df = df.na.fill(0, subset=[col("column_name")]) 其中,column_name是要替换空值的列名。 显示替换后的DataFrame: 代码语言:txt 复制 df.show() 这样,字符串类型列中的空值就被替换为零了。 在腾讯云的产品中,与PySpark相关的产品是腾讯云的弹性MapReduce(EMR)服务。EMR是一种大数据处理和分析的...
7.2 fill(value, subset=None) DataFrame.fillna() and DataFrameNaFunctions.fill() are aliases of each other. 替换null值,是na.fill()的别名。 DataFrame.fillna()和DataFrameNaFunctions.fill()是彼此的别名。 参数:●value– 整形,长整形,浮点型,字符串,或者字典。用来替换空值的值。如果值是字典,则subset...
DataFrame.fillna() and DataFrameNaFunctions.fill() are aliases of each other. Parameters value –int, long, float, string, bool or dict. Value to replace null values with. If the value is a dict, then subset is ignored and value must be a mapping from column name (string) to ...
>>> df4.na.fill(50).show() +---+---+---+ |age|height| name| +---+---+---+ | 10| 80|Alice| | 5| 50| Bob| | 50| 50| Tom| | 50| 50| null| +---+---+---+ >>> df4.na.fill({'age': 50, 'name': 'unknown'}).show() +---+---+---+ |age|height|...
startswith(other) #判断列中每个值是否以指定字符开头,返回布尔值 endswith(“string”) #判断列中每个值是否以指定字符结尾,返回布尔值 isNotNull() #判断列中的值是否不为空 isNull() #判断列中的值是否为空 like(expression) #判断列中的值是否满足相似条件,判断条件跟sql语法相同,支持通配符 ...
列元素查询操作,列的类型为column,它可以使用pyspark.sql.Column中的所有方法 df.columns #获取df中的列名,注意columns后面没有括号 select()#选取某一列或某几列数据 例:df.select(“name”) #使用select返回的是dataframe格式,使用df[]在选中>=2个列时返回的才是dataframe对象,否则返回的是column对象。 df.sel...
The fillNa value replaces the null value and it is an alias for na.fill(), it takes up the value based on the and replaces the null values with the values associated. If the value is a dictionary then the value must be mapped from column name as the replacement value and the subset...
1 Replacing null values in a column in Pyspark Dataframe 0 replace null values in string type column with zero PySpark 3 Fill NaN with condition on other column in pyspark 0 how to fill in null values in Pyspark 1 Replace NaN with null when using CreateDataFrame with pandas data 3...
is literally and empty string notNULL. Neitherna.fillnordropnawill help. You can usena.replacebut as far as I know it has not columnwise equivalent so you'll have to call it for each column: replacements = {'some_col':'some_replacement','another_col':'another_replacemen...
Fill NULL values in specific columns Fill NULL values with column average Fill NULL values with group average Unpack a DataFrame's JSON column to a new DataFrame Query a JSON column Sorting and Searching Filter a column using a condition Filter based on a specific column value Filter based on...