formatting.ipynb

Kernel: Python 3 (Anaconda)

! head data/KNYC.csv

#date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation
2014-7-1,81,72,89,68,83,52,100,1943,1901,0.00,0.12,2.17
2014-7-2,82,72,91,68,83,56,100,2001,1966,0.96,0.13,1.79
2014-7-3,78,69,87,68,83,54,103,1933,1966,1.78,0.12,2.80
2014-7-4,70,65,74,68,84,55,102,1986,1949,0.14,0.13,1.76
2014-7-5,72,63,81,68,84,53,101,1979,1999,0.00,0.12,3.07
2014-7-6,75,66,84,68,84,54,103,1979,2010,0.00,0.13,1.97
2014-7-7,81,72,90,68,84,56,100,1914,2010,0.04,0.13,3.13
2014-7-8,81,71,91,69,84,56,100,1894,1993,0.39,0.14,1.80
2014-7-9,80,71,88,69,84,54,106,1963,1936,0.09,0.14,1.09

import numpy as np

knyc = np.loadtxt("data/KNYC.csv",delimiter=',')

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-4-7c75d4b79cd5> in <module>()
----> 1 knyc = np.loadtxt("data/KNYC.csv",delimiter=',')

/projects/anaconda3/lib/python3.5/site-packages/numpy/lib/npyio.py in loadtxt(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin)
    928 
    929             # Convert each value according to its column and store
--> 930             items = [conv(val) for (conv, val) in zip(converters, vals)]
    931             # Then pack it according to the dtype's nesting
    932             items = pack_items(items, packing)
/projects/anaconda3/lib/python3.5/site-packages/numpy/lib/npyio.py in <listcomp>(.0)
    928 
    929             # Convert each value according to its column and store
--> 930             items = [conv(val) for (conv, val) in zip(converters, vals)]
    931             # Then pack it according to the dtype's nesting
    932             items = pack_items(items, packing)
/projects/anaconda3/lib/python3.5/site-packages/numpy/lib/npyio.py in floatconv(x)
    657         if b'0x' in x:
    658             return float.fromhex(asstr(x))
--> 659         return float(x)
    660 
    661     typ = dtype.type
ValueError: could not convert string to float: b'2014-7-1'

np.loadtxt?

knyc = np.loadtxt("data/KNYC.csv",delimiter=',',usecols=(1,2,3,4,5,6,7,8,9,10,11,12))

print(knyc)

[[ 81.    72.    89.   ...,   0.     0.12   2.17]
 [ 82.    72.    91.   ...,   0.96   0.13   1.79]
 [ 78.    69.    87.   ...,   1.78   0.12   2.8 ]
 ..., 
 [ 68.    62.    73.   ...,   0.29   0.13   1.69]
 [ 70.    63.    76.   ...,   0.     0.12   2.57]
 [ 75.    68.    82.   ...,   0.     0.13   3.07]]

print(knyc[0,6], knyc[0,8], knyc[0,-1])

100.0 1901.0 2.17

knyc.shape

(365, 12)

for i in range(knyc.shape[0]):
    print(knyc[i,6], knyc[i,8], knyc[i,-1])

