其中,"data.csv"是数据集的文件路径,header=True表示第一行是列名,inferSchema=True表示自动推断列的数据类型。 找出重复的行: 代码语言:txt 复制 duplicate_rows = df.groupBy(df.columns).count().filter(col("count") > 1) 这里使用groupBy()按所有列分组,并计算每组的行数。然后使用filter()过滤出行数大...
Output: 基于一列删除 Python3 # remove duplicate rows based on college # column dataframe.dropDuplicates(['college']).show() Output: 基于多列的拖放 Python3 # remove duplicate rows based on college # and ID column dataframe.dropDuplicates(['college', 'student ID']).show() Output:...
Returns a new DataFrame containing the distinct rows in this DataFrame. 去重 drop(*cols) Returns a new DataFrame that drops the specified column. 删除列 dropDuplicates([subset]) Return a new DataFrame with duplicate rows removed, optionally only considering certain columns. 返回删除重复行的新 DataF...
1.先看下造的数据 2.创建SparkSession及读取数据 3.dataframe基本信息的查看 获取列(字段) 查看列(字段)个数 查看记录数 查看维度 打印字段树结构 显示前n条记录 选择某几个字段 查看详细信息 4.基础操作 增加列 修改某一列的类型 filter过滤 过滤+ 选择 条件 某列的不重复值(特征的特征值) groupBy orderBy...
I can also join by conditions, but it creates duplicate column names if the keys have the same name, which is frustrating. For now, the only way I know to avoid this is to pass a list of join keys as in the previous cell. If I want to make nonequi joins, then I need to rename...
Remove duplicate rowsTo de-duplicate rows, use distinct, which returns only the unique rows.Python Копирај df_unique = df_customer.distinct() Handle null valuesTo handle null values, drop rows that contain null values using the na.drop method. This method lets you specify if you...
distinct() # Drop duplicate rows, but consider only specific columns df = df.dropDuplicates(['name', 'height']) # Replace empty strings with null (leave out subset keyword arg to replace in all columns) df = df.replace({"": None}, subset=["name"]) # Convert Python/PySpark/NumPy ...
Reviewing the dataset, you can see that some columns contain duplicate information. For example, the cnt column equals the sum of the casual and registered columns. You should remove the casual and registered columns from the dataset. The index column instant is also not useful as a predictor....
This PySpark SQL cheat sheet covers the basics of working with the Apache Spark DataFrames in Python: from initializing the SparkSession to creating DataFrames, inspecting the data, handling duplicate values, querying, adding, updating or removing columns, grouping, filtering or sorting data. You'...
There is no duplicate records in the proposed test sets; therefore, the performance of the learners are not biased by the methods which have better detection rates on the frequent records. The number of selected records from each difficultylevel group is inversely proportional to the percentage of...