In Python Using Seaborn library to load data from the excel file into data frame to reproduce the below image file name data csv Date 1 Mo 2 Mo 3 Mo 6 Mo 1 Yr 2 Yr 3 Yr 5 Yr 7 Yr 10 Yr 20 Yr 30 Yr 01 02 19 2 40 2 40 2 42 2 51 2 60 2 50 2 47 2 49 2 56 2 66 2 83 2 97 01 03 19 2 42 2 42 2 41 2 47 2 50 2 39 2 35 2 37 2 44 2 56 2 75 2 92 01 04 19 2 40 2 42 2 42 2 51 2 57 2 50 2 47 2 49 2 56 2 67 2 83 2 98 01 07 19 2 42 2 42 2 45 2 54 2 58 2 53 2 51 2 53 2 60 2 70 2 86 2 99 01 08 19 2 40 2 42 2 46 2 54 2 60 2 58 2 57 2 58 2 63 2 73 2 88 3 00 01 09 19 2 40 2 42 2 45 2 52 2 59 2 56 2 54 2 57 2 64 2 74 2 90 3 03 01 10 19 2 42 2 42 2 43 2 51 2 59 2 56 2 54 2 56 2 63 2 74 2 92 3 06 01 11 19 2 41 2 43 2 43 2 50 2 58 2 55 2 51 2 52 2 60 2 71 2 90 3 04 01 14 19 2 42 2 43 2 45 2 52 2 57 2 53 2 51 2 53 2 60 2 71 2 91 3 06 01 15 19 2 41 2 43 2 45 2 52 2 57 2 53 2 51 2 53 2 61 2 72 2 92 3 08 01 16 19 2 41 2 40 2 43 2 49 2 57 2 55 2 53 2 54 2 62 2 73 2 92 3 07 01 17 19 2 41 2 41 2 42 2 50 2 57 2 56 2 55 2 58 2 66 2 75 2 93 3 07 01 18 19 2 40 2 40 2 41 2 50 2 60 2 62 2 60 2 62 2 70 2 79 2 95 3 09 01 22 19 2 38 2 40 2 43 2 51 2 59 2 58 2 55 2 57 2 65 2 74 2 91 3 06 01 23 19 2 37 2 38 2 41 2 51 2 59 2 58 2 57 2 59 2 66 2 76 2 93 3 07 01 24 19 2 38 2 41 2 37 2 50 2 58 2 56 2 54 2 55 2 62 2 72 2 89 3 04 01 25 19 2 36 2 41 2 39 2 51 2 60 2 60 2 58 2 59 2 66 2 76 2 92 3 06 01 28 19 2 39 2 41 2 42 2 51 2 60 2 60 2 58 2 58 2 65 2 75 2 92 3 06 01 29 19 2 39 2 41 2 42 2 51 2 60 2 56 2 54 2 55 2 61 2 72 2 90 3 04 01 30 19 2 40 2 39 2 42 2 50 2 57 2 52 2 49 2 49 2 58 2 70 2 90 3 06 01 31 19 2 42 2 43 2 41 2 46 2 55 2 45 2 43 2 43 2 51 2 63 2 83 2 99 02 01 19 2 41 2 42 2 40 2 46 2 56 2 52 2 50 2 51 2 59 2 70 2 88 3 03 02 04 19 2 41 2 41 2 42 2 49 2 57 2 53 2 52 2 53 2 62 2 73 2 92 3 06 02 05 19 2 39 2 40 2 42 2 50 2 56 2 53 2 50 2 51 2 60 2 71 2 89 3 03 02 06 19 2 40 2 41 2 42 2 50 2 56 2 52 2 50 2 50 2 59 2 70 2 88 3 03 02 07 19 2 43 2 43 2 42 2 49 2 55 2 48 2 46 2 46 2 54 2 65 2 85 3 00 02 08 19 2 43 2 43 2 43 2 49 2 54 2 45 2 43 2 44 2 53 2 63 2 82 2 97 02 11 19 2 44 2 44 2 45 2 51 2 55 2 48 2 47 2 47 2 56 2 65 2 85 3 00 02 12 19 2 42 2 43 2 43 2 50 2 55 2 50 2 48 2 49 2 58 2 68 2 87 3 02 02 13 19 2 42 2 42 2 44 2 51 2 55 2 53 2 52 2 53 2 61 2 71 2 89 3 04 02 14 19 2 45 2 43 2 43 2 51 2 53 2 50 2 48 2 48 2 56 2 66 2 85 3 01 02 15 19 2 43 2 43 2 43 2 50 2 55 2 52 2 50 2 49 2 57 2 66 2 84 3 00 02 19 19 2 44 2 42 2 45 2 52 2 54 2 50 2 47 2 47 2 55 2 65 2 84 2 99 02 20 19 2 41 2 42 2 45 2 51 2 54 2 50 2 47 2 47 2 55 2 65 2 84 3 00 02 21 19 2 43 2 44 2 45 2 51 2 55 2 53 2 49 2 51 2 59 2 69 2 89 3 05 02 22 19 2 43 2 45 2 46 2 51 2 55 2 48 2 46 2 47 2 55 2 65 2 86 3 02 02 25 19 2 44 2 46 2 47 2 51 2 56 2 51 2 48 2 48 2 57 2 67 2 87 3 03 02 26 19 2 43 2 44 2 45 2 51 2 55 2 48 2 44 2 45 2 54 2 64 2 84 3 01 02 27 19 2 43 2 44 2 45 2 52 2 54 2 50 2 48 2 49 2 59 2 69 2 91 3 07 Pairplot all short rates (less than one year rate) 24 22 1 Mo 20 100 orino 1 8 24 22 2 Mo 20 Vedeo 1 8 24 22 the strengthens 20 1 8 26 24 22 6 Mo 20 1 8 1 6 1 75 2 50 1 75 2 50 1 75 2 50 25 200 2 25 1 Mo 200 2 25 2 Mo 200 225 3 Mo 20 6 Mo

