# Filter on equals conditiondf=df.filter(df.is_adult=='Y')# Filter on >, <, >=, <= conditiondf=df.filter(df.age>25)# Multiple conditions require parentheses around each conditiondf=df.filter((df.age>25)&(df.is_adult=='Y'))# Compare against a list of allowed valuesdf=df.filter...
from pyspark.sql import SparkSession from pyspark.sql.functions import when # 创建SparkSession spark = SparkSession.builder.appName("Multiple WHEN Conditions").getOrCreate() # 创建示例数据 data = [("John", 25), ("Alice", 30), ("Mike", 35)] df = spark.createDataFrame(data, ["Name",...
df.filter(df.SalesYTD>4000000).show() sql: selectSalesYTDfromdfwhereSalesYTD>4000000 多条件过滤 df.filter(df.SalesYTD>4000000 & df.Bonus<55000).show() sql: select*fromdfwhereSalesYTD>4000000and Bonus<55000 过滤字符串是否包含 df.filter(col('education').contain('degree').show() 4.等于和不...
df.printSchema() 3.select 功能:选择DataFrame中的指定列(通过传入参数进行指定) 4. filter和where 功能:过滤DataFrame内的数据,返回一个过滤后的DataFrame 5.groupBy 分组 功能:按照指定的列进行数据的分组, 返回值是GroupedData对象 df.groupBy() 传入参数和select一样,支持多种形式。GroupedData对象是一个特殊的Da...
subset_df = df.filter(df["rank"] <11).select("City") display(subset_df) 步驟4:儲存數據框架 您可以將 DataFrame 儲存至數據表,或將數據框架寫入檔案或多個檔案。 將DataFrame 儲存至數據表 根據預設,Azure Databricks 會針對所有數據表使用 Delta Lake 格式。 若要儲存 DataFrame,您必須擁有CREATE目錄和架...
filter() #过滤数据 df = df.filter(df[tenure]>=21)等价于df = df.where(df[tenure]>=21) 在有多个条件时: df .filter(“id = 1 or c1 = 'b’” ).show() 过滤null值或nan值时: from pyspark.sql.functions import isnan, isnull
1. filter按照条件过滤 frompyspark.sql.functionsimport*#重命名列df=df.withColumnRenamed('Item Name','ItemName')df1=df.filter(df.ItemName=='Total income')#另外一种写法df1=df.filter(col('ItemName')=='Total income')display(df1) 2. 使用like()模糊查找字符串 ...
functions.expr import expr field = "emails.unverified" processed = expr(df, field=field, expr=f"transform({field}, x -> (upper(x)))") Field Rename Rename all the fields based on any rename function. (If you only want to rename specific fields filter on them in your rename function)...
df_basket1.agg({'Price':'max'}).show() Maximum value of price column is calculated Minimum value of the column in pyspark with example: Minimum value of the column in pyspark is calculated using aggregate function – agg() function. The agg() Function takes up the column name and ‘min...
Filter with a SQL expression. Note the double and single quotes as I’m passing a SQL where clause into filter(). df1.filter("Sex='female'").show() +---+---+---+---+ |PassengerId| Name| Sex|Survived| +---+---+---+---+ | 2|Florence|female| 1| | 3| Laina|female|...