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[__...
join(new_item_m_value, ["uin", "item_id"], "inner") rfm_values.show() return rfm_values 2.5 RFM模型应用 有了RFM模型,我们就可以通过策略对用户分层了。其实这里就是要为RFM定义阈值来对用户划分,实际情况要依据产品和运营策略,比如是否有运营策略,是否有运营阈值等等因素。 本文就用最简单的中位数...
df[y_machine_label[i]]=y_powers[i] 1. 2. 3. (3)实现数据分隔并换列插入存储 elec_aps = [] for item in data_use['elec_ap']: #print(item.split('_')[-1]) elec_aps.append(item.split('_')[-1]) ### df.replace(to_replace, value) 前面是需要替换的值,后面是替换后的值。 da...
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",...
replace('f','') file = open(file_path,"w+") print(data,file = file) file.close() df_temp = pd.read_csv(file_path,header=None,names=["feature","weight"]) df_importance = df_importance.merge(df_temp, left_on="feature", right_on="feature") df_importance.sort_values(by=['...
() ass_rule_df["antecedent_str"] = ass_rule_df["antecedent"].apply(lambda x: str(x)) ass_rule_df.sort_values( ["antecedent_str", "confidence"], ascending=[True, False], inplace=True ) t2 = datetime.datetime.now() logger.debug()("spent ts:", t2 - t1) return ass_rule_df ...
replace 全量替换 functions 部分替换 groupBy + agg 聚合 explode分割 isin 读取 从hive中读取数据 将数据保存到数据库中 读写csv/json pyspark.sql.functions常见内置函数 1.pyspark.sql.functions.abs(col) 2.pyspark.sql.functions.acos(col) 3.pyspark.sql.functions.add_months(start, months) 4.pyspark.sql...
# 字符串替换(正则) df.withColumn('col1', F.regexp_replace('col', 'jsheng', 'Jsheng')) 列间计算 在pandas中,列间运算比较简单,只需要在df上选择对应的列进行运算就可以搞定。如下: # 不合理住院天数占比 data['reasonable_in_hospital_ratio'] = round(data['平均不合理住院天数'] / data['平...
Write some code that'll convert all the column names to snake_case: def to_snake_case(s): return s.lower().replace(" ", "_") cols = [col(s).alias(to_snake_case(s)) for s in annoying.columns] annoying.select(cols).show() ...
TUPLE: A CQL row is represented as a python tuple with the values in CQL table column order / the order of the selected columns. ROW: A pyspark_cassandra.Row object representing a CQL row. Column values are related between CQL and python as follows: CQLpython ascii unicode string bigint ...