第五次作业

1.统计质量等级对应的天数，例如：
优：5天
良：3天
中度污染：2天
2.找出PMI2.5的最大值和最小值，分别指出是哪一天。

import pandas as pd
import numpy as np

path = open(r"C:\Users\Administrator\Desktop\pmi_days.csv")
data = pd.read_csv(path)

gp = data.groupby('质量等级')

you = dict([x for x in gp])['优']
liang = dict([x for x in gp])['良']
qing = dict([x for x in gp])['轻度污染']
zhong = dict([x for x in gp])['中度污染']
print("优：{}天\n良：{}天\n轻度污染：{}天\n中度污染：{}天".format(len(you.index),len(liang.index),
len(qing.index),len(zhong.index)))

pm = data.sort_values(by='PM2.5')
pm_1 = pm.reset_index(drop=True)#低到高排列日期
print("PM2.5的最大的一天是：{}\t数值：{}".format(pm_1["PM2.5"][29],pm_1["日期"][29]))
print("PM2.5的最小的一天是：{}\t数值：{}".format(pm_1["PM2.5"][0],pm_1["日期"][0]))

习题2：读入文件1980-2018GDP.csv，完成以下操作：
1.按行输出每年GDP数据，表头列名如文件第1行所示。

2.将各年GDP数据转换成字典格式，以年份为keys,其它值为values（数据类型为列表方式）,例如：
{
2017:[827121.7,6.8%,60989]
........
}

import pandas as pd

days_path = open(r"C:\Users\1980-2018GDP.csv")
days_list = pd.read_csv(days_path)
print(days_list, "\t\t\n")
dict_GDP = days_list.set_index('年份').T.to_dict('list')
print("字典：", dict_GDP, "\n")
data_max = max(dict_GDP, key=dict_GDP.get)
data_min = min(dict_GDP, key=dict_GDP.get)
print("GDP最大值：", data_max, dict_GDP[data_max], "\n")
print("GDP最小值：", data_min, dict_GDP[data_min])

3.遍历字典数据，求出GDP的最小值与最大值，并输出数据与对应的年份。

import pandas as p
path = open(r"1980-2018GDP.csv")
list = p.read_csv(path)
print(list, "\t\t\n")
GDP = list.set_index('年份').T.to_dict('list')
print("字典：\n", GDP, "\n")
year_max = max(GDP, key=GDP.get)
year_min = min(GDP, key=GDP.get)
print("GDP最大值：", year_max, GDP[year_max], "\n")
print("GDP最小值：", year_min, GDP[year_min])

字典：{1978: [3645.2, '0.00%', 381, nan], 1979: [4062.6, '7.60%', 419, nan], 1980: [4545.6, '7.80%', 463, '6.00%'], 1981: [4891.6, '5.30%', 492, '2.40%'], 1982: [5323.4, '9.00%', 528, '1.90%'], 1983: [5962.7, '10.90%', 583, '1.50%'], 1984: [7208.1, '15.20%', 695, '2.80%'], 1985: [9016.0, '13.50%', 858, '9.40%'], 1986: [10275.2, '8.90%', 963, '6.50%'], 1987: [12058.6, '11.60%', 1112, '7.30%'], 1988: [15042.8, '11.30%', 1366, '18.80%'], 1989: [16992.3, '4.10%', 1519, '18.00%'], 1990: [18667.8, '3.80%', 1644, '3.10%'], 1991: [21781.5, '9.20%', 1893, '3.40%'], 1992: [26923.5, '14.20%', 2311, '6.40%'], 1993: [35333.9, '14.00%', 2998, '14.70%'], 1994: [48197.9, '13.10%', 4044, '24.10%'], 1995: [60793.7, '10.90%', 5046, '17.10%'], 1996: [71176.6, '10.00%', 5846, '8.30%'], 1997: [78973.0, '9.30%', 6420, '2.80%'], 1998: [84402.3, '7.80%', 6796, '-0.80%'], 1999: [89677.1, '7.60%', 7159, '-1.40%'], 2000: [99214.6, '8.40%', 7858, '0.40%'], 2001: [109655.2, '8.30%', 8622, '0.70%'], 2002: [120332.7, '9.10%', 9398, '-0.80%'], 2003: [135822.8, '10.00%', 10542, '1.20%'], 2004: [159878.3, '10.10%', 12336, '3.90%'], 2005: [183084.8, '10.20%', 14040, '1.80%'], 2006: [211923.8, '11.60%', 16024, '1.50%'], 2007: [249530.6, '11.90%', 18868, '4.80%'], 2008: [300670.0, '9.00%', 23128, '5.90%'], 2009: [340507.0, '9.10%', 25608, '-0.70%'], 2010: [397980.0, '10.30%', 30015, '3.30%'], 2011: [471564.0, '9.20%', 36403, '5.40%'], 2012: [519322.0, '7.80%', 40007, '2.60%'], 2013: [568845.0, '7.70%', 43852, '2.60%'], 2014: [636463.0, '7.40%', 47203, '2%'], 2015: [677000.0, '6.90%',