# 统计字段的不同取值数量cols=df.columns n_unique=[]forcolincols: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 ...
# 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....
* A column is a PK if it is not nullable and has unique values. * 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 wit...
Bucketing works well when the number of unique bucketing column values is large and the bucketing column is used often in queries.Summary In this chapter, we explored how to use tabular data with Spark SQL. These code examples can be reused as the foundation for processing data with Spark ...
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) ...
plt.xlabel('Column name') plt.ylabel('Num unique') plt.xticks(rotation=90) plt.yticks([0, 1, 2, 3, 4]) plt.show() return pd.Series(num_unique, index=filter_col).sort_values(ascending=False) cardinality_plot(pd_melt, categorical) ...
数量distinct_count=data.select(target_column).distinct().count()# 使用collect_set收集所有唯一值unique_values=data.select(F.collect_set(target_column)).first()[0]# 输出结果print(f"Distinct count of{target_column}:{distinct_count}")print(f"Unique values in{target_column}:{unique_values}")...
* Determines if a column referenced by a base table access is a primary key. * A column is a PK if it is not nullable and has unique values. * To determine if a column has unique values in the absence of informational * RI constraints, the number of distinct values is compared to ...
OneSaltingtest led us to introduce a combined column containing values from all other group by columns. Again all such tests failed to improve the behavior of the job. Duplicates eh?, Easy Peasy Since we knew the problem now, Here is how we removed them in SQL (for queries 1,2,4 and...
Returns anewDataFrame that drops rows containingnullor NaN values. If howis"any", then drop rows containing anynullor NaN values. If howis"all", then drop rows onlyifevery columnisnullor NaNforthat row. def drop(how: String, cols: Seq[String]): DataFrame ...