# Quick examples of getting unique values in columns# Example 1: Find unique values of a columnprint(df['Courses'].unique())print(df.Courses.unique())# Example 2: Convert to listprint(df.Courses.unique().tolist())# Example 3: Unique values with drop_duplicatesdf.Courses.drop_duplicates(...
You can get the number of unique values in the column of pandas DataFrame using several ways like using functionsSeries.unique.size,Series.nunique(), andSeries.drop_duplicates().size(). Since the DataFrame column is internally represented as a Series, you can use these functions to perform th...
# in Pythonfrompyspark.sql.functionsimportexpr,col,columndf.select(expr("DEST_COUNTRY_NAME"),col("DEST_COUNTRY_NAME"),column("DEST_COUNTRY_NAME"))\.show(2) However if you mix Column objects and strings, you will get an error, like the following code will result in a compiler error df....
# 初始化spark sessionspark_session=SparkSession.builder \.master("local")\.appName("sparkify")\.getOrCreate()# 加载数据与持久化src="data/mini_sparkify_event_data.json"df=spark_session.read.json(src)# 构建视图(方便查询)df.createOrReplaceTempView("sparkify_table")df.persist()# 查看前5行数据...
* To determine if a column has unique values in the absence of informational * RI constraints, the number of distinct values is compared to the total * number of rows in the table. If their relative difference * is within the expected limits (i.e. 2 * spark.sql.statistics.ndv.maxError...
要创建一个SparkSession,仅仅使用SparkSession.builder 即可:from pyspark.sql import SparkSessionspark_session = SparkSession \.builder \.appName("Python Spark SQL basic example") \.config("spark.some.config.option", "some-value") \.getOrCreate() ...
采集列信息// 若spark.sql.statistics.histogram.enabled设置为true,则会采集直方图信息// 采集直方图信息需要额外一次的表扫描// 使用的是等高直方图// 只支持IntegralType/DoubleType/DecimalType/FloatType/DateType/TimestampType的列采集直方图ANALYZETABLE[db_name.]tablenameCOMPUTESTATISTICSFORCOLUMNScolumn1,column2...
'stats', 'stdev', 'subtract', 'subtractByKey', 'sum', 'sumApprox', 'take', 'takeOrdered', 'takeSample', 'toDF', 'toDebugString', 'toLocalIterator', 'top', 'treeAggregate', 'treeReduce', 'union', 'unpersist', 'values', 'variance', 'zip', 'zipWithIndex', 'zipWithUniqueId']...
avg(DISTINCTColumn): Returns the mean of unique values in the specified column. You can use the following statement in Spark SQL to obtain the mean of uniqueFreightvalues of each shipper, as shown in the following figure. selectShipper, avg(distinctFreight) ...
n_unique.append(df.select(col).distinct().count()) pd.DataFrame(data={'col':cols,'n_unique':n_unique}).sort_values('n_unique', ascending=False) 结果如下,ID类的属性有最多的取值,其他的字段属性相对集中。 📌 类别型取值分布 我们来看看上面分析的尾部,分布比较集中的类别型字段的取值有哪些。