43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
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import requests
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import json
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import matplotlib.pyplot as graph
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import numpy
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import statistics
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import os
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import pandas
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pandas.options.display.float_format = '{:.10f}'.format
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word = input('Please enter a word to research!\n')
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print()
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startYear = 1800
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endYear = 2019
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years = range(startYear, endYear + 1)
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response = requests.get('https://books.google.com/ngrams/json?content=%s&year_start=%s&year_end=%s&corpus=26&smoothing=3' % (word, startYear, endYear))
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data = json.loads(response.content)[0]
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frame = {}
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points = data['timeseries']
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frame['word'] = data['ngram']
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frame['stdev'] = numpy.std(points)
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frame['mean'] = numpy.mean(points)
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frame['median'] = numpy.median(points)
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frame['mode'] = statistics.mode(points)
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frame['range'] = max(points) - min(points)
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frame['q1'] = numpy.percentile(points, 25)
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frame['q3'] = numpy.percentile(points, 75)
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frame['iqr'] = frame['q3'] - frame['q1']
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df = pandas.DataFrame([frame])
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print(df)
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m, b = numpy.polyfit(years, points, 1)
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graph.plot(years, points)
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graph.plot(years, m * years + b)
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graph.title(frame['word'])
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graph.ticklabel_format(style='plain')
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graph.savefig('%s/%s.png' % (os.path.dirname(os.path.realpath(__file__)), word), dpi=100)
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