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In Python Using Seaborn library to load data from the excel file into data frame to reproduce the below image file name: data.csv Date 1

In Python Using Seaborn library to load data from the excel file into data frame to reproduce the below image

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file name: data.csv

Date	1 Mo	2 Mo	3 Mo	6 Mo	1 Yr	2 Yr	3 Yr	5 Yr	7 Yr	10 Yr	20 Yr	30 Yr
01/02/19	2.40	2.40	2.42	2.51	2.60	2.50	2.47	2.49	2.56	2.66	2.83	2.97
01/03/19	2.42	2.42	2.41	2.47	2.50	2.39	2.35	2.37	2.44	2.56	2.75	2.92
01/04/19	2.40	2.42	2.42	2.51	2.57	2.50	2.47	2.49	2.56	2.67	2.83	2.98
01/07/19	2.42	2.42	2.45	2.54	2.58	2.53	2.51	2.53	2.60	2.70	2.86	2.99
01/08/19	2.40	2.42	2.46	2.54	2.60	2.58	2.57	2.58	2.63	2.73	2.88	3.00
01/09/19	2.40	2.42	2.45	2.52	2.59	2.56	2.54	2.57	2.64	2.74	2.90	3.03
01/10/19	2.42	2.42	2.43	2.51	2.59	2.56	2.54	2.56	2.63	2.74	2.92	3.06
01/11/19	2.41	2.43	2.43	2.50	2.58	2.55	2.51	2.52	2.60	2.71	2.90	3.04
01/14/19	2.42	2.43	2.45	2.52	2.57	2.53	2.51	2.53	2.60	2.71	2.91	3.06
01/15/19	2.41	2.43	2.45	2.52	2.57	2.53	2.51	2.53	2.61	2.72	2.92	3.08
01/16/19	2.41	2.40	2.43	2.49	2.57	2.55	2.53	2.54	2.62	2.73	2.92	3.07
01/17/19	2.41	2.41	2.42	2.50	2.57	2.56	2.55	2.58	2.66	2.75	2.93	3.07
01/18/19	2.40	2.40	2.41	2.50	2.60	2.62	2.60	2.62	2.70	2.79	2.95	3.09
01/22/19	2.38	2.40	2.43	2.51	2.59	2.58	2.55	2.57	2.65	2.74	2.91	3.06
01/23/19	2.37	2.38	2.41	2.51	2.59	2.58	2.57	2.59	2.66	2.76	2.93	3.07
01/24/19	2.38	2.41	2.37	2.50	2.58	2.56	2.54	2.55	2.62	2.72	2.89	3.04
01/25/19	2.36	2.41	2.39	2.51	2.60	2.60	2.58	2.59	2.66	2.76	2.92	3.06
01/28/19	2.39	2.41	2.42	2.51	2.60	2.60	2.58	2.58	2.65	2.75	2.92	3.06
