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da385bd938
Check whether the value in the "before" column is zero (specifically, whether the file size in that column is greater than zero since filesizes are non-negative) before dividing the value in the "after" column by it.
59 lines
1.7 KiB
Python
Executable File
59 lines
1.7 KiB
Python
Executable File
#!/usr/bin/env python
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# This is a simple script that reads in a csv file, selects a column, and then
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# forms an "s-curve" graph of that column.
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import argparse
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import csv
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import sys
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def get_data(input_file, before_column, after_column):
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def get_selected_csv_rows(input_file, before_column, after_column):
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for row in csv.DictReader(input_file):
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before = float(row[before_column])
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after = float(row[after_column])
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if before > 0:
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delta = after / before
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else:
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delta = 1
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yield delta
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def f(input_data):
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result = list(enumerate(sorted(input_data)))
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count = float(len(result) - 1)
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return [(x[0] / count, x[1]) for x in result]
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return f(get_selected_csv_rows(input_file, before_column, after_column))
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def main():
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p = argparse.ArgumentParser(description="""
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A script that reads in a csv file, splices out selected before/after
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column, and then outputs a new csv file with that data in s-curve form. An
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s-curve is a graph where one sorts the output %-change and graphs the %-n
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vs %-change.
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NOTE: We assume that the csv has a csv header that maps to the before and
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after column names passed in.
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""")
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p.add_argument('input_file', type=argparse.FileType('r'))
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p.add_argument('before_column_name', type=str)
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p.add_argument('after_column_name', type=str)
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args = p.parse_args()
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data = get_data(args.input_file, args.before_column_name,
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args.after_column_name)
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w = csv.DictWriter(sys.stdout, fieldnames=['N/total', 'New/Old'])
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w.writeheader()
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for d in data:
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w.writerow({'N/total': d[0], 'New/Old': d[1]})
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if __name__ == "__main__":
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main()
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