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app.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+from pyecharts.charts import Bar,Scatter,Pie
+from pyecharts import options as opts
+from pyecharts.charts import Scatter3D
+
+from pyecharts.charts import WordCloud
+from pyecharts.globals import SymbolType
+from pyecharts.charts import Timeline
+
+df =pd.read_csv('flaskProject/data/export.csv')
+def tubiao_country_bar():
+    country_counts = df['country'].value_counts().head(10)
+    bar=(
+        Bar()
+        .add_xaxis(country_counts.index.tolist())
+        .add_yaxis("电影数量",country_counts.tolist())
+        .set_global_opts(title_opts=opts.TitleOpts("按国家分组统计电影数量 Top 10"))
+    )
+    bar.render('country_movie_count.html')
+
+def tubiao_top10_reviews():
+    top10_reviews=df.nlargest(10,'num_reviews')
+    bar=(
+        Bar()
+        .add_xaxis(top10_reviews['title'].tolist())
+        .add_yaxis("评价人数",top10_reviews['num_reviews'].tolist())
+        .set_global_opts(title_opts=opts.TitleOpts(title="评价人数 Top 10"))
+    )
+    bar.render('top10reviews.html')
+
+    scatter=(
+        Scatter()
+        .add_xaxis(df['ranking'].tolist())
+        .add_yaxis('电影评分',df['rating'].tolist())
+        .set_global_opts(
+            title_opts=opts.TitleOpts(title="高分电影评分-排名散点分布图"),
+            xaxis_opts=opts.AxisOpts(type_='value',name="电影排名"),
+            yaxis_opts=opts.AxisOpts(type_='value', name="电影评分")
+        )
+    )
+    scatter.render("rating_ranting_scatter.html")
+
+
+
+
+tubiao_country_bar()
+tubiao_top10_reviews()