0.4307153577, 56: 0.4102051026, 57: 0.4437218609, 58: 0.4212106053, 59: 0.4172086043}, 'MDP_rej_ratio': {0: 0.1660830415, 1: 0.1605802901, 2: 0.152076038, 3: 0.1885942971, 4: 0.152076038, 5: 0.1565782891, 6: 0.1445722861, 7: 0.1570785393, 8: 0.1705852926, 9: 0.1605802901, 10: 0.1740870435, 11: 0.1670835418, 12: 0.1805902951, 13: 0.1740870435, 14: 0.1460730365, 15: 0.1810905453, 16: 0.1425712856, 17: 0.1580790395, 18: 0.1455727864, 19: 0.1590795398, 20: 0.001500750375, 21: 0.00300150075, 22: 0.002501250625, 23: 0.002501250625, 24: 0.0020010005, 25: 0.002501250625, 26: 0.0020010005, 27: 0.001500750375, 28: 0.004002001001, 29: 0.00300150075, 30: 0.0020010005, 31: 0.0, 32: 0.004002001001, 33: 0.0005002501251, 34: 0.0020010005, 35: 0.0, 36: 0.004502251126, 37: 0.002501250625, 38: 0.001500750375, 39: 0.004002001001, 40: 0.3851925963, 41: 0.3851925963, 42: 0.4097048524, 43: 0.3756878439, 44: 0.4112056028, 45: 0.4212106053, 46: 0.3791895948, 47: 0.4127063532, 48: 0.4432216108, 49: 0.4152076038, 50: 0.3871935968, 51: 0.4197098549, 52: 0.3896948474, 53: 0.4107053527, 54: 0.4062031016, 55: 0.4252126063, 56: 0.4112056028, 57: 0.3931965983, 58: 0.4372186093, 59: 0.4157078539}, 'Q-Learning_rej_ratio': {0: 0.1790895448, 1: 0.1645822911, 2: 0.1545772886, 3: 0.1905952976, 4: 0.1510755378, 5: 0.1595797899, 6: 0.148074037, 7: 0.1575787894, 8: 0.1715857929, 9: 0.1590795398, 10: 0.1690845423, 11: 0.168084042, 12: 0.180090045, 13: 0.1785892946, 14: 0.1495747874, 15: 0.1815907954, 16: 0.1435717859, 17: 0.1685842921, 18: 0.1505752876, 19: 0.1670835418, 20: 0.001500750375, 21: 0.00300150075, 22: 0.002501250625, 23: 0.002501250625, 24: 0.0020010005, 25: 0.002501250625, 26: 0.0020010005, 27: 0.001500750375, 28: 0.004002001001, 29: 0.00300150075, 30: 0.0020010005, 31: 0.0, 32: 0.004002001001, 33: 0.0005002501251, 34: 0.0020010005, 35: 0.0, 36: 0.004502251126, 37: 0.002501250625, 38: 0.001500750375, 39: 0.004002001001, 40: 0.3856928464, 41: 0.4167083542, 42: 0.3786893447, 43: 0.4187093547, 44: 0.4157078539, 45: 0.392196098, 46: 0.4032016008, 47: 0.4452226113, 48: 0.4217108554, 49: 0.3876938469, 50: 0.4192096048, 51: 0.388194097, 52: 0.4122061031, 53: 0.4152076038, 54: 0.4172086043, 55: 0.4137068534, 56: 0.3956978489, 57: 0.4342171086, 58: 0.4082041021, 59: 0.4032016008}, 'Parametrized_factor': {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0, 5: 1.0, 6: 1.0, 7: 1.0, 8: 1.0, 9: 1.0, 10: 1.0, 11: 1.0, 12: 1.0, 13: 1.0, 14: 1.0, 15: 1.0, 16: 1.0, 17: 1.0, 18: 1.0, 19: 1.0, 20: 0.2, 21: 0.2, 22: 0.2, 23: 0.2, 24: 0.2, 25: 0.2, 26: 0.2, 27: 0.2, 28: 0.2, 29: 0.2, 30: 0.2, 31: 0.2, 32: 0.2, 33: 0.2, 34: 0.2, 35: 0.2, 36: 0.2, 37: 0.2, 38: 0.2, 39: 0.2, 40: 2.0, 41: 2.0, 42: 2.0, 43: 2.0, 44: 2.0, 45: 2.0, 46: 2.0, 47: 2.0, 48: 2.0, 49: 2.0, 50: 2.0, 51: 2.0, 52: 2.0, 53: 2.0, 54: 2.0, 55: 2.0, 56: 2.0, 57: 2.0, 58: 2.0, 59: 2.0}}
data2 = pd.DataFrame(data)
# figure size
plt.figure(figsize=(12, 8))
ax = sns.pointplot(y="BestFit_rej_ratio", x="Parametrized_factor", data=data2, linestyles='-.', color='g', capsize=.1, scale=.2, errwidth=.5)
ax = sns.pointplot(y="MDP_rej_ratio", x="Parametrized_factor", data=data2, linestyles='-', color='r', capsize=.12, scale=.2, errwidth=.5)
ax = sns.pointplot(y="Q-Learning_rej_ratio", x="Parametrized_factor", data=data2, linestyles=':', color='k', capsize=.15, scale=.5, errwidth=.5)
ax.legend(bbox_to_anchor=(1.15, 1), loc='upper left')
labels = ax.legend(['BestFit', 'MDP', 'Q-Learning'])
colors = ['green', 'red', 'black']
i = 0
for l in labels.get_texts():
l.set_color(colors[i])
i+=1
plt.setp(ax.get_legend().get_texts(), fontsize='12')
# for legend text
ax.set_ylabel('Rejection ratio')
ax.set_xlabel('Parametrized factor')
plt.show()
Problem is that the plot is not respecting the scale of the x-axis values (Parametrized_factor).
How can I solve it?

Inner joining 2 tables on their index columns AND their non-index columns

Problem:
I have two pandas dataframes of ~3GB each, to join on 1) postcode & 2) every combination of house_identifier variables until either a row has found a join (in a for loop) or all variable-to-variable combinations have failed for the row.
After two columns have joined a row in the loop, that row will be appended to a separate list and removed from the dataframes. postcode is a non-unique index.
