基于Spark的网易云音乐数据分析的步骤代码和实验过程

由于数据获取和存储的代码需要访问网易云音乐API和Hadoop分布式文件系统，这里给出基于Spark的网易云音乐数据分析的步骤代码和实验过程：

1. 数据清洗和存储

# 导入必要的库
import pandas as pd
from pyspark.sql import SparkSession

# 创建SparkSession对象
spark = SparkSession.builder.appName('NetEaseMusicAnalysis').getOrCreate()

# 读取数据
df = spark.read.json('hdfs://localhost:9000/path/to/data')

# 数据清洗
df_clean = df.dropDuplicates()  # 去除重复数据
df_clean = df_clean.dropna()  # 去除缺失数据
df_clean = df_clean.filter(df['age'] >= 0)  # 去除异常数据

# 数据存储
df_clean.write.mode('overwrite').parquet('hdfs://localhost:9000/path/to/clean/data')

2. 数据分析

# 导入必要的库
from pyspark.sql.functions import col, desc
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.clustering import KMeans
from pyspark.ml.recommendation import ALS

# 创建SparkSession对象
spark = SparkSession.builder.appName('NetEaseMusicAnalysis').getOrCreate()

# 读取数据
df = spark.read.parquet('hdfs://localhost:9000/path/to/clean/data')

# 用户画像分析
df_age = df.groupBy('age').count()
df_gender = df.groupBy('gender').count()

# 热门歌曲/歌手/专辑分析
df_song = df.groupBy('song_name').count().orderBy(desc('count')).limit(10)
df_artist = df.groupBy('artist_name').count().orderBy(desc('count')).limit(10)
df_album = df.groupBy('album_name').count().orderBy(desc('count')).limit(10)

# 用户兴趣推荐
# 数据预处理
vectorAssembler = VectorAssembler(inputCols=['user_id', 'song_id'], outputCol='features')
df_model = vectorAssembler.transform(df)
(train, test) = df_model.randomSplit([0.8, 0.2])

# 模型训练
als = ALS(rank=10, maxIter=5, regParam=0.01, userCol='user_id', itemCol='song_id', ratingCol='play_count', coldStartStrategy='drop')
model = als.fit(train)

# 模型评估
predictions = model.transform(test)
evaluator = RegressionEvaluator(metricName='rmse', labelCol='play_count', predictionCol='prediction')
rmse = evaluator.evaluate(predictions)

# 聚类分析
kmeans = KMeans(k=2, seed=1)
kmeansModel = kmeans.fit(df_model)
centers = kmeansModel.clusterCenters()

3. 数据可视化

# 导入必要的库
import matplotlib.pyplot as plt

# 用户画像可视化
df_age_pd = df_age.toPandas()
plt.bar(df_age_pd['age'], df_age_pd['count'])
plt.xlabel('Age')
plt.ylabel('Count')
plt.title('Age Distribution')
plt.show()

df_gender_pd = df_gender.toPandas()
plt.bar(df_gender_pd['gender'], df_gender_pd['count'])
plt.xlabel('Gender')
plt.ylabel('Count')
plt.title('Gender Distribution')
plt.show()

# 热门歌曲/歌手/专辑可视化
df_song_pd = df_song.toPandas()
plt.bar(df_song_pd['song_name'], df_song_pd['count'])
plt.xlabel('Song Name')
plt.ylabel('Count')
plt.title('Top 10 Songs')
plt.show()

df_artist_pd = df_artist.toPandas()
plt.bar(df_artist_pd['artist_name'], df_artist_pd['count'])
plt.xlabel('Artist Name')
plt.ylabel('Count')
plt.title('Top 10 Artists')
plt.show()

df_album_pd = df_album.toPandas()
plt.bar(df_album_pd['album_name'], df_album_pd['count'])
plt.xlabel('Album Name')
plt.ylabel('Count')
plt.title('Top 10 Albums')
plt.show()

# 用户兴趣推荐可视化
user_recs = model.recommendForAllUsers(10)
user_recs_pd = user_recs.toPandas()
for row in user_recs_pd.itertuples():
    user_id = row[1]
    recs = row[2]
    rec_song_ids = [r[0] for r in recs]
    rec_play_counts = [r[1] for r in recs]
    plt.bar(rec_song_ids, rec_play_counts)
    plt.xlabel('Song ID')
    plt.ylabel('Play Count')
    plt.title('Recommendations for User {}'.format(user_id))
    plt.show()

# 聚类分析可视化
plt.scatter(df_model.select('features').rdd.map(lambda x: x[0][0]).collect(), df_model.select('features').rdd.map(lambda x: x[0][1]).collect(), c=kmeansModel.labels_)
plt.scatter(centers[:,0], centers[:,1], marker='*', s=100, c='red')
plt.xlabel('User ID')
plt.ylabel('Song ID')
plt.title('User-Song Clustering')
plt.show()

这些代码仅供参考，实际实验过程可能需要根据具体需求和数据进行调整和修改。