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nCov2019数据分析

数据来源

本数据来源于2019新型冠状病毒疫情时间序列数据仓库，其数据来源为丁香园。

%matplotlib inline

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import data_util
import plot_util

from IPython.display import display, Markdown
raw_data = data_util.load_data()

全国数据校验

全国数据是数值较大、自然增长、概率分布、应不受人为干预的数据，所以它应该满足本福特定律（Benford's Law, First-Digit Law），即数据首位数字越小它出现概率越高。比如首位数字是1的概率比9高很多。

本福特定律只是数据真实的必要不充分条件，如果只公布部分数据，且已公布数据也恰好符合上面条件，则可以规避开本数据校验。

raw_city_confirmed = raw_data['city_confirmedCount']

benford_raw = data_util.benford(raw_city_confirmed)
figure = plot_util.plot_bar(benford_raw, '全国数据校验（本福特定律）', 'Digit', 'Percent')

png

city_name = '深圳'
display(Markdown('## ' + city_name + '数据'))

深圳数据

raw_data = data_util.load_data()
display(Markdown('### ' + city_name + '累计数量'))

深圳累计数量

city_daily_data = data_util.aggregate_daily(raw_data, city_name)
city_daily_data = data_util.calculate_dead_cured_rate(city_daily_data)
city_daily_data.tail(5)
<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } 
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</style>
provinceName provinceEnglishName province_zipCode cityName cityEnglishName city_zipCode confirmed suspected cured dead updateTime updateDate dead_rate cured_rate
5580 广东省 Guangdong 440000 深圳 Shenzhen 440300.0 416 0 199 2 2020-02-20 19:07:19.834 2020-02-20 0.480769 47.836538
4495 广东省 Guangdong 440000 深圳 Shenzhen 440300.0 416 0 222 2 2020-02-21 18:12:13.066 2020-02-21 0.480769 53.365385
2753 广东省 Guangdong 440000 深圳 Shenzhen 440300.0 417 0 226 2 2020-02-22 18:01:40.406 2020-02-22 0.479616 54.196643
1065 广东省 Guangdong 440000 深圳 Shenzhen 440300.0 417 0 237 3 2020-02-23 19:19:02.938 2020-02-23 0.719424 56.834532
32 广东省 Guangdong 440000 深圳 Shenzhen 440300.0 417 0 249 3 2020-02-24 18:16:54.754 2020-02-24 0.719424 59.712230 
figure = plot_util.plot_conf_main(city_daily_data, city_name + '累计')

png

display(Markdown('### ' + city_name + '增长速度'))

深圳增长速度

city_daily_data_1st_derivative = data_util.diff(city_daily_data)
city_daily_data_1st_derivative.tail()
<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } 
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.dataframe thead th {
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</style>
provinceName provinceEnglishName province_zipCode cityName cityEnglishName city_zipCode confirmed suspected cured dead updateTime updateDate dead_rate cured_rate
5580 广东省 Guangdong 440000 深圳 Shenzhen 440300.0 0.0 0.0 17.0 0.0 2020-02-20 19:07:19.834 2020-02-20 0.000000 4.086538
4495 广东省 Guangdong 440000 深圳 Shenzhen 440300.0 0.0 0.0 23.0 0.0 2020-02-21 18:12:13.066 2020-02-21 0.000000 5.528846
2753 广东省 Guangdong 440000 深圳 Shenzhen 440300.0 1.0 0.0 4.0 0.0 2020-02-22 18:01:40.406 2020-02-22 -0.001153 0.831258
1065 广东省 Guangdong 440000 深圳 Shenzhen 440300.0 0.0 0.0 11.0 1.0 2020-02-23 19:19:02.938 2020-02-23 0.239808 2.637890
32 广东省 Guangdong 440000 深圳 Shenzhen 440300.0 0.0 0.0 12.0 0.0 2020-02-24 18:16:54.754 2020-02-24 0.000000 2.877698 
figure = plot_util.plot_conf_main(city_daily_data_1st_derivative, city_name + '增长速度')

png

display(Markdown('### ' + city_name + '增长加速度'))

