Rename columnsTo rename a column, use the withColumnRenamed method, which accepts the existing and new column names:Python Копирај df_customer_flag_renamed = df_customer_flag.withColumnRenamed("balance_flag", "balance_flag_renamed") The alias method is especially helpful when you ...
To rename the columns count(1), avg(Age) etc, use toDF(). ( gdf2 .agg({'*': 'count', 'Age': 'avg', 'Fare':'sum'}) .toDF('Pclass', 'counts', 'average_age', 'total_fare') .show() ) +---+---+---+---+ |Pclass|counts| average_age|total_fare| +---+--...
pyspark-rename-column.py pyspark-repace-null.py pyspark-repartition-2.py pyspark-repartition.py pyspark-row.py pyspark-sampling.py pyspark-select-columns.py pyspark-shape-dataframe.py pyspark-show-top-n-rows.py pyspark-sparksession.py pyspark-split-function.py pyspark-sql-case-wh...
Select columns from PySpark DataFrame PySpark Collect() – Retrieve data from DataFrame PySpark withColumn to update or add a column PySpark using where filter function PySpark – Distinct to drop duplicate rows PySpark orderBy() and sort() explained PySpark Groupby Explained with Example PySpark...
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Return a new DataFrame with duplicate rows removed, optionally only considering certain columns. For a static batch DataFrame, it just drops duplicate rows. For a streaming DataFrame, it will keep all data across triggers as intermediate state to drop duplicates rows. You can use withWatermark()...
The alias method is especially helpful when you want to rename your columns as part of aggregations:Python Kopiraj from pyspark.sql.functions import avg df_segment_balance = df_customer.groupBy("c_mktsegment").agg( avg(df_customer["c_acctbal"]).alias("avg_account_balance") ) display(df...
functions.field_rename import rename def capitalize_field_name(field_name: str) -> str: return field_name.upper() renamed_df = rename(df, rename_func=capitalize_field_name()) Fillna This function mimics the vanilla pyspark fillna functionality with added support for filling nested fields. The ...
('N/A')))# Drop duplicate rows in a dataset (distinct)df=df.dropDuplicates()# ordf=df.distinct()# Drop duplicate rows, but consider only specific columnsdf=df.dropDuplicates(['name','height'])# Replace empty strings with null (leave out subset keyword arg to replace in all columns)...