Best Practices¶

As with all software, VerticaPy has a performance cost. To get the best performance, we need to understand the architectures of Vertica and VerticaPy. In this lesson, we'll go through some optimization steps.

1. Optimize your architecture at the database-level¶

At the end of the day, VerticaPy is an abstraction of SQL, so any database-level optimizations you make carry over to VerticaPy.

Optimizing Vertica mostly comes down to optimizing projections. Think in advance about your data architecture before creating a vDataFrame so we only select the essential columns.

In the following example, we use the 'usecols' parameter of the vDataFrame to select the most essential columns in our dataset.

In [13]:

import verticapy as vp
from verticapy.datasets import load_titanic
load_titanic() # loading the dataset in Vertica in case we do not have it
vdf = vp.vDataFrame("public.titanic",
                    usecols = ["survived", "pclass", "age"])
display(vdf)

	123 pclass Int	123 survived Int	123 age Numeric(6,3)
1	1	0	2.0
2	1	0	30.0
3	1	0	25.0
4	1	0	39.0
5	1	0	71.0
6	1	0	47.0
7	1	0	[null]
8	1	0	24.0
9	1	0	36.0
10	1	0	25.0
11	1	0	45.0
12	1	0	42.0
13	1	0	41.0
14	1	0	48.0
15	1	0	[null]
16	1	0	45.0
17	1	0	[null]
18	1	0	33.0
19	1	0	28.0
20	1	0	17.0
21	1	0	49.0
22	1	0	36.0
23	1	0	46.0
24	1	0	[null]
25	1	0	27.0
26	1	0	[null]
27	1	0	47.0
28	1	0	37.0
29	1	0	[null]
30	1	0	70.0
31	1	0	39.0
32	1	0	31.0
33	1	0	50.0
34	1	0	39.0
35	1	0	36.0
36	1	0	[null]
37	1	0	30.0
38	1	0	19.0
39	1	0	64.0
40	1	0	[null]
41	1	0	[null]
42	1	0	37.0
43	1	0	47.0
44	1	0	24.0
45	1	0	71.0
46	1	0	38.0
47	1	0	46.0
48	1	0	[null]
49	1	0	45.0
50	1	0	40.0
51	1	0	55.0
52	1	0	42.0
53	1	0	[null]
54	1	0	55.0
55	1	0	42.0
56	1	0	[null]
57	1	0	50.0
58	1	0	46.0
59	1	0	50.0
60	1	0	32.5
61	1	0	58.0
62	1	0	41.0
63	1	0	[null]
64	1	0	[null]
65	1	0	29.0
66	1	0	30.0
67	1	0	30.0
68	1	0	19.0
69	1	0	46.0
70	1	0	54.0
71	1	0	28.0
72	1	0	65.0
73	1	0	44.0
74	1	0	55.0
75	1	0	47.0
76	1	0	37.0
77	1	0	58.0
78	1	0	64.0
79	1	0	65.0
80	1	0	28.5
81	1	0	[null]
82	1	0	45.5
83	1	0	23.0
84	1	0	29.0
85	1	0	18.0
86	1	0	47.0
87	1	0	38.0
88	1	0	22.0
89	1	0	[null]
90	1	0	31.0
91	1	0	[null]
92	1	0	36.0
93	1	0	55.0
94	1	0	33.0
95	1	0	61.0
96	1	0	50.0
97	1	0	56.0
98	1	0	56.0
99	1	0	24.0
100	1	0	[null]

Rows: 1-100 | Columns: 3

2. Save the current relation when you can¶

The vDataFrame works just like a view. If the final generated relation uses a lot of different functions, it will drastically increase the computation time for each method call.

Smaller transformations won't slow down the process much, but heavier transformations (multiple joins, heavy use of advanced analytical funcions, moving windows, etc.) can cause noticeable slowdown. If you make these kinds of changes, you should save the vDataFrame structure. Let's look at an example.

