feat(商务大数据分析): 五一前作业更新
- 新增 aqi.csv 文件，包含 2020 年 1 月 1 日至 9 月 27 日的空气质量数据- 数据包括日期、AQI、质量等级以及 PM2.5、PM10、SO2、CO、NO2、O3等污染物含量
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task2.py
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task2.py
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import time
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from flask import Flask
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import redis
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app = Flask(__name__)
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cache = redis.Redis(host='redis', port=6379)
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def get_hit_count():
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retries = 5
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while True:
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try:
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return cache.incr('hits')
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except redis.exceptions.ConnectionError as exc:
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if retries == 0:
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raise exc
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retries -= 1
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time.sleep(0.5)
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@app.route('/')
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def hello():
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count = get_hit_count()
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return 'Hello World! I have been seen {} times.\n'.format(count)
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商务大数据分析/20250430/data/2001-2019年劳动力与就业人员数据.npz
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商务大数据分析/20250430/data/2001-2019年劳动力与就业人员数据.npz
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商务大数据分析/20250430/data/aqi.csv
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商务大数据分析/20250430/data/aqi.csv
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日期,AQI,质量等级,PM2.5含量（ppm）,PM10含量（ppm）,SO2含量（ppm）,CO含量（ppm）,NO2含量（ppm）,O3_8h含量（ppm）
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2020/1/1,79,良,58,64,8,0.7,57,23
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2020/1/2,112,轻度污染,84,73,10,1.0,71,7
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2020/1/3,68,良,49,51,7,0.8,49,3
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2020/1/4,90,良,67,57,7,1.2,53,18
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2020/1/5,110,轻度污染,83,65,7,1.0,51,46
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2020/1/6,65,良,47,58,6,1.0,43,6
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2020/1/7,50,优,18,19,5,1.5,40,43
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2020/1/8,69,良,50,49,7,0.9,39,45
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2020/1/9,69,良,50,40,6,0.9,47,33
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2020/1/10,57,良,34,28,5,0.8,45,21
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2020/1/11,47,优,27,21,6,0.7,37,39
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2020/1/12,125,轻度污染,95,74,8,1.0,44,71
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2020/1/13,148,轻度污染,113,94,9,1.3,59,53
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2020/1/14,172,中度污染,130,114,12,1.4,62,65
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2020/1/15,113,轻度污染,85,62,8,1.2,50,32
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2020/1/16,55,良,16,14,5,0.8,44,38
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2020/1/17,82,良,60,43,6,0.9,42,25
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2020/1/18,88,良,65,55,6,0.8,51,31
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2020/1/19,112,轻度污染,84,77,10,1.0,53,77
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2020/1/20,127,轻度污染,96,99,14,1.2,70,90
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2020/1/21,203,重度污染,153,113,9,1.3,43,60
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2020/1/22,148,轻度污染,113,77,6,1.2,43,30
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2020/1/23,58,良,41,37,5,0.6,26,42
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2020/1/24,79,良,58,57,6,0.7,18,78
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2020/1/25,40,优,26,40,5,0.5,17,61
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2020/1/26,29,优,20,17,5,0.5,21,56
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2020/1/27,70,良,51,41,5,0.8,21,57
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2020/1/28,95,良,71,65,6,0.7,19,70
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2020/1/29,84,良,62,60,10,0.7,25,77
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2020/1/30,128,轻度污染,97,85,9,1.0,26,103
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2020/1/31,125,轻度污染,95,82,10,1.0,43,94
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2020/2/1,75,良,55,49,7,0.7,21,102
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2020/2/2,42,优,28,30,8,0.5,26,84
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2020/2/3,109,轻度污染,82,68,9,0.9,30,109
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2020/2/4,92,良,68,60,7,0.8,22,103
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2020/2/5,65,良,47,44,7,0.8,19,106
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2020/2/6,38,优,22,25,5,0.6,11,75
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2020/2/7,33,优,14,16,5,0.6,12,66
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2020/2/8,62,良,44,40,5,0.6,22,45
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2020/2/9,53,良,37,34,7,0.6,25,79
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2020/2/10,48,优,33,36,8,0.7,32,69
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2020/2/11,35,优,24,26,6,0.7,20,47
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2020/2/12,43,优,30,34,5,0.9,23,56
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2020/2/13,22,优,15,18,5,0.8,15,30
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2020/2/14,23,优,14,17,5,0.8,18,32
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2020/2/15,27,优,8,10,5,0.7,14,53
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2020/2/16,42,优,21,31,7,0.5,11,83
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2020/2/17,45,优,19,33,9,0.5,18,90
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2020/2/18,49,优,27,32,8,0.6,28,97
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2020/2/19,55,良,39,37,7,0.6,25,76
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2020/2/20,64,良,46,43,8,0.7,25,111
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2020/2/21,62,良,44,41,9,0.6,31,66
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2020/2/22,83,良,61,89,10,0.9,42,94
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2020/2/23,92,良,68,107,7,0.8,25,109
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2020/2/24,85,良,63,87,8,1.0,44,84
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2020/2/25,95,良,71,79,6,1.2,44,42
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2020/2/26,50,优,35,43,4,0.7,19,87
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2020/2/27,70,良,51,68,4,0.8,19,93
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2020/2/28,38,优,26,25,4,0.7,28,59
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2020/2/29,40,优,28,21,4,0.8,29,41
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2020/3/1,50,优,35,29,5,0.7,27,58
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2020/3/2,54,良,38,37,5,0.8,22,87
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2020/3/3,72,良,52,49,8,1.0,44,64
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2020/3/4,85,良,63,58,6,0.9,33,104
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2020/3/5,56,良,31,41,8,0.7,24,107
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2020/3/6,64,良,46,43,7,0.6,35,84