01/29/19	2.39	2.41	2.42	2.51	2.60	2.56	2.54	2.55	2.61	2.72	2.90	3.04
01/30/19	2.40	2.39	2.42	2.50	2.57	2.52	2.49	2.49	2.58	2.70	2.90	3.06
01/31/19	2.42	2.43	2.41	2.46	2.55	2.45	2.43	2.43	2.51	2.63	2.83	2.99
02/01/19	2.41	2.42	2.40	2.46	2.56	2.52	2.50	2.51	2.59	2.70	2.88	3.03
02/04/19	2.41	2.41	2.42	2.49	2.57	2.53	2.52	2.53	2.62	2.73	2.92	3.06
02/05/19	2.39	2.40	2.42	2.50	2.56	2.53	2.50	2.51	2.60	2.71	2.89	3.03
02/06/19	2.40	2.41	2.42	2.50	2.56	2.52	2.50	2.50	2.59	2.70	2.88	3.03
02/07/19	2.43	2.43	2.42	2.49	2.55	2.48	2.46	2.46	2.54	2.65	2.85	3.00
02/08/19	2.43	2.43	2.43	2.49	2.54	2.45	2.43	2.44	2.53	2.63	2.82	2.97
02/11/19	2.44	2.44	2.45	2.51	2.55	2.48	2.47	2.47	2.56	2.65	2.85	3.00
02/12/19	2.42	2.43	2.43	2.50	2.55	2.50	2.48	2.49	2.58	2.68	2.87	3.02
02/13/19	2.42	2.42	2.44	2.51	2.55	2.53	2.52	2.53	2.61	2.71	2.89	3.04
02/14/19	2.45	2.43	2.43	2.51	2.53	2.50	2.48	2.48	2.56	2.66	2.85	3.01
02/15/19	2.43	2.43	2.43	2.50	2.55	2.52	2.50	2.49	2.57	2.66	2.84	3.00
02/19/19	2.44	2.42	2.45	2.52	2.54	2.50	2.47	2.47	2.55	2.65	2.84	2.99
02/20/19	2.41	2.42	2.45	2.51	2.54	2.50	2.47	2.47	2.55	2.65	2.84	3.00
02/21/19	2.43	2.44	2.45	2.51	2.55	2.53	2.49	2.51	2.59	2.69	2.89	3.05
02/22/19	2.43	2.45	2.46	2.51	2.55	2.48	2.46	2.47	2.55	2.65	2.86	3.02
02/25/19	2.44	2.46	2.47	2.51	2.56	2.51	2.48	2.48	2.57	2.67	2.87	3.03
02/26/19	2.43	2.44	2.45	2.51	2.55	2.48	2.44	2.45	2.54	2.64	2.84	3.01
02/27/19	2.43	2.44	2.45	2.52	2.54	2.50	2.48	2.49	2.59	2.69	2.91	3.07

Pairplot all short rates (less than one year rate) 24 22 1 Mo 20 100-orino *** 1.8 24 22 2 Mo 20 Vedeo!. 1.8 24 22 the strengthens 20 1.8 26 24 22 6 Mo 20 1.8 1.6 1.75 2.50 1.75 2.50 1.75 2.50 25 200 2.25 1 Mo 200 2.25 2 Mo 200 225 3 Mo 20 6 Mo