计算TOP3直播的引流人次占比:
# 假设数据中有一个名为'live_room'的字典,记录了每个直播间的基本信息
total_followers = sum(live_room[room]['followers'] for room in live_room)
top_three_rooms = sorted(live_room, key=lambda x: live_room[x]['follows_from_video'], reverse=True)[:3]
total_top_three_followers = sum(live_room[room]['followers'] for room in top_three_rooms)
top_three_followers_ratio = (total_top_three_followers / total_followers) * 100
print(f'TOP3直播间引流人次占比:{top_three_followers_ratio}%')
分析带货类目分布:
# 假设数据中有一个名为'category_sales'的字典,记录了每个直播间的销售类别及其金额
category_distribution = {}
for room in live_room:
if 'categories_sold' not in live_room[room]:
continue
for category, amount in live_room[room]['categories_sold'].items():
if category in category_distribution:
category_distribution[category] += amount
else:
category_distribution[category] = amount
total_sales = sum(category_distribution.values())
top_categories = sorted(category_distribution.items(), key=lambda x: x[1], reverse=True)[:3]
print(f'引流效率高的直播间的带货类目分布:{top_categories}')
计算粉丝数与引流能力的关系:
# 假设数据中有一个名为'live_room'的字典,记录了每个直播间的基本信息和引流量
followers_vs_traffic = [(live_room[room]['followers'], live_room[room]['follows_from_video']) for room in live_room]
correlation_coefficient = np.corrcoef([data[0] for data in followers_vs_traffic], [data[1] for data in followers_vs_traffic])[0, 1]
print(f'粉丝数与引流能力的相关系数:{correlation_coefficient}')
以上分析数据来源:互联岛