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2020/3/7,67,良,48,53,6,0.9,43,75
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2020/3/8,68,良,49,52,7,0.8,29,105
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2020/3/9,37,优,19,25,5,0.7,29,47
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2020/3/10,62,良,44,38,5,1.0,32,74
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2020/3/11,67,良,48,68,7,1.0,39,113
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2020/3/12,68,良,49,68,7,0.8,44,95
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2020/3/13,67,良,48,70,7,0.9,45,46
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2020/3/14,52,良,24,53,7,0.5,40,96
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2020/3/15,77,良,38,86,12,0.8,61,109
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2020/3/16,70,良,51,80,10,0.7,47,108
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2020/3/17,74,良,43,77,10,0.7,59,122
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2020/3/18,70,良,46,79,16,1.1,56,123
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2020/3/19,77,良,35,104,14,0.9,47,78
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2020/3/20,68,良,37,86,12,0.7,53,112
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2020/3/21,67,良,42,67,10,0.9,53,87
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2020/3/22,55,良,31,60,6,0.8,33,102
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2020/3/23,42,优,19,41,8,0.5,33,83
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2020/3/24,65,良,26,43,9,0.5,33,118
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2020/3/25,63,良,30,48,9,0.8,50,66
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2020/3/26,57,良,27,35,6,0.9,45,24
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2020/3/27,34,优,12,27,6,0.6,27,62
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2020/3/28,39,优,11,24,5,0.4,20,78
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2020/3/29,35,优,22,27,6,0.7,28,57
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2020/3/30,38,优,26,24,5,0.7,29,60
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2020/3/31,48,优,28,34,5,0.7,38,40
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2020/4/1,45,优,23,28,5,0.7,36,90
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2020/4/2,54,良,30,57,7,0.7,39,101
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2020/4/3,67,良,45,63,8,0.8,53,109
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2020/4/4,93,良,60,87,11,1.1,74,125
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2020/4/5,56,良,20,61,7,0.6,27,94
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2020/4/6,65,良,31,50,8,0.6,36,117
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2020/4/7,55,良,30,50,9,0.6,43,105
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2020/4/8,62,良,29,55,9,0.5,45,114
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2020/4/9,85,良,42,68,10,0.6,43,142
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2020/4/10,70,良,43,65,8,0.7,32,124
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2020/4/11,37,优,23,25,5,0.7,27,73
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2020/4/12,70,良,31,52,7,0.6,26,123
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2020/4/13,85,良,39,69,9,0.6,54,141
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2020/4/14,81,良,42,64,10,0.7,49,137
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2020/4/15,75,良,30,64,19,1.1,60,116
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2020/4/16,75,良,33,71,12,0.8,60,111
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2020/4/17,79,良,53,94,17,0.9,63,75
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2020/4/18,66,良,47,82,9,0.8,41,112
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2020/4/19,51,良,19,24,5,0.6,31,101
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2020/4/20,65,良,25,37,9,0.8,52,110
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2020/4/21,45,优,23,40,7,0.5,34,89
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2020/4/22,63,良,29,45,7,0.4,41,115
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2020/4/23,70,良,40,68,8,0.5,54,124
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2020/4/24,85,良,30,69,15,0.7,68,130
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2020/4/25,73,良,26,70,22,1.0,58,120
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2020/4/26,77,良,36,76,14,0.8,47,132
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2020/4/27,51,良,25,49,7,0.6,28,101
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2020/4/28,81,良,25,45,7,0.5,34,137
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2020/4/29,112,轻度污染,34,69,11,0.6,43,173
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2020/4/30,90,良,43,80,12,0.8,55,148
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2020/5/1,54,良,38,53,8,0.9,34,88
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2020/5/2,80,良,39,60,13,1.2,35,136
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2020/5/3,90,良,44,72,22,1.5,52,147
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2020/5/4,132,轻度污染,43,69,18,1.4,40,195
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2020/5/5,49,优,34,49,6,1.0,36,46
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2020/5/6,45,优,29,44,5,0.8,29,89
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2020/5/7,72,良,28,50,6,0.7,24,126
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2020/5/8,40,优,24,40,6,0.8,26,44
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2020/5/9,36,优,25,27,5,0.6,21,72
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2020/5/10,55,良,27,32,6,0.9,43,105
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2020/5/11,87,良,23,55,8,0.8,44,144
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2020/5/12,77,良,15,61,12,0.6,46,132
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2020/5/13,133,轻度污染,31,79,10,0.6,37,196
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2020/5/14,58,良,35,52,7,0.8,29,109
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2020/5/15,44,优,18,26,8,0.9,33,87
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2020/5/16,52,良,22,35,11,1.0,41,100
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2020/5/17,83,良,24,45,14,1.2,36,139
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2020/5/18,60,良,19,64,8,0.7,30,112
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2020/5/19,92,良,29,70,15,0.9,52,150
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2020/5/20,127,轻度污染,35,71,13,0.7,35,189
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2020/5/21,105,轻度污染,23,53,10,0.6,23,165
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2020/5/22,75,良,16,31,6,0.6,16,130
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2020/5/23,85,良,21,41,7,0.6,23,142
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2020/5/24,130,轻度污染,39,70,12,0.9,35,193
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2020/5/25,57,良,40,57,10,0.9,31,103
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2020/5/26,75,良,26,35,6,0.9,25,129
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2020/5/27,109,轻度污染,26,49,9,1.0,34,169
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2020/5/28,99,良,41,66,14,1.2,44,158
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2020/5/29,51,良,33,51,10,0.7,34,99
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2020/5/30,46,优,18,25,5,0.6,18,91
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2020/5/31,80,良,25,49,9,0.9,33,135
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2020/6/1,116,轻度污染,34,62,7,0.8,31,177
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2020/6/2,65,良,23,43,9,0.9,34,117
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2020/6/3,48,优,14,27,7,0.8,24,95
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2020/6/4,47,优,12,28,8,0.8,22,94
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2020/6/5,36,优,10,17,5,0.7,25,71
