In [1]:
import pandas as pd
import glob
import os
pd.options.mode.chained_assignment = None
In [2]:
path = r'C:\Users\Ty Dickinson\Downloads\DailyKBDI\County_Summary_Tables\Old_Process\1995'
all_files = glob.glob(os.path.join(path, "*.txt"))
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
number1995 = range(0,256)
number1995 = [x for x in number1995 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
table1995 = pd.concat((pd.read_fwf(f, header=None, skiprows=number1995,
names=NewNames) for f in all_files))
index1995 = []
for i in range(0,12045):
index1995.insert(i,i)
table1995 = table1995.set_index([index1995])
table1995['Date'] = table1995.index
date = '01-01-1995'
date = pd.to_datetime(date)
x = 0
y = 33
for i in range(0,365):
for j in range(x,y):
table1995.Date[j] = date + pd.DateOffset(days=i)
j += 1
x = y
y += 33
i += 1
spring1 = pd.to_datetime('03-01-1995')
spring2 = pd.to_datetime('05-31-1995')
summer1 = pd.to_datetime('06-01-1995')
summer2 = pd.to_datetime('08-31-1995')
fall1 = pd.to_datetime('09-01-1995')
fall2 = pd.to_datetime('11-30-1995')
table1995 = table1995.set_index(['Date'])
spring1995 = table1995.loc[spring1:spring2]
summer1995 = table1995.loc[summer1:summer2]
fall1995 = table1995.loc[fall1:fall2]
spring1995 = spring1995.sort_values(['County'])
summer1995 = summer1995.sort_values(['County'])
fall1995 = fall1995.sort_values(['County'])
spring1995list = []
summer1995list = []
fall1995list = []
x = 0
y = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(x,y):
if (spring1995.Average[j] >= 500) or (spring1995.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
spring1995list.insert(i, ThresholdDays)
x = y
y += 92
i += 1
a = 0
b = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(a,b):
if (summer1995.Average[j] >= 500) or (summer1995.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
summer1995list.insert(i, ThresholdDays)
a = b
b += 92
i += 1
c = 0
d = 91
for i in range(0,33):
ThresholdDays = 0
for j in range(c,d):
if (fall1995.Average[j] >= 500) or (fall1995.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
fall1995list.insert(i, ThresholdDays)
c = d
d += 91
i += 1
In [3]:
path = r'C:\Users\Ty Dickinson\Downloads\DailyKBDI\County_Summary_Tables\Old_Process\1996'
all_files = glob.glob(os.path.join(path, "*.txt"))
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
number1996 = range(0,256)
number1996 = [x for x in number1996 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
table1996 = pd.concat((pd.read_fwf(f, header=None, skiprows=number1996,
names=NewNames) for f in all_files))
index1996 = []
for i in range(0,12078):
index1996.insert(i,i)
table1996 = table1996.set_index([index1996])
table1996['Date'] = table1996.index
date = '01-01-1996'
date = pd.to_datetime(date)
x = 0
y = 33
for i in range(0,366):
for j in range(x,y):
table1996.Date[j] = date + pd.DateOffset(days=i)
j += 1
x = y
y += 33
i += 1
spring1 = pd.to_datetime('03-01-1996')
spring2 = pd.to_datetime('05-31-1996')
summer1 = pd.to_datetime('06-01-1996')
summer2 = pd.to_datetime('08-31-1996')
fall1 = pd.to_datetime('09-01-1996')
fall2 = pd.to_datetime('11-30-1996')
table1996 = table1996.set_index(['Date'])
spring1996 = table1996.loc[spring1:spring2]
summer1996 = table1996.loc[summer1:summer2]
fall1996 = table1996.loc[fall1:fall2]
spring1996 = spring1996.sort_values(['County'])
summer1996 = summer1996.sort_values(['County'])
fall1996 = fall1996.sort_values(['County'])
spring1996list = []
summer1996list = []
fall1996list = []
x = 0
y = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(x,y):
if (spring1996.Average[j] >= 500) or (spring1996.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
spring1996list.insert(i, ThresholdDays)
x = y
y += 92
i += 1
a = 0
b = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(a,b):
if (summer1996.Average[j] >= 500) or (summer1996.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
summer1996list.insert(i, ThresholdDays)
a = b
b += 92
i += 1
c = 0
d = 91
for i in range(0,33):
ThresholdDays = 0
for j in range(c,d):
if (fall1996.Average[j] >= 500) or (fall1996.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
fall1996list.insert(i, ThresholdDays)
c = d
d += 91
i += 1
In [4]:
path = r'C:\Users\Ty Dickinson\Downloads\DailyKBDI\County_Summary_Tables\Old_Process\1997'
all_files = glob.glob(os.path.join(path, "*.txt"))
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
number1997 = range(0,256)
number1997 = [x for x in number1995 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
table1997 = pd.concat((pd.read_fwf(f, header=None, skiprows=number1997,
names=NewNames) for f in all_files))
index1997 = []
for i in range(0,12045):
index1997.insert(i,i)
table1997 = table1997.set_index([index1997])
table1997['Date'] = table1997.index
date = '01-01-1997'
date = pd.to_datetime(date)
x = 0
y = 33
for i in range(0,365):
for j in range(x,y):
table1997.Date[j] = date + pd.DateOffset(days=i)
j += 1
x = y
y += 33
i += 1
spring1 = pd.to_datetime('03-01-1997')
spring2 = pd.to_datetime('05-31-1997')
summer1 = pd.to_datetime('06-01-1997')
summer2 = pd.to_datetime('08-31-1997')
fall1 = pd.to_datetime('09-01-1997')
fall2 = pd.to_datetime('11-30-1997')
table1997 = table1997.set_index(['Date'])
spring1997 = table1997.loc[spring1:spring2]
summer1997 = table1997.loc[summer1:summer2]
fall1997 = table1997.loc[fall1:fall2]
spring1997 = spring1997.sort_values(['County'])
summer1997 = summer1997.sort_values(['County'])
fall1997 = fall1997.sort_values(['County'])
spring1997list = []
summer1997list = []
fall1997list = []
x = 0
y = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(x,y):
if (spring1997.Average[j] >= 500) or (spring1997.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
spring1997list.insert(i, ThresholdDays)
x = y
y += 92
i += 1
a = 0
b = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(a,b):
if (summer1997.Average[j] >= 500) or (summer1997.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
summer1997list.insert(i, ThresholdDays)
a = b
b += 92
i += 1
c = 0
d = 91
for i in range(0,33):
ThresholdDays = 0
for j in range(c,d):
if (fall1997.Average[j] >= 500) or (fall1997.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
fall1997list.insert(i, ThresholdDays)
c = d
d += 91
i += 1
In [5]:
path = r'C:\Users\Ty Dickinson\Downloads\DailyKBDI\County_Summary_Tables\Old_Process\1998'
all_files = glob.glob(os.path.join(path, "*.txt"))
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
number1998 = range(0,256)
number1998 = [x for x in number1998 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
table1998 = pd.concat((pd.read_fwf(f, header=None, skiprows=number1998,
names=NewNames) for f in all_files))
index1998 = []
for i in range(0,12045):
index1998.insert(i,i)
table1998 = table1995.set_index([index1998])
table1998['Date'] = table1998.index
date = '01-01-1998'
date = pd.to_datetime(date)
x = 0
y = 33
for i in range(0,365):
for j in range(x,y):
table1998.Date[j] = date + pd.DateOffset(days=i)
j += 1
x = y
y += 33
i += 1
spring1 = pd.to_datetime('03-01-1998')
spring2 = pd.to_datetime('05-31-1998')
summer1 = pd.to_datetime('06-01-1998')
summer2 = pd.to_datetime('08-31-1998')
fall1 = pd.to_datetime('09-01-1998')
fall2 = pd.to_datetime('11-30-1998')
table1998 = table1998.set_index(['Date'])
spring1998 = table1998.loc[spring1:spring2]
summer1998 = table1998.loc[summer1:summer2]
fall1998 = table1998.loc[fall1:fall2]
spring1998 = spring1998.sort_values(['County'])
summer1998 = summer1998.sort_values(['County'])
fall1998 = fall1998.sort_values(['County'])
spring1998list = []
summer1998list = []
fall1998list = []
x = 0
y = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(x,y):
if (spring1998.Average[j] >= 500) or (spring1998.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
spring1998list.insert(i, ThresholdDays)
x = y
y += 92
i += 1
a = 0
b = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(a,b):
if (summer1998.Average[j] >= 500) or (summer1998.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
summer1998list.insert(i, ThresholdDays)
a = b
b += 92
i += 1
c = 0
d = 91
for i in range(0,33):
ThresholdDays = 0
for j in range(c,d):
if (fall1998.Average[j] >= 500) or (fall1998.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
fall1998list.insert(i, ThresholdDays)
c = d
d += 91
i += 1
In [6]:
path = r'C:\Users\Ty Dickinson\Downloads\DailyKBDI\County_Summary_Tables\Old_Process\1999'
all_files = glob.glob(os.path.join(path, "*.txt"))
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
number1999 = range(0,256)
number1999 = [x for x in number1999 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
table1999 = pd.concat((pd.read_fwf(f, header=None, skiprows=number1999,
names=NewNames) for f in all_files))
index1999 = []
for i in range(0,8019):
index1999.insert(i,i)
table1999 = table1999.set_index([index1999])
table1999['Date'] = table1999.index
date = '01-01-1999'
date = pd.to_datetime(date)
x = 0
y = 33
for i in range(0,243):
for j in range(x,y):
table1999.Date[j] = date + pd.DateOffset(days=i)
j += 1
x = y
y += 33
i += 1
spring1 = pd.to_datetime('03-01-1999')
spring2 = pd.to_datetime('05-31-1999')
summer1 = pd.to_datetime('06-01-1999')
summer2 = pd.to_datetime('08-31-1999')
table1999 = table1999.set_index(['Date'])
spring1999 = table1999.loc[spring1:spring2]
summer1999 = table1999.loc[summer1:summer2]
spring1999 = spring1999.sort_values(['County'])
summer1999 = summer1999.sort_values(['County'])
spring1999list = []
summer1999list = []
x = 0
y = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(x,y):
if (spring1999.Average[j] >= 500) or (spring1999.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
spring1999list.insert(i, ThresholdDays)
x = y
y += 92
i += 1
a = 0
b = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(a,b):
if (summer1999.Average[j] >= 500) or (summer1999.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
summer1999list.insert(i, ThresholdDays)
a = b
b += 92
i += 1
In [7]:
path = r'C:\Users\Ty Dickinson\Downloads\DailyKBDI\County_Summary_Tables\Old_Process\2002'
all_files = glob.glob(os.path.join(path, "*.txt"))
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
number2002 = range(0,256)
number2002 = [x for x in number2002 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
table2002 = pd.concat((pd.read_fwf(f, header=None, skiprows=number2002,
names=NewNames) for f in all_files))
index2002 = []
for i in range(0,12045):
index2002.insert(i,i)
table2002 = table2002.set_index([index2002])
table2002['Date'] = table2002.index
date = '01-01-2002'
date = pd.to_datetime(date)
x = 0
y = 33
for i in range(0,365):
for j in range(x,y):
table2002.Date[j] = date + pd.DateOffset(days=i)
j += 1
x = y
y += 33
i += 1
spring1 = pd.to_datetime('03-01-2002')
spring2 = pd.to_datetime('05-31-2002')
summer1 = pd.to_datetime('06-01-2002')
summer2 = pd.to_datetime('08-31-2002')
fall1 = pd.to_datetime('09-01-2002')
fall2 = pd.to_datetime('11-30-2002')
table2002 = table2002.set_index(['Date'])
spring2002 = table2002.loc[spring1:spring2]
summer2002 = table2002.loc[summer1:summer2]
fall2002 = table2002.loc[fall1:fall2]
spring2002 = spring2002.sort_values(['County'])
summer2002 = summer2002.sort_values(['County'])