Table columns (not hirarchical)
dataset_1 has these variables:
postcode, house_identifier_1, house_identifier_2, house_identifier_3, id
dataset_2 has these variables:
postcode, house_identifier_a, house_identifier_b, id_2
Column combinations to join in a loop:
table_1_variables = ['number_x', 'number_y', 'number_z']
table_2_variables = ['number_a', 'number_b']
for i in table_1_variables:
for j in table_2_variables:
To join the tables efficiently a stratgy seems to be to first join on the indexes (postcodes), and then on the non-indexed columns. However it seems like this would create a very large intermediary join which would push an 8GB memory over limits and the syntax is also unclear between combining (left_index=True, right_index=True, left_on=, right_on=)
Meanwhile, indexing/reindexing and then sorting_index inside a loop seems very inefficient.
Is there a better way to join or merge theese efficiently?
Example of intersects:
{'id': {27: '{582D0636-8DEF-8F22-E053-6C04A8C01BAC}',
41: '{D9E869FE-7B55-4C36-AC43-695B9033A13B}',
33: '{93E6821E-554E-40FD-E053-6B04A8C0C1DF}',
1: '{288DCE29-0589-E510-E050-A8C06205480E}',
48: '{3A23DDD5-A0E8-41D2-A514-5B09385C301F}',
52: '{CEB16957-F7FA-4D1B-B45F-A390214735BC}',
13: '{404A5AF3-9B20-CD2B-E050-A8C063055C7B}',
16: '{64342BFD-FD07-422C-E053-6C04A8C0FB8A}',
57: '{29A8E769-8A10-4477-9494-FF55EF5FAE4B}',
10: '{404A5AF3-0B58-CD2B-E050-A8C063055C7B}',
21: '{55BDCAE6-0C10-521D-E053-6B04A8C0DD7A}',
31: '{5C676A02-1781-4152-950C-6E5CA2CBC487}',
7: '{68FEB20B-142E-38DA-E053-6C04A8C051AE}',
45: '{8F1B26BD-673F-53DB-E053-6C04A8C03649}',
12: '{2F115F7A-8F81-4124-9FD4-FB76E742B2C1}',
36: '{344AB2D7-4B59-4AB4-8F52-75B29BE8C509}',
20: '{965B6D91-D4B6-95E4-E053-6C04A8C07729}',
56: '{59872FD9-F39D-4BB9-95F6-91E002D948B1}',
22: '{6141DFF0-973F-4FEC-A582-7F310B566031}'},
'id_2': {27: 10002277489,
41: 64023255,
33: 10007367447,
1: 22229221,
48: 10033235735,
52: 100062162615,
13: 50103744,
16: 10022903998,
57: 12015624,
10: 12154940,
21: 10024247587,
31: 100041193990,
7: 10008230730,
45: 10091640210,
12: 202107394,
36: 5062293,
20: 48114659,
56: 10001311242,
22: 10000443154},
'postcode': {27: 'lu72la',
41: 'cf626nt',
33: 'hr40aq',
1: 'bn32pd',
48: 'sg13ae',
52: 'gu97jx',
13: 'ct202ef',
16: 'bh14rn',
57: 'ub24af',
10: 'w55bu',
21: 'po302dp',
31: 'tq148aq',
7: 'e82ag',
45: 'ch47ew',
12: 'ha90ae',
36: 'nw34tt',
20: 'sw192rw',
56: 'so143hw',
22: 'se218hp'},
'house_identifier_1': {27: '76',
41: 'flat6',
33: '49',
1: 'flat10',
48: '145',
52: '31',
13: 'flat19',
16: 'flat7',
57: '76',
10: 'flat1',
21: 'flat1',
31: 'flat43',
7: 'flata',
45: '8',
12: '42',
36: 'flat9',
20: 'flat43',
56: 'flat156',
22: 'flat2'},
'house_identifier_2': {27: 'eastdock',
41: 'courtlands',
33: 'watkinscourt',
1: 'ascothouse',
48: 'monumentcourt',
52: 'sumnercourt',
13: '22-24',
16: '77',
57: 'osterleyviews',
10: '55-59',
21: '138',
31: 'leandercourt',
7: '130',
45: 'greenbankhall',
12: 'danescourt',
36: 'holmefieldcourt',
20: 'bennetscourtyard',
56: 'oceanaboulevard',
22: '124f'},
'house_identifier_3': {27: 'eastdock',
41: 'courtlands',
33: 'watkinscourt',
1: 'ascothouse',
48: 'monumentcourt',
52: 'sumnercourt',
13: None,
16: None,
57: 'osterleyviews',
10: None,
21: None,
31: 'leandercourt',
7: None,
45: 'greenbankhall',
12: 'danescourt',
36: 'holmefieldcourt',
20: 'bennetscourtyard',
56: 'oceanaboulevard',
22: None},
'house_identifier_a': {27: None,
41: None,
33: None,
1: '18-20',
48: None,
52: None,
13: '22-24',
16: '77',
57: None,
10: '55-59',
21: '138',
31: None,
7: '130',
45: None,
12: None,
36: None,
20: None,
56: None,
22: '124f'},
'house_identifier_b': {27: '76',
41: 'flat6',
33: '49',
1: 'flat10',
48: '145',
52: '31',
13: 'flat19',
16: 'flat7',
57: '76',
10: 'flat1',
21: 'flat1',
31: 'flat43',
7: 'flata',
45: '8',
12: '42',
36: 'flat9',
20: 'flat43',
56: 'flat156',
22: 'flat2'}}

Plotly-Dash: How to choose between multiple columns from multiple dataframes in a dictionary?