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2020/6/6,85,良,15,26,6,0.7,19,142
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2020/6/7,80,良,16,37,7,0.5,17,135
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2020/6/8,57,良,28,44,7,0.5,14,108
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2020/6/9,60,良,18,31,6,0.6,15,112
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2020/6/10,45,优,22,36,7,0.9,36,82
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2020/6/11,68,良,33,48,6,1.0,36,121
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2020/6/12,39,优,9,28,6,0.8,27,77
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2020/6/13,53,良,11,23,6,0.8,35,103
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2020/6/14,39,优,12,23,6,0.7,27,78
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2020/6/15,44,优,7,19,6,0.7,35,46
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2020/6/16,50,优,20,29,5,0.8,22,99
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2020/6/17,46,优,17,43,7,0.8,26,91
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2020/6/18,34,优,16,25,5,0.7,23,67
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2020/6/19,60,良,13,25,6,0.8,28,111
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2020/6/20,48,优,13,22,6,0.7,19,95
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2020/6/21,41,优,5,10,5,0.6,20,82
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2020/6/22,34,优,8,10,5,0.6,20,67
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2020/6/23,35,优,9,16,5,0.9,28,40
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2020/6/24,48,优,18,34,8,0.9,34,96
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2020/6/25,71,良,26,41,8,1.0,28,125
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2020/6/26,45,优,13,28,6,0.6,18,89
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2020/6/27,35,优,12,21,5,0.7,28,63
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2020/6/28,47,优,11,18,5,0.7,14,93
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2020/6/29,41,优,8,18,6,0.7,19,81
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2020/6/30,83,良,16,28,6,0.7,26,139
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2020/7/1,126,轻度污染,33,55,6,0.7,18,188
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2020/7/2,47,优,18,35,6,0.6,18,94
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2020/7/3,46,优,10,21,5,0.6,19,91
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2020/7/4,60,良,20,34,6,0.7,18,112
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2020/7/5,35,优,11,21,6,0.7,28,45
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2020/7/6,35,优,8,13,5,0.7,28,46
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2020/7/7,55,良,14,25,9,1.1,36,105
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2020/7/8,80,良,29,43,8,1.3,26,136
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2020/7/9,50,优,22,39,5,0.8,17,99
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2020/7/10,70,良,19,31,5,0.6,12,124
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2020/7/11,30,优,13,23,5,0.7,17,59
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2020/7/12,42,优,15,41,9,0.8,33,64
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2020/7/13,29,优,11,15,5,0.6,23,58
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2020/7/14,46,优,14,25,6,0.7,21,91
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2020/7/15,46,优,7,13,5,0.7,21,91
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2020/7/16,39,优,7,12,5,0.6,21,78
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2020/7/17,32,优,15,24,6,0.6,22,63
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2020/7/18,49,优,18,32,6,0.8,39,20
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2020/7/19,37,优,13,19,6,1.0,29,67
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2020/7/20,49,优,21,30,7,0.7,25,98
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2020/7/21,48,优,20,32,6,0.7,26,96
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2020/7/22,55,良,14,37,6,0.6,21,105
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2020/7/23,38,优,10,27,7,0.6,22,76
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2020/7/24,44,优,12,19,5,0.7,35,75
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2020/7/25,58,良,13,31,7,0.7,39,109
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2020/7/26,70,良,26,44,7,0.7,26,124
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2020/7/27,53,良,22,39,7,1.1,42,44
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2020/7/28,33,优,11,23,6,0.7,26,64
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2020/7/29,56,良,28,48,6,0.9,24,107
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2020/7/30,81,良,24,45,7,0.9,25,137
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2020/7/31,50,优,9,33,7,0.5,24,99
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2020/8/1,99,良,15,33,8,0.6,23,158
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2020/8/2,65,良,25,47,7,0.9,40,117
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2020/8/3,55,良,18,38,6,0.7,23,106
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2020/8/4,48,优,9,30,6,0.6,20,95
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2020/8/5,60,良,23,51,7,1.0,41,111
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2020/8/6,36,优,8,27,6,0.7,20,72
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2020/8/7,36,优,10,27,6,0.6,19,71
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2020/8/8,37,优,16,36,7,0.6,24,73
|
||||
2020/8/9,35,优,18,33,7,0.8,28,66
|
||||
2020/8/10,30,优,11,20,7,0.8,24,53
|
||||
2020/8/11,44,优,15,27,6,0.7,20,88
|
||||
2020/8/12,27,优,8,23,6,0.6,16,53
|
||||
2020/8/13,40,优,11,26,7,0.7,24,80
|
||||
2020/8/14,46,优,10,28,7,0.6,21,91
|
||||
2020/8/15,75,良,15,34,7,0.6,28,129
|
||||
2020/8/16,96,良,19,43,8,0.8,29,155
|
||||
2020/8/17,91,良,21,45,8,0.7,23,149
|
||||
2020/8/18,77,良,30,55,8,0.5,18,132
|
||||
2020/8/19,77,良,26,49,8,0.6,25,132
|
||||
2020/8/20,130,轻度污染,21,41,8,0.8,30,193
|
||||
2020/8/21,88,良,27,45,8,0.9,28,145
|
||||
2020/8/22,87,良,31,53,7,0.8,22,144
|
||||
2020/8/23,112,轻度污染,35,59,8,0.6,17,173
|
||||
2020/8/24,91,良,33,58,10,0.8,28,149
|
||||
2020/8/25,64,良,24,43,9,0.6,26,116
|
||||
2020/8/26,72,良,26,41,8,0.7,20,126
|
||||
2020/8/27,75,良,15,29,8,0.6,21,129
|
||||
2020/8/28,54,良,26,53,12,0.9,39,104
|
||||
2020/8/29,82,良,28,53,11,1.0,35,138
|
||||
2020/8/30,100,良,24,40,9,0.8,32,160
|
||||
2020/8/31,55,良,23,43,10,0.9,44,87
|
||||
2020/9/1,49,优,20,37,10,0.9,39,78
|
||||
2020/9/2,50,优,21,35,8,0.7,26,99
|
||||
2020/9/3,89,良,24,49,11,0.7,45,146
|
||||
2020/9/4,85,良,24,56,8,0.7,42,141
|
||||
2020/9/5,104,轻度污染,31,62,14,0.8,42,164
|
||||
2020/9/6,116,轻度污染,26,54,10,0.6,38,177
|
||||
2020/9/7,114,轻度污染,34,65,11,0.8,58,175
|
||||
2020/9/8,101,轻度污染,38,81,12,0.9,58,161
|
||||
2020/9/9,86,良,46,82,13,0.9,65,143
|
||||
2020/9/10,69,良,50,73,10,1.0,52,84
|
||||
2020/9/11,75,良,24,43,8,0.9,31,130
|
||||
2020/9/12,72,良,26,46,12,0.9,41,126
|
||||
2020/9/13,67,良,29,48,9,0.8,30,120
|
||||
2020/9/14,89,良,33,53,9,0.8,29,146
|
||||
2020/9/15,38,优,16,24,7,0.8,30,64
|
||||
2020/9/16,52,良,19,27,8,0.8,36,102
|
||||
2020/9/17,49,优,15,22,7,0.7,28,98
|
||||
2020/9/18,43,优,14,20,7,0.5,20,86
|
||||
2020/9/19,70,良,21,36,9,0.7,27,124
|
||||
2020/9/20,90,良,26,44,9,0.7,28,148
|
||||
2020/9/21,50,优,19,37,9,0.6,29,100
|
||||
2020/9/22,55,良,31,36,8,0.7,29,105
|
||||
2020/9/23,46,优,17,33,7,0.7,33,92
|
||||
2020/9/24,60,良,27,51,10,0.7,48,106
|
||||
2020/9/25,62,良,21,47,9,0.7,43,114
|
||||
2020/9/26,74,良,29,53,9,0.7,37,128
|
||||
2020/9/27,60,良,18,41,9,0.6,25,112
|
||||
|
507
商务大数据分析/20250430/data/boston_house_prices.csv
Executable file
507
商务大数据分析/20250430/data/boston_house_prices.csv
Executable file
@ -0,0 +1,507 @@
|
||||
犯罪率,居住面积占比,商业用地占比,河流穿行,一氧化氮含量（ppm）,房间数（间）,住宅占比,平均距离,可达性指数,财产税,学生与老师占比,低收入人群,房屋价格（美元）
|
||||
0.00632,18,2.31,0,0.538,6.575,65.2,4.09,1,296,15.3,4.98,24000
|
||||
