例如,假设我们希望过滤出名字以 J 开头并且年龄小于 30 的用户。 # 过滤名字以 'J' 开头且年龄小于 30 的用户filtered_df_multiple_conditions=df.filter((df.Name.startswith("J"))&(df.Age<30))# 显示过滤后的 DataFramefiltered_df_multiple_conditions.show() 1. 2. 3. 4. 5. 在这个示例中,我们...
filter过滤 # filter the records 过滤mobile是vivo的记录 df.filter(df['mobile']=='Vivo').show() 1. 2. 过滤+ 选择 # filter the records df.filter(df['mobile']=='Vivo').select('age','ratings','mobile').show() 1. 2. 条件 # filter the multiple conditions df.filter(df['mobile']==...
# keep rows with certain length data.filter("length(col) > 20") # get distinct value of the column data.select("col").distinct() # remove row which has certain character data.filter(~F.col('col').contains('abc')) 列值处理 (1)列值分割 # split column based on space data = data...
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...
filter(df.is_adult == 'Y') # Filter on >, <, >=, <= condition df = df.filter(df.age > 25) # Multiple conditions require parentheses around each condition df = df.filter((df.age > 25) & (df.is_adult == 'Y')) # Compare against a list of allowed values df = df.filter(...
Multiple filter conditions 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("ac...
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...
Filter with a column expression df1.filter(df1.Sex == 'female').show() +---+---+---+---+ |PassengerId| Name| Sex|Survived| +---+---+---+---+ | 2|Florence|female| 1| | 3| Laina|female| 1| | 4| Lily|female| 1| +---+---+---+---+ Filter with a SQL...
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
>>> df.filter(df.name.like('Al%')).collect()[Row(age=2, name=u'Alice')] name(*alias, **kwargs) name() is an alias for alias(). New in version 2.0. otherwise(value)[source] Evaluates a list of conditions and returns one of multiple possible result expressions. If Column.otherwi...