DataFrame.__getattr__(name)
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Returns the Column denoted by name. |
DataFrame.__getitem__(item)
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Returns the column as a Column. |
DataFrame.agg(*exprs)
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Aggregate on the entire DataFrame without groups (shorthand for df.groupBy().agg()). |
DataFrame.alias(alias)
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Returns a new DataFrame with an alias set. |
DataFrame.approxQuantile(col, probabilities, …)
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Calculates the approximate quantiles of numerical columns of a DataFrame. |
DataFrame.cache()
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Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER). |
DataFrame.checkpoint([eager])
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Returns a checkpointed version of this DataFrame. |
DataFrame.coalesce(numPartitions)
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Returns a new DataFrame that has exactly numPartitions partitions. |
DataFrame.colRegex(colName)
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Selects column based on the column name specified as a regex and returns it as Column. |
DataFrame.collect()
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Returns all the records as a list of Row. |
DataFrame.columns
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Retrieves the names of all columns in the DataFrame as a list. |
DataFrame.corr(col1, col2[, method])
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Calculates the correlation of two columns of a DataFrame as a double value. |
DataFrame.count()
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Returns the number of rows in this DataFrame. |
DataFrame.cov(col1, col2)
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Calculate the sample covariance for the given columns, specified by their names, as a double value. |
DataFrame.createGlobalTempView(name)
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Creates a global temporary view with this DataFrame. |
DataFrame.createOrReplaceGlobalTempView(name)
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Creates or replaces a global temporary view using the given name. |
DataFrame.createOrReplaceTempView(name)
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Creates or replaces a local temporary view with this DataFrame. |
DataFrame.createTempView(name)
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Creates a local temporary view with this DataFrame. |
DataFrame.crossJoin(other)
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Returns the cartesian product with another DataFrame. |
DataFrame.crosstab(col1, col2)
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Computes a pair-wise frequency table of the given columns. |
DataFrame.cube(*cols)
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Create a multi-dimensional cube for the current DataFrame using the specified columns, so we can run aggregations on them. |
DataFrame.describe(*cols)
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Computes basic statistics for numeric and string columns. |
DataFrame.distinct()
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Returns a new DataFrame containing the distinct rows in this DataFrame. |
DataFrame.drop(*cols)
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Returns a new DataFrame without specified columns. |
DataFrame.dropDuplicates([subset])
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Return a new DataFrame with duplicate rows removed, optionally only considering certain columns. |
DataFrame.dropDuplicatesWithinWatermark([subset])
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Return a new DataFrame with duplicate rows removed, |
DataFrame.drop_duplicates([subset])
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drop_duplicates() is an alias for dropDuplicates().
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DataFrame.dropna([how, thresh, subset])
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Returns a new DataFrame omitting rows with null values. |
DataFrame.dtypes
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Returns all column names and their data types as a list. |
DataFrame.exceptAll(other)
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Return a new DataFrame containing rows in this DataFrame but not in another DataFrame while preserving duplicates. |
DataFrame.explain([extended, mode])
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Prints the (logical and physical) plans to the console for debugging purposes. |
DataFrame.fillna(value[, subset])
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Replace null values, alias for na.fill(). |
DataFrame.filter(condition)
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Filters rows using the given condition. |
DataFrame.first()
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Returns the first row as a Row. |
DataFrame.foreach(f)
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Applies the f function to all Row of this DataFrame. |
DataFrame.foreachPartition(f)
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Applies the f function to each partition of this DataFrame. |
DataFrame.freqItems(cols[, support])
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Finding frequent items for columns, possibly with false positives. |
DataFrame.groupBy(*cols)
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Groups the DataFrame using the specified columns, so we can run aggregation on them. |
DataFrame.head([n])
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Returns the first n rows. |
DataFrame.hint(name, *parameters)
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Specifies some hint on the current DataFrame. |
DataFrame.inputFiles()
