I am using the following code:
tmp = (merged_df_2014.groupby(['CRG', pd.to_datetime(merged_df_2014['yearmonth'], format='%Y%m')])
['credit_application'].mean().reset_index(name='probability of application')
)
ax = sns.lineplot(data=tmp, x='yearmonth', y='probability of application', hue='CRG')
ax.tick_params(axis='x', rotation=45)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
on the following data (tmp.to_dict()):
{'CRG': {0: 0.0,
1: 0.0,
2: 0.0,
3: 0.0,
4: 0.0,
5: 0.0,
6: 0.0,
7: 0.0,
8: 0.0,
9: 0.0,
10: 0.0,
11: 0.0,
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: 1.0,
21: 1.0,
22: 1.0,
23: 1.0,
24: 2.0,
25: 2.0,
26: 2.0,
27: 2.0,
28: 2.0,
29: 2.0,
30: 2.0,
31: 2.0,
32: 2.0,
33: 2.0,
34: 2.0,
35: 2.0,
36: 3.0,
37: 3.0,
38: 3.0,
39: 3.0,
40: 3.0,
41: 3.0,
42: 3.0,
43: 3.0,
44: 3.0,
45: 3.0,
46: 3.0,
47: 3.0,
48: 4.0,
49: 4.0,
50: 4.0,
51: 4.0,
52: 4.0,
53: 4.0,
54: 4.0,
55: 4.0,
56: 4.0,
57: 4.0,
58: 4.0,
59: 4.0,
60: 5.0,
61: 5.0,
62: 5.0,
63: 5.0,
64: 5.0,
65: 5.0,
66: 5.0,
67: 5.0,
68: 5.0,
69: 5.0,
70: 5.0,
71: 5.0,
72: 7.0,
73: 7.0,
74: 7.0,
75: 7.0,
76: 7.0,
77: 7.0,
78: 7.0,
79: 7.0,
80: 7.0,
81: 7.0,
82: 7.0,
83: 7.0},
'yearmonth': {0: Timestamp('2014-01-01 00:00:00'),
1: Timestamp('2014-02-01 00:00:00'),
2: Timestamp('2014-03-01 00:00:00'),
3: Timestamp('2014-04-01 00:00:00'),
4: Timestamp('2014-05-01 00:00:00'),
5: Timestamp('2014-06-01 00:00:00'),
6: Timestamp('2014-07-01 00:00:00'),
7: Timestamp('2014-08-01 00:00:00'),
8: Timestamp('2014-09-01 00:00:00'),
9: Timestamp('2014-10-01 00:00:00'),
10: Timestamp('2014-11-01 00:00:00'),
11: Timestamp('2014-12-01 00:00:00'),
12: Timestamp('2014-01-01 00:00:00'),
13: Timestamp('2014-02-01 00:00:00'),
14: Timestamp('2014-03-01 00:00:00'),
15: Timestamp('2014-04-01 00:00:00'),
16: Timestamp('2014-05-01 00:00:00'),
17: Timestamp('2014-06-01 00:00:00'),
18: Timestamp('2014-07-01 00:00:00'),
19: Timestamp('2014-08-01 00:00:00'),
20: Timestamp('2014-09-01 00:00:00'),
21: Timestamp('2014-10-01 00:00:00'),
22: Timestamp('2014-11-01 00:00:00'),
23: Timestamp('2014-12-01 00:00:00'),
24: Timestamp('2014-01-01 00:00:00'),
25: Timestamp('2014-02-01 00:00:00'),
26: Timestamp('2014-03-01 00:00:00'),
27: Timestamp('2014-04-01 00:00:00'),
28: Timestamp('2014-05-01 00:00:00'),
29: Timestamp('2014-06-01 00:00:00'),
30: Timestamp('2014-07-01 00:00:00'),
31: Timestamp('2014-08-01 00:00:00'),
32: Timestamp('2014-09-01 00:00:00'),
33: Timestamp('2014-10-01 00:00:00'),
34: Timestamp('2014-11-01 00:00:00'),
35: Timestamp('2014-12-01 00:00:00'),
36: Timestamp('2014-01-01 00:00:00'),
37: Timestamp('2014-02-01 00:00:00'),
38: Timestamp('2014-03-01 00:00:00'),
39: Timestamp('2014-04-01 00:00:00'),
40: Timestamp('2014-05-01 00:00:00'),
41: Timestamp('2014-06-01 00:00:00'),
42: Timestamp('2014-07-01 00:00:00'),
43: Timestamp('2014-08-01 00:00:00'),
44: Timestamp('2014-09-01 00:00:00'),
45: Timestamp('2014-10-01 00:00:00'),
46: Timestamp('2014-11-01 00:00:00'),
47: Timestamp('2014-12-01 00:00:00'),
48: Timestamp('2014-01-01 00:00:00'),
49: Timestamp('2014-02-01 00:00:00'),
50: Timestamp('2014-03-01 00:00:00'),
51: Timestamp('2014-04-01 00:00:00'),
52: Timestamp('2014-05-01 00:00:00'),
53: Timestamp('2014-06-01 00:00:00'),
54: Timestamp('2014-07-01 00:00:00'),
55: Timestamp('2014-08-01 00:00:00'),
56: Timestamp('2014-09-01 00:00:00'),
