Spark vs. Pandas Dataframe

	Pandas	Spark
Working style	Single machine tool, no parallel mechanism parallelism does not support Hadoop and handles large volumes of data with bottlenecks	Distributed parallel computing framework, built-in parallel mechanism parallelism, all data and operations are automatically distributed on each cluster node. Process distributed data in a way that handles in-memory data. Supports Hadoop and can handle large amounts of data
Delay mechanism	Not lazy-evaluated	lazy-evaluated
Memory Cache	Stand-alone cache	Persist () or cache () saves the converted Rdds in memory
Dataframe variability	Dataframe in Pandas is variable.	Rdds in Spark is immutable, so dataframe is immutable.
Create	Convert from SPARK_DF: PANDAS_DF = Spark_df.topandas ()	Convert from PANDAS_DF: SPARK_DF = Sqlcontext.createdataframe (PANDAS_DF) In addition, Createdataframe supports the conversion of SPARK_DF from list, where the list element can be Tuple,dict,rdd
List,dict,ndarray Conversion	Existing Rdds conversions
CSV Data Set Read	Structured data file reads
HDF5 Read	JSON data Set Read
Excel reads	Hive Table Read
	External database Read
Index indexes	Automatically created	There are no index indexes and you need to create additional columns if needed
Row structure	Series structure, belonging to the pandas Dataframe structure	Row structure, which belongs to the spark dataframe structure
Column structure	Series structure, belonging to the pandas Dataframe structure	Column structure, which belongs to the spark DataFrame structure, such as: dataframe[name:string]
Column Name	Duplicate names are not allowed	Allow duplicate names Modify column names by using the alias method
Column Additions	df["XX"] = 0	Df.withcolumn ("xx", 0). Show () will error From Pyspark.sql import functions Df.withcolumn ("xx", Functions.lit (0)). Show ()
Column modification	Originally has df["XX"] column, df["xx"] = 1	Originally has df["XX"] column, Df.withcolumn ("xx", 1). Show ()
Show		DF does not output specific content, output specific content with the Show method Output form: Dataframe[age:bigint, name:string]
DF Output Specific Content	Df.show () Output specific content
No tree structure output form	Print a summary in the form of a tree: Df.printschema ()
	Df.collect ()
Sort	Df.sort_index () Sort by axis
Df.sort () Sort by value in column	Df.sort () Sort by value in column
Select or slice	Df.name Output Specific content	Df[] do not output specific content, output specific content with the Show method df["name"] do not output specific content, output specific content with the Show method
df[] Output specific content, df["name"] Output specific content	Df.select () Select one or more columns Df.select ("name") Slice df.select (df[' name '], df[' age ']+1)
DF[0] DF.IX[0]	Df.first ()
Df.head (2)	Df.head (2) or Df.take (2)
Df.tail (2)
Slice Df.ix[:3] or df.ix[: "XX"] or df[: "XX"]
Df.loc[] Select by label
df.iloc[] Select by location
Filter	df[df[' age ']>21]	Df.filter (df[' age ']>21) or df.where (df[' age ']>21)
Integration	Df.groupby ("Age") Df.groupby ("A"). AVG ("B")	Df.groupby ("Age") Df.groupby ("A"). AVG ("B"). Show () apply a single function From Pyspark.sql import functions Df.groupby ("A"). Agg (Functions.avg ("B"), Functions.min ("B"), Functions.max ("B")). Show () apply multiple functions
Statistics	Df.count () outputs the number of non-empty rows for each column	Df.count () Output Total rows
Df.describe () Describes the count, mean, STD, min, 25%, 50%, 75%, max for some columns	Df.describe () Describes the count, mean, StdDev, Min, max for some columns
Merge	Concat method under Pandas, supports axial merging
Merge method under Pandas, support multi-column merge The same name is automatically added suffix, the corresponding key is reserved only one copy	The Join method under Spark is Df.join () The same name does not automatically add suffixes, only the key values exactly match to keep a copy
Df.join () supports multi-column merging
Df.append () supports multi-line merging
Missing data processing	Automatically add NaNs to missing data	Do not automatically add NaNs and do not throw errors
Fillna function: Df.fillna ()	Fillna function: Df.na.fill ()
Dropna function: Df.dropna ()	Dropna function: Df.na.drop ()
SQL statements	Import Sqlite3 Pd.read_sql ("Select name, age from People WHERE age >= and age <= 19″)	Form registration: Registering the DATAFRAME structure as a SQL statement usage type Df.registertemptable ("people") or sqlcontext.registerdataframeastable (DF, "people") Sqlcontext.sql ("Select name, age from People WHERE age >= and age <= 19″)
Feature registration: Registering functions as SQL statement usage type Sqlcontext.registerfunction ("stringlengthstring", Lambda X:len (x)) Sqlcontext.sql ("Select stringlengthstring (' Test ')")
They convert each other.	PANDAS_DF = Spark_df.topandas ()	SPARK_DF = Sqlcontext.createdataframe (PANDAS_DF)
function application	Df.apply (f) Apply function f to each column of DF	Df.foreach (f) or Df.rdd.foreach (f) Apply the function f to each column of DF Df.foreachpartition (f) or df.rdd.foreachPartition (f) applies each block of DF to the application function f
Map-reduce operation	Map (func, list), Reduce (func, list) return type seq	Df.map (func), Df.reduce (func) return type Seqrdds
diff operation	There is a diff operation to process the time series data (pandas will compare the current line to the previous row)	No diff operation (the upper and lower lines of spark are independent of each other, distributed storage)

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