2.Use Regular expression to replace String Column Value #Replace part of string with another stringfrompyspark.sql.functionsimportregexp_replace df.withColumn('address', regexp_replace('address','Rd','Road')) \ .show(truncate=False)# createVar[f"{table_name}_df"] = getattr(sys.modules[__...
df.na.replace("old_value", "new_value", subset=["col1", "col2"]) 这些方法都返回一个新的 DataFrame,原始 DataFrame 不会被修改。 以下是一个使用 .na 方法处理缺失值的示例 from pyspark.sql import SparkSessionspark = SparkSession.builder.getOrCreate()# 创建一个包含缺失值的 DataFramedata = ...
## df['value'] = df['value'].str[0] 可去掉空值行,但会将第一列数据变为该行数组元素的第一个元素 ##可借助辅助列(value_2),后再删除该辅助列,也可以直接if判断操作 df['value_2'] = df['value'].str[0] ### 再去除有空行存在的列,必须加inplace=True,否则原数据并不会改变 df.dropna(i...
format(column_name)) -- Example with the column types for column_name, column_type in dataset.dtypes: -- Replace all columns values by "Test" dataset = dataset.withColumn(column_name, F.lit("Test")) 12. Iteration Dictionaries # Define a dictionary my_dictionary = { "dog": "Alice",...
(2000, 1, 3, 12, 0)) ], schema='a long, b double, c string, d date, e timestamp') df.createOrReplaceTempView("t1") # UDF- 匿名函数 spark.udf.register('xtrim', lambda x: re.sub('[ \n\r\t]', '', x), 'string') # UDF 显式函数 def xtrim2(record): return re.sub(...
from pyspark.sql.functions import when from pyspark.sql.functions import lit df.withColumn(col1,when(df[col1] == lit('value'),'replace_value').otherwise(df['col1']) 17. pyspark dataframe sample函数 df.sample(withReplacement = False,fraction = 0.5,seed = None 18. 筛选有空值的行 df.whe...
add_sheet("sheet1") # 消费时间,消费用户id,消费物品id,消费维度(次数,金额),消费值 column_names = ['ftime', 'uin', 'item_id', 'pay_dimension', 'value'] column_count = len(column_names) for i in range(column_count): worksheet.write(0, i, column_names[i]) # 向构建好字段的excel...
df = df.withColumn("split_col", split(df["value"], ";")) 将拆分后的数据按行展开: 代码语言:txt 复制 df = df.withColumn("exploded_col", explode(df["split_col"])) 创建临时视图以便后续查询操作: 代码语言:txt 复制 df.createOrReplaceTempView("temp_view") 执行SQL查询语句: 代码语言:...
value – 一个文字值或一个Column表达式 >>> df.select(when(df['age'] == 2, 3).otherwise(4).alias("age")).collect() [Row(age=3), Row(age=4)] >>> df.select(when(df.age == 2, df.age + 1).alias("age")).collect() [Row(age=3), Row(age=None)] df3 = df.withColumn(...
To create a new column, use the withColumn method. The following example creates a new column that contains a boolean value based on whether the customer account balance c_acctbal exceeds 1000:Python Kopiraj df_customer_flag = df_customer.withColumn("balance_flag", col("c_acctbal") > ...