In this code snippet, we create a DataFramedfwith two columns: “name” of type StringType and “age” of type StringType. Let’s say we want to change the data type of the “age” column from StringType to Int
(1)列操作 # add a new column data = data.withColumn("newCol",df.oldCol+1) # replace the old column data = data.withColumn("oldCol",newCol) # rename the column data.withColumnRenamed("oldName","newName") # change column data type data.withColumn("oldColumn", data.oldColumn.cast("in...
# To convert the type of a column using the .cast() method, you can write code like this: dataframe = dataframe.withColumn("col", dataframe.col.cast("new_type")) # Cast the columns to integers model_data = model_data.withColumn("arr_delay", model_data.arr_delay.cast("integer")) m...
...rows = self.ws.max_row columns = self.ws.max_column return rows, columns # 获取指定单元格的值...cellvalue = self.ws.cell(row=row, column=column).value return cellvalue # 修改指定单元格值...mytest.getCellValue(row, 4) # 获取所有选项 Selects = mytest.getCellValue(row, 5) ...
# To convert the type of a column using the .cast() method, you can write code like this:dataframe=dataframe.withColumn("col",dataframe.col.cast("new_type"))# Cast the columns to integersmodel_data=model_data.withColumn("arr_delay",model_data.arr_delay.cast("integer"))model_data=model...
# Import the data to a DataFrame departures_df = spark.read.csv('2015-departures.csv.gz', header=True) # Remove any duration of 0 departures_df = departures_df.filter(departures_df[3] > 0) # Add an ID column departures_df = departures_df.withColumn('id', F.monotonically_increasing_id...
col_dtypes (dict): dictionary of columns names and their datatype Returns: Spark dataframe """ selects = list() for column in df.columns: if column in col_dtypes.keys(): schema = StructType([StructField('root', col_dtypes[column])]) ...
To explicitly select a column from a specific DataFrame, you can use the [] operator or the . operator. (The . operator cannot be used to select columns starting with an integer, or ones that contain a space or special character.) This can be especially helpful when you are joining Data...
arguments can either be the column name as a string (one for each column) or a column object (using thedf.colNamesyntax). When you pass a column object, you can perform operations like addition or subtraction on the column to change the data contained in it, much like inside.withColumn(...
data.txt pandas-pyspark-dataframe.py pyspark-add-month.py pyspark-add-new-column.py pyspark-aggregate.py pyspark-array-string.py pyspark-arraytype.py pyspark-broadcast-dataframe.py pyspark-cast-column.py pyspark-change-string-double.py pyspark-collect.py pyspark-column-functions.py...