I want to plot a double y-axis plot where the user can select which variable to look at. I want the dropdown option to change the original y-axis, but also want the second y-axis to change with the variable change. The data comes from two dictionaries, where each dictionary has 2 keys which are dataframes. Here are the dicts:
df_vals.keys()
['corn','soybeans']
{'time': {0: Timestamp('2020-09-20 00:00:00'), 1: Timestamp('2020-09-21 00:00:00'), 2: Timestamp('2020-09-22 00:00:00'), 3: Timestamp('2020-09-23 00:00:00'), 4: Timestamp('2020-09-24 00:00:00'), 5: Timestamp('2020-09-25 00:00:00'), 6: Timestamp('2020-09-26 00:00:00'), 7: Timestamp('2020-09-27 00:00:00'), 8: Timestamp('2020-09-28 00:00:00'), 9: Timestamp('2020-09-29 00:00:00'), 10: Timestamp('2020-09-30 00:00:00'), 11: Timestamp('2020-10-01 00:00:00'), 12: Timestamp('2020-10-02 00:00:00'), 13: Timestamp('2020-10-03 00:00:00'), 14: Timestamp('2020-10-04 00:00:00'), 15: Timestamp('2020-10-05 00:00:00')}, '2m_temp_24hdelta_prod': {0: -0.33056551535638423, 1: -0.06352654836543402, 2: -0.276225357129493, 3: -0.7788107233764435, 4: -0.12182345812042986, 5: 0.4844870528180379, 6: -0.7795401737865336, 7: -1.3736563255451468, 8: -0.7925949910485673, 9: -0.6084065411465024, 10: -0.6532739602739552, 11: -0.5334182358612463, 12: -0.3214601096392593, 13: 0.005672046711691303, 14: 0.294874775721931, 15: 0.0}, '2m_temp_24hdelta_area': {0: -0.2942234811255382, 1: -0.06695257736951654, 2: -0.2382484006633631, 3: -0.747444491858053, 4: -0.008792915536150941, 5: 0.25843822574146474, 6: -0.8653840134962728, 7: -1.3306357244341183, 8: -0.77807373911064, 9: -0.6101878063800394, 10: -0.6602593986352374, 11: -0.5377113682438022, 12: -0.3489377042704134, 13: -0.12997682663355925, 14: 0.08031697715852158, 15: 0.0}, 'total_precip_24hdelta_prod': {0: -1.4417379991236094, 1: -3.24276961794111, 2: -1.9549294932106442, 3: 1.0805492310759035, 4: -2.8572102912370547, 5: -0.6036102993252254, 6: 0.068152851219848, 7: -0.37760433118519804, 8: -0.2182212103223477, 9: 0.14385291749053375, 10: -0.09611101762074367, 11: -0.44170367466228744, 12: -0.9926136091953488, 13: -0.26638156415402015, 14: -0.0435076228174961, 15: 0.0}, 'total_precip_24hdelta_area': {0: -0.8969484573159082, 1: -2.002193332285897, 2: -1.3265684841453602, 3: 0.5232970017014504, 4: -1.9744318826572878, 5: -0.4756557183821801, 6: 0.09401319045911638, 7: -0.2480262049680989, 8: -0.16164720991353132, 9: 0.12918646107950632, 10: -0.025385234499880355, 11: -0.37094005589542123, 12: -0.7621485373750772, 13: -0.21974376949919702, 14: -0.06387744429696394, 15: 0.0}}
{'time': {0: Timestamp('2020-09-20 00:00:00'), 1: Timestamp('2020-09-21 00:00:00'), 2: Timestamp('2020-09-22 00:00:00'), 3: Timestamp('2020-09-23 00:00:00'), 4: Timestamp('2020-09-24 00:00:00'), 5: Timestamp('2020-09-25 00:00:00'), 6: Timestamp('2020-09-26 00:00:00'), 7: Timestamp('2020-09-27 00:00:00'), 8: Timestamp('2020-09-28 00:00:00'), 9: Timestamp('2020-09-29 00:00:00'), 10: Timestamp('2020-09-30 00:00:00'), 11: Timestamp('2020-10-01 00:00:00'), 12: Timestamp('2020-10-02 00:00:00'), 13: Timestamp('2020-10-03 00:00:00'), 14: Timestamp('2020-10-04 00:00:00'), 15: Timestamp('2020-10-05 00:00:00')}, '2m_temp_24hdelta_prod': {0: -0.41072100441995785, 1: -0.11297222900273206, 2: -0.3134558546203517, 3: -0.7055797268403201, 4: -0.14038170579478787, 5: 0.2625117005223226, 6: -0.7101408224478941, 7: -1.2573241419225902, 8: -0.7906424533010389, 9: -0.661536032998284, 10: -0.6822789549363446, 11: -0.5744259308508103, 12: -0.4206210457410846, 13: -0.08431581171659296, 14: 0.2654961632050054, 15: 0.0}, '2m_temp_24hdelta_area': {0: -0.36772899690348704, 1: -0.05348366544356093, 2: -0.24183851104639115, 3: -0.6953305273210354, 4: -0.045424744173602496, 5: 0.19001500780368247, 6: -0.821935103347599, 7: -1.265563237441084, 8: -0.7328581189116925, 9: -0.6143048783236709, 10: -0.6633462127739507, 11: -0.5190203572096471, 12: -0.39498226425933547, 13: -0.10526773964830015, 14: 0.19258302497068705, 15: 0.0}, 'total_precip_24hdelta_prod': {0: -0.6776698356563055, 1: -1.7516299539550721, 2: -1.270815712012518, 3: 0.5996647217068156, 4: -2.2466045890287396, 5: -0.6706621597096377, 6: 0.06719919310410405, 7: -0.3572052717181586, 8: -0.2512178851633781, 9: 0.1313636195701381, 10: 0.08179245850142033, 11: -0.3036658888458674, 12: -0.8200237460316371, 13: -0.32927495598480583, 14: -0.08189579932441349, 15: 0.0}, 'total_precip_24hdelta_area': {0: -0.7530386453128719, 1: -1.7027556154743797, 2: -1.0907657669920294, 3: 0.429094034006764, 4: -1.9424324271143407, 5: -0.5370968484676638, 6: 0.044404490985521246, 7: -0.30794153270377544, 8: -0.2471378005907392, 9: 0.1212115468745879, 10: 0.07486462848670472, 11: -0.3507116185903665, 12: -0.7247205225779287, 13: -0.25431240691233936, 14: -0.06960793926681186, 15: 0.0}}
df_deltas.keys()
['corn','soybeans']
