-
Notifications
You must be signed in to change notification settings - Fork 5
/
method_chaining.py
executable file
·152 lines (119 loc) · 3.59 KB
/
method_chaining.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
import pandas as pd
chess_games = pd.read_csv("../input/chess/games.csv")
kepler = pd.read_csv("../input/kepler-exoplanet-search-results/cumulative.csv")
wine_reviews = pd.read_csv("../input/wine-reviews/winemag-data-130k-v2.csv", index_col=0)
ramen_reviews = pd.read_csv("../input/ramen-ratings/ramen-ratings.csv", index_col=0)
def check_q1(ans):
expected = chess_games['winner'].value_counts() / len(chess_games)
return ans.equals(expected)
def answer_q1():
print("""chess_games['winner'].value_counts() / len(chess_games)""")
def check_q2(ans):
expected = (chess_games
.opening_name
.map(lambda n: n.split(":")[0].split("|")[0].split("#")[0].strip())
.value_counts()
)
return ans.equals(expected)
def answer_q2():
print("""(chess_games
.opening_name
.map(lambda n: n.split(":")[0].split("|")[0].split("#")[0].strip())
.value_counts()
)""")
def check_q3(ans):
expected = (chess_games
.assign(n=0)
.groupby(['white_id', 'victory_status'])
.n
.apply(len)
.reset_index()
)
return ans.equals(expected)
def answer_q3():
print("""(chess_games
.assign(n=0)
.groupby(['white_id', 'victory_status'])
.n
.apply(len)
.reset_index()
)""")
def check_q4(ans):
expected = (chess_games
.assign(n=0)
.groupby(['white_id', 'victory_status'])
.n
.apply(len)
.reset_index()
.pipe(lambda df: df.loc[df.white_id.isin(chess_games.white_id.value_counts().head(20).index)])
)
return ans.equals(expected)
def answer_q4():
print("""(chess_games
.assign(n=0)
.groupby(['white_id', 'victory_status'])
.n
.apply(len)
.reset_index()
.pipe(lambda df: df.loc[df.white_id.isin(chess_games.white_id.value_counts().head(20).index)])
)""")
def check_q5(ans):
expected = kepler.assign(n=0).groupby(['koi_pdisposition', 'koi_disposition']).n.count()
return ans.plot.bar() if ans.equals(expected) else False
def answer_q5():
print("""kepler.assign(n=0).groupby(['koi_pdisposition', 'koi_disposition']).n.count()""")
def check_q6(ans):
expected = (((wine_reviews['points'].dropna() - 80) / 4)
.value_counts()
.sort_index()
.rename_axis("Wine Ratings")
)
return ans.plot.bar() if ans.head(10).equals(expected.head(10)) else False
def answer_q6():
print("""(((wine_reviews['points'].dropna() - 80) / 4)
.value_counts()
.sort_index()
.rename_axis("Wine Ratings")
)""")
def check_q7(ans):
expected = (ramen_reviews
.Stars
.replace('Unrated', None)
.dropna()
.astype('float64')
.value_counts()
.rename_axis("Ramen Reviews")
.sort_index())
return ans.plot.bar() if ans.head(10).equals(expected.head(10)) else False
def answer_q7():
print("""(ramen_reviews
.Stars
.replace('Unrated', None)
.dropna()
.astype('float64')
.value_counts()
.rename_axis("Ramen Reviews")
.sort_index())""")
def check_q8(ans):
expected = (ramen_reviews
.Stars
.replace('Unrated', None)
.dropna()
.astype('float64')
.map(lambda v: int(v) if v - int(v) < 0.5 else int(v) + 0.5)
.value_counts()
.rename_axis("Ramen Reviews")
.sort_index()
)
return ans.plot.bar() if ans.head(10).equals(expected.head(10)) else False
def answer_q8():
print("""(ramen_reviews
.Stars
.replace('Unrated', None)
.dropna()
.astype('float64')
.map(lambda v: int(v) if v - int(v) < 0.5 else int(v) + 0.5)
.value_counts()
.rename_axis("Ramen Reviews")
.sort_index()
)""")