fall2002 = fall2002.sort_values(['County'])
spring2002list = []
summer2002list = []
fall2002list = []
x = 0
y = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(x,y):
if (spring2002.Average[j] >= 500) or (spring2002.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
spring2002list.insert(i, ThresholdDays)
x = y
y += 92
i += 1
a = 0
b = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(a,b):
if (summer2002.Average[j] >= 500) or (summer2002.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
summer2002list.insert(i, ThresholdDays)
a = b
b += 92
i += 1
c = 0
d = 91
for i in range(0,33):
ThresholdDays = 0
for j in range(c,d):
if (fall2002.Average[j] >= 500) or (fall2002.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
fall2002list.insert(i, ThresholdDays)
c = d
d += 91
i += 1
In [8]:
path = r'C:\Users\Ty Dickinson\Downloads\DailyKBDI\County_Summary_Tables\Old_Process\2003'
all_files = glob.glob(os.path.join(path, "*.txt"))
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
number2003 = range(0,256)
number2003 = [x for x in number2003 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
table2003 = pd.concat((pd.read_fwf(f, header=None, skiprows=number2003,
names=NewNames) for f in all_files))
index2003 = []
for i in range(0,12045):
index2003.insert(i,i)
table2003 = table2003.set_index([index2003])
table2003['Date'] = table2003.index
date = '01-01-2003'
date = pd.to_datetime(date)
x = 0
y = 33
for i in range(0,365):
for j in range(x,y):
table2003.Date[j] = date + pd.DateOffset(days=i)
j += 1
x = y
y += 33
i += 1
spring1 = pd.to_datetime('03-01-2003')
spring2 = pd.to_datetime('05-31-2003')
summer1 = pd.to_datetime('06-01-2003')
summer2 = pd.to_datetime('08-31-2003')
fall1 = pd.to_datetime('09-01-2003')
fall2 = pd.to_datetime('11-30-2003')
table2003 = table2003.set_index(['Date'])
spring2003 = table2003.loc[spring1:spring2]
summer2003 = table2003.loc[summer1:summer2]
fall2003 = table2003.loc[fall1:fall2]
spring2003 = spring2003.sort_values(['County'])
summer2003 = summer2003.sort_values(['County'])
fall2003 = fall2003.sort_values(['County'])
spring2003list = []
summer2003list = []
fall2003list = []
x = 0
y = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(x,y):
if (spring2003.Average[j] >= 500) or (spring2003.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
spring2003list.insert(i, ThresholdDays)
x = y
y += 92
i += 1
a = 0
b = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(a,b):
if (summer2003.Average[j] >= 500) or (summer2003.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
summer2003list.insert(i, ThresholdDays)
a = b
b += 92
i += 1
c = 0
d = 91
for i in range(0,33):
ThresholdDays = 0
for j in range(c,d):
if (fall2003.Average[j] >= 500) or (fall2003.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
fall2003list.insert(i, ThresholdDays)
c = d
d += 91
i += 1
In [9]:
path = r'C:\Users\Ty Dickinson\Downloads\DailyKBDI\County_Summary_Tables\Old_Process\2004'
all_files = glob.glob(os.path.join(path, "*.txt"))
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
number2004 = range(0,256)
number2004 = [x for x in number2004 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
table2004 = pd.concat((pd.read_fwf(f, header=None, skiprows=number2004,
names=NewNames) for f in all_files))
index2004 = []
for i in range(0,12078):
index2004.insert(i,i)
table2004 = table2004.set_index([index2004])
table2004['Date'] = table2004.index
date = '01-01-2004'
date = pd.to_datetime(date)
x = 0
y = 33
for i in range(0,366):
for j in range(x,y):
table2004.Date[j] = date + pd.DateOffset(days=i)
j += 1
x = y
y += 33
i += 1
spring1 = pd.to_datetime('03-01-2004')
spring2 = pd.to_datetime('05-31-2004')
summer1 = pd.to_datetime('06-01-2004')
summer2 = pd.to_datetime('08-31-2004')
fall1 = pd.to_datetime('09-01-2004')
fall2 = pd.to_datetime('11-30-2004')
table2004 = table2004.set_index(['Date'])
spring2004 = table2004.loc[spring1:spring2]
summer2004 = table2004.loc[summer1:summer2]
fall2004 = table2004.loc[fall1:fall2]
spring2004 = spring2004.sort_values(['County'])
summer2004 = summer2004.sort_values(['County'])
fall2004 = fall2004.sort_values(['County'])
spring2004list = []
summer2004list = []
fall2004list = []
x = 0
y = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(x,y):
if (spring2004.Average[j] >= 500) or (spring2004.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
spring2004list.insert(i, ThresholdDays)
x = y
y += 92
i += 1
a = 0
b = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(a,b):
if (summer2004.Average[j] >= 500) or (summer2004.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
summer2004list.insert(i, ThresholdDays)
a = b
b += 92
i += 1
c = 0
d = 91
for i in range(0,33):
ThresholdDays = 0
for j in range(c,d):
if (fall2004.Average[j] >= 500) or (fall2004.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
fall2004list.insert(i, ThresholdDays)
c = d
d += 91
i += 1
In [10]:
path = r'C:\Users\Ty Dickinson\Downloads\DailyKBDI\County_Summary_Tables\Old_Process\2005'
all_files = glob.glob(os.path.join(path, "*.txt"))
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
number2005 = range(0,256)
number2005 = [x for x in number2005 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
table2005 = pd.concat((pd.read_fwf(f, header=None, skiprows=number2005,
names=NewNames) for f in all_files))
index2005 = []
for i in range(0,12045):
index2005.insert(i,i)
table2005 = table2005.set_index([index2005])
table2005['Date'] = table2005.index
date = '01-01-2005'
date = pd.to_datetime(date)
x = 0
y = 33
for i in range(0,365):