In [14]:

# Doing multiple operation
vdf = vp.vDataFrame("public.titanic")
vdf["sex"].label_encode()["boat"].fillna(method = "0ifnull")["name"].str_extract(
    ' ([A-Za-z]+)\.').eval("family_size", expr = "parch + sibsp + 1").drop(
    columns = ["cabin", "body", "ticket", "home.dest"])["fare"].fill_outliers().fillna()
print(vdf.current_relation())

795 elements were filled.
(
   SELECT
     "pclass",
     "survived",
     "name",
     "sex",
     "age",
     "sibsp",
     "parch",
     COALESCE("fare", 32.9113074018842) AS "fare",
     "embarked",
     "boat",
     "family_size" 
   FROM
 (
   SELECT
     "pclass",
     "survived",
     REGEXP_SUBSTR("name", ' ([A-Za-z]+)\.') AS "name",
     DECODE("sex", 'female', 0, 'male', 1, 2) AS "sex",
     COALESCE("age", 30.1524573721163) AS "age",
     "sibsp",
     "parch",
     (CASE WHEN "fare" < -176.6204982585513 THEN -176.6204982585513 WHEN "fare" > 244.5480856064831 THEN 244.5480856064831 ELSE "fare" END) AS "fare",
     COALESCE("embarked", 'S') AS "embarked",
     DECODE("boat", NULL, 0, 1) AS "boat",
     parch + sibsp + 1 AS "family_size" 
   FROM
 (
   SELECT
     "pclass",
     "survived",
     "name",
     "sex",
     "age",
     "sibsp",
     "parch",
     "fare",
     "embarked",
     "boat" 
   FROM
 "public"."titanic") 
VERTICAPY_SUBTABLE) 
VERTICAPY_SUBTABLE) 
VERTICAPY_SUBTABLE

We can look at the query plan of the new relation. This will help us understand how Vertica will execute the different aggregations.

In [15]:

print(vdf.explain())

------------------------------ 
QUERY PLAN DESCRIPTION: 

EXPLAIN SELECT * FROM (SELECT "pclass", "survived", "name", "sex", "age", "sibsp", "parch", COALESCE("fare", 32.9113074018842) AS "fare", "embarked", "boat", "family_size" FROM (SELECT "pclass", "survived", REGEXP_SUBSTR("name", ' ([A-Za-z]+)\.') AS "name", DECODE("sex", 'female', 0, 'male', 1, 2) AS "sex", COALESCE("age", 30.1524573721163) AS "age", "sibsp", "parch", (CASE WHEN "fare" < -176.6204982585513 THEN -176.6204982585513 WHEN "fare" > 244.5480856064831 THEN 244.5480856064831 ELSE "fare" END) AS "fare", COALESCE("embarked", 'S') AS "embarked", DECODE("boat", NULL, 0, 1) AS "boat", parch + sibsp + 1 AS "family_size" FROM (SELECT "pclass", "survived", "name", "sex", "age", "sibsp", "parch", "fare", "embarked", "boat" FROM "public"."titanic") VERTICAPY_SUBTABLE) VERTICAPY_SUBTABLE) VERTICAPY_SUBTABLE

Access Path:
+-STORAGE ACCESS for titanic [Cost: 67, Rows: 1K (NO STATISTICS)] (PATH ID: 1)
|  Projection: public.titanic_super
|  Materialize: titanic.pclass, titanic.survived, titanic.name, titanic.sex, titanic.age, titanic.sibsp, titanic.parch, titanic.fare, titanic.embarked, titanic.boat


----------------------------------------------- 
PLAN: BASE QUERY PLAN (GraphViz Format)
----------------------------------------------- 
digraph G {
graph [rankdir=BT, label = "BASE QUERY PLAN
	Query: EXPLAIN SELECT * FROM (SELECT \"pclass\", \"survived\", \"name\", \"sex\", \"age\", \"sibsp\", \"parch\", COALESCE(\"fare\", 32.9113074018842) AS \"fare\", \"embarked\", \"boat\", \"family_size\" FROM (SELECT \"pclass\", \"survived\", REGEXP_SUBSTR(\"name\", \' ([A-Za-z]+)\.\') AS \"name\", DECODE(\"sex\", \'female\', 0, \'male\', 1, 2) AS \"sex\", COALESCE(\"age\", 30.1524573721163) AS \"age\", \"sibsp\", \"parch\", (CASE WHEN \"fare\" \< -176.6204982585513 THEN -176.6204982585513 WHEN \"fare\" \> 244.5480856064831 THEN 244.5480856064831 ELSE \"fare\" END) AS \"fare\", COALESCE(\"embarked\", \'S\') AS \"embarked\", DECODE(\"boat\", NULL, 0, 1) AS \"boat\", parch + sibsp + 1 AS \"family_size\" FROM (SELECT \"pclass\", \"survived\", \"name\", \"sex\", \"age\", \"sibsp\", \"parch\", \"fare\", \"embarked\", \"boat\" FROM \"public\".\"titanic\") VERTICAPY_SUBTABLE) VERTICAPY_SUBTABLE) VERTICAPY_SUBTABLE
	
