# coding=utf-8
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import sys
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reload(sys)
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sys.setdefaultencoding('utf8')
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from datetime import datetime, date, timedelta
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members = pd.read_csv('datasets/BotC.csv')
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print members.head(1)
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#generate the full set of days from the first date to the last in the dataset
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months = []
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days = []
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dInit = date(2017, 6, 8)
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dEnd = date(2017, 1, 9)
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delta = dEnd - dInit
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for i in range(delta.days+1):
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day = dInit + timedelta(days=i)
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dayString = day.strftime("%d/%m/%y")
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dayDatetime = datetime.strptime(dayString, '%d/%m/%y')
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days.append(dayDatetime)
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#add the dates of shops creation to the days array
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for memberDate in members['Date']:
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if isinstance(memberDate, basestring):
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memberDay = memberDate
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memberDayDatetime = datetime.strptime(memberDay, '%B %d, %Y')
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days.append(memberDayDatetime)
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#count days frequency in days array
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unique, counts = np.unique(days, return_counts=True)
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countDays = dict(zip(unique, counts))
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realCounts = []
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for count in counts:
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realCounts.append(count-1)
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#count the total acumulation of shops created in each days
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totalCount = 0
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globalCount = []
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for k in realCounts:
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totalCount = totalCount + k
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globalCount.append(totalCount)
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dates = countDays.values()
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counts = countDays.values()
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#plot the data
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plt.title("New members registered each day")
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plt.plot(unique, realCounts)
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plt.show()
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plt.title("Total members each day")
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plt.plot(unique, globalCount)
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plt.show()
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plt.title("New members and total members each day")
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plt.plot(unique, realCounts, label="new members registered each day")
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plt.plot(unique, globalCount, label="total members each day")
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plt.legend(loc='upper left')
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plt.show()
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# place of the account
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places = []
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for place in members["Place"]:
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if isinstance(place, basestring):
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places.append(place)
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placesNames, placesCount = np.unique(places, return_counts=True)
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plt.title("Membership places")
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plt.pie(placesCount, labels=placesNames, autopct='%1.1f%%', shadow=True, startangle=90)
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plt.axis('equal')
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plt.show()
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