df1 = spark.createDataFrame([ Row(id=1, value='foo'), Row(id=2, value=None) ]) df1.select( df1['value'] == 'foo', df1['value'].eqNullSafe('foo'), df1['value'].eqNullSafe(None) ).show() 18.getField获取字段 Column.ge
Row(value='# Apache Spark') 现在,我们可以通过以下方式计算包含单词Spark的行数: lines_with_spark = text_file.filter(text_file.value.contains("Spark")) 在这里,我们使用filter()函数过滤了行,并在filter()函数内部指定了text_file_value.contains包含单词"Spark",然后将这些结果放入了lines_with_spark变量...
from pyspark.sql import Window from pyspark.sql.functions import row_number, monotonically_increasing_id window_spec = Window.orderBy(monotonically_increasing_id()) df = df.withColumn("index", row_number().over(window_spec) - 1) ''' +---+---+---+ | cfrnid| 0830|index| +---+---...
write(0, i, column_names[i]) # 向构建好字段的excel表写入所有的数据记录 row_count = 200 # 付费总次数(天) pay_dimension_cnt = "pay_cnt" # 付费总金额(天) pay_dimension_amt = "pay_amt" for i in range(0, row_count, 2): # 随机时间(一个月内) random_ftime = random.randint(...
spark.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING) USING hive")spark.sql("LOAD DATA LOCAL INPATH 'data/kv1.txt' INTO TABLE src")df=spark.sql("SELECT key, value FROM src WHERE key < 10 ORDER BY key")df.show(5)#5.2读取mysql数据 ...
示例二 from pyspark.sql import Row from pyspark.sql.functions import explode eDF = spark.createDataFrame([Row( a=1, intlist=[1, 2, 3], mapfield={"a": "b"})]) eDF.select(explode(eDF.intlist).alias("anInt")).show() +---+ |anInt| +---+ | 1| | 2| | 3| +---+ isin...
pyspark.sql.DataFrame、pyspark.sql.Column和 pyspark.sql.Row 一,SparkSession类 在操作DataFrame之前,首先需要创建SparkSession,通过SparkSession来操作DataFrame。 1,创建SparkSession 通过Builder类来创建SparkSession,在Databricks Notebook中,spark是默认创建,表示一个SparkSession对象: ...
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import SparkConf,SparkContextfrom pyspark.sql import SparkSessionimport jsonimport pandas as pdimport numpy as npimport osfrom pyspark.sql import SQLContextfrom pyspark.sql import Rowfrom pyspark.sql.types import DoubleType,IntegerType,StringType,DateType,StructType,StructField#from common_value import ...
#extract first row as this is our header head=df.first()[0] schema=[‘fname’,’lname’,’age’,’dep’] print(schema) Output: ['fname', 'lname', 'age', 'dep'] 下一步是根据列分隔符对数据集进行分割: #filter the header, separate the columns and apply the schema df_new=df....