for j in range(x,y):
table2005.Date[j] = date + pd.DateOffset(days=i)
j += 1
x = y
y += 33
i += 1
spring1 = pd.to_datetime('03-01-2005')
spring2 = pd.to_datetime('05-31-2005')
summer1 = pd.to_datetime('06-01-2005')
summer2 = pd.to_datetime('08-31-2005')
fall1 = pd.to_datetime('09-01-2005')
fall2 = pd.to_datetime('11-30-2005')
table2005 = table2005.set_index(['Date'])
spring2005 = table2005.loc[spring1:spring2]
summer2005 = table2005.loc[summer1:summer2]
fall2005 = table2005.loc[fall1:fall2]
spring2005 = spring2005.sort_values(['County'])
summer2005 = summer2005.sort_values(['County'])
fall2005 = fall2005.sort_values(['County'])
spring2005list = []
summer2005list = []
fall2005list = []
x = 0
y = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(x,y):
if (spring2005.Average[j] >= 500) or (spring2005.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
spring2005list.insert(i, ThresholdDays)
x = y
y += 92
i += 1
a = 0
b = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(a,b):
if (summer2005.Average[j] >= 500) or (summer2005.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
summer2005list.insert(i, ThresholdDays)
a = b
b += 92
i += 1
c = 0
d = 91
for i in range(0,33):
ThresholdDays = 0
for j in range(c,d):
if (fall2005.Average[j] >= 500) or (fall2005.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
fall2005list.insert(i, ThresholdDays)
c = d
d += 91
i += 1
In [11]:
path = r'C:\Users\Ty Dickinson\Downloads\DailyKBDI\County_Summary_Tables\Old_Process\2006'
all_files = glob.glob(os.path.join(path, "*.txt"))
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
number2006 = range(0,256)
number2006 = [x for x in number2006 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
table2006 = pd.concat((pd.read_fwf(f, header=None, skiprows=number2006,
names=NewNames) for f in all_files))
index2006 = []
for i in range(0,12045):
index2006.insert(i,i)
table2006 = table2006.set_index([index2006])
table2006['Date'] = table2006.index
date = '01-01-2006'
date = pd.to_datetime(date)
x = 0
y = 33
for i in range(0,365):
for j in range(x,y):
table2006.Date[j] = date + pd.DateOffset(days=i)
j += 1
x = y
y += 33
i += 1
spring1 = pd.to_datetime('03-01-2006')
spring2 = pd.to_datetime('05-31-2006')
summer1 = pd.to_datetime('06-01-2006')
summer2 = pd.to_datetime('08-31-2006')
fall1 = pd.to_datetime('09-01-2006')
fall2 = pd.to_datetime('11-30-2006')
table2006 = table2006.set_index(['Date'])
spring2006 = table2006.loc[spring1:spring2]
summer2006 = table2006.loc[summer1:summer2]
fall2006 = table2006.loc[fall1:fall2]
spring2006 = spring2006.sort_values(['County'])
summer2006 = summer2006.sort_values(['County'])
fall2006 = fall2006.sort_values(['County'])
spring2006list = []
summer2006list = []
fall2006list = []
x = 0
y = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(x,y):
if (spring2006.Average[j] >= 500) or (spring2006.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
spring2006list.insert(i, ThresholdDays)
x = y
y += 92
i += 1
a = 0
b = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(a,b):
if (summer2006.Average[j] >= 500) or (summer2006.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
summer2006list.insert(i, ThresholdDays)
a = b
b += 92
i += 1
c = 0
d = 91
for i in range(0,33):
ThresholdDays = 0
for j in range(c,d):
if (fall2006.Average[j] >= 500) or (fall2006.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
fall2006list.insert(i, ThresholdDays)
c = d
d += 91
i += 1
In [12]:
path = r'C:\Users\Ty Dickinson\Downloads\DailyKBDI\County_Summary_Tables\Old_Process\2007'
all_files = glob.glob(os.path.join(path, "*.txt"))
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
number2007 = range(0,256)
number2007 = [x for x in number2007 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
table2007 = pd.concat((pd.read_fwf(f, header=None, skiprows=number2007,
names=NewNames) for f in all_files))
index2007 = []
for i in range(0,12045):
index2007.insert(i,i)
table2007 = table2007.set_index([index2007])
table2007['Date'] = table2007.index
date = '01-01-2007'
date = pd.to_datetime(date)
x = 0
y = 33
for i in range(0,365):
for j in range(x,y):
table2007.Date[j] = date + pd.DateOffset(days=i)
j += 1
x = y
y += 33
i += 1
spring1 = pd.to_datetime('03-01-2007')
spring2 = pd.to_datetime('05-31-2007')
summer1 = pd.to_datetime('06-01-2007')
summer2 = pd.to_datetime('08-31-2007')
fall1 = pd.to_datetime('09-01-2007')
fall2 = pd.to_datetime('11-30-2007')
table2007 = table2007.set_index(['Date'])
spring2007 = table2007.loc[spring1:spring2]
summer2007 = table2007.loc[summer1:summer2]
fall2007 = table2007.loc[fall1:fall2]
spring2007 = spring2007.sort_values(['County'])
summer2007 = summer2007.sort_values(['County'])
fall2007 = fall2007.sort_values(['County'])
spring2007list = []
summer2007list = []
fall2007list = []
x = 0
y = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(x,y):
if (spring2007.Average[j] >= 500) or (spring2007.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
spring2007list.insert(i, ThresholdDays)
x = y
y += 92
i += 1
a = 0
b = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(a,b):
if (summer2007.Average[j] >= 500) or (summer2007.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
summer2007list.insert(i, ThresholdDays)
a = b
b += 92
i += 1
c = 0
d = 91
for i in range(0,33):
ThresholdDays = 0
for j in range(c,d):
if (fall2007.Average[j] >= 500) or (fall2007.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
fall2007list.insert(i, ThresholdDays)
c = d
d += 91
i += 1
In [13]:
path = r'C:\Users\Ty Dickinson\Downloads\DailyKBDI\County_Summary_Tables\Old_Process\2008'
