# 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()
df.groupBy('mobile').sum().show(5,False) 1. max # Value counts df.groupBy('mobile').max().show(5,False) 1. 2. min # Value counts df.groupBy('mobile').min().show(5,False) 1. 2. agg # Aggregation df.groupBy('mobile').agg({'experience':'sum'}).show(5,False) 1. 2. 5...
Remove duplicate rowsTo 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...
those rows will be kept in the result, even if there are duplicate keys in the left DataFrame. Think of left semi joins as filters on a DataFrame
I can filter a subset of rows. The method filter() takes column expressions or SQL expressions. Think of the WHERE clause in SQL queries. Filter with a column expression df1.filter(df1.Sex == 'female').show() +---+---+---+---+ |PassengerId| Name| Sex|Survived| +---+--...
“”“ # Columns of duplicate Rows of DF 浏览25提问于2019-06-21得票数 0 1回答 Pyspark:如果其他列为空,则在pyspark列中填充固定值 、 我有一个有两列的pyspark dataframe。如果另一列中的行值为空,我想用固定值填充一列。因此,在customer_df中,如果customer_address为null,则将城市列填充为“unknown...
["source_column"], )try: df_output = math_functions.IsEven.apply( data_frame=input_df, spark_context=sc, source_column="source_column", target_column="target_column", value=None, true_string="Even", false_string="Not even", ) df_output.show()except:print("Unexpected Error happened "...
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"]) # Convert Python/PySpark/NumPy ...
创作活动我正在尝试将csv转换为Parquet。我使用python 3.6和spark 2.3.1 64位。我无法找到给定追溯的...
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"...