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May 15, 2016 22:38
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import pandas | |
variables_of_interest = { | |
'S3BQ1A5': ('ever used cannabis', {1: 'Yes', 2: 'No', 9: 'Unknown'}), | |
'S3BQ1A6': ('ever used cocaine/crack', {1: 'Yes', 2: 'No', 9: 'Unknown'}), | |
'S3BQ1A9A': ('ever used heroin', {1: 'Yes', 2: 'No', 9: 'Unknown'}), | |
'S3CD5Q13A': ('age at onset of cannabis abuse', { | |
'5-64': 'Age', | |
99: 'Unknown', | |
'BL': 'NA, didnt meet symptom criteria for lifetime cannabis abuse'}), | |
'S3CD6Q13A': ('age at onset of cocaine abuse', { | |
'5-52': 'Age', | |
99: 'Unknown', | |
'BL': 'NA, didnt meet symptom criteria for lifetime cocaine abuse'}), | |
'S3CD9Q13A': ('age at onset of heroin abuse', { | |
'9-62': 'Age', | |
99: 'Unknown', | |
'BL': 'NA, didnt meet symptom criteria for lifetime heroin abuse'}), | |
'S3BD5Q2F': ('age when began using cannabis most', { | |
'5-64': 'Age', | |
99: 'Unknown', | |
'BL': 'NA, never or unknown if ever used cannabis'}), | |
'S3BD6Q2F': ('age when began using cocaine or crack most', { | |
'5-64': 'Age', | |
99: 'Unknown', | |
'BL': 'NA, never or unknown if ever used cocaine or crack'}), | |
'S3BD9Q2F': ('age when began using heroin most', { | |
'12-62': 'Age', | |
99: 'Unknown', | |
'BL': 'NA, never or unknown if ever used heroin'}) | |
} | |
def load_data(): | |
pandas.set_option('display.float_format', lambda x: '%f' % x) | |
data = pandas.read_csv('nesarc_pds.csv', low_memory=False) | |
data.columns = map(str.upper, data.columns) | |
print ('# of observations is ' + str(len(data))) | |
print ('# of variables is ' + str(len(data.columns))) | |
return data | |
def concat_description(tuple_value): | |
result = tuple_value[0] + '( ' | |
for key, value in tuple_value[1].iteritems(): | |
result += str(key) + ' : ' + value + ' ' | |
result += ')' | |
return result | |
def main(): | |
data = load_data() | |
for code in variables_of_interest: | |
data[code] = data[code].convert_objects(convert_numeric=True) | |
for code, tuple_value in variables_of_interest.iteritems(): | |
description = concat_description(tuple_value) | |
print ('counts for ' + code + ' -- ' + description) | |
counts = data[code].value_counts(sort=False) | |
print (counts) | |
print ('percentages for ' + code + ' -- ' + description) | |
percentages = data[code].value_counts(sort=False, normalize=True) | |
print (percentages) | |
if __name__ == 'main': | |
main() |
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