{'time': {0: Timestamp('2020-09-20 00:00:00'), 1: Timestamp('2020-09-20 06:00:00'), 2: Timestamp('2020-09-20 12:00:00'), 3: Timestamp('2020-09-20 18:00:00'), 4: Timestamp('2020-09-21 00:00:00'), 5: Timestamp('2020-09-21 06:00:00'), 6: Timestamp('2020-09-21 12:00:00'), 7: Timestamp('2020-09-21 18:00:00'), 8: Timestamp('2020-09-22 00:00:00'), 9: Timestamp('2020-09-22 06:00:00'), 10: Timestamp('2020-09-22 12:00:00'), 11: Timestamp('2020-09-22 18:00:00'), 12: Timestamp('2020-09-23 00:00:00'), 13: Timestamp('2020-09-23 06:00:00'), 14: Timestamp('2020-09-23 12:00:00'), 15: Timestamp('2020-09-23 18:00:00'), 16: Timestamp('2020-09-24 00:00:00'), 17: Timestamp('2020-09-24 06:00:00'), 18: Timestamp('2020-09-24 12:00:00'), 19: Timestamp('2020-09-24 18:00:00'), 20: Timestamp('2020-09-25 00:00:00'), 21: Timestamp('2020-09-25 06:00:00'), 22: Timestamp('2020-09-25 12:00:00'), 23: Timestamp('2020-09-25 18:00:00'), 24: Timestamp('2020-09-26 00:00:00'), 25: Timestamp('2020-09-26 06:00:00'), 26: Timestamp('2020-09-26 12:00:00'), 27: Timestamp('2020-09-26 18:00:00'), 28: Timestamp('2020-09-27 00:00:00'), 29: Timestamp('2020-09-27 06:00:00'), 30: Timestamp('2020-09-27 12:00:00'), 31: Timestamp('2020-09-27 18:00:00'), 32: Timestamp('2020-09-28 00:00:00'), 33: Timestamp('2020-09-28 06:00:00'), 34: Timestamp('2020-09-28 12:00:00'), 35: Timestamp('2020-09-28 18:00:00'), 36: Timestamp('2020-09-29 00:00:00'), 37: Timestamp('2020-09-29 06:00:00'), 38: Timestamp('2020-09-29 12:00:00'), 39: Timestamp('2020-09-29 18:00:00'), 40: Timestamp('2020-09-30 00:00:00'), 41: Timestamp('2020-09-30 06:00:00'), 42: Timestamp('2020-09-30 12:00:00'), 43: Timestamp('2020-09-30 18:00:00'), 44: Timestamp('2020-10-01 00:00:00'), 45: Timestamp('2020-10-01 06:00:00'), 46: Timestamp('2020-10-01 12:00:00'), 47: Timestamp('2020-10-01 18:00:00'), 48: Timestamp('2020-10-02 00:00:00'), 49: Timestamp('2020-10-02 06:00:00'), 50: Timestamp('2020-10-02 12:00:00'), 51: Timestamp('2020-10-02 18:00:00'), 52: 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As you can see, the dataframes are related to each other. The first dataframe contains 6-hourly data from a forecast model that goes out 15 days for 4 different variables. The second dataframe contains 24-hourly data that describes the change in the value of the specific variable in df_vals from 24 hours ago (the delta). As such, I want to plot a line graph for the actual values on the y1 axis, and a bar graph to show the change in 24 hours on the y2 axis.
I have been able to plot the values from df_vals as a simple line graph and make it interactive so the user can switch between between different dataframes, but also different variables within the dataframe. Code for that here:
app = JupyterDash(__name__)
cols=df_vals['corn'].columns[1:]
app.layout = html.Div([
html.Div([
html.Div([
dcc.Dropdown(
id='variables',
options=[{'label': i, 'value': i} for i in cols],
value='2m_temp_prod'
)
]),
html.Div([
dcc.Dropdown(
id='crop_dropdown',
options=[{'label': i, 'value': i} for i in df_vals.keys()],
value='corn'
)
]),
dcc.Graph(id='plot')])
])
#app.callback(
Output('crop-dropdown', 'options'),
[Input('variables', 'value')])
def update_crop_dropdown(crop):
return [{'label': i, 'value': i} for i in df_vals[crop]]
#app.callback(
Output('plot', 'figure'),
[Input('crop_dropdown', 'value'),
Input('variables', 'value')])
def update_graph(crops, variable_name):
if crops:
return go.Figure(data=go.Scatter(x=df_vals[crops]['time'], y=df_vals[crops][variable_name], mode='lines'))
else:
return go.Figure(data=[])
app.run_server(mode='inline')
I want the similar column names in df_vals and df_deltas to be plotted together. For example, the column name '2m_temp_prod' in df_vals and '2m_temp_24hdelta_prod' in df_deltas would be plotted together. The same idea applies for the other 3 columns.
I am confused how I let the app know that I want to update both the first and second y-axis, but only provide one dropdown to do so.
An example plot is provided where I was able to accomplish this task, but as a stand alone, non-interactive plot. The purpose of the example is to make sure what I am trying to achieve is clear.

I've prepared a setup using Dash that should take all your considerations into account.
You can select crop type ['corn', 'soybeans', 'winterwheat', 'springwheat']
You can select which columns to display on the primary y-axis for each croptype in df_vals.
And you can select which columns to display on the secondary y-axis for each croptype in df_deltas.
Since the datasets in df_vals and df_deltas are displayed on the primary and secondary y-axis respectively, they are not part of the menu selections. The buttons provided should do be sufficient if I'm understanding your question correctly.
Plot 1: Crop=Corn, df_vals=2m_temp_prod, df_deltas=2m_prod_24hdelta_prod
Plot 2: Crop=Soybeans, df_vals=total_precip_prod, df_deltas=total_precip_24hdelta_area
Complete code:
from jupyter_dash import JupyterDash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State, ClientsideFunction
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import plotly.graph_objs as go
from dash.dependencies import Input, Output
import numpy as np