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Returns a best-effort snapshot of the files that compose this DataFrame. |
DataFrame.intersect(other)
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Return a new DataFrame containing rows only in both this DataFrame and another DataFrame. |
DataFrame.intersectAll(other)
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Return a new DataFrame containing rows in both this DataFrame and another DataFrame while preserving duplicates. |
DataFrame.isEmpty()
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Checks if the DataFrame is empty and returns a boolean value. |
DataFrame.isLocal()
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Returns True if the collect() and take() methods can be run locally (without any Spark executors). |
DataFrame.isStreaming
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Returns True if this DataFrame contains one or more sources that continuously return data as it arrives. |
DataFrame.join(other[, on, how])
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Joins with another DataFrame, using the given join expression. |
DataFrame.limit(num)
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Limits the result count to the number specified. |
DataFrame.localCheckpoint([eager])
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Returns a locally checkpointed version of this DataFrame. |
DataFrame.mapInPandas(func, schema[, barrier])
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Maps an iterator of batches in the current DataFrame using a Python native function that takes and outputs a pandas DataFrame, and returns the result as a DataFrame. |
DataFrame.mapInArrow(func, schema[, barrier])
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Maps an iterator of batches in the current DataFrame using a Python native function that takes and outputs a PyArrow’s RecordBatch, and returns the result as a DataFrame. |
DataFrame.melt(ids, values, …)
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Unpivot a DataFrame from wide format to long format, optionally leaving identifier columns set. |
DataFrame.na
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Returns a DataFrameNaFunctions for handling missing values. |
DataFrame.observe(observation, *exprs)
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Define (named) metrics to observe on the DataFrame. |
DataFrame.offset(num)
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Returns a new :class: DataFrame by skipping the first n rows. |
DataFrame.orderBy(*cols, **kwargs)
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Returns a new DataFrame sorted by the specified column(s). |
DataFrame.persist([storageLevel])
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Sets the storage level to persist the contents of the DataFrame across operations after the first time it is computed. |
DataFrame.printSchema([level])
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Prints out the schema in the tree format. |
DataFrame.randomSplit(weights[, seed])
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Randomly splits this DataFrame with the provided weights. |
DataFrame.rdd
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Returns the content as an pyspark.RDD of Row. |
DataFrame.registerTempTable(name)
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Registers this DataFrame as a temporary table using the given name. |
DataFrame.repartition(numPartitions, *cols)
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Returns a new DataFrame partitioned by the given partitioning expressions. |
DataFrame.repartitionByRange(numPartitions, …)
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Returns a new DataFrame partitioned by the given partitioning expressions. |
DataFrame.replace(to_replace[, value, subset])
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Returns a new DataFrame replacing a value with another value. |
DataFrame.rollup(*cols)
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Create a multi-dimensional rollup for the current DataFrame using the specified columns, so we can run aggregation on them. |
DataFrame.sameSemantics(other)
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Returns True when the logical query plans inside both DataFrames are equal and therefore return the same results. |
DataFrame.sample([withReplacement, …])
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Returns a sampled subset of this DataFrame. |
DataFrame.sampleBy(col, fractions[, seed])
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Returns a stratified sample without replacement based on the fraction given on each stratum. |
DataFrame.schema
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Returns the schema of this DataFrame as a pyspark.sql.types.StructType. |
DataFrame.select(*cols)
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Projects a set of expressions and returns a new DataFrame. |
DataFrame.selectExpr(*expr)
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Projects a set of SQL expressions and returns a new DataFrame. |
DataFrame.semanticHash()
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Returns a hash code of the logical query plan against this DataFrame. |
DataFrame.show([n, truncate, vertical])
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Prints the first n rows to the console. |
DataFrame.sort(*cols, **kwargs)
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Returns a new DataFrame sorted by the specified column(s). |
DataFrame.sortWithinPartitions(*cols, **kwargs)
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Returns a new DataFrame with each partition sorted by the specified column(s). |
DataFrame.sparkSession
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Returns Spark session that created this DataFrame. |
DataFrame.stat
