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['mobile']=='Vivo').filter(df['experience'] >10).show() 1. 2. # filter the multiple conditions df.filter((df['mobile']=='Vivo')&(df['experience'] >10)).show() 1. 2. 某列的不重复值(特征的特征值) # Distinct Values in a column df.select('mobile').distinct()....
# create a new col based on another col's value data = data.withColumn('newCol', F.when(condition, value)) # multiple conditions data = data.withColumn("newCol", F.when(condition1, value1) .when(condition2, value2) .otherwise(value3)) 自定义函数(UDF) # 1. define a python function...
from pyspark.sql.functions import col df_that_one_customer = df_customer.filter(col("c_custkey") == 412449) To filter on multiple conditions, use logical operators. For example, & and | enable you to AND and OR conditions, respectively. The following example filters rows where the c_nati...
condition is the criteria used to filter the columns you want to keep. Let’s work again with our DataFrame df and select all the columns except the team column: df_sel = df.select([col for col in df.columns if col != "team"]) Powered By Complex conditions with .selectExpr() If...
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I can also join by conditions, but it creates duplicate column names if the keys have the same name, which is frustrating. For now, the only way I know to avoid this is to pass a list of join keys as in the previous cell. If I want to make nonequi joins, then I need to rename...
The key thing to remember if you have multiple filter conditions is that filter accepts standard Python expressions. Use bitwise operators to handle and/or conditions. from pyspark.sql.functions import col # OR df = auto_df.filter((col("mpg") > "30") | (col("acceleration") < "10"))...
functions.redact import redact redacted_df = redact(df, field="customDimensions.metabolicsConditions") Whitelist Preserving all fields listed in parameters. All other fields will be dropped from nestedfunctions.functions.whitelist import whitelist whitelisted_df = whitelist(df, ["addresses.postalCode", ...
Maximum or minimum value of the column in pyspark can be accomplished using aggregate() function. Maximum or Minimum value of the group in pyspark example