	All Nodes Vector: 
	
	  node[0]=v_testdb_node0001 (initiator) Up
	", labelloc=t, labeljust=l ordering=out]
0[label = "Root 
	OutBlk=[UncTuple(11)]", color = "green", shape = "house"];
1[label = "NewEENode 
	OutBlk=[UncTuple(11)]", color = "green", shape = "box"];
2[label = "StorageUnionStep: titanic_super
	Unc: Integer(8)
	Unc: Integer(8)
	Unc: Varchar(164)
	Unc: Integer(8)
	Unc: Numeric(16, 13)
	Unc: Integer(8)
	Unc: Integer(8)
	Unc: Numeric(18, 13)
	Unc: Varchar(20)
	Unc: Integer(8)
	Unc: Integer(8)", color = "purple", shape = "box"];
3[label = "ExprEval: 
	  titanic.pclass
	  titanic.survived
	  regexp_substr(titanic.name, E\' ([A-Za-z]+)\\.\', 1, 1, \'\', 0)
	  CASE titanic.sex WHEN NULLSEQUAL \'female\' THEN 0 WHEN NULLSEQUAL \'male\' THEN 1 ELSE 2 END
	  coalesce(titanic.age, 30.1524573721163)
	  titanic.sibsp
	  titanic.parch
	  coalesce(CASE WHEN (titanic.fare \< (-176.6204982585513)) THEN (-176.6204982585513) WHEN (titanic.fare \> 244.5480856064831) THEN 244.5480856064831 ELSE titanic.fare END, 32.9113074018842)
	  coalesce(titanic.embarked, \'S\')
	  CASE titanic.boat WHEN NULLSEQUAL NULL THEN 0 ELSE 1 END
	  ((titanic.parch + titanic.sibsp) + 1)
	Unc: Integer(8)
	Unc: Integer(8)
	Unc: Varchar(164)
	Unc: Integer(8)
	Unc: Numeric(16, 13)
	Unc: Integer(8)
	Unc: Integer(8)
	Unc: Numeric(18, 13)
	Unc: Varchar(20)
	Unc: Integer(8)
	Unc: Integer(8)", color = "brown", shape = "box"];
4[label = "ScanStep: titanic_super
	pclass
	survived
	name
	sex
	age
	sibsp
	parch
	fare
	embarked
	boat
	Unc: Integer(8)
	Unc: Integer(8)
	Unc: Varchar(164)
	Unc: Varchar(20)
	Unc: Numeric(6, 3)
	Unc: Integer(8)
	Unc: Integer(8)
	Unc: Numeric(10, 5)
	Unc: Varchar(20)
	Unc: Varchar(100)", color = "brown", shape = "box"];
1->0 [label = "V[0] C=11", color = "black", style="bold", arrowtail="inv"];
2->1 [label = "0", color = "blue"];
3->2 [label = "0", color = "blue"];
4->3 [label = "0", color = "blue"];}

We did plenty of operations and we must keep in mind that each method call will use this relation for its computations. We can save the result as a table in the Vertica database and use the parameter 'inplace' to change the current relation of the vDataFrame by the new one.

In [17]:

vp.drop("public.titanic_clean", method = "table")
vdf.to_db("public.titanic_clean",
          relation_type = "table",
          inplace = True)
print(vdf.current_relation())

"public"."titanic_clean"

When we're dealing with very large datasets, we have to think a bit before saving some transformations. Ideally, you'll want to do a proper data exploration first and then perform the heavier transformations only when they're really neeed.