all_files = glob.glob(os.path.join(path, "*.txt"))
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
number2008 = range(0,256)
number2008 = [x for x in number2008 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
table2008 = pd.concat((pd.read_fwf(f, header=None, skiprows=number2008,
names=NewNames) for f in all_files))
index2008 = []
for i in range(0,12012):
index2008.insert(i,i)
table2008 = table2008.set_index([index2008])
table2008['Date'] = table2008.index
date = '01-01-2008'
date = pd.to_datetime(date)
date2 = pd.to_datetime('08-05-2008')
x = 0
y = 33
#Missing August 3, August 4
for i in range(0,215):
for j in range(x,y):
table2008.Date[j] = date + pd.DateOffset(days=i)
j += 1
x = y
y += 33
i += 1
for i in range(216,366):
for j in range(x,y):
table2008.Date[j] = date2 + pd.DateOffset(days=i-216)
j += 1
x = y
y += 33
i += 1
spring1 = pd.to_datetime('03-01-2008')
spring2 = pd.to_datetime('05-31-2008')
summer1 = pd.to_datetime('06-01-2008')
summer2 = pd.to_datetime('08-31-2008')
fall1 = pd.to_datetime('09-01-2008')
fall2 = pd.to_datetime('11-30-2008')
table2008 = table2008.set_index(['Date'])
spring2008 = table2008.loc[spring1:spring2]
summer2008 = table2008.loc[summer1:summer2]
fall2008 = table2008.loc[fall1:fall2]
spring2008 = spring2008.sort_values(['County'])
summer2008 = summer2008.sort_values(['County'])
fall2008 = fall2008.sort_values(['County'])
spring2008list = []
summer2008list = []
fall2008list = []
x = 0
y = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(x,y):
if (spring2008.Average[j] >= 500) or (spring2008.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
spring2008list.insert(i, ThresholdDays)
x = y
y += 92
i += 1
a = 0
b = 90
for i in range(0,33):
ThresholdDays = 0
for j in range(a,b):
if (summer2008.Average[j] >= 500) or (summer2008.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
summer2008list.insert(i, ThresholdDays)
a = b
b += 90
i += 1
c = 0
d = 91
for i in range(0,33):
ThresholdDays = 0
for j in range(c,d):
if (fall2008.Average[j] >= 500) or (fall2008.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
fall2008list.insert(i, ThresholdDays)
c = d
d += 91
i += 1
In [14]:
path = r'C:\Users\Ty Dickinson\Downloads\DailyKBDI\County_Summary_Tables\Old_Process\2009'
all_files = glob.glob(os.path.join(path, "*.txt"))
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
number2009 = range(0,256)
number2009 = [x for x in number2009 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
table2009 = pd.concat((pd.read_fwf(f, header=None, skiprows=number2009,
names=NewNames) for f in all_files))
index2009 = []
for i in range(0,12045):
index2009.insert(i,i)
table2009 = table2009.set_index([index2009])
table2009['Date'] = table2009.index
date = '01-01-2009'
date = pd.to_datetime(date)
x = 0
y = 33
for i in range(0,365):
for j in range(x,y):
table2009.Date[j] = date + pd.DateOffset(days=i)
j += 1
x = y
y += 33
i += 1
spring1 = pd.to_datetime('03-01-2009')
spring2 = pd.to_datetime('05-31-2009')
summer1 = pd.to_datetime('06-01-2009')
summer2 = pd.to_datetime('08-31-2009')
fall1 = pd.to_datetime('09-01-2009')
fall2 = pd.to_datetime('11-30-2009')
table2009 = table2009.set_index(['Date'])
spring2009 = table2009.loc[spring1:spring2]
summer2009 = table2009.loc[summer1:summer2]
fall2009 = table2009.loc[fall1:fall2]
spring2009 = spring2009.sort_values(['County'])
summer2009 = summer2009.sort_values(['County'])
fall2009 = fall2009.sort_values(['County'])
spring2009list = []
summer2009list = []
fall2009list = []
x = 0
y = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(x,y):
if (spring2009.Average[j] >= 500) or (spring2009.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
spring2009list.insert(i, ThresholdDays)
x = y
y += 92
i += 1
a = 0
b = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(a,b):
if (summer2009.Average[j] >= 500) or (summer2009.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
summer2009list.insert(i, ThresholdDays)
a = b
b += 92
i += 1
c = 0
d = 91
for i in range(0,33):
ThresholdDays = 0
for j in range(c,d):
if (fall2009.Average[j] >= 500) or (fall2009.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
fall2009list.insert(i, ThresholdDays)
c = d
d += 91
i += 1
In [15]:
path = r'C:\Users\Ty Dickinson\Downloads\DailyKBDI\County_Summary_Tables\Old_Process\2010'
all_files = glob.glob(os.path.join(path, "*.txt"))
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
number2010 = range(0,256)
number2010 = [x for x in number2010 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
table2010 = pd.concat((pd.read_fwf(f, header=None, skiprows=number2010,
names=NewNames) for f in all_files))
index2010 = []
for i in range(0,11979):
index2010.insert(i,i)
table2010 = table2010.set_index([index2010])
table2010['Date'] = table2010.index
date = '01-01-2010'
date = pd.to_datetime(date)
date2 = pd.to_datetime('08-03-2010')
x = 0
y = 33
#missing August 1, August 2
for i in range(0,212):
for j in range(x,y):
table2010.Date[j] = date + pd.DateOffset(days=i)
j += 1
x = y
y += 33
i += 1
for i in range(213,365):
for j in range(x,y):
table2010.Date[j] = date2 + pd.DateOffset(days=i-213)
j += 1
x = y
y += 33
i += 1
spring1 = pd.to_datetime('03-01-2010')
spring2 = pd.to_datetime('05-31-2010')
summer1 = pd.to_datetime('06-01-2010')
summer2 = pd.to_datetime('08-31-2010')
fall1 = pd.to_datetime('09-01-2010')
fall2 = pd.to_datetime('11-30-2010')
table2010 = table2010.set_index(['Date'])
spring2010 = table2010.loc[spring1:spring2]
summer2010 = table2010.loc[summer1:summer2]
fall2010 = table2010.loc[fall1:fall2]