0.02731,0,7.07,0,0.469,6.421,78.9,4.9671,2,242,17.8,9.14,21600
|
||||
0.02729,0,7.07,0,0.469,7.185,61.1,4.9671,2,242,17.8,4.03,34700
|
||||
0.03237,0,2.18,0,0.458,6.998,45.8,6.0622,3,222,18.7,2.94,33400
|
||||
0.06905,0,2.18,0,0.458,7.147,54.2,6.0622,3,222,18.7,5.33,36200
|
||||
0.02985,0,2.18,0,0.458,6.43,58.7,6.0622,3,222,18.7,5.21,28700
|
||||
0.08829,12.5,7.87,0,0.524,6.012,66.6,5.5605,5,311,15.2,12.43,22900
|
||||
0.14455,12.5,7.87,0,0.524,6.172,96.1,5.9505,5,311,15.2,19.15,27100
|
||||
0.21124,12.5,7.87,0,0.524,5.631,100,6.0821,5,311,15.2,29.93,16500
|
||||
0.17004,12.5,7.87,0,0.524,6.004,85.9,6.5921,5,311,15.2,17.1,18900
|
||||
0.22489,12.5,7.87,0,0.524,6.377,94.3,6.3467,5,311,15.2,20.45,15000
|
||||
0.11747,12.5,7.87,0,0.524,6.009,82.9,6.2267,5,311,15.2,13.27,18900
|
||||
0.09378,12.5,7.87,0,0.524,5.889,39,5.4509,5,311,15.2,15.71,21700
|
||||
0.62976,0,8.14,0,0.538,5.949,61.8,4.7075,4,307,21,8.26,20400
|
||||
0.63796,0,8.14,0,0.538,6.096,84.5,4.4619,4,307,21,10.26,18200
|
||||
0.62739,0,8.14,0,0.538,5.834,56.5,4.4986,4,307,21,8.47,19900
|
||||
1.05393,0,8.14,0,0.538,5.935,29.3,4.4986,4,307,21,6.58,23100
|
||||
0.7842,0,8.14,0,0.538,5.99,81.7,4.2579,4,307,21,14.67,17500
|
||||
0.80271,0,8.14,0,0.538,5.456,36.6,3.7965,4,307,21,11.69,20200
|
||||
0.7258,0,8.14,0,0.538,5.727,69.5,3.7965,4,307,21,11.28,18200
|
||||
1.25179,0,8.14,0,0.538,5.57,98.1,3.7979,4,307,21,21.02,13600
|
||||
0.85204,0,8.14,0,0.538,5.965,89.2,4.0123,4,307,21,13.83,19600
|
||||
1.23247,0,8.14,0,0.538,6.142,91.7,3.9769,4,307,21,18.72,15200
|
||||
0.98843,0,8.14,0,0.538,5.813,100,4.0952,4,307,21,19.88,14500
|
||||
0.75026,0,8.14,0,0.538,5.924,94.1,4.3996,4,307,21,16.3,15600
|
||||
0.84054,0,8.14,0,0.538,5.599,85.7,4.4546,4,307,21,16.51,13900
|
||||
0.67191,0,8.14,0,0.538,5.813,90.3,4.682,4,307,21,14.81,16600
|
||||
0.95577,0,8.14,0,0.538,6.047,88.8,4.4534,4,307,21,17.28,14800
|
||||
0.77299,0,8.14,0,0.538,6.495,94.4,4.4547,4,307,21,12.8,18400
|
||||
1.00245,0,8.14,0,0.538,6.674,87.3,4.239,4,307,21,11.98,21000
|
||||
1.13081,0,8.14,0,0.538,5.713,94.1,4.233,4,307,21,22.6,12700
|
||||
1.35472,0,8.14,0,0.538,6.072,100,4.175,4,307,21,13.04,14500
|
||||
1.38799,0,8.14,0,0.538,5.95,82,3.99,4,307,21,27.71,13200
|
||||
1.15172,0,8.14,0,0.538,5.701,95,3.7872,4,307,21,18.35,13100
|
||||
1.61282,0,8.14,0,0.538,6.096,96.9,3.7598,4,307,21,20.34,13500
|
||||
0.06417,0,5.96,0,0.499,5.933,68.2,3.3603,5,279,19.2,9.68,18900
|
||||
0.09744,0,5.96,0,0.499,5.841,61.4,3.3779,5,279,19.2,11.41,20000
|
||||
0.08014,0,5.96,0,0.499,5.85,41.5,3.9342,5,279,19.2,8.77,21000
|
||||
0.17505,0,5.96,0,0.499,5.966,30.2,3.8473,5,279,19.2,10.13,24700
|
||||
0.02763,75,2.95,0,0.428,6.595,21.8,5.4011,3,252,18.3,4.32,30800
|
||||
0.03359,75,2.95,0,0.428,7.024,15.8,5.4011,3,252,18.3,1.98,34900
|
||||
0.12744,0,6.91,0,0.448,6.77,2.9,5.7209,3,233,17.9,4.84,26600
|
||||
0.1415,0,6.91,0,0.448,6.169,6.6,5.7209,3,233,17.9,5.81,25300
|
||||
0.15936,0,6.91,0,0.448,6.211,6.5,5.7209,3,233,17.9,7.44,24700
|
||||
0.12269,0,6.91,0,0.448,6.069,40,5.7209,3,233,17.9,9.55,21200
|
||||
0.17142,0,6.91,0,0.448,5.682,33.8,5.1004,3,233,17.9,10.21,19300
|
||||
0.18836,0,6.91,0,0.448,5.786,33.3,5.1004,3,233,17.9,14.15,20000
|
||||
0.22927,0,6.91,0,0.448,6.03,85.5,5.6894,3,233,17.9,18.8,16600
|
||||
0.25387,0,6.91,0,0.448,5.399,95.3,5.87,3,233,17.9,30.81,14400
|
||||
0.21977,0,6.91,0,0.448,5.602,62,6.0877,3,233,17.9,16.2,19400
|
||||
0.08873,21,5.64,0,0.439,5.963,45.7,6.8147,4,243,16.8,13.45,19700
|
||||
0.04337,21,5.64,0,0.439,6.115,63,6.8147,4,243,16.8,9.43,20500
|
||||
0.0536,21,5.64,0,0.439,6.511,21.1,6.8147,4,243,16.8,5.28,25000
|
||||
0.04981,21,5.64,0,0.439,5.998,21.4,6.8147,4,243,16.8,8.43,23400
|
||||
0.0136,75,4,0,0.41,5.888,47.6,7.3197,3,469,21.1,14.8,18900
|
||||
0.01311,90,1.22,0,0.403,7.249,21.9,8.6966,5,226,17.9,4.81,35400
|
||||
0.02055,85,0.74,0,0.41,6.383,35.7,9.1876,2,313,17.3,5.77,24700
|
||||
0.01432,100,1.32,0,0.411,6.816,40.5,8.3248,5,256,15.1,3.95,31600
|
||||
0.15445,25,5.13,0,0.453,6.145,29.2,7.8148,8,284,19.7,6.86,23300
|
||||
0.10328,25,5.13,0,0.453,5.927,47.2,6.932,8,284,19.7,9.22,19600
|
||||
0.14932,25,5.13,0,0.453,5.741,66.2,7.2254,8,284,19.7,13.15,18700
|
||||
0.17171,25,5.13,0,0.453,5.966,93.4,6.8185,8,284,19.7,14.44,16000
|
||||
0.11027,25,5.13,0,0.453,6.456,67.8,7.2255,8,284,19.7,6.73,22200
|
||||
0.1265,25,5.13,0,0.453,6.762,43.4,7.9809,8,284,19.7,9.5,25000
|
||||
0.01951,17.5,1.38,0,0.4161,7.104,59.5,9.2229,3,216,18.6,8.05,33000
|
||||
0.03584,80,3.37,0,0.398,6.29,17.8,6.6115,4,337,16.1,4.67,23500
|
||||
0.04379,80,3.37,0,0.398,5.787,31.1,6.6115,4,337,16.1,10.24,19400
|
||||
0.05789,12.5,6.07,0,0.409,5.878,21.4,6.498,4,345,18.9,8.1,22000
|
||||
0.13554,12.5,6.07,0,0.409,5.594,36.8,6.498,4,345,18.9,13.09,17400
|
||||
0.12816,12.5,6.07,0,0.409,5.885,33,6.498,4,345,18.9,8.79,20900
|
||||
0.08826,0,10.81,0,0.413,6.417,6.6,5.2873,4,305,19.2,6.72,24200
|
||||
0.15876,0,10.81,0,0.413,5.961,17.5,5.2873,4,305,19.2,9.88,21700
|
||||
0.09164,0,10.81,0,0.413,6.065,7.8,5.2873,4,305,19.2,5.52,22800
|
||||
0.19539,0,10.81,0,0.413,6.245,6.2,5.2873,4,305,19.2,7.54,23400
|
||||
0.07896,0,12.83,0,0.437,6.273,6,4.2515,5,398,18.7,6.78,24100
|
||||
0.09512,0,12.83,0,0.437,6.286,45,4.5026,5,398,18.7,8.94,21400
|
||||
0.10153,0,12.83,0,0.437,6.279,74.5,4.0522,5,398,18.7,11.97,20000
|
||||
0.08707,0,12.83,0,0.437,6.14,45.8,4.0905,5,398,18.7,10.27,20800
|
||||
0.05646,0,12.83,0,0.437,6.232,53.7,5.0141,5,398,18.7,12.34,21200
|
||||
0.08387,0,12.83,0,0.437,5.874,36.6,4.5026,5,398,18.7,9.1,20300
|
||||
0.04113,25,4.86,0,0.426,6.727,33.5,5.4007,4,281,19,5.29,28000
|
||||
0.04462,25,4.86,0,0.426,6.619,70.4,5.4007,4,281,19,7.22,23900
|
||||
0.03659,25,4.86,0,0.426,6.302,32.2,5.4007,4,281,19,6.72,24800
|
||||
0.03551,25,4.86,0,0.426,6.167,46.7,5.4007,4,281,19,7.51,22900
|
||||
0.05059,0,4.49,0,0.449,6.389,48,4.7794,3,247,18.5,9.62,23900
|
||||
0.05735,0,4.49,0,0.449,6.63,56.1,4.4377,3,247,18.5,6.53,26600
|
||||
0.05188,0,4.49,0,0.449,6.015,45.1,4.4272,3,247,18.5,12.86,22500
|
||||
0.07151,0,4.49,0,0.449,6.121,56.8,3.7476,3,247,18.5,8.44,22200
|
||||
0.0566,0,3.41,0,0.489,7.007,86.3,3.4217,2,270,17.8,5.5,23600
|
||||
0.05302,0,3.41,0,0.489,7.079,63.1,3.4145,2,270,17.8,5.7,28700
|
||||
0.04684,0,3.41,0,0.489,6.417,66.1,3.0923,2,270,17.8,8.81,22600
|
||||
0.03932,0,3.41,0,0.489,6.405,73.9,3.0921,2,270,17.8,8.2,22000
|
||||
0.04203,28,15.04,0,0.464,6.442,53.6,3.6659,4,270,18.2,8.16,22900
|
||||
0.02875,28,15.04,0,0.464,6.211,28.9,3.6659,4,270,18.2,6.21,25000
|
||||
0.04294,28,15.04,0,0.464,6.249,77.3,3.615,4,270,18.2,10.59,20600
|
||||
0.12204,0,2.89,0,0.445,6.625,57.8,3.4952,2,276,18,6.65,28400
|
||||
0.11504,0,2.89,0,0.445,6.163,69.6,3.4952,2,276,18,11.34,21400
|
||||
0.12083,0,2.89,0,0.445,8.069,76,3.4952,2,276,18,4.21,38700
|
||||
0.08187,0,2.89,0,0.445,7.82,36.9,3.4952,2,276,18,3.57,43800
|
||||
0.0686,0,2.89,0,0.445,7.416,62.5,3.4952,2,276,18,6.19,33200
|
||||
0.14866,0,8.56,0,0.52,6.727,79.9,2.7778,5,384,20.9,9.42,27500
|
||||
0.11432,0,8.56,0,0.52,6.781,71.3,2.8561,5,384,20.9,7.67,26500
|
||||
0.22876,0,8.56,0,0.52,6.405,85.4,2.7147,5,384,20.9,10.63,18600
|
||||
0.21161,0,8.56,0,0.52,6.137,87.4,2.7147,5,384,20.9,13.44,19300
|
||||
0.1396,0,8.56,0,0.52,6.167,90,2.421,5,384,20.9,12.33,20100
|
||||
0.13262,0,8.56,0,0.52,5.851,96.7,2.1069,5,384,20.9,16.47,19500
|
||||
0.1712,0,8.56,0,0.52,5.836,91.9,2.211,5,384,20.9,18.66,19500
|
||||
0.13117,0,8.56,0,0.52,6.127,85.2,2.1224,5,384,20.9,14.09,20400
|
||||
0.12802,0,8.56,0,0.52,6.474,97.1,2.4329,5,384,20.9,12.27,19800
|
||||
0.26363,0,8.56,0,0.52,6.229,91.2,2.5451,5,384,20.9,15.55,19400
|
||||
0.10793,0,8.56,0,0.52,6.195,54.4,2.7778,5,384,20.9,13,21700
|
||||
0.10084,0,10.01,0,0.547,6.715,81.6,2.6775,6,432,17.8,10.16,22800
|
||||
0.12329,0,10.01,0,0.547,5.913,92.9,2.3534,6,432,17.8,16.21,18800
|
||||
0.22212,0,10.01,0,0.547,6.092,95.4,2.548,6,432,17.8,17.09,18700
|
||||
0.14231,0,10.01,0,0.547,6.254,84.2,2.2565,6,432,17.8,10.45,18500