3. Stick to essential columns¶

Columnar databases perform faster queries when there are fewer columns. Since Vertica is a columnar MPP database, so it's important to understand that most of the optimizations are made in projections. VerticaPy doesn't manage this part, so it's important that the data you're working with is well-organized, particularly for larger volumes of data.

Most vDataFrame methods will automatically pick up all numerical columns - even if doing so has a significant performance impact - so it's important to be a little picky and only select the essential columns for any given use case. Let's look at an example.

In [23]:

vdf = vp.vDataFrame("public.titanic")
vp.set_option("sql_on", True)
vdf.avg()

Computing the different aggregations.

SELECT
AVG("pclass"),
AVG("survived"),
AVG("age"),
AVG("sibsp"),
AVG("parch"),
AVG("fare"),
AVG("body")
FROM
"public"."titanic" LIMIT 1

Out[23]:

	avg
"pclass"	2.28444084278768
"survived"	0.364667747163695
"age"	30.1524573721163
"sibsp"	0.504051863857374
"parch"	0.378444084278768
"fare"	33.9637936739659
"body"	164.14406779661

Rows: 1-7 | Columns: 2

Here, we didn't use the 'columns' parameter to pick on any specific columns, so we ended up computing the average of all the numerical columns of the vDataFrame. This isn't a big deal when we're dealing with smaller volumes of data (less than a TB), but with larger volumes of data, we have to be more careful with which columns we use.

In [24]:

vdf.avg(columns = ["age", "survived"])

Out[24]:

	avg
"age"	30.1524573721163
"survived"	0.364667747163695

Rows: 1-2 | Columns: 2

If you just want to exclude a few columns, you can simply get a list of all the columns and specify the unwanted columns with the 'get_columns' method.

In [25]:

vdf.get_columns()

Out[25]:

['"pclass"',
 '"survived"',
 '"name"',
 '"sex"',
 '"age"',
 '"sibsp"',
 '"parch"',
 '"ticket"',
 '"fare"',
 '"cabin"',
 '"embarked"',
 '"boat"',
 '"body"',
 '"home.dest"']

In [26]:

vdf.get_columns(exclude_columns = ["boat", "embarked"])

Out[26]:

['"pclass"',
 '"survived"',
 '"name"',
 '"sex"',
 '"age"',
 '"sibsp"',
 '"parch"',
 '"ticket"',
 '"fare"',
 '"cabin"',
 '"body"',
 '"home.dest"']

If you only want numerical columns, you can use the 'numcol'. This works the same way as 'get_columns', so you can also exclude columns in the same way.

In [27]:

vdf.numcol()

Out[27]:

['"pclass"', '"survived"', '"age"', '"sibsp"', '"parch"', '"fare"', '"body"']

In [28]:

vdf.numcol(exclude_columns = ["body", "sibsp"])

Out[28]:

['"pclass"', '"survived"', '"age"', '"parch"', '"fare"']

Let's compute a correlation matrix of our numerical columns excluding 'body' and 'sibsp'.

In [29]:

vdf.corr(columns = vdf.numcol(exclude_columns = ["parch", "sibsp"]))

Getting the version.

SELECT
version()

Computing the pearson Corr Matrix.

SELECT
CORR_MATRIX("pclass", "survived", "age", "fare", "body") OVER ()
FROM
"public"."titanic"

Out[29]:

	"pclass"	"survived"	"age"	"fare"	"body"
"pclass"	1.0	-0.335856950271864	-0.400828642351015	-0.561687581153705	-0.0472355333131433
"survived"	-0.335856950271864	1.0	-0.0422446185581737	0.264150360783869	[null]
"age"	-0.400828642351015	-0.0422446185581737	1.0	0.178575164117464	0.0581765649177871
"fare"	-0.561687581153705	0.264150360783869	0.178575164117464	1.0	-0.0372842548942878
"body"	-0.0472355333131433	[null]	0.0581765649177871	-0.0372842548942878	1.0

Rows: 1-5 | Columns: 6

Let's turn off the SQL code generation.

