File size: 7,491 Bytes
a64569a |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 |
import pandas as pd
class GsmAnalysis:
hf_rate_coef = {
10: 1.1,
20: 1.2,
40: 1.4,
60: 1.6,
70: 1.7,
80: 1.8,
99: 2.0,
100: 1.4,
}
erlangB_table = {
1: 0.0204,
2: 0.2234,
3: 0.6022,
4: 1.092,
5: 1.657,
6: 2.276,
7: 2.935,
8: 3.627,
9: 4.345,
10: 5.084,
11: 5.841,
12: 6.614,
13: 7.401,
14: 8.2,
15: 9.009,
16: 9.828,
17: 10.66,
18: 11.49,
19: 12.33,
20: 13.18,
21: 14.04,
22: 14.9,
23: 15.76,
24: 16.63,
25: 17.5,
26: 18.38,
27: 19.26,
28: 20.15,
29: 21.04,
30: 21.93,
31: 22.83,
32: 23.72,
33: 24.63,
34: 25.53,
35: 26.43,
36: 27.34,
37: 28.25,
38: 29.17,
39: 30.08,
40: 31,
41: 31.91,
42: 32.84,
43: 33.76,
44: 34.68,
45: 35.61,
46: 36.53,
47: 37.46,
48: 38.39,
49: 39.32,
50: 40.25,
51: 41.19,
52: 42.12,
53: 43.06,
54: 44,
55: 44.93,
56: 45.88,
57: 46.81,
58: 47.75,
59: 48.7,
60: 49.64,
61: 50.59,
62: 51.53,
63: 52.48,
64: 53.43,
65: 54.38,
66: 55.32,
67: 56.27,
68: 57.22,
69: 58.18,
70: 59.13,
71: 60.08,
72: 61.04,
73: 61.99,
74: 62.94,
75: 63.9,
76: 64.86,
77: 65.81,
78: 66.77,
79: 67.73,
80: 68.69,
81: 69.64,
82: 70.61,
83: 71.57,
84: 72.53,
85: 73.49,
86: 74.45,
87: 75.41,
88: 76.38,
89: 77.34,
90: 78.3,
91: 79.27,
92: 80.23,
93: 81.2,
94: 82.16,
95: 83.13,
96: 84.09,
97: 85.06,
98: 86.03,
99: 87,
100: 87.97,
101: 88.94,
102: 89.91,
103: 90.88,
104: 91.85,
105: 92.82,
106: 93.79,
107: 94.76,
108: 95.73,
109: 96.71,
110: 97.68,
111: 98.65,
112: 99.63,
113: 100.6,
114: 101.57,
115: 102.54,
116: 103.52,
117: 104.49,
118: 105.47,
119: 106.44,
120: 107.42,
121: 108.4,
122: 109.37,
123: 110.35,
124: 111.32,
125: 112.3,
126: 113.28,
127: 114.25,
128: 115.23,
129: 116.21,
130: 117.19,
131: 118.17,
132: 119.15,
133: 120.12,
134: 121.1,
135: 122.08,
136: 123.07,
137: 124.04,
138: 125.02,
139: 126.01341,
140: 127.00918,
141: 127.96752,
142: 128.98152,
143: 129.92152,
144: 130.88534,
145: 131.96461,
146: 132.89897,
147: 133.86373,
148: 134.82569,
149: 135.76295,
150: 136.82988,
151: 137.79,
152: 138.77,
153: 139.75,
154: 140.74,
155: 141.72,
156: 142.7,
157: 143.69,
158: 144.67,
159: 145.66,
160: 146.64,
161: 147.63,
162: 148.61,
163: 149.6,
164: 150.58,
165: 151.57,
166: 152.55,
167: 153.54,
168: 154.53,
169: 155.51,
170: 156.5,
171: 157.48,
172: 158.47,
173: 159.46,
174: 160.44,
175: 161.43,
176: 162.42,
177: 163.41,
178: 164.39,
179: 165.38,
180: 166.37,
181: 167.36,
182: 168.35,
183: 169.33,
184: 170.32,
185: 171.31,
186: 172.3,
187: 173.29,
188: 174.28,
189: 175.27,
190: 176.26,
191: 177.25,
192: 178.24,
193: 179.23,
194: 180.22,
195: 181.21,
196: 182.2,
197: 183.19,
198: 184.18,
199: 185.17,
200: 186.16,
}
def kpi_naming_cleaning(df: pd.DataFrame) -> pd.DataFrame:
"""
Clean KPI column names by replacing special characters and standardizing format.