|
||||
0.17134,0,10.01,0,0.547,5.928,88.2,2.4631,6,432,17.8,15.76,18300
|
||||
0.13158,0,10.01,0,0.547,6.176,72.5,2.7301,6,432,17.8,12.04,21200
|
||||
0.15098,0,10.01,0,0.547,6.021,82.6,2.7474,6,432,17.8,10.3,19200
|
||||
0.13058,0,10.01,0,0.547,5.872,73.1,2.4775,6,432,17.8,15.37,20400
|
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|
||||
9.33889,0,18.1,0,0.679,6.38,95.6,1.9682,24,666,20.2,24.08,9500
|
||||
8.49213,0,18.1,0,0.584,6.348,86.1,2.0527,24,666,20.2,17.64,14500
|
||||
10.0623,0,18.1,0,0.584,6.833,94.3,2.0882,24,666,20.2,19.69,14100
|
||||
6.44405,0,18.1,0,0.584,6.425,74.8,2.2004,24,666,20.2,12.03,16100
|
||||
5.58107,0,18.1,0,0.713,6.436,87.9,2.3158,24,666,20.2,16.22,14300
|
||||
13.9134,0,18.1,0,0.713,6.208,95,2.2222,24,666,20.2,15.17,11700
|
||||
11.1604,0,18.1,0,0.74,6.629,94.6,2.1247,24,666,20.2,23.27,13400
|
||||
14.4208,0,18.1,0,0.74,6.461,93.3,2.0026,24,666,20.2,18.05,9600
|
||||
15.1772,0,18.1,0,0.74,6.152,100,1.9142,24,666,20.2,26.45,8700
|
||||
13.6781,0,18.1,0,0.74,5.935,87.9,1.8206,24,666,20.2,34.02,8400
|
||||
9.39063,0,18.1,0,0.74,5.627,93.9,1.8172,24,666,20.2,22.88,12800
|
||||
22.0511,0,18.1,0,0.74,5.818,92.4,1.8662,24,666,20.2,22.11,10500
|
||||
9.72418,0,18.1,0,0.74,6.406,97.2,2.0651,24,666,20.2,19.52,17100
|
||||
5.66637,0,18.1,0,0.74,6.219,100,2.0048,24,666,20.2,16.59,18400
|
||||
9.96654,0,18.1,0,0.74,6.485,100,1.9784,24,666,20.2,18.85,15400
|
||||
12.8023,0,18.1,0,0.74,5.854,96.6,1.8956,24,666,20.2,23.79,10800
|
||||
10.6718,0,18.1,0,0.74,6.459,94.8,1.9879,24,666,20.2,23.98,11800
|
||||
6.28807,0,18.1,0,0.74,6.341,96.4,2.072,24,666,20.2,17.79,14900
|
||||
9.92485,0,18.1,0,0.74,6.251,96.6,2.198,24,666,20.2,16.44,12600
|
||||
9.32909,0,18.1,0,0.713,6.185,98.7,2.2616,24,666,20.2,18.13,14100
|
||||
7.52601,0,18.1,0,0.713,6.417,98.3,2.185,24,666,20.2,19.31,13000
|
||||
6.71772,0,18.1,0,0.713,6.749,92.6,2.3236,24,666,20.2,17.44,13400
|
||||
5.44114,0,18.1,0,0.713,6.655,98.2,2.3552,24,666,20.2,17.73,15200
|
||||
5.09017,0,18.1,0,0.713,6.297,91.8,2.3682,24,666,20.2,17.27,16100
|
||||
8.24809,0,18.1,0,0.713,7.393,99.3,2.4527,24,666,20.2,16.74,17800
|
||||
9.51363,0,18.1,0,0.713,6.728,94.1,2.4961,24,666,20.2,18.71,14900
|
||||
4.75237,0,18.1,0,0.713,6.525,86.5,2.4358,24,666,20.2,18.13,14100
|
||||
4.66883,0,18.1,0,0.713,5.976,87.9,2.5806,24,666,20.2,19.01,12700
|
||||
8.20058,0,18.1,0,0.713,5.936,80.3,2.7792,24,666,20.2,16.94,13500
|
||||
7.75223,0,18.1,0,0.713,6.301,83.7,2.7831,24,666,20.2,16.23,14900
|
||||
6.80117,0,18.1,0,0.713,6.081,84.4,2.7175,24,666,20.2,14.7,20000
|
||||
4.81213,0,18.1,0,0.713,6.701,90,2.5975,24,666,20.2,16.42,16400
|
||||
3.69311,0,18.1,0,0.713,6.376,88.4,2.5671,24,666,20.2,14.65,17700
|
||||
6.65492,0,18.1,0,0.713,6.317,83,2.7344,24,666,20.2,13.99,19500
|
||||
5.82115,0,18.1,0,0.713,6.513,89.9,2.8016,24,666,20.2,10.29,20200
|
||||
7.83932,0,18.1,0,0.655,6.209,65.4,2.9634,24,666,20.2,13.22,21400
|
||||
3.1636,0,18.1,0,0.655,5.759,48.2,3.0665,24,666,20.2,14.13,19900
|
||||
3.77498,0,18.1,0,0.655,5.952,84.7,2.8715,24,666,20.2,17.15,19000
|
||||
4.42228,0,18.1,0,0.584,6.003,94.5,2.5403,24,666,20.2,21.32,19100
|
||||
15.5757,0,18.1,0,0.58,5.926,71,2.9084,24,666,20.2,18.13,19100
|
||||
13.0751,0,18.1,0,0.58,5.713,56.7,2.8237,24,666,20.2,14.76,20100
|
||||
4.34879,0,18.1,0,0.58,6.167,84,3.0334,24,666,20.2,16.29,19900
|
||||
4.03841,0,18.1,0,0.532,6.229,90.7,3.0993,24,666,20.2,12.87,19600
|
||||
3.56868,0,18.1,0,0.58,6.437,75,2.8965,24,666,20.2,14.36,23200
|
||||
4.64689,0,18.1,0,0.614,6.98,67.6,2.5329,24,666,20.2,11.66,29800
|
||||
8.05579,0,18.1,0,0.584,5.427,95.4,2.4298,24,666,20.2,18.14,13800
|
||||
6.39312,0,18.1,0,0.584,6.162,97.4,2.206,24,666,20.2,24.1,13300
|
||||
4.87141,0,18.1,0,0.614,6.484,93.6,2.3053,24,666,20.2,18.68,16700
|
||||
15.0234,0,18.1,0,0.614,5.304,97.3,2.1007,24,666,20.2,24.91,12000
|
||||
10.233,0,18.1,0,0.614,6.185,96.7,2.1705,24,666,20.2,18.03,14600
|
||||
14.3337,0,18.1,0,0.614,6.229,88,1.9512,24,666,20.2,13.11,21400
|
||||
5.82401,0,18.1,0,0.532,6.242,64.7,3.4242,24,666,20.2,10.74,23000
|
||||
5.70818,0,18.1,0,0.532,6.75,74.9,3.3317,24,666,20.2,7.74,23700
|
||||
5.73116,0,18.1,0,0.532,7.061,77,3.4106,24,666,20.2,7.01,25000
|
||||
2.81838,0,18.1,0,0.532,5.762,40.3,4.0983,24,666,20.2,10.42,21800
|
||||
2.37857,0,18.1,0,0.583,5.871,41.9,3.724,24,666,20.2,13.34,20600
|
||||
3.67367,0,18.1,0,0.583,6.312,51.9,3.9917,24,666,20.2,10.58,21200
|
||||
5.69175,0,18.1,0,0.583,6.114,79.8,3.5459,24,666,20.2,14.98,19100
|
||||
4.83567,0,18.1,0,0.583,5.905,53.2,3.1523,24,666,20.2,11.45,20600
|
||||
0.15086,0,27.74,0,0.609,5.454,92.7,1.8209,4,711,20.1,18.06,15200
|
||||
0.18337,0,27.74,0,0.609,5.414,98.3,1.7554,4,711,20.1,23.97,7000
|
||||
0.20746,0,27.74,0,0.609,5.093,98,1.8226,4,711,20.1,29.68,8100
|
||||
0.10574,0,27.74,0,0.609,5.983,98.8,1.8681,4,711,20.1,18.07,13600
|
||||
0.11132,0,27.74,0,0.609,5.983,83.5,2.1099,4,711,20.1,13.35,20100
|
||||
0.17331,0,9.69,0,0.585,5.707,54,2.3817,6,391,19.2,12.01,21800
|
||||
0.27957,0,9.69,0,0.585,5.926,42.6,2.3817,6,391,19.2,13.59,24500
|
||||
0.17899,0,9.69,0,0.585,5.67,28.8,2.7986,6,391,19.2,17.6,23100
|
||||
0.2896,0,9.69,0,0.585,5.39,72.9,2.7986,6,391,19.2,21.14,19700
|
||||
0.26838,0,9.69,0,0.585,5.794,70.6,2.8927,6,391,19.2,14.1,18300
|
||||
0.23912,0,9.69,0,0.585,6.019,65.3,2.4091,6,391,19.2,12.92,21200
|
||||
0.17783,0,9.69,0,0.585,5.569,73.5,2.3999,6,391,19.2,15.1,17500
|
||||
0.22438,0,9.69,0,0.585,6.027,79.7,2.4982,6,391,19.2,14.33,16800
|
||||
0.06263,0,11.93,0,0.573,6.593,69.1,2.4786,1,273,21,9.67,22400
|
||||
0.04527,0,11.93,0,0.573,6.12,76.7,2.2875,1,273,21,9.08,20600
|
||||
0.06076,0,11.93,0,0.573,6.976,91,2.1675,1,273,21,5.64,23900
|
||||
0.10959,0,11.93,0,0.573,6.794,89.3,2.3889,1,273,21,6.48,22000
|
||||
0.04741,0,11.93,0,0.573,6.03,80.8,2.505,1,273,21,7.88,11900
|
||||
|
15000
商务大数据分析/20250430/data/hr.csv
Executable file
15000
商务大数据分析/20250430/data/hr.csv
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BIN
商务大数据分析/20250430/data/student_grade.xlsx
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商务大数据分析/20250430/data/student_grade.xlsx
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100
商务大数据分析/20250430/data/worldcloud.csv
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100
商务大数据分析/20250430/data/worldcloud.csv
Executable file
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|
||||
词语,频数
|
||||
东风,1379
|
||||
何处,1231
|
||||
人间,1164
|
||||
风流,843
|
||||
归去,818
|
||||
春风,800
|
||||
西风,782
|
||||
归来,768
|
||||
江南,760
|
||||
相思,759
|
||||
梅花,725
|
||||
千里,668
|
||||
多少,653
|
||||
回首,649
|
||||
如今,647
|
||||
明月,646
|
||||
阑干,632
|
||||
年年,605
|
||||
万里,587
|
||||
一笑,579
|
||||
黄昏,561
|
||||
当年,537
|
||||
芳草,533
|
||||
天涯,531
|
||||
相逢,523
|
||||
尊前,519
|
||||
一枝,510
|
||||
风雨,500
|
||||
流水,481
|
||||
风吹,474
|
||||
依旧,469
|
||||
多情,458
|
||||
风月,452
|
||||
当时,451
|
||||
故人,445
|
||||
斜阳,444
|
||||
无人,443
|
||||
不知,426
|
||||
深处,424
|
||||
不见,416
|
||||
时节,407
|
||||
凄凉,404
|
||||
平生,394
|
||||
春色,393
|
||||
无限,381
|
||||
一点,374
|
||||
功名,366
|
||||
杨柳,363
|
||||
天上,361
|
||||
匆匆,357
|
||||
芙蓉,356
|
||||
今日,354
|
||||
扁舟,351
|
||||
西湖,350
|
||||
憔悴,349
|
||||
消息,347
|
||||
桃花,343
|
||||
何事,335
|
||||
一片,333
|
||||
神仙,332
|
||||
一声,331
|
||||
黄花,330
|
||||
心事,330
|
||||
鸳鸯,328
|
||||
十分,327
|
||||
人生,324
|
||||
断肠,323
|
||||
佳人,323
|
||||
长安,321
|
||||
东君,319
|
||||
桃李,319
|
||||
而今,318
|
||||
为谁,317
|
||||
无情,307
|
||||
去年,306
|
||||
天气,306
|
||||
不是,305
|
||||
海棠,305
|
||||
少年,305
|
||||
今夜,304
|
||||
不似,303
|
||||
十年,303
|
||||
行人,300
|
||||
谁知,300
|
||||
寂寞,299
|
||||
肠断,297
|
||||
江上,297
|
||||
悠悠,297
|
||||
富贵,295
|
||||
时候,295
|
||||
昨夜,295
|
||||
几度,292
|
||||
月明,292
|
||||
何时,291
|
||||
青山,291
|
||||
蓬莱,290
|
||||
往事,290
|
||||
如何,287
|
||||
惟有,287
|
||||
|
8979
商务大数据分析/20250430/data/商品销售数据.csv
Executable file
8979
商务大数据分析/20250430/data/商品销售数据.csv
Executable file