In [30]:

vp.set_option("sql_on", False)

4. Use the help function¶

The 'help' function is very useful for quickly viewing parameters.

In [31]:

help(vdf.agg)

Help on method aggregate in module verticapy.vdataframe:

aggregate(func:list, columns:list=[], ncols_block:int=20, processes:int=1) method of verticapy.vdataframe.vDataFrame instance
    ---------------------------------------------------------------------------
    Aggregates the vDataFrame using the input functions.
    
    Parameters
    ----------
    func: list
        List of the different aggregations.
            aad            : average absolute deviation
            approx_median  : approximate median
            approx_q%      : approximate q quantile 
                             (ex: approx_50% for the approximate median)
            approx_unique  : approximative cardinality
            count          : number of non-missing elements
            cvar           : conditional value at risk
            dtype          : virtual column type
            iqr            : interquartile range
            kurtosis       : kurtosis
            jb             : Jarque-Bera index 
            mad            : median absolute deviation
            max            : maximum
            mean           : average
            median         : median
            min            : minimum
            mode           : most occurent element
            percent        : percent of non-missing elements 
            q%             : q quantile (ex: 50% for the median)
                             Use the 'approx_q%' (approximate quantile) 
                             aggregation to get better performances.
            prod           : product
            range          : difference between the max and the min
            sem            : standard error of the mean
            skewness       : skewness
            sum            : sum
            std            : standard deviation
            topk           : kth most occurent element (ex: top1 for the mode)
            topk_percent   : kth most occurent element density
            unique         : cardinality (count distinct)
            var            : variance
                Other aggregations will work if supported by your version of 
                the database.
    columns: list, optional
        List of the vColumn's names. If empty, depending on the aggregations,
        all or only numerical vColumns will be used.
    ncols_block: int, optional
        Number of columns used per query. Setting this parameter divides
        what would otherwise be one large query into many smaller queries called
        "blocks." The size of each block is determined by the ncols_block parameter.
    processes: int, optional
        Number of child processes to create. Setting this with the ncols_block parameter
        lets you parallelize a single query into many smaller queries, where each child 
        process creates its own connection to the database and sends one query. This can 
        improve query performance, but consumes more resources. If processes is set to 1, 
        the queries are sent iteratively from a single process.
    
    Returns
    -------
    tablesample
        An object containing the result. For more information, see
        utilities.tablesample.
    
    See Also
    --------
    vDataFrame.analytic : Adds a new vColumn to the vDataFrame by using an advanced 
        analytical function on a specific vColumn.

5. Close your connections¶

More connections to the database will increase the concurrency on the system, so try to close all of your connections after using them. VerticaPy simplifies the connection process by allowing the user to create an auto-connection, but it will not close it until you use the 'close_connection' function.

To demonstrate, let's create a database connection. Once we're done, we'll close it.

In [32]:

import verticapy as vp
vp.connect("VerticaDSN")

We can use it to create a vDataFrame perform some operations on the data.

In [34]:

vdf = vp.vDataFrame("public.titanic")
vdf["sex"].label_encode()["boat"].fillna(method = "0ifnull")["name"].str_extract(
    ' ([A-Za-z]+)\.').eval("family_size", expr = "parch + sibsp + 1").drop(
    columns = ["cabin", "body", "ticket", "home.dest"])["fare"].fill_outliers().fillna()

795 elements were filled.

Out[34]:

