64 lines
2.3 KiB
Python
64 lines
2.3 KiB
Python
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import pandas as pd
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def countPercentOfNumbers(row):
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numberOfChars = len(row['value'])
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numberOfDigitsInString = sum(c.isdigit() for c in row['value'])
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if (numberOfDigitsInString == 0):
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return 0
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else:
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return (numberOfDigitsInString/numberOfChars) * 100
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def countPercentOfLetters(row):
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numberOfChars = len(row['value'])
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numberOfAlphasInString = sum(c.isalpha() for c in row['value'])
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if (numberOfAlphasInString == 0):
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return 0
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else:
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return (numberOfAlphasInString/numberOfChars) * 100
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def countPercentOfSpaces(row):
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numberOfChars = len(row['value'])
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numberOfSpacesInString = sum(c.isspace() for c in row['value'])
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if (numberOfSpacesInString == 0):
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return 0
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else:
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return (numberOfSpacesInString / numberOfChars) * 100
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def countPercentOfSpecials(row):
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numberOfChars = len(row['value'])
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numberOfDigitsInString = sum(c.isdigit() for c in row['value'])
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numberOfAlphasInString = sum(c.isalpha() for c in row['value'])
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numberOfSpacesInString = sum(c.isspace() for c in row['value'])
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numberOfSpecials = numberOfChars - numberOfDigitsInString - numberOfAlphasInString - numberOfSpacesInString
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if (numberOfSpecials == 0):
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return 0
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else:
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return (numberOfSpecials/numberOfChars) * 100
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df = pd.read_csv('../datasets/out.csv',
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usecols=range(2),
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lineterminator='\n',
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header=None)
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df = df.rename(columns={0: "type", 1: "value"})
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df['numberOfChars'] = df.apply(lambda row: len(row['value']), axis=1)
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print('Mean amount of chars by type')
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print(df.groupby(['type'])["numberOfChars"].mean())
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df['percentOfLetters'] = df.apply(countPercentOfLetters, axis=1)
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df['percentOfNumbers'] = df.apply(countPercentOfNumbers, axis=1)
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df['percentOfSpecialChars'] = df.apply(countPercentOfSpecials, axis=1)
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df['percentOfSpaces'] = df.apply(countPercentOfSpaces, axis=1)
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print('Mean percent of alphanumeric by type')
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print(df.groupby(['type'])["percentOfLetters"].mean())
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print('Mean percent of digits by type')
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print(df.groupby(['type'])["percentOfNumbers"].mean())
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print('Mean percent of special characters by type')
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print(df.groupby(['type'])["percentOfSpecialChars"].mean())
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print('Mean percent of spaces by type')
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print(df.groupby(['type'])["percentOfSpaces"].mean())
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