100.0 1901.0 2.17
100.0 1966.0 1.79
103.0 1966.0 2.8
102.0 1949.0 1.76
101.0 1999.0 3.07
103.0 2010.0 1.97
100.0 2010.0 3.13
100.0 1993.0 1.8
106.0 1936.0 1.09
102.0 1993.0 1.79
98.0 1988.0 1.94
99.0 1966.0 2.68
101.0 1966.0 3.16
100.0 1954.0 1.6
102.0 1995.0 2.33
99.0 1980.0 1.38
100.0 1953.0 3.13
101.0 1953.0 1.76
102.0 1977.0 1.82
101.0 1980.0 1.97
104.0 1977.0 2.26
104.0 2011.0 1.86
100.0 2011.0 2.41
97.0 1999.0 3.75
97.0 1999.0 1.64
98.0 1940.0 3.8
98.0 1963.0 2.65
97.0 1999.0 3.11
99.0 1949.0 3.47
98.0 1988.0 3.56
102.0 1933.0 2.29
100.0 1933.0 2.85
100.0 1955.0 2.49
97.0 2005.0 2.71
100.0 1944.0 3.25
101.0 1944.0 1.44
97.0 1955.0 3.31
104.0 1918.0 2.18
99.0 2001.0 2.6
103.0 2001.0 4.1
98.0 1949.0 4.64
102.0 1944.0 2.39
97.0 1944.0 3.62
99.0 2005.0 2.7
99.0 1988.0 5.81
97.0 1988.0 1.52
96.0 1944.0 4.8
95.0 1944.0 2.86
94.0 2002.0 3.95
94.0 2002.0 2.53
97.0 1955.0 3.63
96.0 1955.0 4.19
95.0 1916.0 1.85
92.0 1916.0 3.03
94.0 1972.0 3.61
95.0 1948.0 1.86
103.0 1948.0 3.24
101.0 1948.0 4.16
100.0 1948.0 3.99
99.0 1953.0 2.68
98.0 1973.0 2.3
100.0 1953.0 3.76
97.0 1953.0 3.84
102.0 1953.0 2.12
99.0 1929.0 3.32
97.0 1929.0 3.48
94.0 1985.0 2.45
97.0 1881.0 3.26
101.0 1881.0 2.07
93.0 1919.0 4.86
94.0 1915.0 0.86
97.0 1983.0 1.38
99.0 1983.0 2.9
94.0 1961.0 2.35
94.0 1952.0 3.94
93.0 1931.0 3.82
92.0 1927.0 4.16
93.0 1915.0 5.02
93.0 1991.0 3.37
91.0 1891.0 3.92
94.0 1983.0 4.3
93.0 1983.0 2.32
95.0 1895.0 5.54
95.0 1914.0 2.34
97.0 1895.0 8.28
89.0 1959.0 2.26
90.0 1970.0 2.36
91.0 1970.0 2.34
90.0 1933.0 3.13
88.0 1881.0 3.84
88.0 1945.0 2.18
89.0 1986.0 2.64
88.0 1927.0 4.98
90.0 1927.0 2.16
87.0 1919.0 1.55
88.0 1941.0 4.05
94.0 1941.0 1.99
90.0 1941.0 2.39
88.0 1944.0 4.09
87.0 2007.0 4.3
86.0 1916.0 7.33
91.0 1939.0 2.16
85.0 1949.0 3.06
86.0 1954.0 4.26
87.0 1954.0 2.75
84.0 1920.0 1.76
84.0 1956.0 1.7
87.0 1897.0 2.15
90.0 1938.0 2.28
82.0 1928.0 2.45
83.0 1963.0 4.35
80.0 1969.0 2.78
84.0 1920.0 2.17
88.0 1979.0 1.51
85.0 1947.0 2.97
79.0 2001.0 2.51
79.0 1963.0 3.3
78.0 1964.0 3.4
82.0 1963.0 1.88
83.0 1919.0 2.49
78.0 1971.0 3.67
82.0 1961.0 1.64
81.0 1946.0 2.41
84.0 1950.0 1.69
83.0 1950.0 1.7
79.0 2003.0 2.6
78.0 1975.0 1.44
78.0 1961.0 1.94
74.0 1948.0 1.47
78.0 1938.0 2.96
76.0 1975.0 7.4
75.0 1975.0 3.65
73.0 1985.0 1.7
74.0 1949.0 1.41
76.0 1879.0 2.39
73.0 1931.0 2.06
72.0 1993.0 2.23
80.0 1993.0 2.43
72.0 1928.0 2.39
71.0 1953.0 1.54
73.0 1928.0 1.24
72.0 1921.0 1.95
77.0 1985.0 3.37
74.0 1900.0 1.33
72.0 1931.0 2.03
72.0 1931.0 1.84
73.0 1979.0 1.95
73.0 1979.0 1.36
67.0 1946.0 1.91
72.0 1896.0 2.15
70.0 2011.0 2.14
69.0 1990.0 2.01
70.0 1991.0 1.11
70.0 2006.0 1.72
66.0 1970.0 2.16
69.0 1998.0 1.63
74.0 1998.0 1.84
70.0 2001.0 1.28
71.0 2001.0 1.6
75.0 1998.0 1.98
65.0 1927.0 1.54
66.0 1966.0 2.54
70.0 1946.0 1.62
64.0 1879.0 2.41
68.0 1931.0 1.6
64.0 1923.0 3.03
67.0 1881.0 2.22
67.0 2008.0 1.34
63.0 1971.0 2.25
62.0 2000.0 2.28
63.0 1984.0 1.3
58.0 1931.0 1.58
60.0 2002.0 1.82
65.0 2013.0 2.49
71.0 2013.0 2.18
66.0 1990.0 1.61
63.0 1996.0 1.42
64.0 1982.0 1.3
63.0 1982.0 1.66
63.0 1949.0 2.14
65.0 1982.0 1.35
70.0 1984.0 2.52