from plotly.subplots import make_subplots
from pandas import Timestamp
# data
df_vals = {'corn': pd.DataFrame({'time': {0: Timestamp('2020-09-20 00:00:00'), 1: Timestamp('2020-09-20 06:00:00'), 2: Timestamp('2020-09-20 12:00:00'), 3: Timestamp('2020-09-20 18:00:00'), 4: Timestamp('2020-09-21 00:00:00'), 5: Timestamp('2020-09-21 06:00:00'), 6: Timestamp('2020-09-21 12:00:00'), 7: Timestamp('2020-09-21 18:00:00'), 8: Timestamp('2020-09-22 00:00:00'), 9: Timestamp('2020-09-22 06:00:00'), 10: Timestamp('2020-09-22 12:00:00'), 11: Timestamp('2020-09-22 18:00:00'), 12: Timestamp('2020-09-23 00:00:00'), 13: Timestamp('2020-09-23 06:00:00'), 14: Timestamp('2020-09-23 12:00:00'), 15: Timestamp('2020-09-23 18:00:00'), 16: Timestamp('2020-09-24 00:00:00'), 17: Timestamp('2020-09-24 06:00:00'), 18: Timestamp('2020-09-24 12:00:00'), 19: Timestamp('2020-09-24 18:00:00'), 20: Timestamp('2020-09-25 00:00:00'), 21: Timestamp('2020-09-25 06:00:00'), 22: Timestamp('2020-09-25 12:00:00'), 23: Timestamp('2020-09-25 18:00:00'), 24: Timestamp('2020-09-26 00:00:00'), 25: Timestamp('2020-09-26 06:00:00'), 26: Timestamp('2020-09-26 12:00:00'), 27: Timestamp('2020-09-26 18:00:00'), 28: Timestamp('2020-09-27 00:00:00'), 29: Timestamp('2020-09-27 06:00:00'), 30: Timestamp('2020-09-27 12:00:00'), 31: Timestamp('2020-09-27 18:00:00'), 32: Timestamp('2020-09-28 00:00:00'), 33: Timestamp('2020-09-28 06:00:00'), 34: Timestamp('2020-09-28 12:00:00'), 35: Timestamp('2020-09-28 18:00:00'), 36: Timestamp('2020-09-29 00:00:00'), 37: Timestamp('2020-09-29 06:00:00'), 38: Timestamp('2020-09-29 12:00:00'), 39: Timestamp('2020-09-29 18:00:00'), 40: Timestamp('2020-09-30 00:00:00'), 41: Timestamp('2020-09-30 06:00:00'), 42: Timestamp('2020-09-30 12:00:00'), 43: Timestamp('2020-09-30 18:00:00'), 44: Timestamp('2020-10-01 00:00:00'), 45: Timestamp('2020-10-01 06:00:00'), 46: Timestamp('2020-10-01 12:00:00'), 47: Timestamp('2020-10-01 18:00:00'), 48: Timestamp('2020-10-02 00:00:00'), 49: Timestamp('2020-10-02 06:00:00'), 50: Timestamp('2020-10-02 12:00:00'), 51: Timestamp('2020-10-02 18:00:00'), 52: Timestamp('2020-10-03 00:00:00'), 53: Timestamp('2020-10-03 06:00:00'), 54: Timestamp('2020-10-03 12:00:00'), 55: Timestamp('2020-10-03 18:00:00'), 56: Timestamp('2020-10-04 00:00:00'), 57: Timestamp('2020-10-04 06:00:00'), 58: Timestamp('2020-10-04 12:00:00'), 59: Timestamp('2020-10-04 18:00:00'), 60: Timestamp('2020-10-05 00:00:00')}, '2m_temp_prod': {0: 299.346777266481, 1: 294.0395117035824, 2: 292.9592738522704, 3: 301.3180464757325, 4: 300.62356652087135, 5: 295.3372490145483, 6: 293.70255086586366, 7: 301.74835531223954, 8: 300.63094530701716, 9: 295.19553201608596, 10: 293.61613871139605, 11: 302.1631229776387, 12: 300.9153730135594, 13: 295.15968865817507, 14: 293.4150837111033, 15: 301.41874976967574, 16: 300.2591302298784, 17: 294.35648855874496, 18: 292.60893943975975, 19: 300.69793228145136, 20: 299.21051997362474, 21: 293.03793188187217, 22: 290.7864320360987, 23: 298.83351270175194, 24: 297.3528693941268, 25: 291.19369512544074, 26: 289.09521928196455, 27: 297.6441935083638, 28: 296.7296754953959, 29: 290.9897312443865, 30: 289.32126319033677, 31: 298.7574828448876, 32: 298.12901959834915, 33: 292.5899489699376, 34: 290.89859256270717, 35: 300.43330551865887, 36: 299.4213926560918, 37: 293.6473005538043, 38: 291.78953606425546, 39: 300.96031965551913, 40: 299.8616920790521, 41: 293.9476866306047, 42: 292.00448821963647, 43: 301.2204786022793, 44: 300.1545728526989, 45: 294.30968496479613, 46: 292.3264718663577, 47: 301.6422264431679, 48: 300.6382119018213, 49: 294.9066383821488, 50: 292.9206232319892, 51: 302.28532470110673, 52: 301.118337633183, 53: 295.580361365072, 54: 293.5071232611944, 55: 302.4993713158521, 56: 301.1771411161832, 57: 295.87429820309063, 58: 293.8387870006977, 59: 302.3844741742686, 60: 300.92076629492163}, '2m_temp_area': {0: 299.79923425166567, 1: 294.4433522483179, 2: 293.1829305163771, 3: 301.76751592578194, 4: 300.97964970416314, 5: 295.58816311209137, 6: 293.944118250348, 7: 302.20448177813586, 8: 301.0782745090335, 9: 295.36833103932497, 10: 293.7448705435944, 11: 302.2414068531493, 12: 300.97154909133104, 13: 295.09171791304016, 14: 293.29872586829305, 15: 301.63398450993316, 16: 300.39729350367077, 17: 294.4765532352668, 18: 292.6348308697987, 19: 300.7196467769189, 20: 299.2413397435659, 21: 293.1571305856655, 22: 290.9298399188299, 23: 299.0361571649767, 24: 297.6784967259681, 25: 291.6225850820778, 26: 289.5307387179231, 27: 298.1675193109706, 28: 297.2851193736129, 29: 291.6047803054954, 30: 289.8725165929944, 31: 299.2259541567033, 32: 298.5645974740215, 33: 292.9844928941141, 34: 291.16692080415885, 35: 300.6640689360243, 36: 299.63105315816887, 37: 293.7973759485704, 38: 291.8748714049971, 39: 301.07558737962773, 40: 299.9769492884242, 41: 294.06556034682825, 42: 292.0989825175383, 43: 301.3224275326778, 44: 300.2429318269633, 45: 294.4016189433437, 46: 292.39193470959646, 47: 301.691478296914, 48: 300.693790365317, 49: 294.95729295702677, 