Backslashes and unicode - python

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'}

Related

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)

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'}}

Visualization for rolling percentage for accuracy

Is there a way to visualize the results below?
29% of the results has accuracy >= 90%
59% of the results has accuracy >= 80%
88% of the results has accuracy >= 70%
98% of the results has accuracy >= 60%
2 entries with 59% and 57%
Below is the code with test result (accuracy) and rolling percentage
df1 = pd.DataFrame( {'Test Results': {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: 0.9452757159999999, 21: 0.9450125009999999, 22: 0.941873717, 23: 0.9336287990000001, 24: 0.932937845, 25: 0.928728552, 26: 0.9217334709999999, 27: 0.9212251640000001, 28: 0.903416839, 29: 0.900519655, 30: 0.8946950090000001, 31: 0.894315668, 32: 0.893705918, 33: 0.8911087870000001, 34: 0.88341769, 35: 0.875316697, 36: 0.873369453, 37: 0.8724485759999999, 38: 0.870855835, 39: 0.8682100159999999, 40: 0.866413987, 41: 0.866225758, 42: 0.86607366, 43: 0.861137576, 44: 0.8570423090000001, 45: 0.855989443, 46: 0.8363952240000001, 47: 0.835153771, 48: 0.831573322, 49: 0.8297349890000001, 50: 0.827174152, 51: 0.82533598, 52: 0.8244730259999999, 53: 0.8231749909999999, 54: 0.821542323, 55: 0.81994456, 56: 0.8193151340000001, 57: 0.817544275, 58: 0.8043684740000001, 59: 0.804280654, 60: 0.804033467, 61: 0.8024588559999999, 62: 0.799949648, 63: 0.7998811370000001, 64: 0.7993836879999999, 65: 0.7978083340000001, 66: 0.794987453, 67: 0.7942367459999999, 68: 0.788060922, 69: 0.7833455859999999, 70: 0.783237411, 71: 0.781810415, 72: 0.77947656, 73: 0.775430937, 74: 0.769843183, 75: 0.763548283, 76: 0.76318018, 77: 0.75891367, 78: 0.7588845790000001, 79: 0.758045414, 80: 0.7545263740000001, 81: 0.7514679670000001, 82: 0.74697755, 83: 0.74597623, 84: 0.743042806, 85: 0.740900129, 86: 0.740844543, 87: 0.7246525220000001, 88: 0.7133019070000001, 89: 0.7124335999999999, 90: 0.703856728, 91: 0.694475843, 92: 0.68836286, 93: 0.683978596, 94: 0.679002735, 95: 0.664945699, 96: 0.662761826, 97: 0.649950991, 98: 0.638550239, 99: 0.60593566, 100: 0.603891537, 101: 0.602900777, 102: 0.594660442, 103: 0.565978017}, 'Rolling Percentage': {0: 0.009615385, 1: 0.019230768999999998, 2: 0.028846154, 3: 0.038461537999999997, 4: 0.048076923, 5: 0.057692308, 6: 0.067307692, 7: 0.076923077, 8: 0.086538462, 9: 0.096153846, 10: 0.10576923099999999, 11: 0.115384615, 12: 0.125, 13: 0.134615385, 14: 0.144230769, 15: 0.153846154, 16: 0.16346153800000002, 17: 0.173076923, 18: 0.182692308, 19: 0.192307692, 20: 0.201923077, 21: 0.21153846199999998, 22: 0.22115384600000002, 23: 0.23076923100000002, 24: 0.240384615, 25: 0.25, 26: 0.259615385, 27: 0.269230769, 28: 0.278846154, 29: 0.288461538, 30: 0.298076923, 31: 0.307692308, 32: 0.317307692, 33: 0.326923077, 34: 0.33653846200000004, 35: 0.346153846, 36: 0.355769231, 37: 0.365384615, 38: 0.375, 39: 0.384615385, 40: 0.394230769, 41: 0.403846154, 42: 0.41346153799999996, 43: 0.423076923, 44: 0.43269230799999997, 45: 0.44230769200000003, 46: 0.451923077, 47: 0.46153846200000004, 48: 0.471153846, 49: 0.480769231, 50: 0.490384615, 51: 0.5, 52: 0.509615385, 53: 0.519230769, 54: 0.528846154, 55: 0.538461538, 56: 0.548076923, 57: 0.557692308, 58: 0.567307692, 59: 0.576923077, 60: 0.586538462, 61: 0.596153846, 62: 0.605769231, 63: 0.615384615, 64: 0.625, 65: 0.634615385, 66: 0.644230769, 67: 0.653846154, 68: 0.663461538, 69: 0.673076923, 70: 0.682692308, 71: 0.692307692, 72: 0.701923077, 73: 0.711538462, 74: 0.721153846, 75: 0.7307692309999999, 76: 0.740384615, 77: 0.75, 78: 0.759615385, 79: 0.7692307690000001, 80: 0.778846154, 81: 0.788461538, 82: 0.798076923, 83: 0.807692308, 84: 0.817307692, 85: 0.826923077, 86: 0.836538462, 87: 0.846153846, 88: 0.8557692309999999, 89: 0.865384615, 90: 0.875, 91: 0.884615385, 92: 0.8942307690000001, 93: 0.903846154, 94: 0.913461538, 95: 0.923076923, 96: 0.932692308, 97: 0.942307692, 98: 0.951923077, 99: 0.961538462, 100: 0.971153846, 101: 0.9807692309999999, 102: 0.990384615, 103: 1.0}} )