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商务大数据分析/20250430/data/某淘宝店铺的订单转化率统计数据.xlsx
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商务大数据分析/20250430/data/某淘宝店铺的订单转化率统计数据.xlsx
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BIN
商务大数据分析/20250430/data/运动员的最大携氧能力、体重和运动后心率数据.xlsx
Executable file
BIN
商务大数据分析/20250430/data/运动员的最大携氧能力、体重和运动后心率数据.xlsx
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336
商务大数据分析/20250430/task.ipynb
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336
商务大数据分析/20250430/task.ipynb
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2057
商务大数据分析/20250430/task2.ipynb
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2057
商务大数据分析/20250430/task2.ipynb
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11
商务大数据分析/20250430/test
Normal file
11
商务大数据分析/20250430/test
Normal file
@ -0,0 +1,11 @@
|
||||
# 创建一个新的图形窗口，设置图形大小为宽 10 英寸，高 8 英寸
|
||||
plt.figure(figsize=(10, 8))
|
||||
# 使用 seaborn 库的 heatmap 函数绘制热力图
|
||||
# data 参数传入之前计算得到的相关系数矩阵 correlation_matrix
|
||||
# annot=True 表示在热力图上显示具体的相关系数数值
|
||||
# cmap='coolwarm' 指定使用的颜色映射，这里使用冷暖色调
|
||||
sns.heatmap(data=correlation_matrix, annot=True, cmap='coolwarm')
|
||||
# 设置图形的标题为 '空气质量特征相关性热力图'，并使用之前设置的中文字体
|
||||
plt.title('空气质量特征相关性热力图', fontproperties=my_font)
|
||||
# 显示绘制好的图形
|
||||
plt.show()
|
||||
BIN
商务大数据分析/20250430/tmp/sincos(x).jpg
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商务大数据分析/20250430/tmp/sincos(x).jpg
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|
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商务大数据分析/20250430/tmp/sincos.jpg
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商务大数据分析/20250430/tmp/sincos.jpg
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|
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BIN
商务大数据分析/20250430/tmp/y=x^2.jpg
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|
After Width: | Height: | Size: 19 KiB |
BIN
商务大数据分析/20250430/tmp/某企业季度销售额对比展示.jpg
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商务大数据分析/20250430/tmp/某企业季度销售额对比展示.jpg
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|
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商务大数据分析/20250430/tmp/某企业季度销售额对比展示1.jpg
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商务大数据分析/20250430/tmp/某企业季度销售额对比展示1.jpg
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|
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商务大数据分析/20250430/tmp/超星平台AI分析：学生图表制作常见问题 vs 专业解决方案.jpg
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商务大数据分析/20250430/tmp/超星平台AI分析：学生图表制作常见问题 vs 专业解决方案.jpg
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|
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199
商务大数据分析/20250430/任务5.1 掌握Matplotlib基础绘图.py
Normal file
199
商务大数据分析/20250430/任务5.1 掌握Matplotlib基础绘图.py
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@ -0,0 +1,199 @@
|
||||
# 代码5-1
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import warnings
|
||||
# 忽略所有警告
|
||||
warnings.filterwarnings("ignore")
|
||||
# %matplotlib inline表示在行中显示图片，在命令行运行报错
|
||||
data = np.arange(0, 1.1, 0.01)
|
||||
plt.title('lines') # 添加标题
|
||||
plt.xlabel('x') # 添加x轴的名称
|
||||
plt.ylabel('y') # 添加y轴的名称
|
||||
plt.xlim((0, 1)) # 确定x轴范围
|
||||
plt.ylim((0, 1)) # 确定y轴范围
|
||||
plt.xticks([0, 0.2, 0.4, 0.6, 0.8, 1]) # 规定x轴刻度
|
||||
plt.yticks([0, 0.2, 0.4, 0.6, 0.8, 1]) # 确定y轴刻度
|
||||
plt.plot(data, data ** 2) # 添加y=x^2曲线
|
||||
plt.plot(data, data ** 4) # 添加y=x^4曲线
|
||||
plt.legend(['y=x^2', 'y=x^4'])
|
||||
plt.savefig('../tmp/y=x^2.jpg')
|
||||
plt.show()
|
||||
|
||||
|
||||
# 代码5-2
|
||||
x = np.arange(0, np.pi * 2, 0.01)
|
||||
# 第一幅子图
|
||||
p1 = plt.figure(figsize=(8, 6), dpi=80) # 确定画布大小
|
||||
# 创建一个2行1列的子图，并开始绘制第一幅
|
||||
ax1 = p1.add_subplot(2, 1, 1)
|
||||
plt.title('lines') # 添加标题
|
||||
plt.xlabel('x') # 添加x轴的名称
|
||||
plt.ylabel('y') # 添加y轴的名称
|
||||
plt.xlim((0, 1)) # 确定x轴范围
|
||||
plt.ylim((0, 1)) # 确定y轴范围
|
||||
plt.xticks([0, 0.2, 0.4, 0.6, 0.8, 1]) # 规定x轴刻度
|
||||
plt.yticks([0, 0.2, 0.4, 0.6, 0.8, 1]) # 确定y轴刻度
|
||||
plt.plot(x, x ** 2) # 添加y=x^2曲线
|
||||
plt.plot(x, x ** 4) # 添加y=x^4曲线
|
||||
plt.legend(['y=x^2', 'y=x^4'])
|
||||
# 第二幅子图
|
||||
ax2 = p1.add_subplot(2, 1, 2) # 开始绘制第二幅
|
||||
plt.title('sin/cos(x)') # 添加标题
|
||||
plt.xlabel('x') # 添加x轴的名称
|
||||
plt.ylabel('y') # 添加y轴的名称
|
||||
plt.xlim((0, np.pi * 2)) # 确定x轴范围
|
||||
plt.ylim((-1, 1)) # 确定y轴范围
|
||||
plt.xticks([0, np.pi / 2, np.pi, np.pi * 1.5, np.pi * 2]) # 规定x轴刻度
|
||||
plt.yticks([-1, -0.5, 0, 0.5, 1]) # 确定y轴刻度
|
||||
plt.plot(x, np.sin(x)) # 添加sin(x)曲线
|
||||
plt.plot(x, np.cos(x)) # 添加cos(x)曲线
|
||||
plt.legend(['y=sin(x)', 'y=cos(x)'])
|
||||
plt.tight_layout() # 调整两个子图间距
|
||||
plt.savefig('../tmp/sincos(x).jpg')
|
||||
plt.show()
|
||||
|
||||
|
||||
# 代码5-3
|
||||
# 原图
|
||||
x = np.linspace(0, 4 * np.pi) # 生成x轴数据
|
||||
y = np.sin(x) # 生成y轴数据
|
||||
plt.plot(x, y, label='$sin(x)$') # 绘制sin曲线图
|
||||
plt.title('sin(x)')
|
||||
plt.xlabel('x')
|
||||
plt.ylabel('y')
|
||||
plt.show()
|
||||
|
||||
# 修改rc参数后的图
|
||||
plt.rcParams['lines.linestyle'] = '-.'
|
||||
plt.rcParams['lines.linewidth'] = 3
|
||||
plt.plot(x, y, label='$sin(x)$') # 绘制三角函数
|
||||
plt.title('sin(x)')
|
||||
plt.xlabel('x')
|
||||
plt.ylabel('y')
|
||||
plt.show()
|
||||
|
||||
|
||||
# 代码5-4
|
||||
# 无法显示中文标题
|
||||
plt.plot(x, y, label='$sin(x)$') # 绘制三角函数
|
||||
plt.title('sin(x)曲线')
|
||||
plt.xlabel('x')
|
||||
plt.ylabel('y')
|
||||
plt.show()
|
||||
|
||||
# 设置rc参数显示中文标题
|
||||
# 设置字体为SimHei显示中文
|
||||
plt.rcParams['font.sans-serif'] = 'SimHei'
|
||||
plt.rcParams['axes.unicode_minus'] = False # 设置正常显示符号
|
||||
plt.plot(x, y, label='$sin(x)$') # 绘制三角函数
|
||||
plt.title('sin(x)曲线')
|
||||
plt.xlabel('x')
|
||||
plt.ylabel('y')
|
||||
plt.show()
|
||||
|
||||
|
||||
# 代码5-5
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
plt.rcParams['font.sans-serif'] = 'SimHei' # 设置中文显示
|
||||
plt.rcParams['axes.unicode_minus'] = False
|
||||
data = np.load('../data/2001-2019年劳动力与就业人员数据.npz',
|
||||
encoding='ASCII', allow_pickle=True)
|
||||
columns = data['arr_0'] # 提取其中的columns数组，视为数据的标签
|
||||
values = data['arr_1'] # 提取其中的values数组，视为数据的存在位置
|
||||
plt.figure(figsize=(12, 6), dpi=1080) # 设置画布
|
||||
plt.scatter(values[:, 0], values[:, 1], marker='o') # 绘制散点图
|
||||
plt.xlabel('年份（年）')
|
||||
plt.ylabel('劳动力人数（万人）')
|
||||
plt.ylim(70000, 85000) # 设置y轴范围
|
||||
plt.xticks(range(2001, 2020, 1), labels=values[:, 0])
|
||||
plt.title('2001～2019年劳动力人数散点图') # 添加图表标题
|
||||
plt.show()
|
||||
|
||||
|
||||
# 代码5-6
|
||||
p = plt.figure(figsize=(12, 6), dpi=1080) # 设置画布
|
||||
# 绘制散点图1
|
||||
plt.scatter(values[:, 0], values[:, 3], marker='o', c='b')
|
||||
# 绘制散点图2
|
||||
plt.scatter(values[:, 0], values[:, 4], marker='o', c='r')
|
||||
plt.xlabel('年份（年）')
|
||||
plt.ylabel('人数（万人）')
|
||||
plt.ylim(20000, 60000) # 设置y轴范围
|
||||
plt.xticks(range(2001, 2020, 1), labels=values[:, 0])
|
||||
plt.legend(['城镇就业人员', '乡村就业人员']) # 设置图例
|
||||
plt.title('2001～2019年城乡就业人数散点图') # 添加图表标题
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
# 代码5-7
|
||||
p = plt.figure(figsize=(12, 6), dpi=1080) #设置画布
|
||||
plt.plot(values[:, 0], values[:, 2], color='r', linestyle='-')
|
||||
plt.xlabel('年份（年）')
|
||||
plt.ylabel('人数（万人）')
|
||||
plt.ylim(70000, 80000) # 设置y轴范围
|
||||
plt.xticks(range(2001, 2020, 1), labels=values[:, 0])
|
||||
plt.title('2001～2019年就业人数折线图')
|
||||
plt.show()
|
||||
|
||||
|
||||
# 代码5-8
|
||||
p = plt.figure(figsize=(12, 6), dpi=1080) #设置画布
|
||||
plt.plot(values[:, 0], values[:, 2], c='b', linestyle = '-',
|
||||
marker = 'o') # 绘制点线图
|
||||
plt.xlabel('年份（年）')
|
||||
plt.ylabel('人数（万人）')
|
||||
plt.ylim(70000, 80000) # 设置y轴范围
|
||||
plt.xticks(range(2001, 2020, 1), labels=values[:, 0])
|
||||
plt.title('2001～2019年就业人数点线图')
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
# 代码5-9
|
||||
p = plt.figure(figsize=(12, 6), dpi=1080) #设置画布
|
||||
plt.plot(values[:, 0], values[:, 3], 'bs-',
|
||||
values[:, 0], values[:, 4], 'ro-.')