50: 292.94815058578274, 51: 302.1922658603409, 52: 301.01878139937173, 53: 295.47413908911483, 54: 293.41960123543845, 55: 302.34370446075303, 56: 301.05227283650527, 57: 295.72013507339597, 58: 293.6867376233815, 59: 302.19133411599717, 60: 300.81799340051333}, 'total_precip_prod': {0: 0.0, 1: 0.19106968995229195, 2: 0.1557650112297922, 3: 0.42176822127339436, 4: 0.3635719686978164, 5: 0.624238127750141, 6: 0.7363658332723646, 7: 0.6637602529624484, 8: 0.8303668371501847, 9: 0.9645353650727154, 10: 0.9080621631493283, 11: 0.7407109615632305, 12: 1.2188155467167008, 13: 1.4934816133864144, 14: 0.8673540204216558, 15: 0.4536446670226782, 16: 0.5838893851141779, 17: 0.28997017910749373, 18: 0.396575809101194, 19: 0.3017425722928852, 20: 0.2600037974259506, 21: 0.10090977019496428, 22: 0.1120663639424084, 23: 0.11820662603244433, 24: 0.11351422163952127, 25: 0.0354462928350286, 26: 0.08973231687769569, 27: 0.11707786940722151, 28: 0.07097838855834729, 29: 0.03411315769121203, 30: 0.09051350215127027, 31: 0.11314915610119855, 32: 0.14753233086329737, 33: 0.1845242398275337, 34: 0.2518174918229658, 35: 0.17011312187047897, 36: 0.2749232044764252, 37: 0.2972017724997405, 38: 0.3552475267861772, 39: 0.27943871692889416, 40: 0.31981605728486306, 41: 0.19735958986038185, 42: 0.2806487307465678, 43: 0.2830918175327961, 44: 0.3288561686915008, 45: 0.1744303376099874, 46: 0.30609645936303326, 47: 0.21925050665230758, 48: 0.20281755796768022, 49: 0.06620804878284778, 50: 0.17684099550656973, 51: 0.19853145136367492, 52: 0.2563854573673008, 53: 0.24201876323456878, 54: 0.41939251801295807, 55: 0.36402178359658277, 56: 0.37120895070913373, 57: 0.281792653746079, 58: 0.5868870658107626, 59: 0.4927117680726645, 60: 0.5223743530274055}, 'total_precip_area': {0: 0.0, 1: 0.2869521154242563, 2: 0.21660564402622137, 3: 0.4856913604264373, 4: 0.5011640941544891, 5: 0.7562519498474788, 6: 0.7839821183163472, 7: 0.5862595162666312, 8: 0.8974715199444484, 9: 0.9610750641626696, 10: 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0.24311804406545334, 52: 0.3062432174815857, 53: 0.27031885272433986, 54: 0.40763842665734645, 55: 0.3546261915245615, 56: 0.4085152708749088, 57: 0.30056439476727764, 58: 0.5493651343645845, 59: 0.4937984226857489, 60: 0.531137675746978}}),
'soybeans':pd.DataFrame({'time': {0: Timestamp('2020-09-20 00:00:00'), 1: Timestamp('2020-09-20 06:00:00'), 2: Timestamp('2020-09-20 12:00:00'), 3: Timestamp('2020-09-20 18:00:00'), 4: Timestamp('2020-09-21 00:00:00'), 5: Timestamp('2020-09-21 06:00:00'), 6: Timestamp('2020-09-21 12:00:00'), 7: Timestamp('2020-09-21 18:00:00'), 8: Timestamp('2020-09-22 00:00:00'), 9: Timestamp('2020-09-22 06:00:00'), 10: Timestamp('2020-09-22 12:00:00'), 11: Timestamp('2020-09-22 18:00:00'), 12: Timestamp('2020-09-23 00:00:00'), 13: Timestamp('2020-09-23 06:00:00'), 14: Timestamp('2020-09-23 12:00:00'), 15: Timestamp('2020-09-23 18:00:00'), 16: Timestamp('2020-09-24 00:00:00'), 17: Timestamp('2020-09-24 06:00:00'), 18: Timestamp('2020-09-24 12:00:00'), 19: Timestamp('2020-09-24 18:00:00'), 20: Timestamp('2020-09-25 00:00:00'), 21: Timestamp('2020-09-25 06:00:00'), 22: Timestamp('2020-09-25 12:00:00'), 23: Timestamp('2020-09-25 18:00:00'), 24: Timestamp('2020-09-26 00:00:00'), 25: Timestamp('2020-09-26 06:00:00'), 26: Timestamp('2020-09-26 12:00:00'), 27: Timestamp('2020-09-26 18:00:00'), 28: Timestamp('2020-09-27 00:00:00'), 29: Timestamp('2020-09-27 06:00:00'), 30: Timestamp('2020-09-27 12:00:00'), 31: Timestamp('2020-09-27 18:00:00'), 32: Timestamp('2020-09-28 00:00:00'), 33: Timestamp('2020-09-28 06:00:00'), 34: Timestamp('2020-09-28 12:00:00'), 35: Timestamp('2020-09-28 18:00:00'), 36: Timestamp('2020-09-29 00:00:00'), 37: Timestamp('2020-09-29 06:00:00'), 38: Timestamp('2020-09-29 12:00:00'), 39: Timestamp('2020-09-29 18:00:00'), 40: Timestamp('2020-09-30 00:00:00'), 41: Timestamp('2020-09-30 06:00:00'), 42: Timestamp('2020-09-30 12:00:00'), 43: Timestamp('2020-09-30 18:00:00'), 44: Timestamp('2020-10-01 00:00:00'), 45: Timestamp('2020-10-01 06:00:00'), 46: Timestamp('2020-10-01 12:00:00'), 47: Timestamp('2020-10-01 18:00:00'), 48: Timestamp('2020-10-02 00:00:00'), 49: Timestamp('2020-10-02 06:00:00'), 50: Timestamp('2020-10-02 12:00:00'), 51: Timestamp('2020-10-02 18:00:00'), 52: Timestamp('2020-10-03 00:00:00'), 53: Timestamp('2020-10-03 06:00:00'), 54: Timestamp('2020-10-03 12:00:00'), 55: Timestamp('2020-10-03 18:00:00'), 56: Timestamp('2020-10-04 00:00:00'), 57: Timestamp('2020-10-04 06:00:00'), 58: Timestamp('2020-10-04 12:00:00'), 59: Timestamp('2020-10-04 18:00:00'), 60: Timestamp('2020-10-05 00:00:00')}, '2m_temp_prod': {0: 299.4681144428137, 1: 294.4593329079582, 2: 293.54097959722486, 3: 301.26850218370805, 4: 300.4714986353434, 5: 295.6384695410705, 6: 294.2218375466852, 7: 301.7426496375417, 8: 300.4512511041961, 9: 295.3463775610583, 10: 294.00147574962756, 11: 302.04475406462933, 12: 300.7236658622478, 13: 295.24334771914255, 14: 293.7074020631231, 15: 301.7624504225804, 16: 300.4964931687989, 