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Returns a DataFrameStatFunctions for statistic functions. |
DataFrame.storageLevel
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Get the DataFrame’s current storage level. |
DataFrame.subtract(other)
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Return a new DataFrame containing rows in this DataFrame but not in another DataFrame. |
DataFrame.summary(*statistics)
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Computes specified statistics for numeric and string columns. |
DataFrame.tail(num)
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Returns the last num rows as a list of Row. |
DataFrame.take(num)
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Returns the first num rows as a list of Row. |
DataFrame.to(schema)
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Returns a new DataFrame where each row is reconciled to match the specified schema. |
DataFrame.toDF(*cols)
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Returns a new DataFrame that with new specified column names |
DataFrame.toJSON([use_unicode])
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Converts a DataFrame into a RDD of string. |
DataFrame.toLocalIterator([prefetchPartitions])
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Returns an iterator that contains all of the rows in this DataFrame. |
DataFrame.toPandas()
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Returns the contents of this DataFrame as Pandas pandas.DataFrame. |
DataFrame.to_pandas_on_spark([index_col])
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|
DataFrame.transform(func, *args, **kwargs)
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Returns a new DataFrame. |
DataFrame.union(other)
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Return a new DataFrame containing the union of rows in this and another DataFrame. |
DataFrame.unionAll(other)
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Return a new DataFrame containing the union of rows in this and another DataFrame. |
DataFrame.unionByName(other[, …])
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Returns a new DataFrame containing union of rows in this and another DataFrame. |
DataFrame.unpersist([blocking])
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Marks the DataFrame as non-persistent, and remove all blocks for it from memory and disk. |
DataFrame.unpivot(ids, values, …)
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Unpivot a DataFrame from wide format to long format, optionally leaving identifier columns set. |
DataFrame.where(condition)
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where() is an alias for filter().
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DataFrame.withColumn(colName, col)
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Returns a new DataFrame by adding a column or replacing the existing column that has the same name. |
DataFrame.withColumns(*colsMap)
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Returns a new DataFrame by adding multiple columns or replacing the existing columns that have the same names. |
DataFrame.withColumnRenamed(existing, new)
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Returns a new DataFrame by renaming an existing column. |
DataFrame.withColumnsRenamed(colsMap)
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Returns a new DataFrame by renaming multiple columns. |
DataFrame.withMetadata(columnName, metadata)
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Returns a new DataFrame by updating an existing column with metadata. |
DataFrame.withWatermark(eventTime, …)
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Defines an event time watermark for this DataFrame. |
DataFrame.write
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Interface for saving the content of the non-streaming DataFrame out into external storage. |
DataFrame.writeStream
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Interface for saving the content of the streaming DataFrame out into external storage. |
DataFrame.writeTo(table)
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Create a write configuration builder for v2 sources. |
DataFrame.pandas_api([index_col])
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Converts the existing DataFrame into a pandas-on-Spark DataFrame. |
DataFrameNaFunctions.drop([how, thresh, subset])
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Returns a new DataFrame omitting rows with null values. |
DataFrameNaFunctions.fill(value[, subset])
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Replace null values, alias for na.fill(). |
DataFrameNaFunctions.replace(to_replace[, …])
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Returns a new DataFrame replacing a value with another value. |
DataFrameStatFunctions.approxQuantile(col, …)
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Calculates the approximate quantiles of numerical columns of a DataFrame. |
DataFrameStatFunctions.corr(col1, col2[, method])
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Calculates the correlation of two columns of a DataFrame as a double value. |
DataFrameStatFunctions.cov(col1, col2)
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Calculate the sample covariance for the given columns, specified by their names, as a double value. |
DataFrameStatFunctions.crosstab(col1, col2)
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Computes a pair-wise frequency table of the given columns. |
DataFrameStatFunctions.freqItems(cols[, support])
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Finding frequent items for columns, possibly with false positives. |
DataFrameStatFunctions.sampleBy(col, fractions)
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Returns a stratified sample without replacement based on the fraction given on each stratum. |