spring2010 = spring2010.sort_values(['County'])
summer2010 = summer2010.sort_values(['County'])
fall2010 = fall2010.sort_values(['County'])
spring2010list = []
summer2010list = []
fall2010list = []
x = 0
y = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(x,y):
if (spring2010.Average[j] >= 500) or (spring2010.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
spring2010list.insert(i, ThresholdDays)
x = y
y += 92
i += 1
a = 0
b = 90
for i in range(0,33):
ThresholdDays = 0
for j in range(a,b):
if (summer2010.Average[j] >= 500) or (summer2010.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
summer2010list.insert(i, ThresholdDays)
a = b
b += 90
i += 1
c = 0
d = 91
for i in range(0,33):
ThresholdDays = 0
for j in range(c,d):
if (fall2010.Average[j] >= 500) or (fall2010.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
fall2010list.insert(i, ThresholdDays)
c = d
d += 91
i += 1
In [16]:
#August 1, 2010
number2011 = range(0,256)
number2011 = [x for x in number2011 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
table20100801 = pd.read_fwf('http://twc.tamu.edu/weather_images/summ/summ20100801.txt',
header=None, skiprows=number2011, names=NewNames)
ThresholdDays = 0
for i in range(0,33):
if (table20100801.Average[i] >= 500) or (table20100801.Maximum[i] >= 650):
ThresholdDays += 1
Aug1Total = ThresholdDays
In [17]:
#August 2, 2010
number2011 = range(0,256)
number2011 = [x for x in number2011 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
table20100802 = pd.read_fwf('http://twc.tamu.edu/weather_images/summ/summ20100802.txt',
header=None, skiprows=number2011, names=NewNames)
ThresholdDays = 0
for i in range(0,33):
if (table20100802.Average[i] >= 500) or (table20100802.Maximum[i] >= 650):
ThresholdDays += 1
Aug2Total = ThresholdDays
In [18]:
path = r'C:\Users\Ty Dickinson\Downloads\DailyKBDI\County_Summary_Tables\Old_Process\2011'
all_files = glob.glob(os.path.join(path, "*.txt"))
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
number2011 = range(0,256)
number2011 = [x for x in number2011 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
table2011 = pd.concat((pd.read_fwf(f, header=None, skiprows=number2011,
names=NewNames) for f in all_files))
index2011 = []
for i in range(0,11913):
index2011.insert(i,i)
table2011 = table2011.set_index([index2011])
table2011['Date'] = table2011.index
date = '01-01-2011'
date = pd.to_datetime(date)
date2 = pd.to_datetime('10-12-2011')
date3 = pd.to_datetime('11-06-2011')
date4 = pd.to_datetime('11-20-2011')
date5 = pd.to_datetime('11-29-2011')
x = 0
y = 33
#missing Oct. 11, Nov. 5, 19, 28
for i in range(0,283):
for j in range(x,y):
table2011.Date[j] = date + pd.DateOffset(days=i)
j += 1
x = y
y += 33
i += 1
for i in range(284,308):
for j in range(x,y):
table2011.Date[j] = date2 + pd.DateOffset(days=i-284)
j += 1
x = y
y += 33
i += 1
for i in range(309,322):
for j in range(x,y):
table2011.Date[j] = date3 + pd.DateOffset(days=i-309)
j += 1
x = y
y += 33
i += 1
for i in range(323,331):
for j in range(x,y):
table2011.Date[j] = date4 + pd.DateOffset(days=i-323)
j += 1
x = y
y += 33
i += 1
for i in range(332,365):
for j in range(x,y):
table2011.Date[j] = date5 + pd.DateOffset(days=i-332)
j += 1
x = y
y += 33
i += 1
spring1 = pd.to_datetime('03-01-2011')
spring2 = pd.to_datetime('05-31-2011')
summer1 = pd.to_datetime('06-01-2011')
summer2 = pd.to_datetime('08-31-2011')
fall1 = pd.to_datetime('09-01-2011')
fall2 = pd.to_datetime('11-30-2011')
table2011 = table2011.set_index(['Date'])
spring2011 = table2011.loc[spring1:spring2]
summer2011 = table2011.loc[summer1:summer2]
fall2011 = table2011.loc[fall1:fall2]
spring2011 = spring2011.sort_values(['County'])
summer2011 = summer2011.sort_values(['County'])
fall2011 = fall2011.sort_values(['County'])
spring2011list = []
summer2011list = []
fall2011list = []
x = 0
y = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(x,y):
if (spring2011.Average[j] >= 500) or (spring2011.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
spring2011list.insert(i, ThresholdDays)
x = y
y += 92
i += 1
a = 0
b = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(a,b):
if (summer2011.Average[j] >= 500) or (summer2011.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
summer2011list.insert(i, ThresholdDays)
a = b
b += 92
i += 1
c = 0
d = 87
for i in range(0,33):
ThresholdDays = 0
for j in range(c,d):
if (fall2011.Average[j] >= 500) or (fall2011.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
fall2011list.insert(i, ThresholdDays)
c = d
d += 87
i += 1
In [19]:
#October 11, 2011
number2011 = range(0,256)
number2011 = [x for x in number2011 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
table20111011 = pd.read_fwf('http://twc.tamu.edu/weather_images/summ/summ20111011.txt',
header=None, skiprows=number2011, names=NewNames)
ThresholdDays = 0
for i in range(0,33):
if (table20111011.Average[i] >= 500) or (table20111011.Maximum[i] >= 650):
ThresholdDays += 1
Oct11Total = ThresholdDays
In [20]:
#November 5, 2011
number2011 = range(0,256)
number2011 = [x for x in number2011 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
table20111105 = pd.read_fwf('http://twc.tamu.edu/weather_images/summ/summ20111105.txt',
header=None, skiprows=number2011, names=NewNames)
ThresholdDays = 0
for i in range(0,33):
if (table20111105.Average[i] >= 500) or (table20111105.Maximum[i] >= 650):
ThresholdDays += 1
Nov5Total = ThresholdDays
In [21]:
#November 19, 2011
number2011 = range(0,256)
number2011 = [x for x in number2011 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
table20111119 = pd.read_fwf('http://twc.tamu.edu/weather_images/summ/summ20111119.txt',