17: 294.75437504400884, 18: 293.1177542350017, 19: 301.0425274988938, 20: 299.46808452778663, 21: 293.52076913001173, 22: 291.3670613175431, 23: 299.3793965992101, 24: 297.7263093286383, 25: 291.7095202331552, 26: 289.6578323905783, 27: 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1.0056286133748165, 12: 1.4698900412759783, 13: 1.003917146674298, 14: 0.6760869472842964, 15: 0.46827391738334945, 16: 0.6192899499044445, 17: 0.30060294065641396, 18: 0.41819106385838506, 19: 0.42650150247172797, 20: 0.37798023230687594, 21: 0.13500750520245205, 22: 0.14298402753104766, 23: 0.15948345822813867, 24: 0.18379226168252366, 25: 0.06000665823151941, 26: 0.1208072573649105, 27: 0.1350174494403403, 28: 0.11033956479303804, 29: 0.06970661157888385, 30: 0.16430003817188624, 31: 0.20396331622388164, 32: 0.2434592812259675, 33: 0.2349487648431431, 34: 0.2746628172448139, 35: 0.2223445586696201, 36: 0.32000417926146, 37: 0.2856880861377167, 38: 0.3563173295637319, 39: 0.2961985136805571, 40: 0.39572744360892087, 41: 0.25261863325235423, 42: 0.3480723202390621, 43: 0.33450860690381784, 44: 0.40810079238884994, 45: 0.2246481215656477, 46: 0.322536138199169, 47: 0.26302935652500986, 48: 0.26877015748360655, 49: 0.10435240067851569, 50: 0.23626983714582017, 51: 0.2921901873852093, 52: 0.3573174778311772, 53: 0.2690948499009291, 54: 0.43485595266861593, 55: 0.3857033635552736, 56: 0.45008050524046406, 57: 0.31559742112580763, 58: 0.5897464914238096, 59: 0.5591084829706243, 60: 0.6031174118554716}})}
df_deltas = {'corn': pd.DataFrame({'time': {0: Timestamp('2020-09-20 00:00:00'), 1: Timestamp('2020-09-21 00:00:00'), 2: Timestamp('2020-09-22 00:00:00'), 3: Timestamp('2020-09-23 00:00:00'), 4: Timestamp('2020-09-24 00:00:00'), 5: Timestamp('2020-09-25 00:00:00'), 6: Timestamp('2020-09-26 00:00:00'), 7: Timestamp('2020-09-27 00:00:00'), 8: Timestamp('2020-09-28 00:00:00'), 9: Timestamp('2020-09-29 00:00:00'), 10: Timestamp('2020-09-30 00:00:00'), 11: Timestamp('2020-10-01 00:00:00'), 12: Timestamp('2020-10-02 00:00:00'), 13: Timestamp('2020-10-03 00:00:00'), 14: Timestamp('2020-10-04 00:00:00'), 15: Timestamp('2020-10-05 00:00:00')}, '2m_temp_24hdelta_prod': {0: -0.33056551535638423, 1: -0.06352654836543402, 2: -0.276225357129493, 3: -0.7788107233764435, 4: -0.12182345812042986, 5: 0.4844870528180379, 6: -0.7795401737865336, 7: -1.3736563255451468, 8: -0.7925949910485673, 9: -0.6084065411465024, 10: -0.6532739602739552, 11: -0.5334182358612463, 12: -0.3214601096392593, 13: 0.005672046711691303, 14: 0.294874775721931, 15: 0.0}, '2m_temp_24hdelta_area': {0: -0.2942234811255382, 1: -0.06695257736951654, 2: -0.2382484006633631, 3: -0.747444491858053, 4: -0.008792915536150941, 5: 0.25843822574146474, 6: -0.8653840134962728, 7: -1.3306357244341183, 8: -0.77807373911064, 9: -0.6101878063800394, 10: -0.6602593986352374, 11: -0.5377113682438022, 12: -0.3489377042704134, 13: -0.12997682663355925, 14: 0.08031697715852158, 15: 0.0}, 'total_precip_24hdelta_prod': {0: -1.4417379991236094, 1: -3.24276961794111, 2: -1.9549294932106442, 3: 1.0805492310759035, 4: -2.8572102912370547, 5: -0.6036102993252254, 6: 0.068152851219848, 7: -0.37760433118519804, 8: -0.2182212103223477, 9: 0.14385291749053375, 10: -0.09611101762074367, 11: -0.44170367466228744, 12: -0.9926136091953488, 13: -0.26638156415402015, 14: -0.0435076228174961, 15: 0.0}, 'total_precip_24hdelta_area': {0: -0.8969484573159082, 1: -2.002193332285897, 2: -1.3265684841453602, 3: 0.5232970017014504, 4: -1.9744318826572878, 5: -0.4756557183821801, 6: 0.09401319045911638, 7: -0.2480262049680989, 8: -0.16164720991353132, 9: 0.12918646107950632, 10: -0.025385234499880355, 11: -0.37094005589542123, 12: -0.7621485373750772, 13: -0.21974376949919702, 14: -0.06387744429696394, 15: 0.0}}),
'soybeans':pd.DataFrame({'time': {0: Timestamp('2020-09-20 00:00:00'), 1: Timestamp('2020-09-21 00:00:00'), 2: Timestamp('2020-09-22 00:00:00'), 3: Timestamp('2020-09-23 00:00:00'), 4: Timestamp('2020-09-24 00:00:00'), 5: Timestamp('2020-09-25 00:00:00'), 6: Timestamp('2020-09-26 00:00:00'), 7: Timestamp('2020-09-27 00:00:00'), 8: Timestamp('2020-09-28 00:00:00'), 9: Timestamp('2020-09-29 00:00:00'), 10: Timestamp('2020-09-30 00:00:00'), 11: Timestamp('2020-10-01 00:00:00'), 12: Timestamp('2020-10-02 00:00:00'), 13: Timestamp('2020-10-03 00:00:00'), 14: Timestamp('2020-10-04 00:00:00'), 15: Timestamp('2020-10-05 00:00:00')}, '2m_temp_24hdelta_prod': {0: -0.41072100441995785, 1: -0.11297222900273206, 2: -0.3134558546203517, 3: -0.7055797268403201, 4: -0.14038170579478787, 5: 0.2625117005223226, 6: -0.7101408224478941, 7: -1.2573241419225902, 8: -0.7906424533010389, 9: -0.661536032998284, 10: -0.6822789549363446, 11: -0.5744259308508103, 12: -0.4206210457410846, 13: -0.08431581171659296, 14: 0.2654961632050054, 15: 0.0}, '2m_temp_24hdelta_area': {0: -0.36772899690348704, 1: -0.05348366544356093, 2: -0.24183851104639115, 3: -0.6953305273210354, 4: -0.045424744173602496, 5: 0.19001500780368247, 6: -0.821935103347599, 7: -1.265563237441084, 