Args:
df: DataFrame with KPI column names to clean
Returns:
DataFrame with cleaned column names
"""
name_df = df.copy()
name_df.columns = name_df.columns.str.replace("[ /(),-.']", "_", regex=True)
name_df.columns = name_df.columns.str.replace("___", "_")
name_df.columns = name_df.columns.str.replace("__", "_")
name_df.columns = name_df.columns.str.replace("%", "perc")
name_df.columns = name_df.columns.str.rstrip("_")
return name_df
def create_daily_date(df: pd.DataFrame) -> pd.DataFrame:
"""
Create a daily date column from PERIOD_START_TIME and drop unnecessary columns.
Args:
df: DataFrame containing PERIOD_START_TIME column
Returns:
DataFrame with new date column and unnecessary columns removed
"""
date_df = df.copy()
date_df[["mois", "jour", "annee"]] = date_df["PERIOD_START_TIME"].str.split(
".", expand=True
)
date_df["date"] = date_df["annee"] + "-" + date_df["mois"] + "-" + date_df["jour"]
# Remove unnecessary columns
date_df = date_df.drop(["annee", "mois", "jour", "PERIOD_START_TIME"], axis=1)
return date_df
def create_hourly_date(df: pd.DataFrame):
date_df = df
date_df[["date_t", "hour"]] = date_df["PERIOD_START_TIME"].str.split(
" ", expand=True
)
date_df[["mois", "jour", "annee"]] = date_df["date_t"].str.split(".", expand=True)
date_df["datetime"] = (
date_df["annee"]
+ "-"
+ date_df["mois"]
+ "-"
+ date_df["jour"]
+ " "
+ date_df["hour"]
)
date_df["date"] = date_df["annee"] + "-" + date_df["mois"] + "-" + date_df["jour"]
# Remove columns 'année' and 'mois'
date_df = date_df.drop(
["annee", "mois", "jour", "date_t", "PERIOD_START_TIME"], axis=1
)
return date_df
def create_dfs_per_kpi(
df: pd.DataFrame = None,
pivot_date_column: str = "date",
pivot_name_column: str = "BTS_name",
kpi_columns_from: int = None,
) -> pd.DataFrame:
"""
Create pivoted DataFrames for each KPI and perform analysis.
Args:
df: DataFrame containing KPI data
Returns:
DataFrame with combined analysis results
"""
kpi_columns = df.columns[kpi_columns_from:]
# print(kpi_columns)
pivoted_kpi_dfs = {}
# Loop through each KPI and create pivoted DataFrames
for kpi in kpi_columns:
temp_df = df[[pivot_date_column, pivot_name_column, kpi]].copy()
# remove duplicates
temp_df = temp_df.drop_duplicates(
subset=[pivot_name_column, pivot_date_column], keep="first"
)
temp_df = temp_df.reset_index()
# Pivot the dataframe
pivot_df = temp_df.pivot(
index=pivot_name_column, columns=pivot_date_column, values=kpi
)
# print(pivot_df)
pivot_df.columns = pd.MultiIndex.from_product([[kpi], pivot_df.columns])
pivot_df.columns.names = ["KPI", "Date"]
# Store in dictionary with KPI name as key
pivoted_kpi_dfs[kpi] = pivot_df
return pivoted_kpi_dfs
|