Python - Assigning Multiple Keys for a Single Value

I'd like to be able to assign the following keys to these values in Python:
Numbers 01 - 10 : 5.01
Numbers 11 - 20 : 7.02
Numbers 21 - 30 : 9.03
Numbers 31 - 40 : 11.04
Numbers 41 - 50 : 15.00
Numbers 51 - 60 : 17.08
Numbers 61 - 70 : 19.15
I know that this is possible:
rates = dict.fromkeys(range(1, 11), 5.01)
rates.update(dict.fromkeys(range(11, 21), 7.02)
# ...etc
and that's okay. However, is there a way to do this in one line or one initializer list in Python?

Use a dictionary comprehension and an initial mapping:
numbers = {1: 5.01, 11: 7.02, 21: 9.03, 31: 11.04, 41: 15.0, 51: 71.08, 61: 19.15}
numbers = {k: v for start, v in numbers.items() for k in range(start, start + 10)}
Demo:
>>> from pprint import pprint
>>> numbers = {1: 5.01, 11: 7.02, 21: 9.03, 31: 11.04, 41: 15.0, 51: 71.08, 61: 19.15}
>>> numbers = {k: v for start, v in numbers.items() for k in range(start, start + 10)}
>>> pprint(numbers)
{1: 5.01,
2: 5.01,
3: 5.01,
4: 5.01,
5: 5.01,
6: 5.01,
7: 5.01,
8: 5.01,
9: 5.01,
10: 5.01,
11: 7.02,
12: 7.02,
13: 7.02,
14: 7.02,
15: 7.02,
16: 7.02,
17: 7.02,
18: 7.02,
19: 7.02,
20: 7.02,
21: 9.03,
22: 9.03,
23: 9.03,
24: 9.03,
25: 9.03,
26: 9.03,
27: 9.03,
28: 9.03,
29: 9.03,
30: 9.03,
31: 11.04,
32: 11.04,
33: 11.04,
34: 11.04,
35: 11.04,
36: 11.04,
37: 11.04,
38: 11.04,
39: 11.04,
40: 11.04,
41: 15.0,
42: 15.0,
43: 15.0,
44: 15.0,
45: 15.0,
46: 15.0,
47: 15.0,
48: 15.0,
49: 15.0,
50: 15.0,
51: 71.08,
52: 71.08,
53: 71.08,
54: 71.08,
55: 71.08,
56: 71.08,
57: 71.08,
58: 71.08,
59: 71.08,
60: 71.08,
61: 19.15,
62: 19.15,
63: 19.15,
64: 19.15,
65: 19.15,
66: 19.15,
67: 19.15,
68: 19.15,
69: 19.15,
70: 19.15}
The dictionary expression produces both a key and a value for each iteration of the loops. There are two loops in that expression, and you need to read them from left to right as nested in that order. Written out as a non-comprehension set of loops, you'd get:
numbers = {1: 5.01, 11: 7.02, 21: 9.03, 31: 11.04, 41: 15.0, 51: 71.08, 61: 19.15}
output = {}
# loop over the (key, value) pairs in the numbers dictionary
for start, v in numbers.items():
for k in range(start, start + 10):
output[k] = v
numbers = output
Essentially the keys in the original numbers dictionary are turned into ranges to form 10 new keys in the output dictionary, all with the same value.

save output as text file in networkx

I have printed an output in python shell like:
>>>{1: 117.33282674772036, 2: 119.55324074074075, 3: 116.45497076023392, 4: 113.77561475409836, 5: 112.93896713615024, 6: 114.23583333333333, 7: 124.92402972749794, 8: 121.40603448275863, 9: 116.4946452476573, 10: 112.89107142857142, 11: 122.33312577833125, 12: 116.57083333333334, 13: 122.2856334841629, 14: 125.26688815060908, 15: 129.13817204301074, 16: 128.78991596638656, 17: 127.54600301659126, 18: 133.65972222222223, 19: 127.28315789473685, 20: 125.07205882352942, 21: 124.79464285714286, 22: 131.36170212765958, 23: 130.17974002689377, 24: 138.37055555555557, 25: 132.72380952380954, 26: 138.44230769230768, 27: 134.82251082251082, 28: 147.12448979591838, 29: 149.86879730866275, 30: 145.04521072796936, 31: 143.72442396313363, 32: 148.12940140845072, 33: 140.06355218855219, 34: 145.44537815126051, 35: 146.50366300366301, 36: 146.2173611111111, 37: 152.36319881525361, 38: 156.42249459264599, 39: 154.6977564102564, 40: 155.47647058823529, 41: 158.72357723577235, 42: 162.23746031746032, 43: 149.30991931656382, 44: ........
It represents adjacent neighbors. How can I save this output in a text file in python line-by-line?
like:
1:117.3328268788
2:119.5532822788

Something like this:
with open('some_file.txt', 'w') as f:
for k in sorted(your_dic):
f.write("{}:{}\n".format(k, your_dic[k]))

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