df_replaced = df.na.replace("Alice", "Lucy", subset=["name"])# 显示处理后的 DataFramedf_without_na.show()df_filled.show()df_replaced.show() 在上述示例中,我们首先创建了包含缺失值的 DataFrame。然后使用 .na.drop() 方法删除了包含任何缺失值的行,使
7),'font_scale':0.7}sns.set_context("notebook",rc=viz_dict)sns.set_style("darkgrid")cmap=sns.cubehelix_palette(dark=0,light=1,as_cmap=True)sns.heatmap(event_log.toPandas().replace('',np.nan).isnull(),cbar=False,cmap=cmap);...
df = df.filter(df[tenure]>=21)等价于df = df.where(df[tenure]>=21) 在有多个条件时: df .filter(“id = 1 or c1 = ‘b’” ).show() 过滤null值或nan值时: from pyspark.sql.functions import isnan, isnull df = df.filter(isnull("tenure")) df.show() # 把a列里面数据为null的筛...
df.createOrReplaceGlobalTempView("test") query='''select * from global_temp.test where age>26 ''' spark.sql(query).show() #创建一个新Session也能使用全局表 spark.newSession().sql(query).show() +---+---+---+---+ | id| name|age| sal| +---+---+---+---+ | 1|James| ...
from pyspark.sql.functions import isnull df = df.filter(isnull("col_a")) 1 2 输出list类型,list中每个元素是Row类: list = df.collect() 1 注:此方法将所有数据全部导入到本地,返回一个Array对象 查询概况 df.describe().show() 1 以及查询类型,之前是type,现在是df.printSchema() ...
...(“id = 1 or c1 = ‘b’” ).show() ###对null或nan数据进行过滤: from pyspark.sql.functions import isnan, isnull...udf 函数应用 from pyspark.sql.functions import udf from pyspark.sql.types import StringType import datetime...()) # 使用 df.withColumn('day', udfday(df.day)) ...
25. regexp_extract，regex_replace字符串处理 26.round 四舍五入函数 27.split对固定模式的字符串进行...
# 丢弃空值,DataFrame.dropna(how='any', thresh=None, subset=None) df.dropna(how='all', subset=['sex']).show...# 修改df里的某些值 df1 = df.na.replace({"M": "Male", "F": "Female"}) df1.show() # DataFrame.union # 相当于SQL...DataFrame的列操作APIs 这里主要针对的是列进行...
Use the spark.table() method with the argument "flights" to create a DataFrame containing the values of the flights table in the .catalog. Save it as flights. Show the head of flights using flights.show(). The column air_time contains the duration of the flight in minutes. ...
replace({"": None}, subset=["name"]) # Convert Python/PySpark/NumPy NaN operator to null df = df.replace(float("nan"), None) String Operations String Filters # Contains - col.contains(string) df = df.filter(df.name.contains('o')) # Starts With - col.startswith(string) df = df...