Output: 基于一列删除 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:...
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。
df.drop('age').show() df.drop(df.age).show() df.join(df2, df.name == df2.name, 'inner').drop('name').sort('age').show() #创建新的column或更新重名column,指定column不存在不操作 df.withColumn('age2', df.age + 2).show() df.withColumns({'age2': df.age + 2, 'age3': ...
# 1. df.dropDuplicate() :数据去重,无参数按整理去重;也可指定列去重 pd_data = pd.DataFrame({'name':['张三','李四','王五','张三','李四','王五'] ,'score':[65,35,89,65,67,97]}) df = spark.createDataFrame(pd_data) df.show() df.dropDuplicates().show() df.dropDuplicates(['na...
You can also drop multiple columns at once:Python Копирај df_customer_flag_renamed.drop("c_phone", "balance_flag_renamed") Row operationsSpark provides many basic row operations:Filter rows Remove duplicate rows Handle null values Append rows Sort rows Filter rows...
# apply pandas udf on multiple columns of dataframe df.withColumn("product", prod_udf(df['ratings'],df['experience'])).show(10,False) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 6.删除 去重dropDuplicates # duplicate values df.count() # 33 ...
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"])...
Select columns from PySpark DataFrame PySpark Collect() – Retrieve data from DataFrame PySpark withColumn to update or add a column PySpark using where filter function PySpark – Distinct to drop duplicate rows PySpark orderBy() and sort() explained PySpark Groupby Explained with Example PySpark...
Reviewing the dataset, you can see that some columns contain duplicate information. For example, the cnt column equals the sum of the casual and registered columns. You should remove the casual and registered columns from the dataset. The index column instant is also not useful as a predictor....
This PySpark SQL cheat sheet covers the basics of working with the Apache Spark DataFrames in Python: from initializing the SparkSession to creating DataFrames, inspecting the data, handling duplicate values, querying, adding, updating or removing columns, grouping, filtering or sorting data. You'...