65.0 1984.0 1.69
63.0 1965.0 2.31
62.0 1966.0 2.05
68.0 1876.0 1.92
64.0 2000.0 2.42
66.0 1950.0 2.73
64.0 1993.0 1.5
72.0 2007.0 1.65
64.0 1907.0 2.67
65.0 1998.0 1.25
64.0 1937.0 1.42
60.0 1876.0 1.72
63.0 1975.0 1.46
64.0 1890.0 2.35
68.0 1932.0 1.44
70.0 1932.0 2.06
67.0 1932.0 1.27
58.0 1995.0 1.44
63.0 1990.0 1.36
66.0 1990.0 2.1
64.0 1951.0 2.39
61.0 2006.0 1.41
63.0 2006.0 3.45
61.0 1959.0 1.7
62.0 1906.0 2.55
68.0 1967.0 2.18
60.0 1967.0 1.8
72.0 1950.0 2.19
69.0 1916.0 1.94
66.0 1916.0 1.87
69.0 2002.0 1.03
64.0 2006.0 1.19
63.0 1947.0 1.51
67.0 1989.0 2.12
59.0 1988.0 2.98
64.0 1991.0 1.55
68.0 1991.0 2.1
70.0 1991.0 1.43
68.0 2008.0 2.74
54.0 1938.0 2.96
61.0 1965.0 1.15
63.0 1990.0 1.74
61.0 2001.0 2.63
65.0 1960.0 2.74
62.0 1999.0 1.66
64.0 1951.0 2.42
63.0 1946.0 1.59
73.0 1949.0 1.73
71.0 1954.0 1.4
67.0 1976.0 1.49
68.0 1981.0 1.5
66.0 1997.0 2.15
69.0 1939.0 3.07
68.0 1930.0 1.86
69.0 1997.0 2.39
70.0 1985.0 1.38
75.0 1985.0 1.69
75.0 1930.0 2.11
65.0 1890.0 1.87
72.0 1997.0 1.56
67.0 1976.0 1.21
73.0 1972.0 2.95
72.0 1972.0 2.41
65.0 1991.0 2.25
70.0 1974.0 1.65
72.0 1880.0 1.81
68.0 1935.0 2.63
74.0 1946.0 1.87
76.0 1987.0 1.78
69.0 2000.0 1.82
74.0 2006.0 1.62
73.0 1977.0 2.94
71.0 1890.0 2.33
85.0 1990.0 3.86
75.0 1946.0 1.02
77.0 1990.0 1.81
82.0 1990.0 2.03
75.0 1945.0 1.42
77.0 1989.0 3.1
76.0 1918.0 2.19
83.0 1945.0 1.93
84.0 1921.0 2.37
78.0 2012.0 3.44
76.0 1923.0 1.6
76.0 1988.0 2.05
79.0 1963.0 4.25
76.0 1922.0 1.42
83.0 1998.0 1.79
84.0 1945.0 2.98
86.0 1945.0 2.03
82.0 1998.0 2.45
86.0 1998.0 2.2
83.0 1917.0 1.89
81.0 1967.0 1.93
81.0 1981.0 1.9
80.0 1892.0 1.99
80.0 1928.0 2.76
79.0 1947.0 2.52
92.0 2010.0 1.35
90.0 1991.0 1.93
86.0 1991.0 3.42
86.0 1922.0 4.31
84.0 1955.0 1.1
90.0 1977.0 2.12
88.0 1977.0 1.26
85.0 1941.0 2.72
87.0 1941.0 7.57
92.0 2002.0 3.29
96.0 2002.0 1.29
96.0 1976.0 2.19
92.0 1976.0 1.96
90.0 1927.0 1.96
87.0 1923.0 2.28
86.0 2001.0 2.45
86.0 2007.0 2.34
87.0 2001.0 2.17
91.0 1915.0 1.68
92.0 2009.0 1.88
92.0 1915.0 2.04
90.0 1990.0 2.74
89.0 1974.0 0.91
91.0 1942.0 4.97
87.0 2001.0 2.48
90.0 2001.0 1.1
90.0 2001.0 1.66
92.0 2001.0 2.02
90.0 1980.0 1.55
92.0 1986.0 1.46
93.0 2000.0 3.82
91.0 2000.0 3.02
94.0 1979.0 1.42
94.0 1979.0 2.1
92.0 1993.0 1.67
93.0 1881.0 1.84
89.0 1956.0 1.66
88.0 1900.0 3.38
90.0 1900.0 1.16
90.0 1951.0 2.66
92.0 1974.0 1.05
90.0 1936.0 2.18
99.0 1962.0 2.02
96.0 1996.0 2.03
93.0 1996.0 1.94
96.0 1941.0 1.25
94.0 1964.0 2.7
93.0 1975.0 2.07
95.0 1880.0 0.86
95.0 1880.0 1.28
96.0 1880.0 2.62
94.0 1959.0 1.16
97.0 1969.0 3.99
97.0 1987.0 2.19
96.0 1939.0 3.13
96.0 1895.0 2.6
96.0 1895.0 2.79
95.0 1895.0 3.01
99.0 1925.0 2.75
99.0 1925.0 2.8
98.0 1925.0 2.62
96.0 1925.0 4.16
95.0 1933.0 1.47
97.0 1933.0 2.55
96.0 2008.0 2.07
95.0 1973.0 1.14
93.0 1973.0 2.18
96.0 1961.0 1.71
99.0 1956.0 2.54
96.0 1994.0 1.13
97.0 1891.0 1.31
96.0 1957.0 1.82
95.0 1929.0 2.3
98.0 1994.0 2.16
98.0 1923.0 1.39
97.0 1988.0 1.7
98.0 1988.0 1.96
96.0 1888.0 1.75
96.0 1888.0 1.46
99.0 1952.0 1.28
100.0 1952.0 4.29
101.0 1966.0 2.11
96.0 1991.0 1.69
101.0 1934.0 2.57
99.0 1964.0 3.07

