To drop multiple columns from a PySpark DataFrame, we can pass a list of column names to the .drop() method. We can do this in two ways: # Option 1: Passing the names as a list df_dropped = df.drop(["team", "pl
This line creates a list of columns to drop: It iterates over each column in null_percentage.columns. For each column col, it checks if the percentage of nulls (null_percentage.first()[col]) is greater than the threshold (0.3).
sample=result.sample(False,0.5,0)# randomly select50%oflines — 1.2 列元素操作 — 获取Row元素的所有列名: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 r=Row(age=11,name='Alice')print r.columns #['age','name'] 选择一列或多列:select 代码语言:javascript 代码运行次数:0 运行 AI代码...
我希望将列放在包含banned_columns列表中任何单词的pyspark中,并从其余列中形成一个新的dataframe。banned_columns = ["basket","cricket","ball"] drop_these = [columns_to_drop for columns_to_drop in df.columnsif col 浏览0提问于2018-07-16得票数 1 回答已采纳 4回答 如何在Python中排除Spark datafram...
在PySpark 中,df.na.drop() 和 df.dropna() 都是 DataFrame 对象的方法,用于处理缺失值。它们之间的区别如下:df.na.drop(**{subset:[col,col]}):这个方法用于删除包含任何缺失值(null 或 NaN)的行。默认情况下,该方法会删除包含任何缺失值的整行数据。你可以通过传递额外的参数来指定其他条件,例如只删除某...
('device_id','age').dropDuplicates(['age']) # 按指定字段去重 print('摄像头id列表',) device_dif.show() # show 是action动作 print('摄像头数目',device_dif.count()) # count 是action动作 # 统计 print('===统计===') df.stat.freqItems(['device_id','gender'], 0.3).show() # 显...
columns #删除age的列 df = df.na.drop() # 按行将行中含有na的整行删除 df13 = df8.dropna(subset=[‘customerID’, ‘tenure’]) # 指定删除’customerID’或’tenure’中任一一列包含na的行 fillna() #填充空值,与df.na.fill()相同 train.fillna(-1).show() #将所有为na的值填充为-1,可...
agg_row = data.select([(count(when(isnan(c)|col(c).isNull(),c))/data.count()).alias(c) for c in data.columns if c not in {'date_recored', 'public_meeting', 'permit'}]).collect() 进行最后处理,请注意drop函数的用法 agg_dict_list = [row.asDict() for row in agg_row] ag...
# 2、或者df2 = df.na.drop() (3)平均值填充缺失值 frompyspark.sql.functionsimportwhenimportpyspark.sql.functionsasF# 计算各个数值列的平均值defmean_of_pyspark_columns(df, numeric_cols): col_with_mean = []forcolinnumeric_cols: mean_value = df.select(F.avg(df[col])) ...
You can apply this for a subset of columns by specifying this, as shown below:Python Kopiraj df_customer_no_nulls = df_customer.na.drop("all", subset=["c_acctbal", "c_custkey"]) To fill in missing values, use the fill method. You can choose to apply this to all columns or ...