|
||||
plt.xlabel('年份（年）')
|
||||
plt.ylabel('人数（万人）')
|
||||
plt.ylim(20000, 60000) # 设置y轴范围
|
||||
plt.xticks(range(2001, 2020, 1), labels=values[:, 0])
|
||||
plt.legend(['城镇就业人员', '乡村就业人员'])
|
||||
plt.title('2001～2019年城乡就业人数点线图')
|
||||
plt.show()
|
||||
|
||||
|
||||
# 代码5-10
|
||||
columns = data['arr_0'] # 提取其中的columns数组，视为数据的标签
|
||||
values = data['arr_1'] # 提取其中的values数组，视为数据的存在位置
|
||||
# 绘制柱形图
|
||||
labels = ['城镇就业人员', '乡村就业人员']
|
||||
p = plt.figure(figsize=(6, 6), dpi=1080)
|
||||
plt.bar(range(2), values[-1, 3:5], width=0.5)
|
||||
plt.xlabel('类别')
|
||||
plt.ylabel('就业人数（万人）')
|
||||
plt.xticks(range(2), labels)
|
||||
plt.title('2019年城乡就业人数柱形图')
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
# 代码5-11
|
||||
label = ['城镇就业人员', '乡村就业人员']
|
||||
explode = [0.01, 0.01] # 设定各项距离圆心n个半径
|
||||
p = plt.figure(figsize=(6, 6), dpi=1080) # 设置画布
|
||||
plt.pie(values[-1, 3:5], explode=explode,
|
||||
labels=label, autopct='%1.1f%%')
|
||||
plt.title('2019年城乡就业人数分布饼图')
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
# 代码5-12
|
||||
label= ['城镇就业人员', '乡村就业人员']
|
||||
gdp = (list(values[:, 3]),list(values[:, 4]))
|
||||
p = plt.figure(figsize=(6, 6), dpi=1080)
|
||||
plt.boxplot(gdp, notch=True, labels=label, meanline=True)
|
||||
plt.title('2001～2019年城乡就业人数分布箱线图')
|
||||
plt.show()
|
||||
365
商务大数据分析/20250430/任务5.2 掌握seaborn基础绘图.py
Executable file
365
商务大数据分析/20250430/任务5.2 掌握seaborn基础绘图.py
Executable file
@ -0,0 +1,365 @@
|
||||
# 代码5-13
|
||||
# 导库
|
||||
from matplotlib import pyplot as plt
|
||||
import pandas as pd
|
||||
import seaborn as sns
|
||||
import warnings
|
||||
# 忽略所有警告
|
||||
warnings.filterwarnings("ignore")
|
||||
# 设置中文字体
|
||||
plt.rcParams['font.sans-serif'] = ['SimHei']
|
||||
sns.set_style({'font.sans-serif':['simhei', 'Arial']})
|
||||
|
||||
# 加载数据
|
||||
hr = pd.read_csv('../data/hr.csv', encoding='gbk')
|
||||
|
||||
data = hr.head(100)
|
||||
# 使用Matplotlib库绘图
|
||||
color_map = dict(zip(data['薪资'].unique(), ['b', 'y', 'r']))
|
||||
for species, group in data.groupby('薪资'):
|
||||
plt.scatter(group['每月平均工作小时数（小时）'],
|
||||
group['满意度'],
|
||||
color=color_map[species], alpha=0.4,
|
||||
edgecolors=None, label=species)
|
||||
plt.legend(frameon=True, title='薪资')
|
||||
plt.xlabel('平均每个月工作时长（小时）')
|
||||
plt.ylabel('满意度水平')
|
||||
plt.title('满意度水平与平均每个月工作小时')
|
||||
plt.show()
|
||||
|
||||
# 使用seaborn库绘图
|
||||
sns.lmplot('每月平均工作小时数（小时）', '满意度', data, hue='薪资', fit_reg=False, height=4)
|
||||
plt.xlabel('平均每个月工作时长（小时）')
|
||||
plt.ylabel('满意度水平')
|
||||
plt.title('满意度水平与平均每个月工作小时')
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
# 代码5-14
|
||||
import numpy as np
|
||||
plt.rcParams['axes.unicode_minus'] = False
|
||||
x = np.arange(1, 10, 2)
|
||||
y1 = x + 1
|
||||
y2 = x + 3
|
||||
y3 = x + 5
|
||||
# 绘制3条不同的直线
|
||||
# 使用Matplotlib库绘图
|
||||
plt.title('Matplotlib库的绘图风格')
|
||||
plt.plot(x, y1)
|
||||
plt.plot(x, y2)
|
||||
plt.plot(x, y3)
|
||||
plt.show()
|
||||
|
||||
# 使用seaborn库绘图
|
||||
# 第1部分
|
||||
sns.set_style('darkgrid') # 全黑风格
|
||||
sns.set_style({'font.sans-serif':['simhei', 'Arial']})
|
||||
plt.title('seaborn库的绘图风格')
|
||||
# 第2部分
|
||||
sns.lineplot(x, y1)
|
||||
sns.lineplot(x, y2)
|
||||
sns.lineplot(x, y3)
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
# 代码5-15
|
||||
x = np.arange(1, 10, 2)
|
||||
y1 = x + 1
|
||||
y2 = x + 3
|
||||
y3 = x + 5
|
||||
def showLine(flip=1):
|
||||
sns.lineplot(x, y1)
|
||||
sns.lineplot(x, y2)
|
||||
sns.lineplot(x, y3)
|
||||
pic = plt.figure(figsize=(12, 8))
|
||||
with sns.axes_style('darkgrid'): # 使用darkgrid主题
|
||||
pic.add_subplot(2, 3, 1)
|
||||
showLine()
|
||||
plt.title('darkgrid')
|
||||
with sns.axes_style('whitegrid'): # 使用whitegrid主题
|
||||
pic.add_subplot(2, 3, 2)
|
||||
showLine()
|
||||
plt.title('whitegrid')
|
||||
with sns.axes_style('dark'): # 使用dark主题
|
||||
pic.add_subplot(2, 3, 3)
|
||||
showLine()
|
||||
plt.title('dark')
|
||||
with sns.axes_style('white'): # 使用white主题
|
||||
pic.add_subplot(2, 3, 4)
|
||||
showLine()
|
||||
plt.title('white')
|
||||
with sns.axes_style('ticks'): # 使用ticks主题
|
||||
pic.add_subplot(2, 3, 5)
|
||||
showLine()
|
||||
plt.title('ticks')
|
||||
sns.set_style(style='darkgrid', rc={'font.sans-serif': ['MicrosoftYaHei', 'SimHei'],
|
||||
'grid.color': 'black'}) # 修改主题中参数
|
||||
pic.add_subplot(2, 3, 6)
|
||||
showLine()
|
||||
plt.title('修改参数')
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
# 代码5-16
|
||||
sns.set()
|
||||
x = np.arange(1, 10, 2)
|
||||
y1 = x + 1
|
||||
y2 = x + 3
|
||||
y3 = x + 5
|
||||
def showLine(flip=1):
|
||||
sns.lineplot(x, y1)
|
||||
sns.lineplot(x, y2)
|
||||
sns.lineplot(x, y3)
|
||||
# 恢复默认参数
|
||||
pic = plt.figure(figsize=(8, 8), dpi=100)
|
||||
with sns.plotting_context('paper'): # 选择paper类型
|
||||
pic.add_subplot(2, 2, 1)
|
||||
showLine()
|
||||
plt.title('paper')
|
||||
with sns.plotting_context('notebook'): # 选择notebook类型
|
||||
pic.add_subplot(2, 2, 2)
|
||||
showLine()
|
||||
plt.title('notebook')
|
||||
with sns.plotting_context('talk'): # 选择talk类型
|
||||
pic.add_subplot(2, 2, 3)
|
||||
showLine()
|
||||
plt.title('talk')
|
||||
with sns.plotting_context('poster'): # 选择poster类型
|
||||
pic.add_subplot(2, 2, 4)
|
||||
showLine()
|
||||
plt.title('poster')
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
# 代码5-17
|
||||
with sns.axes_style('white'):
|
||||
showLine()
|
||||
sns.despine() # 默认无参数状态，就是删除上方和右方的边框
|
||||
plt.title('控制图形边框')
|
||||
plt.show()
|
||||
|
||||
|
||||
with sns.axes_style('white'):
|
||||
data = np.random.normal(size=(20, 6)) + np.arange(6) / 2
|
||||
sns.boxplot(data=data)
|
||||
sns.despine(offset=10, left=False, bottom=False)
|
||||
plt.title('控制图形边框')
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
|
||||
# 代码5-18
|
||||
sns.palplot(sns.color_palette())
|
||||
|
||||
|
||||
|
||||
# 代码5-19
|
||||
palette = sns.color_palette('muted')
|
||||
sns.palplot(palette)
|
||||
|
||||
|
||||
|
||||
# 代码5-20
|
||||
sns.palplot(sns.color_palette('hls', 8))
|
||||
|
||||
|
||||
|
||||
# 代码5-21
|
||||
sns.palplot(sns.hls_palette(8, l=.3, s=.8)) # l控制亮度，s控制饱和度
|
||||
|
||||
|
||||
#代码5-22
|
||||
sns.palplot(sns.color_palette('husl', 8))