深圳增长加速度

city_daily_data_2nd_derivative = data_util.diff(city_daily_data_1st_derivative)
city_daily_data_2nd_derivative.tail()
<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } 
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.dataframe thead th {
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}
</style>
provinceName provinceEnglishName province_zipCode cityName cityEnglishName city_zipCode confirmed suspected cured dead updateTime updateDate dead_rate cured_rate
5580 广东省 Guangdong 440000 深圳 Shenzhen 440300.0 0.0 0.0 -2.0 0.0 2020-02-20 19:07:19.834 2020-02-20 0.000000 -0.480769
4495 广东省 Guangdong 440000 深圳 Shenzhen 440300.0 0.0 0.0 6.0 0.0 2020-02-21 18:12:13.066 2020-02-21 0.000000 1.442308
2753 广东省 Guangdong 440000 深圳 Shenzhen 440300.0 1.0 0.0 -19.0 0.0 2020-02-22 18:01:40.406 2020-02-22 -0.001153 -4.697588
1065 广东省 Guangdong 440000 深圳 Shenzhen 440300.0 -1.0 0.0 7.0 1.0 2020-02-23 19:19:02.938 2020-02-23 0.240961 1.806632
32 广东省 Guangdong 440000 深圳 Shenzhen 440300.0 0.0 0.0 1.0 -1.0 2020-02-24 18:16:54.754 2020-02-24 -0.239808 0.239808 
figure = plot_util.plot_conf_main(city_daily_data_2nd_derivative, city_name + '增长加速度')
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 8722 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 8722 missing from current font.
  font.set_text(s, 0, flags=flags)

png

display(Markdown('### ' + city_name + '死亡治愈率'))

深圳死亡治愈率

figure = plot_util.plot_conf_dead_cured_ratio(city_daily_data, city_name + '死亡治愈率%')

png

black_province_name = '湖北省'
display(Markdown('## 全国数据（除' + black_province_name + '）'))

全国数据（除湖北省）

display(Markdown('因' + black_province_name + '灾情特别严重且现已隔离（' + black_province_name + '加油），它的数据可能和全国其他地区有较大差别。为更精确预计其他地区的未来发展趋势，这里考虑排除其以外的全国其他地区情况。'))

因湖北省灾情特别严重且现已隔离（湖北省加油），它的数据可能和全国其他地区有较大差别。为更精确预计其他地区的未来发展趋势，这里考虑排除其以外的全国其他地区情况。

display(Markdown('## 全国累计（除' + black_province_name + '）'))

全国累计（除湖北省）

white_daily_data = data_util.aggregate_daily_except(raw_data, province_name=black_province_name)
white_daily_data = data_util.calculate_dead_cured_rate(white_daily_data)
figure = plot_util.plot_conf_main(white_daily_data, '全国累计（除' + black_province_name + '）')

png

display(Markdown('## 全国增长速度（除' + black_province_name + '）'))

全国增长速度（除湖北省）

white_daily_data_1st_derivative = data_util.diff(white_daily_data)
white_daily_data_1st_derivative.tail()
<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } 
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province_zipCode city_zipCode confirmed suspected cured dead dead_rate cured_rate
updateDate
2020-02-20 151280000 139543970.0 25.0 0.0 572.0 2.0 0.014783 4.479124
2020-02-21 153240000 140806270.0 476.0 0.0 610.0 4.0 0.008554 2.937501
2020-02-22 140740000 129510470.0 -348.0 0.0 418.0 6.0 0.064324 4.700671
2020-02-23 152910000 141346370.0 196.0 0.0 583.0 -2.0 -0.025925 3.689261
2020-02-24 130300000 123221439.0 -456.0 0.0 71.0 -1.0 0.015976 2.751535 
figure = plot_util.plot_conf_main(white_daily_data_1st_derivative, '全国增长速度（除' + black_province_name + '）')

png

display(Markdown('## 全国增长加速度（除' + black_province_name + '）'))

全国增长加速度（除湖北省）

white_daily_data_2nd_derivative = data_util.diff(white_daily_data_1st_derivative)
white_daily_data_2nd_derivative.tail()
<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } 
.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
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}
</style>
province_zipCode city_zipCode confirmed suspected cured dead dead_rate cured_rate
updateDate
2020-02-20 151280000 139543970.0 -29.0 0.0 57.0 -4.0 -0.030846 0.530595
2020-02-21 153240000 140806270.0 451.0 0.0 38.0 2.0 -0.006229 -1.541622
2020-02-22 140740000 129510470.0 -824.0 0.0 -192.0 2.0 0.055770 1.763170
2020-02-23 152910000 141346370.0 544.0 0.0 165.0 -8.0 -0.090249 -1.011410
2020-02-24 130300000 123221439.0 -652.0 0.0 -512.0 1.0 0.041901 -0.937726 
figure = plot_util.plot_conf_main(white_daily_data_2nd_derivative, '全国增长加速度（除' + black_province_name + '）')

png

display(Markdown('## 全国增死亡治愈率（除' + black_province_name + '）'))

全国增死亡治愈率（除湖北省）

figure = plot_util.plot_conf_dead_cured_ratio(white_daily_data, '全国增死亡治愈率%（除' + black_province_name + '）')

png

city_name = '武汉'
display(Markdown('## ' + city_name + '数据'))

武汉数据

raw_data = data_util.load_data()
display(Markdown('### ' + city_name + '累计数量'))