number = 367.3
print("Here is a number",number)

Here is a number 367.3

print("Here is a number: {}".format(number))

Here is a number: 367.3

print("Here is a num{}ber".format(number))

Here is a num367.3ber

print("Today is the {}th day of May. It is the {}th hour of the day.".format(16,13))

Today is the 16th day of May. It is the 13th hour of the day.

print("Here is a number {:010.1f} also it's {}".format(number,number))

Here is a number 00000367.3 also it's 367.3

nd = 10
for i in range(20):
    print("{:5.1f} {:5d} {:4.{ndigits}f}".format(knyc[i,6], int(knyc[i,8]), knyc[i,-1],ndigits=nd))

100.0  1901 2.1700000000
100.0  1966 1.7900000000
103.0  1966 2.8000000000
102.0  1949 1.7600000000
101.0  1999 3.0700000000
103.0  2010 1.9700000000
100.0  2010 3.1300000000
100.0  1993 1.8000000000
106.0  1936 1.0900000000
102.0  1993 1.7900000000
 98.0  1988 1.9400000000
 99.0  1966 2.6800000000
101.0  1966 3.1600000000
100.0  1954 1.6000000000
102.0  1995 2.3300000000
 99.0  1980 1.3800000000
100.0  1953 3.1300000000
101.0  1953 1.7600000000
102.0  1977 1.8200000000
101.0  1980 1.9700000000

a = 2.3
b = 7.3

a/b

0.3150684931506849