def replace_null_with_empty_array(array_column): if array_column is None: return [] else: return array_column replace_null_with_empty_array_udf = udf(replace_null_with_empty_array, ArrayType(IntegerType())) 使用UDF替换空值为空数组: 代码语言:txt 复制 df = df.withColumn("array_column", ...
pyspark.sql.functions.replace() 函数用于替换字符串中的特定子字符串。它的语法如下: replace(str, search, replace) 其中:str:要进行替换操作的字符串列或表达式。search:要搜索并替换的子字符串。replace:用于替换匹配项的新字符串。 这个函数将在给定的字符串列或表达式中查找所有匹配 search 的子字符串,并用...
In PySpark,fillna() from DataFrame class or fill() from DataFrameNaFunctions is used to replace NULL/None values on all or selected multiple columns with either zero(0), empty string, space, or any constant literal values. AdvertisementsWhile working on PySpark DataFrame we often need to ...
跟cast()是同一个函数 cast(dataType) #转换数据类型 startswith(other) #判断列中每个值是否以指定字符开头,返回布尔值 endswith(“string”) #判断列中每个值是否以指定字符结尾,返回布尔值 isNotNull() #判断列中的值
(table_name.replace(".", "")) data.registerTempTable(temp_table_name) print(data.columns) columns = ",".join([column for column in data.columns if column != "dt"]) print(columns) insert_model = "into" if is_overwrite: insert_model = "overwrite" # 写入hive insert_sql_str = ""...
1|0fill关键字的用法 Replace null values, alias for na.fill(). DataFrame.fillna() and DataFrameNaFunctions.fill() are aliases of each other. Parameters value –int, long, float, string, bool or dict. Value to replace null values with. If the value is a dict, then subset is ignored ...
df.na.replace(['Alice', 'Bob'], ['A', 'B'], 'name').show()+---+---+---+| age|height|name|+---+---+---+| 10| 80| A|| 5| null| B||null| 10| Tom||null| null|null|+---+---+---+df.show()+---+---+---+| age|height| name|+---+---+---+| 10...
代码运行次数:0 运行 AI代码解释 #5.1读取hive数据 spark.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING) USING hive")spark.sql("LOAD DATA LOCAL INPATH 'data/kv1.txt' INTO TABLE src")df=spark.sql("SELECT key, value FROM src WHERE key < 10 ORDER BY key")df.show(5)#5.2...
#Register the DataFrame as a SQL temporary viewdf.CreateOrReplaceTempView("people") sqlDF = spark.sql("SELECT * FROM people") sqlDF.show()#+---+---+#| age| name|#+---+---+#+null|Jackson|#| 30| Martin|#| 19| Melvin|#+---|---| 您需要从某个表中选择所有...
") return df print("int-->0,double-->mean,string-->unknow") df = df.na.replace('', 'unkown') # 将空字符串填充为unkown df = df.fillna('unkown', subset = string_tz) # 将string的NULL填充为unkown df = df.fillna(0, subset = int_tz) # 用均值填充连续类特征中的null值 # 计算...