武汉累计数量

city_daily_data = data_util.aggregate_daily(raw_data, city_name)
city_daily_data = data_util.calculate_dead_cured_rate(city_daily_data)
city_daily_data.tail(5)
<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } 
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.dataframe thead th {
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</style>
provinceName provinceEnglishName province_zipCode cityName cityEnglishName city_zipCode confirmed suspected cured dead updateTime updateDate dead_rate cured_rate
5336 湖北省 Hubei 420000 武汉 Wuhan 420100.0 45027 0 5598 1585 2020-02-20 22:08:31.581 2020-02-20 3.520110 12.432540
4068 湖北省 Hubei 420000 武汉 Wuhan 420100.0 45346 0 6281 1684 2020-02-21 20:57:17.698 2020-02-21 3.713668 13.851277
2545 湖北省 Hubei 420000 武汉 Wuhan 420100.0 45660 0 7292 1774 2020-02-22 20:55:28.727 2020-02-22 3.885239 15.970215
928 湖北省 Hubei 420000 武汉 Wuhan 420100.0 46201 0 8189 1856 2020-02-23 20:06:03.658 2020-02-23 4.017229 17.724725
0 湖北省 Hubei 420000 武汉 Wuhan 420100.0 46607 0 8950 1987 2020-02-24 19:12:21.027 2020-02-24 4.263308 19.203124 
figure = plot_util.plot_conf_main(city_daily_data, city_name + '累计')

png

display(Markdown('### ' + city_name + '增长速度'))

武汉增长速度

city_daily_data_1st_derivative = data_util.diff(city_daily_data)
city_daily_data_1st_derivative.tail()
<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } 
.dataframe tbody tr th {
    vertical-align: top;
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.dataframe thead th {
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}
</style>
provinceName provinceEnglishName province_zipCode cityName cityEnglishName city_zipCode confirmed suspected cured dead updateTime updateDate dead_rate cured_rate
5336 湖北省 Hubei 420000 武汉 Wuhan 420100.0 615.0 0.0 558.0 88.0 2020-02-20 22:08:31.581 2020-02-20 0.149400 1.084256
4068 湖北省 Hubei 420000 武汉 Wuhan 420100.0 319.0 0.0 683.0 99.0 2020-02-21 20:57:17.698 2020-02-21 0.193558 1.418736
2545 湖北省 Hubei 420000 武汉 Wuhan 420100.0 314.0 0.0 1011.0 90.0 2020-02-22 20:55:28.727 2020-02-22 0.171570 2.118938
928 湖北省 Hubei 420000 武汉 Wuhan 420100.0 541.0 0.0 897.0 82.0 2020-02-23 20:06:03.658 2020-02-23 0.131990 1.754510
0 湖北省 Hubei 420000 武汉 Wuhan 420100.0 406.0 0.0 761.0 131.0 2020-02-24 19:12:21.027 2020-02-24 0.246079 1.478399 
figure = plot_util.plot_conf_main(city_daily_data_1st_derivative, city_name + '增长速度')

png

display(Markdown('### ' + city_name + '增长加速度'))

武汉增长加速度

city_daily_data_2nd_derivative = data_util.diff(city_daily_data_1st_derivative)
city_daily_data_2nd_derivative.tail()
<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } 
.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
provinceName provinceEnglishName province_zipCode cityName cityEnglishName city_zipCode confirmed suspected cured dead updateTime updateDate dead_rate cured_rate
5336 湖北省 Hubei 420000 武汉 Wuhan 420100.0 -1045.0 0.0 -229.0 -28.0 2020-02-20 22:08:31.581 2020-02-20 0.008947 -0.315955
4068 湖北省 Hubei 420000 武汉 Wuhan 420100.0 -296.0 0.0 125.0 11.0 2020-02-21 20:57:17.698 2020-02-21 0.044159 0.334480
2545 湖北省 Hubei 420000 武汉 Wuhan 420100.0 -5.0 0.0 328.0 -9.0 2020-02-22 20:55:28.727 2020-02-22 -0.021988 0.700201
928 湖北省 Hubei 420000 武汉 Wuhan 420100.0 227.0 0.0 -114.0 -8.0 2020-02-23 20:06:03.658 2020-02-23 -0.039580 -0.364428
0 湖北省 Hubei 420000 武汉 Wuhan 420100.0 -135.0 0.0 -136.0 49.0 2020-02-24 19:12:21.027 2020-02-24 0.114089 -0.276111 
figure = plot_util.plot_conf_main(city_daily_data_2nd_derivative, city_name + '增长加速度')

png

display(Markdown('### ' + city_name + '死亡治愈率'))

武汉死亡治愈率

figure = plot_util.plot_conf_dead_cured_ratio(city_daily_data, city_name + '死亡治愈率%')

png

display(Markdown('### ' + city_name + '数据校验'))

武汉数据校验

city_confirmed = city_daily_data['confirmed']

benford_raw = data_util.benford(city_confirmed)
figure = plot_util.plot_bar(benford_raw, city_name + '数据校验（本福特定律）', 'Digit', 'Percent')

png

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