header=None, skiprows=number2011, names=NewNames)
ThresholdDays = 0
for i in range(0,33):
if (table20111119.Average[i] >= 500) or (table20111119.Maximum[i] >= 650):
ThresholdDays += 1
Nov19Total = ThresholdDays
In [22]:
#November 28, 2011
number2011 = range(0,256)
number2011 = [x for x in number2011 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
table20111128 = pd.read_fwf('http://twc.tamu.edu/weather_images/summ/summ20111128.txt',
header=None, skiprows=number2011, names=NewNames)
ThresholdDays = 0
for i in range(0,33):
if (table20111128.Average[i] >= 500) or (table20111128.Maximum[i] >= 650):
ThresholdDays += 1
Nov28Total = ThresholdDays
In [23]:
path = r'C:\Users\Ty Dickinson\Downloads\DailyKBDI\County_Summary_Tables\Old_Process\2012'
all_files = glob.glob(os.path.join(path, "*.txt"))
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
number2012 = range(0,256)
number2012 = [x for x in number2012 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
table2012 = pd.concat((pd.read_fwf(f, header=None, skiprows=number2012,
names=NewNames) for f in all_files))
index2012 = []
for i in range(0,12045):
index2012.insert(i,i)
table2012 = table2012.set_index([index2012])
table2012['Date'] = table2012.index
date = '01-01-2012'
date = pd.to_datetime(date)
date2 = pd.to_datetime('12-29-2012')
x = 0
y = 33
#Missing December 28
for i in range(0,362):
for j in range(x,y):
table2012.Date[j] = date + pd.DateOffset(days=i)
j += 1
x = y
y += 33
i += 1
for i in range(363,366):
for j in range(x,y):
table2012.Date[j] = date2 + pd.DateOffset(days=i-363)
j += 1
x = y
y += 33
i += 1
spring1 = pd.to_datetime('03-01-2012')
spring2 = pd.to_datetime('05-31-2012')
summer1 = pd.to_datetime('06-01-2012')
summer2 = pd.to_datetime('08-31-2012')
fall1 = pd.to_datetime('09-01-2012')
fall2 = pd.to_datetime('11-30-2012')
table2012 = table2012.set_index(['Date'])
spring2012 = table2012.loc[spring1:spring2]
summer2012 = table2012.loc[summer1:summer2]
fall2012 = table2012.loc[fall1:fall2]
spring2012 = spring2012.sort_values(['County'])
summer2012 = summer2012.sort_values(['County'])
fall2012 = fall2012.sort_values(['County'])
spring2012list = []
summer2012list = []
fall2012list = []
x = 0
y = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(x,y):
if (spring2012.Average[j] >= 500) or (spring2012.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
spring2012list.insert(i, ThresholdDays)
x = y
y += 92
i += 1
a = 0
b = 92
for i in range(0,33):
ThresholdDays = 0
for j in range(a,b):
if (summer2012.Average[j] >= 500) or (summer2012.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
summer2012list.insert(i, ThresholdDays)
a = b
b += 92
i += 1
c = 0
d = 91
for i in range(0,33):
ThresholdDays = 0
for j in range(c,d):
if (fall2012.Average[j] >= 500) or (fall2012.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
fall2012list.insert(i, ThresholdDays)
c = d
d += 91
i += 1
In [24]:
#December 28, 2012
number2011 = range(0,256)
number2011 = [x for x in number2011 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
table20121228 = pd.read_fwf('http://twc.tamu.edu/weather_images/summ/summ20121228.txt',
header=None, skiprows=number2011, names=NewNames)
ThresholdDays = 0
for i in range(0,33):
if (table20121228.Average[i] >= 500) or (table20121228.Maximum[i] >= 650):
ThresholdDays += 1
In [25]:
path = r'C:\Users\Ty Dickinson\Downloads\DailyKBDI\County_Summary_Tables\Old_Process\2013'
all_files = glob.glob(os.path.join(path, "*.txt"))
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
number2013 = range(0,256)
number2013 = [x for x in number2013 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
table2013 = pd.concat((pd.read_fwf(f, header=None, skiprows=number2013,
names=NewNames) for f in all_files))
index2013 = []
for i in range(0,11946):
index2013.insert(i,i)
table2013 = table2013.set_index([index2013])
table2013['Date'] = table2013.index
date = '01-01-2013'
date = pd.to_datetime(date)
date2 = pd.to_datetime('01-20-2013')
date3 = pd.to_datetime('03-20-2013')
date4 = pd.to_datetime('06-30-2013')
x = 0
y = 33
#missing Jan. 19, March 19, June 29
for i in range(0,18):
for j in range(x,y):
table2013.Date[j] = date + pd.DateOffset(days=i)
j += 1
x = y
y += 33
i += 1
for i in range(19,77):
for j in range(x,y):
table2013.Date[j] = date2 + pd.DateOffset(days=i-19)
j += 1
x = y
y += 33
i += 1
for i in range(78,179):
for j in range(x,y):
table2013.Date[j] = date3 + pd.DateOffset(days=i-78)
j += 1
x = y
y += 33
i += 1
for i in range(180,365):
for j in range(x,y):
table2013.Date[j] = date4 + pd.DateOffset(days=i-180)
j += 1
x = y
y += 33
i += 1
spring1 = pd.to_datetime('03-01-2013')
spring2 = pd.to_datetime('05-31-2013')
summer1 = pd.to_datetime('06-01-2013')
summer2 = pd.to_datetime('08-31-2013')
fall1 = pd.to_datetime('09-01-2013')
fall2 = pd.to_datetime('11-30-2013')
table2013 = table2013.set_index(['Date'])
spring2013 = table2013.loc[spring1:spring2]
summer2013 = table2013.loc[summer1:summer2]
fall2013 = table2013.loc[fall1:fall2]
spring2013 = spring2013.sort_values(['County'])
summer2013 = summer2013.sort_values(['County'])
fall2013 = fall2013.sort_values(['County'])
spring2013list = []
summer2013list = []
fall2013list = []
x = 0
y = 91
for i in range(0,33):
ThresholdDays = 0
for j in range(x,y):
if (spring2013.Average[j] >= 500) or (spring2013.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
spring2013list.insert(i, ThresholdDays)
x = y
y += 91
i += 1
a = 0
b = 91
for i in range(0,33):
ThresholdDays = 0
for j in range(a,b):