|
||||
|
||||
|
||||
|
||||
# 代码5-23
|
||||
plt.plot(x, y1, sns.xkcd_rgb['pale red'], lw=3)
|
||||
plt.plot(x, y2, sns.xkcd_rgb['medium green'], lw=3)
|
||||
plt.plot(x, y3, sns.xkcd_rgb['denim blue'], lw=3)
|
||||
plt.title('线条颜色示例')
|
||||
plt.show()
|
||||
|
||||
# 自定义定性调色板
|
||||
colors = ['windows blue', 'amber', 'greyish', 'faded green', 'dusty purple']
|
||||
sns.palplot(sns.xkcd_palette(colors))
|
||||
|
||||
|
||||
|
||||
# 代码5-24
|
||||
sns.palplot(sns.color_palette('Greens'))
|
||||
sns.palplot(sns.color_palette('YlOrRd_r'))
|
||||
sns.palplot(sns.color_palette('YlOrRd_d'))
|
||||
|
||||
|
||||
|
||||
# 代码5-25
|
||||
sns.palplot(sns.cubehelix_palette(8, start=1, rot=0))
|
||||
|
||||
x, y = np.random.multivariate_normal([0, 0], [[1, -.5], [-.5, 1]], size=300).T
|
||||
cmap = sns.cubehelix_palette(as_cmap=True) # 生产调色板对象
|
||||
sns.kdeplot(x, y, cmap=cmap, shade=True)
|
||||
plt.title('连续调色板')
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
# 代码5-26
|
||||
sns.palplot(sns.light_palette('blue'))
|
||||
sns.palplot(sns.dark_palette('yellow'))
|
||||
# 使用husl颜色空间作为种子
|
||||
pal = sns.dark_palette((200, 80, 60), input='husl', reverse=True, as_cmap=True)
|
||||
sns.kdeplot(x, y, cmap=pal)
|
||||
plt.title('自定义连续调色板')
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
# 代码5-27
|
||||
sns.palplot(sns.color_palette('BrBG', 7))
|
||||
sns.palplot(sns.color_palette('RdBu_r', 7))
|
||||
|
||||
|
||||
|
||||
# 代码5-28
|
||||
sns.palplot(sns.color_palette('coolwarm', 7))
|
||||
|
||||
|
||||
|
||||
# 代码5-29
|
||||
sns.palplot(sns.diverging_palette(240, 10, n=7))
|
||||
sns.palplot(sns.diverging_palette(150, 275, s=80, l=55, n=7))
|
||||
# 创建中间是暗色的调色板
|
||||
sns.palplot(sns.diverging_palette(250, 15, s=75, l=40, n=7, center='dark'))
|
||||
# 通过sep参数控制中间区域渐变宽度
|
||||
sns.palplot(sns.diverging_palette(150, 275, s=80, l=55, n=7, sep=80))
|
||||
|
||||
|
||||
|
||||
# 代码5-30
|
||||
x = np.arange(1, 10, 2)
|
||||
y1 = x + 1
|
||||
y2 = x + 3
|
||||
y3 = x + 5
|
||||
def showLine(flip=1):
|
||||
sns.lineplot(x, y1)
|
||||
sns.lineplot(x, y2)
|
||||
sns.lineplot(x, y3)
|
||||
# 使用默认调色板
|
||||
showLine()
|
||||
plt.title('默认调色板')
|
||||
plt.show()
|
||||
|
||||
# sns.set_palette函数设置调色板
|
||||
sns.set_palette('YlOrRd_d')
|
||||
showLine()
|
||||
plt.title('使用set_palette设置调色板')
|
||||
plt.show()
|
||||
|
||||
sns.set() # 恢复所有默认设置
|
||||
plt.rcParams['font.sans-serif'] = ['SimHei']
|
||||
plt.rcParams['axes.unicode_minus'] = False
|
||||
pic = plt.figure(figsize=(8, 4))
|
||||
with sns.color_palette('PuBuGn_d'): # 临时配置调色板
|
||||
pic.add_subplot(1, 2, 1)
|
||||
showLine()
|
||||
plt.title('使用color_palette设置调色板')
|
||||
pic.add_subplot(1, 2, 2) # 使用默认调色板
|
||||
showLine()
|
||||
plt.title('默认调色板')
|
||||
plt.show()
|
||||
|
||||
|
||||
# 代码5-31
|
||||
boston = pd.read_csv('../data/boston_house_prices.csv', encoding='gbk')
|
||||
plt.rcParams['axes.unicode_minus'] = False
|
||||
corr = boston.corr() # 特征的相关系数矩阵
|
||||
sns.heatmap(corr)
|
||||
plt.title('特征矩阵热力图')
|
||||
plt.show()
|
||||
|
||||
|
||||
# 代码5-32
|
||||
plt.figure(figsize=(10, 10))
|
||||
sns.heatmap(corr, annot=True, fmt='.2f')
|
||||
plt.title('特征矩阵热力图')
|
||||
plt.show()
|
||||
|
||||
|
||||
# 代码5-33
|
||||
# 提取部门为销售部、离职为1的数据
|
||||
sale = hr.iloc[(hr['部门'].values=='销售部') & (hr['离职'].values==1), :]
|
||||
sns.stripplot(x=sale['每月平均工作小时数（小时）'])
|
||||
plt.title('简单水平分布散点图')
|
||||
plt.show()
|
||||
|
||||
|
||||
# 代码5-34
|
||||
# 提取离职为1的数据
|
||||
hr1 = hr.iloc[hr['离职'].values==1, :]
|
||||
plt.figure(figsize=(10, 5))
|
||||
plt.subplot(121)
|
||||
plt.xticks(rotation=70)
|
||||
sns.stripplot(x='部门', y='每月平均工作小时数（小时）', data=hr1) # 默认添加随机噪声
|
||||
plt.title('默认随机噪声抖动')
|
||||
plt.subplot(122)
|
||||
plt.xticks(rotation=70)
|
||||
sns.stripplot(x='部门', y='每月平均工作小时数（小时）',
|
||||
data=hr1, jitter=False) # 不添加随机噪声
|
||||
plt.title('无随机噪声抖动')
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
# 代码5-35
|
||||
# 提取高薪在职的数据
|
||||
hr2 = hr.iloc[(hr['薪资'].values=='高') & (hr['离职'].values==0), :]
|
||||
sns.stripplot(x='5年内升职', y='每月平均工作小时数（小时）',
|
||||
hue='部门', data=hr2, jitter=True)
|
||||
plt.title('前5年是否晋升与平均每月工作时长')
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
# 代码5-36
|
||||
plt.figure(figsize=(10, 13))
|
||||
plt.subplot(211)
|
||||
plt.xticks(rotation=70)
|
||||
plt.title('不同部门的平均每月工作时长')
|
||||
sns.stripplot(x='部门', y='每月平均工作小时数（小时）', hue='5年内升职', data=hr2)
|
||||
plt.subplot(212)
|
||||
plt.xticks(rotation=70)
|
||||
sns.stripplot(x='部门', y='每月平均工作小时数（小时）', hue='5年内升职',
|
||||
data=hr2, dodge=True)
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
# 代码5-37
|
||||
sns.swarmplot(x='部门', y='每月平均工作小时数（小时）', data=hr2)
|
||||
plt.xticks(rotation=70)
|
||||
plt.title('不同部门的平均每月工作时长')
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
# 代码5-38
|
||||
sns.swarmplot(x='部门', y='每月平均工作小时数（小时）',
|
||||
hue='5年内升职', data=hr2)
|
||||
plt.xticks(rotation=30)
|
||||
plt.title('不同部门的平均每月工作时长')
|
||||
plt.show()
|
||||
|
||||
|
||||
# 代码5-39
|
||||
fig, axes = plt.subplots(1, 2, figsize=(8, 4))
|
||||
axes[0].set_title('修改前的线性回归拟合图')
|
||||
axes[1].set_title('修改后的线性回归拟合图')
|
||||
sns.regplot(x='房间数（间）', y='房屋价格（美元）', data=boston, ax=axes[0])
|
||||
sns.regplot(x='房间数（间）', y='房屋价格（美元）', data=boston, ci=50, ax=axes[1])
|
||||
plt.show()
|
||||
28
数据可视化技术/test.py
Normal file
28
数据可视化技术/test.py
Normal file
@ -0,0 +1,28 @@
|
||||
import time
|
||||
from flask import Flask
|
||||
import redis
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
# 使用服务名 "redis" 作为 host，docker-compose 会自动识别
|
||||
def get_redis_client():
|
||||
return redis.Redis(host='hadoop102', port=6379)
|
||||
|
||||
def get_hit_count():
|
||||
retries = 5
|
||||
while True:
|
||||
try:
|
||||
return get_redis_client().incr('hits')
|
||||
except redis.exceptions.ConnectionError as exc:
|
||||
if retries == 0:
|
||||
raise exc
|
||||
retries -= 1
|
||||
time.sleep(0.5)
|
||||
|
||||
@app.route('/')
|
||||
def hello():
|
||||
count = get_hit_count()
|
||||
return f'Hello World! I have been seen {count} times.\n'
|
||||
|
||||
if __name__ == '__main__':
|
||||
app.run(debug=True)
|
||||
Loading…
x
Reference in New Issue
Block a user