dataframe.filter((dataframe.student_ID.isin(Id_list))| (dataframe.college.isin(college_list))).show() 输出: 方法四:使用Startswith和endswith 这里我们将使用pyspark的startswith和endswith函数。 startswith():该函数以一个字符为参数,在字符串的第一个字符开始的列中搜索,如果条件满足则返回True。 语法:...
# subset or filter the data with # multiple condition df=df.filter((df.Gender=='Male')|(df.Percentage>90)) df.show() 输出: 注:本文由VeryToolz翻译自Subset or Filter data with multiple conditions in PySpark,非经特殊声明,文中代码和图片版权归原作者srishivansh5404所有,本译文的传播和使用请遵循...
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']==...
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# 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...
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...
1.3 Using Multiple Conditions To filter rows with NULL values on multiple columns, use either AND or & operator. df.filter("state IS NULL AND gender IS NULL").show() df.filter(df.state.isNull() & df.gender.isNull()).show()
analytics and processing purpose. It can be used with single or multiple conditions to filter the data or can be used to generate a new column of it. This can also be used in the PySpark SQL function, just as the like operation to filter the columns associated with the character value ...
conditions.append(condition) # Combine all the conditions using AND (every column must meet its own condition) combined_condition = conditions[0] for condition in conditions[1:]: combined_condition &= condition # Filter the DataFrame based on the combined condition filtered_df = df.filter(combine...
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