In [1]:
import pandas as pd
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df = pd.read_csv("data/200log.csv")
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df['unit'] = 1
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success = df[df.url_status_code ==200].unit.count()
success_grouped = df[df.url_status_code ==200].groupby('name').unit.count()
fail = df[df.url_status_code !=200].unit.count()
fail_grouped = df[df.url_status_code !=200].groupby('name').unit.count()
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fail_grouped
Out[8]:
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total_requests = df.url_status_code.sum()
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print "Summary of URL Requests"
print "-" * 50
print "Percentage successful: %r" % (round(float(success)/ (success + fail), 2))
print "Total Succesful:", success
print "Total Unsuccesful:", fail
print "\n"
print 'Quantity of Articles Available'
print "-" * 50
for index, elem in enumerate(success_grouped):
print success_grouped.index[index].capitalize(),": ", success_grouped[index]
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print success
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df.url_status_code.count()
Out[14]:
In [17]:
df.groupby('name').count()
Out[17]:
In [18]:
df.count()
Out[18]:
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