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Kernel: Python 2 (SageMath)
import pandas as pd
import matplotlib.pyplot as plt
#http://www.data.jma.go.jp/gmd/risk/obsdl/#

df = pd.read_csv('data.csv', index_col='年月日', parse_dates=['年月日'])

weather = df[['最低気温(℃)', '最高気温(℃)', '降雪量合計(cm)', '日照時間(時間)']]
weather.columns = ['min_temp', 'temp', 's_dep', 'dl']
weather.index.names = ['dt']

w16 = weather.loc['2016/10/1': '2017/2/1']
w15 = weather.loc['2015/10/1': '2016/2/1']

idx16 = w16.index
idx15 = w15.index

plt.figure(figsize=(14, 6))

plt.subplot(211)
plt.bar(idx16, w15['s_dep'],color='g',label='15', alpha=0.7)
plt.bar(idx16, w16['s_dep'],color='r',label='16', alpha=0.7)
plt.legend()
plt.title('amount of snow (by day)')


plt.subplot(212)
#plt.plot(idx16, w16['temp'], color='r')
#plt.plot(idx16, w15['temp'], color='g')
plt.plot(idx16, w15['temp'].rolling(window=5, min_periods=2).mean(), color='lightsage', label='15(max)')
plt.plot(idx16, w16['temp'].rolling(window=5, min_periods=2).mean(), color='tomato', label='16(max)')
plt.plot(idx16, w15['min_temp'].rolling(window=5, min_periods=2).mean(), color='darkgreen', label='15(min)')
plt.plot(idx16, w16['min_temp'].rolling(window=5, min_periods=2).mean(), color='red', label='16(min)')


plt.title('max/min temperature')


plt.legend()
plt.tight_layout()
plt.show()
Image in a Jupyter notebook
for y, w in {'2015': w15, '2016': w16}.items():
    print(y)
    print(w.describe())
    print('sum s_dep: {}'.format(w['s_dep'].sum()))
    print('sum dl: {}'.format(w['dl'].sum()))
2015 min_temp temp s_dep dl count 124.000000 124.000000 124.000000 124.000000 mean 0.132258 6.455645 2.000000 3.607258 std 5.699031 7.195903 5.068635 2.925086 min -10.700000 -5.400000 0.000000 0.000000 25% -4.500000 0.275000 0.000000 1.175000 50% 0.100000 5.500000 0.000000 3.200000 75% 4.500000 12.750000 1.000000 5.600000 max 11.700000 22.100000 30.000000 10.700000 sum s_dep: 248 sum dl: 447.3 2016 min_temp temp s_dep dl count 124.000000 124.000000 124.000000 124.000000 mean -1.355645 5.238710 2.854839 3.175806 std 6.112114 7.216475 6.443871 2.825907 min -12.800000 -7.400000 0.000000 0.000000 25% -6.025000 -0.400000 0.000000 0.500000 50% -2.150000 3.300000 0.000000 2.700000 75% 1.800000 10.525000 3.000000 5.100000 max 15.300000 24.700000 44.000000 10.400000 sum s_dep: 354 sum dl: 393.8 
plt.figure(figsize=(14,8))


plt.subplot(221)
w15['s_dep'].hist(cumulative=False,color='g',alpha=0.6,label='15')
w16['s_dep'].hist(cumulative=False,color='r',alpha=0.6,label='16')
plt.title('amount of snow (histgram)')
plt.legend()


plt.subplot(222)
w16['s_dep'].hist(cumulative=True,color='r',alpha=1,label='16',histtype='step')
w15['s_dep'].hist(cumulative=True,color='g',alpha=1,label='15',histtype='step')
plt.title('amount of snow (cumulative)')
plt.legend()

plt.subplot(223)
w15['temp'].hist(cumulative=False,color='g',alpha=0.6,label='15', range=(-15,25))
w16['temp'].hist(cumulative=False,color='r',alpha=0.6,label='16', range=(-15,25))
plt.title('max temp (histgram)')
plt.legend()

plt.subplot(224)
w15['min_temp'].hist(cumulative=False,color='g',alpha=0.6,label='15', range=(-15,25))
w16['min_temp'].hist(cumulative=False,color='r',alpha=0.6,label='16', range=(-15,25))
plt.title('min temp (histgram)')
plt.legend()



plt.show()
Image in a Jupyter notebook
for y,d in {'2015': w15, '2016': w16}.items():
    print(y + '年')
    print('最高気温 <= 0 {}'.format(d[d.temp<= 0].count()['temp']))
    print('最高気温 <= -5: {}'.format(d[d.min_temp<= -10].count()['temp']))
    print('最高気温 <= -10: {}'.format(d[d.min_temp<= -10].count()['temp']))
    print('最高気温 <= -15: {}'.format(d[d.min_temp<= -15].count()['temp']))
2015年 最高気温 <= 0 27 最高気温 <= -5: 1 最高気温 <= -10: 1 最高気温 <= -15: 0 2016年 最高気温 <= 0 37 最高気温 <= -5: 4 最高気温 <= -10: 4 最高気温 <= -15: 0