How to realize column-column conversion, that is, wide table and narrow table transformation in spark

Spark in how to achieve row and column conversion, that is, wide table narrow table conversion, many novices are not very clear about this, in order to help you solve this problem, the following editor will explain in detail for you, people with this need can come to learn, I hope you can gain something.

From time to time, the code spark column turns to from pyspark import SparkContext, SparkConffrom pyspark.sql import SparkSession, SQLContext, Row, functions as Ffrom pyspark.sql.functions import array, col, explode, struct, litconf = SparkConf (). SetAppName ("test"). SetMaster ("local [*]") sc = SparkContext (conf=conf) spark = SQLContext (sc) # df is datasource, by will exclude columndef df_columns_to_line (df) By): # Filter dtypes and split into column names and type description df_a = df.select ([col (c) .cast ("string") for c in df.columns]) cols, dtypes = zip (* (c, t) for (c, t) in df_a.dtypes if c not in by) # Spark SQL supports only homogeneous columns assert len (set (dtypes)) = = 1, "All columns have to be of the same type" # Create and explode an array of (column_name Column_value) structs kvs = explode (array ([struct (lit (c) .alias ("feature"), col (c) .alias ("value") for c in cols]) .alias ("kvs") return df_a.select (by + [kvs]) .select (by + ["kvs.feature", "kvs.value"]) df = sc.parallelize ([(1,0.0,0.6), (1,0.6) ToDF (["A", "col_1", "col_2"]) df_row_data = df_columns_to_line (df) ["A"]) df.show () df_row_data.show () > > df.show () +-- + | A | col_1 | col_2 | +-- + | 1 | 0. 0 | 0. 6 | 1 | 0. 6 | 0. 7 | +-> > df_row_data.show () +-+ -+ | A | feature | value | +-- + | 1 | col_1 | 0.0 | 1 | col_2 | 0.6 | 1 | col_1 | 0.6 | | 1 | col_2 | 0.7 | +-- +

Note that feature and value are the final two column names that are redefined after the original multiple column names are converted into row data.

Spark row transfer column df_features = df_row_data.select ('feature'). Distinct (). Collect () features = map (lambda r:r.feature, df_features) df_column_data = df_row_data.groupby ("A"). Pivot (' feature', features) .agg (F.first ('value') Ignorenulls=True)) df_column_data.show () +-- + | A | col_2 | col_1 | +-- + | 1 | 0.6 | 0.0 | +-+

Row to column is relatively simple, based on the above results directly conversion, the key is the use of pivot function

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