	123 pclass Int	123 survived Int	Abc name Varchar(164)	123 sex Int	123 age Float	123 sibsp Int	123 parch Int	123 fare Float	Abc embarked Varchar(20)	123 boat Bool	123 family_size Integer
1	1	0	Miss.	0	2.0	1	2	151.55	S	0	4
2	1	0	Mr.	1	30.0	1	2	151.55	S	0	4
3	1	0	Mrs.	0	25.0	1	2	151.55	S	0	4
4	1	0	Mr.	1	39.0	0	0	0.0	S	0	1
5	1	0	Mr.	1	71.0	0	0	49.5042	C	0	1
6	1	0	Col.	1	47.0	1	0	227.525	C	0	2
7	1	0	Mr.	1	30.1524573721163	0	0	25.925	S	0	1
8	1	0	Mr.	1	24.0	0	1	244.5480856064831	C	0	2
9	1	0	Mr.	1	36.0	0	0	75.2417	C	1	1
10	1	0	Mr.	1	25.0	0	0	26.0	C	0	1
11	1	0	Mr.	1	45.0	0	0	35.5	S	0	1
12	1	0	Mr.	1	42.0	0	0	26.55	S	0	1
13	1	0	Mr.	1	41.0	0	0	30.5	S	0	1
14	1	0	Mr.	1	48.0	0	0	50.4958	C	0	1
15	1	0	Dr.	1	30.1524573721163	0	0	39.6	C	0	1
16	1	0	Major.	1	45.0	0	0	26.55	S	0	1
17	1	0	Mr.	1	30.1524573721163	0	0	31.0	S	0	1
18	1	0	Mr.	1	33.0	0	0	5.0	S	0	1
19	1	0	Mr.	1	28.0	0	0	47.1	S	0	1
20	1	0	Mr.	1	17.0	0	0	47.1	S	0	1
21	1	0	Mr.	1	49.0	0	0	26.0	S	0	1
22	1	0	Mr.	1	36.0	1	0	78.85	S	0	2
23	1	0	Mr.	1	46.0	1	0	61.175	S	0	2
24	1	0	Mr.	1	30.1524573721163	0	0	0.0	S	0	1
25	1	0	Mr.	1	27.0	1	0	136.7792	C	0	2
26	1	0	Mr.	1	30.1524573721163	0	0	52.0	S	0	1
27	1	0	Mr.	1	47.0	0	0	25.5875	S	0	1
28	1	0	Mr.	1	37.0	1	1	83.1583	C	0	3
29	1	0	Mr.	1	30.1524573721163	0	0	26.55	S	0	1
30	1	0	Capt.	1	70.0	1	1	71.0	S	0	3
31	1	0	Mr.	1	39.0	1	0	71.2833	C	0	2
32	1	0	Mr.	1	31.0	1	0	52.0	S	0	2
33	1	0	Mr.	1	50.0	1	0	106.425	C	0	2
34	1	0	Mr.	1	39.0	0	0	29.7	C	0	1
35	1	0	Miss.	0	36.0	0	0	31.6792	C	0	1
36	1	0	Mr.	1	30.1524573721163	0	0	221.7792	S	0	1
37	1	0	Mr.	1	30.0	0	0	27.75	C	0	1
38	1	0	Mr.	1	19.0	3	2	244.5480856064831	S	0	6
39	1	0	Mr.	1	64.0	1	4	244.5480856064831	S	0	6
40	1	0	Mr.	1	30.1524573721163	0	0	26.55	S	0	1
41	1	0	Mr.	1	30.1524573721163	0	0	0.0	S	0	1
42	1	0	Mr.	1	37.0	1	0	53.1	S	0	2
43	1	0	Mr.	1	47.0	0	0	38.5	S	0	1
44	1	0	Mr.	1	24.0	0	0	79.2	C	0	1
45	1	0	Mr.	1	71.0	0	0	34.6542	C	0	1
46	1	0	Mr.	1	38.0	0	1	153.4625	S	0	2
47	1	0	Mr.	1	46.0	0	0	79.2	C	0	1
48	1	0	Mr.	1	30.1524573721163	0	0	42.4	S	0	1
49	1	0	Mr.	1	45.0	1	0	83.475	S	0	2
50	1	0	Mr.	1	40.0	0	0	0.0	S	0	1
51	1	0	Mr.	1	55.0	1	1	93.5	S	0	3
52	1	0	Mr.	1	42.0	0	0	42.5	S	0	1
53	1	0	Mr.	1	30.1524573721163	0	0	51.8625	S	0	1
54	1	0	Mr.	1	55.0	0	0	50.0	S	0	1
55	1	0	Mr.	1	42.0	1	0	52.0	S	0	2
56	1	0	Mr.	1	30.1524573721163	0	0	30.6958	C	1	1