8: -0.7328581189116925, 9: -0.6143048783236709, 10: -0.6633462127739507, 11: -0.5190203572096471, 12: -0.39498226425933547, 13: -0.10526773964830015, 14: 0.19258302497068705, 15: 0.0}, 'total_precip_24hdelta_prod': {0: -0.6776698356563055, 1: -1.7516299539550721, 2: -1.270815712012518, 3: 0.5996647217068156, 4: -2.2466045890287396, 5: -0.6706621597096377, 6: 0.06719919310410405, 7: -0.3572052717181586, 8: -0.2512178851633781, 9: 0.1313636195701381, 10: 0.08179245850142033, 11: -0.3036658888458674, 12: -0.8200237460316371, 13: -0.32927495598480583, 14: -0.08189579932441349, 15: 0.0}, 'total_precip_24hdelta_area': {0: -0.7530386453128719, 1: -1.7027556154743797, 2: -1.0907657669920294, 3: 0.429094034006764, 4: -1.9424324271143407, 5: -0.5370968484676638, 6: 0.044404490985521246, 7: -0.30794153270377544, 8: -0.2471378005907392, 9: 0.1212115468745879, 10: 0.07486462848670472, 11: -0.3507116185903665, 12: -0.7247205225779287, 13: -0.25431240691233936, 14: -0.06960793926681186, 15: 0.0}})}
# managed indexes
df_vals['corn'].set_index('time', inplace=True)
df_vals['soybeans'].set_index('time', inplace=True)
df_deltas['corn'].set_index('time', inplace=True)
df_deltas['soybeans'].set_index('time', inplace=True)
# Dash app setup
app = JupyterDash(external_stylesheets=[dbc.themes.SLATE])
# controls
controls = dbc.Card(
[ dbc.FormGroup(
[
dbc.Label("Crop"),
dcc.Dropdown(
id="Crop",
options=[
{"label": col, "value": col} for col in list(df_vals.keys())
],
value=list(df_vals.keys())[0],
clearable=False,
),
]
),
dbc.FormGroup(
[
dbc.Label("df_vals"),
dcc.Dropdown(
id="val_cols",
options=[
{"label": col, "value": col} for col in df_vals[list(df_vals.keys())[0]].columns
],
value=df_vals[list(df_vals.keys())[0]].columns[0],
clearable=False,
),
]
),
dbc.FormGroup(
[
dbc.Label("df_deltas"),
dcc.Dropdown(
id="delta_cols",
options=[
{"label": col, "value": col} for col in df_deltas[list(df_deltas.keys())[0]].columns
],
value=df_deltas[list(df_deltas.keys())[0]].columns[0],
clearable=False
),
]
),
],
body=True,
)
# Layout
app.layout = dbc.Container(
[
html.H1("Plotly Dash"),
html.Hr(),
dbc.Row([
dbc.Col([
dbc.Row([
dbc.Col(controls)
], align="start"),
dbc.Row([
dbc.Col([
html.Br(),
dbc.Row([
dbc.Col([html.Div(id = 'txt1')
])
]),
html.Br(),
dbc.Row([
dbc.Col([html.Div(id = 'txt2')])
])
])
])
],xs = 4)
,
dbc.Col([
dbc.Row([
dbc.Col([html.Div(id = 'plot_title')],)
]),
dbc.Row([
dbc.Col(dcc.Graph(id="crop-graph")),
#dbc.Col(dcc.Graph(id="cluster-graph"))
])
])
],),
],
fluid=True,
)
#app.callback(
Output("crop-graph", "figure"),
[ Input("Crop", "value"),
Input("val_cols", "value"),
Input("delta_cols", "value"),
#Input("cluster-count", "value"),
],
)
def crop_graph(Crop, val, delta):
# plotly figure setup
fig = make_subplots(specs=[[{"secondary_y": True}]])
# traces for df_vals
fig.add_trace(go.Scatter(name=val, x=df_vals[Crop].index, y=df_vals[Crop][val], mode = 'lines'), secondary_y=False,
)
# traces for df_deltas
fig.add_trace(go.Scatter(name=delta, x=df_deltas[Crop].index, y=df_deltas[Crop][delta], mode = 'lines'), secondary_y=True,
)
# aesthetics
fig.update_layout(template="plotly_dark", plot_bgcolor='#272B30', paper_bgcolor='#272B30')
fig.update_layout(margin= {'t':150, 'b':0, 'r': 0, 'l': 0, 'pad': 0})
fig.update_layout(hovermode = 'x')
fig.update_layout(showlegend=True, legend=dict(x=1,y=0.85))
fig.update_layout(title = dict(text = 'Crop: '+ Crop + ', Vals: ' + val + ', Deltas: '+delta))
return(fig)
app.run_server(mode='external', port = 8099)

Backslashes and unicode

I am making a romaji to hiragana translator and am getting an error when I try this concatenation. I made a list of keys and am using a for loop to make a dictionary by the sequential nature of unicode.
combos = {}
for hexy in range(12363, 12435):
combos[sounds[12363 - hexy]] = ('\u%s' % str(chr(hex(hexy))))
I get the error
SyntaxError: (unicode error) 'unicodeescape' codec can't decode bytes in position 0-1: truncated \uXXXX escape
I have tried doubling the backslashes, separating the u and backslash, using a + instead of value insertion, and checking out when other people got similar errors.

If you want the character, just use chr(hexy).
If you want an escape code and assuming Python 3.6+, use f'\\u{hexy:04x}'.
for example (dropped sounds since it wasn't defined):
combos = {}
for i,hexy in enumerate(range(12363, 12435)):
combos[i] = chr(hexy)
print(combos)
Result:
{0: 'か', 1: 'が', 2: 'き', 3: 'ぎ', 4: 'く', 5: 'ぐ', 6: 'け', 7: 'げ', 8: 'こ', 9: 'ご', 10: 'さ', 11: 'ざ', 12: 'し', 13: 'じ', 14: 'す', 15: 'ず', 16: 'せ', 17: 'ぜ', 18: 'そ', 19: 'ぞ', 20: 'た', 21: 'だ', 22: 'ち', 23: 'ぢ', 24: 'っ', 25: 'つ', 26: 'づ', 27: 'て', 28: 'で', 29: 'と', 30: 'ど', 31: 'な', 32: 'に', 33: 'ぬ', 34: 'ね', 35: 'の', 36: 'は', 37: 'ば', 38: 'ぱ', 39: 'ひ', 40: 'び', 41: 'ぴ', 42: 'ふ', 43: 'ぶ', 44: 'ぷ', 45: 'へ', 46: 'べ', 47: 'ぺ', 48: 'ほ', 49: 'ぼ', 50: 'ぽ', 51: 'ま', 52: 'み', 53: 'む', 54: 'め', 55: 'も', 56: 'ゃ', 57: 'や', 58: 'ゅ', 59: 'ゆ', 60: 'ょ', 61: 'よ', 62: 'ら', 63: 'り', 64: 'る', 65: 'れ', 66: 'ろ', 67: 'ゎ', 68: 'わ', 69: 'ゐ', 70: 'ゑ', 71: 'を'}