Assignment 4

0. Name & Z-ID (5 pts)

The first cell of your notebook should be a markdown cell with a line for your name and a line for your Z-ID. If you wish to add other information (the assignment name, a description of the assignment), you may do so after these two lines.

1. Read Data (15 pts)

Either download the file and upload it to Jupyter, or use the provided template notebook which contains a cell to download the file. Then, use an iterator to read the file illinois-climate-monthly.txt into a list of dictionaries named data (a format similar to what we used in Assignment 3). Remember that a file object will provide an iterator if you pass it to the iter function. Read the header first (the first line), and then the rest of the file. The header will serve as the keys for each dictionary while the other lines are values. Do not assign each key-value pair individually; this is tedious. You do not need to convert values (e.g. to floats) in this step (see Step 3). When you finish, data` should look like:

[{'StateCode': '11',
  'Division': '00',
  'YearMonth': '199001',
  'PCP': '2.05',
  'TAVG': '36.4',
  'PDSI': '-1.86',
  'PHDI': '-1.86',
  'ZNDX': '-.39',
  'PMDI': '-1.86',
  'CDD': '0',
  'HDD': '971',
  'SP01': '.18',
  'SP02': '-.93',
  'SP03': '-1.28',
  'SP06': '-.75',
  'SP09': '-.93',
  'SP12': '-.92',
  'SP24': '-1.91',
  'TMIN': '27.2',
  'TMAX': '45.5'},
  ...
]

2. Split Year and Month (5 pts)

For each data item, create two new key-value pairs, Year and Month, from the YearMonth column. Add these pairs to the existing dictionaries in the list.

3. Compute Yearly Values (15 pts)

Create a new list of dictionaries named year_data that has one entry per year that and contains four key-value pairs (Year, PCP, TAVG, PSDI). PCP should be the sum of all PCP values for that year; TAVG and PSDI should be the average of the TAVG and PSDI values for the year. You will need to convert the individual values to floats before computing sums or averages. year_data should look like:

[{'Year': 1990,
  'PCP': 49.94,
  'TAVG': 53.55833333333333,
  'PDSI': 1.9158333333333335},
 {'Year': 1991,
  'PCP': 36.120000000000005,
  'TAVG': 53.74166666666667,
  'PDSI': -1.0808333333333333},
 ...
]

4. Write Output (20 pts)

Write the new year_data list of dictionaries to a file named illinois-climate-yearly.txt in a similar format to the original file. This means writing the header values as the first line. Second, write the floating point values for PCP with 2 digits after the decimal point, TAVG with 1 digit after the decimal point, and PSDI with 2 digits after the decimal point. All the numbers should be right-aligned with at least two spaces between each column. Finally, the PSDI averages should be written without a leading 0. In other words, values of 0.34 or -0.65 would be written as .34 and -.65. There is no format option for not writing the leading zero in Python so you need to write code to do this transformation. The output file should look like:

Year    PCP   TAVG   PDSI 
1990  49.94   53.6   1.92
1991  36.12   53.7  -1.08
1992  35.81   51.9    .08
1993  51.18   50.9   3.76
1994  35.51   52.0   -.35
1995  38.91   51.6   -.16
1996  38.82   50.1    .63
1997  35.10   51.0   -.18
1998  44.49   55.2   1.40
1999  35.71   53.5    .08
2000  39.77   52.2   -.01
2001  39.65   53.4    .67
2002  39.06   53.2    .35
2003  37.78   51.7   -.12
2004  39.97   52.6    .79
2005  31.43   53.7  -1.89
2006  40.64   54.0   -.32
2007  36.69   53.7   -.54
2008  50.17   50.9   2.98
2009  50.96   51.4   4.71
2010  40.68   52.9   3.78
2011  46.10   53.1   1.27
2012  30.11   55.8  -2.35
2013  42.49   51.1    .42
2014  41.29   49.5   1.09
2015  48.80   52.9   2.73
2016  39.75   54.7   2.75
2017  37.80   54.4    .46
2018  45.79   52.5   1.60
2019  49.87   52.1   4.34