57: Timestamp('2014-10-01 00:00:00'),
58: Timestamp('2014-11-01 00:00:00'),
59: Timestamp('2014-12-01 00:00:00'),
60: Timestamp('2014-01-01 00:00:00'),
61: Timestamp('2014-02-01 00:00:00'),
62: Timestamp('2014-03-01 00:00:00'),
63: Timestamp('2014-04-01 00:00:00'),
64: Timestamp('2014-05-01 00:00:00'),
65: Timestamp('2014-06-01 00:00:00'),
66: Timestamp('2014-07-01 00:00:00'),
67: Timestamp('2014-08-01 00:00:00'),
68: Timestamp('2014-09-01 00:00:00'),
69: Timestamp('2014-10-01 00:00:00'),
70: Timestamp('2014-11-01 00:00:00'),
71: Timestamp('2014-12-01 00:00:00'),
72: Timestamp('2014-01-01 00:00:00'),
73: Timestamp('2014-02-01 00:00:00'),
74: Timestamp('2014-03-01 00:00:00'),
75: Timestamp('2014-04-01 00:00:00'),
76: Timestamp('2014-05-01 00:00:00'),
77: Timestamp('2014-06-01 00:00:00'),
78: Timestamp('2014-07-01 00:00:00'),
79: Timestamp('2014-08-01 00:00:00'),
80: Timestamp('2014-09-01 00:00:00'),
81: Timestamp('2014-10-01 00:00:00'),
82: Timestamp('2014-11-01 00:00:00'),
83: Timestamp('2014-12-01 00:00:00')},
'probability of application': {0: 0.029411764705882353,
1: 0.029411764705882353,
2: 0.0058823529411764705,
3: 0.03488372093023256,
4: 0.029411764705882353,
5: 0.01764705882352941,
6: 0.028901734104046242,
7: 0.023529411764705882,
8: 0.047337278106508875,
9: 0.04046242774566474,
10: 0.04093567251461988,
11: 0.04142011834319527,
12: 0.03676470588235294,
13: 0.03676470588235294,
14: 0.051470588235294115,
15: 0.014705882352941176,
16: 0.058823529411764705,
17: 0.029197080291970802,
18: 0.021897810218978103,
19: 0.0364963503649635,
20: 0.021897810218978103,
21: 0.043795620437956206,
22: 0.0364963503649635,
23: 0.021897810218978103,
24: 0.08870967741935484,
25: 0.04032258064516129,
26: 0.07258064516129033,
27: 0.0967741935483871,
28: 0.07258064516129033,
29: 0.08064516129032258,
30: 0.056451612903225805,
31: 0.072,
32: 0.06349206349206349,
33: 0.03937007874015748,
34: 0.06299212598425197,
35: 0.15625,
36: 0.050359712230215826,
37: 0.05755395683453238,
38: 0.07553956834532374,
39: 0.06115107913669065,
40: 0.07885304659498207,
41: 0.06785714285714285,
42: 0.07857142857142857,
43: 0.06382978723404255,
44: 0.09574468085106383,
45: 0.06028368794326241,
46: 0.0711743772241993,
47: 0.06761565836298933,
48: 0.08955223880597014,
49: 0.11940298507462686,
50: 0.08955223880597014,
51: 0.014925373134328358,
52: 0.16417910447761194,
53: 0.1791044776119403,
54: 0.13432835820895522,
55: 0.04477611940298507,
56: 0.13432835820895522,
57: 0.05970149253731343,
58: 0.1044776119402985,
59: 0.08955223880597014,
60: 0.16666666666666666,
61: 0.08333333333333333,
62: 0.125,
63: 0.20833333333333334,
64: 0.041666666666666664,
65: 0.125,
66: 0.2916666666666667,
67: 0.125,
68: 0.16666666666666666,
69: 0.16666666666666666,
70: 0.16666666666666666,
71: 0.16666666666666666,
72: 0.0916030534351145,
73: 0.11363636363636363,
74: 0.06060606060606061,
75: 0.07633587786259542,
76: 0.10606060606060606,
77: 0.1076923076923077,
78: 0.1297709923664122,
79: 0.1,
80: 0.15384615384615385,
81: 0.11450381679389313,
82: 0.10687022900763359,
83: 0.09448818897637795}}
and I am getting this plot:
As you can see, the legend is not displaying all 7 values of the categorical variable "CRG" (0,1,2,3,4,5,7). Instead, it shows only 5 of them and somehow "averages" the labels creating non-existing values such as "1.5" and "4.5"
How can I fix this?
