change code from jupyter notebook to .py file

src/hyopthesis about temperature and number of complaints per day.ipynb → src/.ipynb_checkpoints/hyopthesis about temperature and number of complaints per day-checkpoint.ipynb

@@ -87,7 +87,7 @@
     "fig.tight_layout()\n",
     "\n",
     "plt.title('Correalation between Average Temperature per Month and Number of days with extremely high complaints per Month')\n",
-    "plt.savefig('../plots/cor2.png')"
+    "plt.savefig('../plots/cor1.png')"
    ]
   },
   {

src/col1.py

@@ -0,0 +1,54 @@
+import matplotlib.pyplot as plt
+from datetime import datetime
+import numpy as np
+import pandas as pd
+#%matplotlib inline
+f = open('../output/count_day.out','r')
+xx = []
+yy = []
+for line in f.readlines():
+    day = line.split('\t')[0]
+    xx.append(day)
+    yy.append(int(line.split('\t')[1]))
+date = [datetime.strptime(x,'%Y-%m-%d') for x in xx]
+
+yy1 = yy[-2651:]
+xx1 = [i for i in xx if i.split('-')[0] != '2009']
+
+weather1 = pd.read_csv('../output/weather2010-2011.csv')
+weather2 = pd.read_csv('../output/weather2012-2013.csv')
+weather3 = pd.read_csv('../output/weather2014-2015.csv')
+weather4 = pd.read_csv('../output/weather2016-2017.csv')
+
+frames = [weather1, weather2, weather3, weather4]
+weather = pd.concat(frames)
+
+weather = weather[['YEARMODA', 'TEMP']]
+weather = weather.groupby(['YEARMODA'])['TEMP'].mean()
+weather.index.name = None
+weather = np.asarray(weather)
+weather = pd.DataFrame({'Temperatue':weather}, index = xx1)
+
+complaints = pd.DataFrame({'Complaints': yy1}, index = xx1)
+
+df = pd.concat([weather, complaints], axis=1)
+
+fig, ax1 = plt.subplots(figsize = (18, 12))
+t = np.arange(1, 2652)
+ax1.plot(t, df['Temperatue'], 'b', label = 'Average Temperature per Month')
+ax1.set_xlabel('Date Index')
+ax1.set_ylabel('Average Temperature per Day', color='b')
+ax1.tick_params('y', colors='b')
+
+ax2 = ax1.twinx()
+ax2.plot(t, df['Complaints'], 'g')
+ax2.set_ylabel('Number of Complaints per Day', color='g')
+ax2.tick_params('y', colors='g')
+
+fig.tight_layout()
+
+plt.title('Correalation between Average Temperature per Month and Number of days with extremely high complaints per Month')
+plt.savefig('../plots/cor1.png')
+
+from scipy.stats import pearsonr 
+print(pearsonr(df['Temperatue'], df['Complaints']))

src/col2.py

@@ -0,0 +1,69 @@
+import matplotlib.pyplot as plt
+from datetime import datetime
+import numpy as np
+import pandas as pd
+#%matplotlib inline
+f = open('../output/count_day.out','r')
+xx = []
+yy = []
+for line in f.readlines():
+    day = line.split('\t')[0]
+    xx.append(day)
+    yy.append(int(line.split('\t')[1]))
+date = [datetime.strptime(x,'%Y-%m-%d') for x in xx]
+
+xx1 = [i for i in xx if i.split('-')[0] != '2009']
+yy1 = yy[-2651:]
+
+index = sorted(range(len(yy)), key=lambda i: yy[i], reverse=True)[:100]
+lv = [yy[i] for i in index]
+ld = [xx[i] for i in index]
+
+p1=[]
+for j in range(1, 13):
+    p1.append(sum([1 for i in ld if int(i.split('-')[1]) == j]))
+
+weather1 = pd.read_csv('../output/weather2010-2011.csv')
+weather2 = pd.read_csv('../output/weather2012-2013.csv')
+weather3 = pd.read_csv('../output/weather2014-2015.csv')
+weather4 = pd.read_csv('../output/weather2016-2017.csv')
+
+frames = [weather1, weather2, weather3, weather4]
+weather = pd.concat(frames)
+weather = weather[['YEARMODA', 'TEMP']]
+weather = weather.groupby(['YEARMODA'])['TEMP'].mean()
+weather.index.name = None
+weather = np.asarray(weather)
+weather = pd.DataFrame({'Temperatue':weather}, index = xx1)
+
+complaints = pd.DataFrame({'Complaints': yy1}, index = xx1)
+df = pd.concat([weather, complaints], axis=1)
+
+df.index = [i.split('-')[1] for i in df.index]
+del df['Complaints']
+df1 = df.groupby(df.index)['Temperatue'].mean()
+df1.index = [1, 10, 11, 12, 2, 3, 4, 5, 6, 7, 8, 9]
+
+tem = []
+for i in range(1, 13):
+    tem.append(df1.ix[i])
+
+fig, ax1 = plt.subplots(figsize = (12, 8))
+t = np.arange(1, 13)
+ax1.plot(t, tem, 'b', label = 'Average Temperature per Month')
+ax1.set_xlabel('Month')
+ax1.set_ylabel('Average Temperature per Day', size = 'xx-large', color='b')
+ax1.tick_params('y', colors='b')
+
+ax2 = ax1.twinx()
+ax2.plot(t, p1, 'g')
+ax2.set_ylabel('Number of days with extremely high complaints per Month', size = 'xx-large', color='g')
+ax2.tick_params('y', colors='g')
+
+fig.tight_layout()
+
+plt.title('Correalation between Average Temperature per Month and Number of days with extremely high complaints per Month')
+plt.savefig('../plots/cor2.png')
+
+from scipy.stats import pearsonr 
+print(pearsonr(tem, p1))