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from .utils import *
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import re
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def getAllTypes():
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return list(set(typeList('types')))
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# 评分
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def getAllRateDataByType(type):
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if type == 'all':
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rateList = df['rate'].values
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# rateList = df['rate'].map(lambda x: float(x)).values
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rateList.sort()
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else:
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typeList = df['types'].map(lambda x: x.split(sep=','))
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oldRateList = df['rate'].values
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rateList = []
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for i, item in enumerate(typeList):
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if type in item:
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rateList.append(oldRateList[i])
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rateObj = {}
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for i in rateList:
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if rateObj.get(i, -1) == -1:
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rateObj[i] = 1
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else:
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rateObj[i] = rateObj[i] + 1
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return list(rateObj.keys()), list(rateObj.values())
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def getStart(searchIpt):
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searchdata = list(df.loc[df['title'].str.contains(searchIpt)]['title'])
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if searchdata:
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# print(startes, searchName)
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searchName = searchdata[0]
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startes = list(df.loc[df['title'].str.contains(searchIpt)]['starts'])[0].split(',')
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startData = [
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{
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'name': '五星',
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'value': 0
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},
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{
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'name': '四星',
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'value': 0
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},
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{
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'name': '三星',
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'value': 0
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},
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{
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'name': '二星',
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'value': 0
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},
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{
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'name': '一星',
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'value': 0
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}
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]
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for i, item in enumerate(startes):
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# startData[i]['value'] = float(re.sub('%', '', item))
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startData[i]['value'] = float(re.sub('%', '', item))
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else:
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searchName = '无此电影'
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startData = ''
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return startData, searchName
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# 豆瓣年度评价评分柱状图
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def getYearMeanData():
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# 确保 'rate' , 'year' 列是数值类型
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df['rate'] = pd.to_numeric(df['rate'], errors='coerce')
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df['year'] = pd.to_numeric(df['year'], errors='coerce')
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# 将非数值类型的数据转换为 NaN
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df.dropna(subset=['rate', 'year'], inplace=True)
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# 获取唯一的年份列表，并去除可能的 NaN 值
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yearList = list(set(df['year'].dropna().unique()))
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# 尝试删除列表中的第一个 0，如果它存在的话
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if 0 in yearList:
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yearList.remove(0)
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# 计算每个年份的电影评分平均值
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meanList = []
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for year in yearList:
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meanList.append(df[df['year'] == year]['rate'].mean())
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return yearList, meanList
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