duplicate_values = duplicate_rows.select(df.columns) 使用select()选择与原始数据框相同的列,即提取重复行的值。 替换重复行的值: 代码语言:txt 复制 df = df.dropDuplicates() 使用dropDuplicates()方法删除重复的行,即保留每个重复组中的第一行,并更新数据框。
Python3 # remove duplicate rows based on college # column dataframe.dropDuplicates(['college']).show() Output: 基于多列的拖放 Python3 # remove duplicate rows based on college # and ID column dataframe.dropDuplicates(['college', 'student ID']).show() Output:发表评论: 发送 推荐阅...
Return a new DataFrame with duplicate rows removed, optionally only considering certain columns. 返回删除重复行的新 DataFrame,可选择仅考虑某些列。 drop_duplicates([subset]) drop_duplicates() is an alias for dropDuplicates(). dropna([how, thresh, subset]) Returns a new DataFrame omitting rows wit...
6.删除 去重dropDuplicates # duplicate values df.count() # 33 # drop duplicate values df=df.dropDuplicates() # validate new count df.count() # 26 1. 2. 3. 4. 5. 6. 7. 8. 删除某列 # drop column of dataframe df_new=df.drop('mobile') df_new.show(10) 1. 2. 3. 4. 7.保...
dropDuplicates() # or df = df.distinct() # Drop duplicate rows, but consider only specific columns df = df.dropDuplicates(['name', 'height']) # Replace empty strings with null (leave out subset keyword arg to replace in all columns) df = df.replace({"": None}, subset=["name"])...
To de-duplicate rows, use distinct, which returns only the unique rows.Python Копирај df_unique = df_customer.distinct() Handle null valuesTo handle null values, drop rows that contain null values using the na.drop method. This method lets you specify if you want to drop rows...
PySpark – Distinct to drop duplicate rows PySpark orderBy() and sort() explained PySpark Groupby Explained with Example PySpark Join Types Explained with Examples PySpark Union and UnionAll Explained PySpark UDF (User Defined Function) PySpark flatMap() Transformation PySpark map Transformation PySpark...
AWS Glue 提供了以下可在 PySpark ETL 操作中使用的内置转换。您的数据在一个称为DynamicFrame的数据结构中从转换传递到转换,该数据结构是 Apache Spark SQLDataFrame的扩展。DynamicFrame包含您的数据,并引用其架构来处理您的数据。 此外,其中的大多数转换也将作为DynamicFrame类的方法存在。更多相关信息,请参阅Dynamic...
In the Aggregation drop down, select "AVG". display(train.select("hr", "cnt")) Visualization 02468101214161820220100200300400 hrcnt 24 aggregated rows. Train the machine learning pipeline Now that you have reviewed the data and prepared it as a DataFrame with numeric values, you're ready to...
Duplicate Values >>> df = df.dropDuplicates() Powered By Queries >>> from pyspark.sql import functions as F Powered By Select >>> df.select("firstName").show() #Show all entries in firstName column>>> df.select("firstName","lastName") \ .show()>>> df.select("firstName"...