if (summer2013.Average[j] >= 500) or (summer2013.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
summer2013list.insert(i, ThresholdDays)
a = b
b += 91
i += 1
c = 0
d = 91
for i in range(0,33):
ThresholdDays = 0
for j in range(c,d):
if (fall2013.Average[j] >= 500) or (fall2013.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
fall2013list.insert(i, ThresholdDays)
c = d
d += 91
i += 1
In [26]:
#January 19, 2013
number2011 = range(0,256)
number2011 = [x for x in number2011 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
table20130119 = pd.read_fwf('http://twc.tamu.edu/weather_images/summ/summ20130119.txt',
header=None, skiprows=number2011, names=NewNames)
ThresholdDays = 0
for i in range(0,33):
if (table20130119.Average[i] >= 500) or (table20130119.Maximum[i] >= 650):
ThresholdDays += 1
In [27]:
#March 19, 2013
number2011 = range(0,256)
number2011 = [x for x in number2011 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
table20130319 = pd.read_fwf('http://twc.tamu.edu/weather_images/summ/summ20130319.txt',
header=None, skiprows=number2011, names=NewNames)
ThresholdDays = 0
for i in range(0,33):
if (table20130319.Average[i] >= 500) or (table20130319.Maximum[i] >= 650):
ThresholdDays += 1
March19Total = ThresholdDays
In [28]:
#June 29, 2013
number2011 = range(0,256)
number2011 = [x for x in number2011 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
table20130629 = pd.read_fwf('http://twc.tamu.edu/weather_images/summ/summ20130629.txt',
header=None, skiprows=number2011, names=NewNames)
ThresholdDays = 0
for i in range(0,33):
if (table20130629.Average[i] >= 500) or (table20130629.Maximum[i] >= 650):
ThresholdDays += 1
June29Total = ThresholdDays
In [29]:
path = r'C:\Users\Ty Dickinson\Downloads\DailyKBDI\County_Summary_Tables\Old_Process\2014'
all_files = glob.glob(os.path.join(path, "*.txt"))
NewNames = ['County', 'Average', 'Maximum', 'Minimum']
number2014 = range(0,256)
number2014 = [x for x in number2014 if not x in[
8,11,12,16,17,28,29,47,60,65,70,76,83,87,90,95,
106,129,131,134,137,144,145,151,160,164,194,
228,233,234,247,248,255
]]
table2014 = pd.concat((pd.read_fwf(f, header=None, skiprows=number2014,
names=NewNames) for f in all_files))
index2014 = []
for i in range(0,12045):
index2014.insert(i,i)
table2014 = table2014.set_index([index2014])
table2014['Date'] = table2014.index
date = '01-01-2014'
date = pd.to_datetime(date)
x = 0
y = 33
for i in range(0,365):
for j in range(x,y):
table2014.Date[j] = date + pd.DateOffset(days=i)
j += 1
x = y
y += 33
i += 1
spring1 = pd.to_datetime('03-01-2014')
spring2 = pd.to_datetime('05-31-2014')
summer1 = pd.to_datetime('06-01-2014')
summer2 = pd.to_datetime('08-31-2014')
fall1 = pd.to_datetime('09-01-2014')
fall2 = pd.to_datetime('11-30-2014')
table2014 = table2014.set_index(['Date'])
spring2014 = table2014.loc[spring1:spring2]
summer2014 = table2014.loc[summer1:summer2]
fall2014 = table2014.loc[fall1:fall2]
spring2014 = spring2014.sort_values(['County'])
summer2014 = summer2014.sort_values(['County'])
fall2014 = fall2014.sort_values(['County'])
spring2014list = []
summer2014list = []
fall2014list = []
x = 0
y = 91
for i in range(0,33):
ThresholdDays = 0
for j in range(x,y):
if (spring2014.Average[j] >= 500) or (spring2014.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
spring2014list.insert(i, ThresholdDays)
x = y
y += 91
i += 1
a = 0
b = 91
for i in range(0,33):
ThresholdDays = 0
for j in range(a,b):
if (summer2014.Average[j] >= 500) or (summer2014.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
summer2014list.insert(i, ThresholdDays)
a = b
b += 91
i += 1
c = 0
d = 91
for i in range(0,33):
ThresholdDays = 0
for j in range(c,d):
if (fall2014.Average[j] >= 500) or (fall2014.Maximum[j] >= 650):
ThresholdDays += 1
j += 1
fall2014list.insert(i, ThresholdDays)
c = d
d += 91
i += 1
In [30]:
Spring = [sum(spring1995list), sum(spring1996list), sum(spring1997list),
sum(spring1998list), sum(spring1999list), 1244, 264,
sum(spring2002list), sum(spring2003list), sum(spring2004list),
sum(spring2005list), sum(spring2006list), sum(spring2007list),
sum(spring2008list), sum(spring2009list), sum(spring2010list),
sum(spring2011list), sum(spring2012list),
(sum(spring2013list) + March19Total), sum(spring2014list)
]
Summer = [sum(summer1995list), sum(summer1996list), sum(summer1997list),
sum(summer1998list), sum(summer1999list), 1599, 1653,
sum(summer2002list), sum(summer2003list), sum(summer2004list),
sum(summer2005list), sum(summer2006list), sum(summer2007list),
sum(summer2008list), sum(summer2009list),
(sum(summer2010list) + Aug1Total + Aug2Total), sum(summer2011list),
sum(summer2012list), (sum(summer2013list) + June29Total),
sum(summer2014list)
]
Fall = [sum(fall1995list), sum(fall1996list), sum(fall1997list),
sum(fall1998list), 2562, 1528, 466, sum(fall2002list),
sum(fall2003list), sum(fall2004list), sum(fall2005list),
sum(fall2006list), sum(fall2007list), sum(fall2008list),
sum(fall2009list), sum(fall2010list),
(sum(fall2011list) + Oct11Total + Nov5Total + Nov19Total + Nov28Total),
sum(fall2012list), sum(fall2013list), sum(fall2014list)
]
years = []
for i in range(1995, 2015):
years.insert(i,i)
dfspring = pd.DataFrame({'Spring':Spring})
dfsummer = pd.DataFrame({'Summer':Summer})
dffall = pd.DataFrame({'Fall':Fall})
dftot = pd.concat([dfspring, dfsummer, dffall], axis=1)
dftot = dftot.set_index([years])
dftot.to_csv('KBDI Threshold Days.csv')
In [31]:
#NOTES
#1995-Summer 1999 taken from folders
#Fall 1999-Summer 2001 taken from single csv
#2002-2014 taken from folders
#folders use avg. >= 500 OR max >= 650
#single csv uses only avg. >= 500
In [ ]: