Python技術棧與Spark交叉資料分析雙向整合技術實戰！
阿新 • • 發佈：2018-12-30
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- Python Spark DataFrame 基礎
df = spark.read.parquet('/sql/users.parquet')
df.show()
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
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- Python Spark DataFrame 聚合統計
CustomerID,Genre,Age,Annual Income (k$),Spending Score (1-100)
0001,Male,19,15,39
0002,Male,21,15,81
0003,Female,20,16,6
0004,Female,23,16,77
0005,Female,31,17,40
0006,Female,22,17,76
df = spark.read.csv('/sql/customers.csv',header=True)
df.printSchema()
df.show()
root
|-- CustomerID: string (nullable = true)
|-- Genre: string (nullable = true)
|-- Age: string (nullable = true)
|-- Annual Income (k$): string (nullable = true)
|-- Spending Score (1-100): string (nullable = true)
+----------+------+---+------------------+----------------------+
|CustomerID| Genre|Age|Annual Income (k$)|Spending Score (1-100)|
+----------+------+---+------------------+----------------------+
| 0001| Male| 19| 15| 39|
| 0002| Male| 21| 15| 81|
| 0003|Female| 20| 16| 6|
| 0004|Female| 23| 16| 77|
| 0005|Female| 31| 17| 40|
| 0006|Female| 22| 17| 76|
| 0007|Female| 35| 18| 6|
| 0008|Female| 23| 18| 94|
| 0009| Male| 64| 19| 3|
| 0010|Female| 30| 19| 72|
| 0011| Male| 67| 19| 14|
| 0012|Female| 35| 19| 99|
| 0013|Female| 58| 20| 15|
| 0014|Female| 24| 20| 77|
| 0015| Male| 37| 20| 13|
| 0016| Male| 22| 20| 79|
| 0017|Female| 35| 21| 35|
| 0018| Male| 20| 21| 66|
| 0019| Male| 52| 23| 29|
| 0020|Female| 35| 23| 98|
+----------+------+---+------------------+----------------------+
df.agg({"Age": "max","Annual Income (k$)":"mean","Spending Score (1-100)":"mean"}).show()
+---------------------------+-----------------------+--------+
|avg(Spending Score (1-100))|avg(Annual Income (k$))|max(Age)|
+---------------------------+-----------------------+--------+
| 50.2| 60.56| 70|
+---------------------------+-----------------------+--------+
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- alias(alias)為DataFrame定義一個別名,稍後再函式中就可以利用這個別名來做相關的運 算，例如說自關聯Join:
df1 = df.alias('cus1')
type(df1)
df2 = df.alias('cus2')
df3 = df1.join(df2,col('cus1.CustomerId')==col('cus2.CustomerId'),'inner')
df3.count()
200
+----------+------+---+------------------+----------------------+----------+------+---+------------------+----------------------+
|CustomerID| Genre|Age|Annual Income (k$)|Spending Score (1-100)|CustomerID| Genre|Age|Annual Income (k$)|Spending Score (1-100)|
+----------+------+---+------------------+----------------------+----------+------+---+------------------+----------------------+
| 0001| Male| 19| 15| 39| 0001| Male| 19| 15| 39|
| 0002| Male| 21| 15| 81| 0002| Male| 21| 15| 81|
| 0003|Female| 20| 16| 6| 0003|Female| 20| 16| 6|
| 0004|Female| 23| 16| 77| 0004|Female| 23| 16| 77|
| 0005|Female| 31| 17| 40| 0005|Female| 31| 17| 40|
| 0006|Female| 22| 17| 76| 0006|Female| 22| 17| 76|
| 0007|Female| 35| 18| 6| 0007|Female| 35| 18| 6|
| 0008|Female| 23| 18| 94| 0008|Female| 23| 18| 94|
| 0009| Male| 64| 19| 3| 0009| Male| 64| 19| 3|
| 0010|Female| 30| 19| 72| 0010|Female| 30| 19| 72|
+----------+------+---+------------------+----------------------+----------+------+---+------------------+----------------------+
only showing top 10 rows
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- cache()，將DataFrame快取到StorageLevel對應的快取級別中，預設是 MEMORY_AND_DISK
df = spark.read.csv('/sql/customers.csv',header=True)
a = df.cache()
a.show()
+----------+------+---+------------------+----------------------+
|CustomerID| Genre|Age|Annual Income (k$)|Spending Score (1-100)|
+----------+------+---+------------------+----------------------+
| 0001| Male| 19| 15| 39|
| 0002| Male| 21| 15| 81|
| 0003|Female| 20| 16| 6|
| 0004|Female| 23| 16| 77|
| 0005|Female| 31| 17| 40|
| 0006|Female| 22| 17| 76|
| 0007|Female| 35| 18| 6|
| 0008|Female| 23| 18| 94|
| 0009| Male| 64| 19| 3|
| 0010|Female| 30| 19| 72|
| 0011| Male| 67| 19| 14|
| 0012|Female| 35| 19| 99|
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- checkpoint(eager=True) 對DataFrame設定斷點，這個方法是Spark2.1引入的方法，這個方法的呼叫會斬斷在這個 DataFrame上的邏輯執行計劃，將前後的依賴關係持久化到checkpoint檔案中去。
sc
sc.setCheckpointDir('/datas/checkpoint')
a.checkpoint()
a.show()
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- coalesce(numPartitions) 重分割槽演算法，傳入的引數是DataFrame的分割槽數量。
注意通過read方法讀取檔案，建立的DataFrame預設的分割槽數為檔案的個數，即一個檔案對
應一個分割槽，在分割槽數少於coalesce指定的分割槽數的時候，呼叫coalesce是不起作用的
df = spark.read.csv('/sql/customers.csv',header=True)
df.rdd.getNumPartitions()
1
spark.read.csv('/sql/customers.csv',header=True).coalesce(3).rdd.getNumPartitions()
1
df = spark.range(0,20,2,3)
df.rdd.getNumPartitions()
df.coalesce(2).rdd.getNumPartitions()
2
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- repartition(numPartitions, *cols)這個方法和coalesce(numPartitions) 方法一樣，都是 對DataFrame進行重新的分割槽，但是repartition這個方法會使用hash演算法，在整個叢集中進 行shuffle，效率較低。repartition方法不僅可以指定分割槽數，還可以指定按照哪些列來做分 區。
df = spark.read.csv('/sql/customers.csv',header=True)
df.rdd.getNumPartitions()
1
df2 = df.repartition(3)
df2.rdd.getNumPartitions()
3
df2.columns
df3 = df2.repartition(6,'Genre')
df3.show(20)
+----------+------+---+------------------+----------------------+
|CustomerID| Genre|Age|Annual Income (k$)|Spending Score (1-100)|
+----------+------+---+------------------+----------------------+
| 0003|Female| 20| 16| 6|
| 0004|Female| 23| 16| 77|
| 0005|Female| 31| 17| 40|
| 0006|Female| 22| 17| 76|
| 0007|Female| 35| 18| 6|
| 0008|Female| 23| 18| 94|
| 0010|Female| 30| 19| 72|
| 0012|Female| 35| 19| 99|
| 0013|Female| 58| 20| 15|
df3.rdd.getNumPartitions()
6
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- colRegex(colName)用正則表示式的方式返回我們想要的列。
df = spark.createDataFrame([("a", 1), ("b", 2), ("c", 3)], ["Col1", "a"])
df.select(df.colRegex("`(Col1)?+.+`")).show()
+---+
| a|
+---+
| 1|
| 2|
| 3|
+---+
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- collect()，返回DataFrame中的所有資料，注意資料量大了容易造成Driver節點記憶體溢 出！
df = spark.createDataFrame([("a", 1), ("b", 2), ("c", 3)], ["Col1", "a"])
df.collect()
[Row(Col1='a', a=1), Row(Col1='b', a=2), Row(Col1='c', a=3)]
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- columns,以列表的形式返回DataFrame的所有列名
df = spark.read.csv('/sql/customers.csv',header=True)
df.columns
df = spark.read.csv('/sql/customers.csv',header=True)
df.columns
['CustomerID', 'Genre', 'Age', 'Annual Income (k$)', 'Spending Score (1-100)']
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- SparkSQL DataFrame 轉換為 PandasDataFrame
df = spark.read.csv('/sql/customers.csv',header=True)
pdf = df.toPandas()
pdf.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 200 entries, 0 to 199
Data columns (total 5 columns):
CustomerID 200 non-null object
Genre 200 non-null object
Age 200 non-null object
Annual Income (k$) 200 non-null object
Spending Score (1-100) 200 non-null object
dtypes: object(5)
memory usage: 7.9+ KB
pdf['Age'] = pdf['Age'].astype('int')
pdf["Annual Income (k$)"]=pdf["Annual Income (k$)"].astype('int')
pdf["Spending Score (1-100)"]=pdf["Spending Score (1-100)"].astype('int')
pdf.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 200 entries, 0 to 199
Data columns (total 5 columns):
CustomerID 200 non-null object
Genre 200 non-null object
Age 200 non-null int64
Annual Income (k$) 200 non-null int64
Spending Score (1-100) 200 non-null int64
dtypes: int64(3), object(2)
memory usage: 7.9+ KB
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- PandasDataFrame 轉換為 SparkSQL DataFrame
df1 = spark.createDataFrame(pdf)
df1.corr("Age","Annual Income (k$)")
df1.corr("Spending Score (1-100)","Annual Income (k$)")
0.009902848094037492
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- count()返回DataFrame中Row的數量
df = spark.read.csv('/sql/customers.csv',header=True)
df.count()
200
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- createGlobalTempView(name)使用DataFrame建立一個全域性的臨時表，其生命週期 和啟動的app的週期一致，即啟動的spark應用存在則這個臨時的表就一直能訪問。直到 sparkcontext的stop方法的呼叫退出應用為止。建立的臨時表儲存在global_temp這個庫 中