57	1	0	Miss.	0	50.0	0	0	28.7125	C	0	1
58	1	0	Mr.	1	46.0	0	0	26.0	S	0	1
59	1	0	Mr.	1	50.0	0	0	26.0	S	0	1
60	1	0	Mr.	1	32.5	0	0	211.5	C	0	1
61	1	0	Mr.	1	58.0	0	0	29.7	C	0	1
62	1	0	Mr.	1	41.0	1	0	51.8625	S	0	2
63	1	0	Mr.	1	30.1524573721163	0	0	26.55	S	0	1
64	1	0	Mr.	1	30.1524573721163	0	0	27.7208	C	0	1
65	1	0	Mr.	1	29.0	0	0	30.0	S	0	1
66	1	0	Mr.	1	30.0	0	0	45.5	S	0	1
67	1	0	Mr.	1	30.0	0	0	26.0	S	0	1
68	1	0	Mr.	1	19.0	1	0	53.1	S	0	2
69	1	0	Mr.	1	46.0	0	0	75.2417	C	0	1
70	1	0	Mr.	1	54.0	0	0	51.8625	S	0	1
71	1	0	Mr.	1	28.0	1	0	82.1708	C	0	2
72	1	0	Mr.	1	65.0	0	0	26.55	S	0	1
73	1	0	Dr.	1	44.0	2	0	90.0	Q	0	3
74	1	0	Mr.	1	55.0	0	0	30.5	S	0	1
75	1	0	Mr.	1	47.0	0	0	42.4	S	0	1
76	1	0	Mr.	1	37.0	0	1	29.7	C	0	2
77	1	0	Mr.	1	58.0	0	2	113.275	C	0	3
78	1	0	Mr.	1	64.0	0	0	26.0	S	0	1
79	1	0	Mr.	1	65.0	0	1	61.9792	C	0	2
80	1	0	Mr.	1	28.5	0	0	27.7208	C	0	1
81	1	0	Mr.	1	30.1524573721163	0	0	0.0	S	0	1
82	1	0	Mr.	1	45.5	0	0	28.5	S	0	1
83	1	0	Mr.	1	23.0	0	0	93.5	S	0	1
84	1	0	Mr.	1	29.0	1	0	66.6	S	0	2
85	1	0	Mr.	1	18.0	1	0	108.9	C	0	2
86	1	0	Mr.	1	47.0	0	0	52.0	S	0	1
87	1	0	Jonkheer.	1	38.0	0	0	0.0	S	0	1
88	1	0	Mr.	1	22.0	0	0	135.6333	C	0	1
89	1	0	Mr.	1	30.1524573721163	0	0	227.525	C	0	1
90	1	0	Mr.	1	31.0	0	0	50.4958	S	0	1
91	1	0	Mr.	1	30.1524573721163	0	0	50.0	S	0	1
92	1	0	Mr.	1	36.0	0	0	40.125	C	0	1
93	1	0	Mr.	1	55.0	1	0	59.4	C	0	2
94	1	0	Mr.	1	33.0	0	0	26.55	S	0	1
95	1	0	Mr.	1	61.0	1	3	244.5480856064831	C	0	5
96	1	0	Mr.	1	50.0	1	0	55.9	S	0	2
97	1	0	Mr.	1	56.0	0	0	26.55	S	0	1
98	1	0	Mr.	1	56.0	0	0	30.6958	C	0	1
99	1	0	Mr.	1	24.0	1	0	60.0	S	0	2
100	1	0	Mr.	1	30.1524573721163	0	0	26.0	S	0	1

Rows: 1-100 | Columns: 11

We can then close the connection when we are done.

In [35]:

vp.close_connection()

It is very important to follow the previous process when you are working in an environment with multiple users.

6. Understand the time complexity of methods¶

Some techniques are more computationally expensive than others. For example, a 'kendall' correlation is very expensive compared to a 'pearson' correlation. This is because a 'kendall' correlation uses a cross join, giving it a time complexity of O(n*n) (where 'n' is the number of rows). We'll demonstrate this with the 'titanic' dataset.

In [41]:

import time
vdf = vp.vDataFrame("public.titanic")
start_time = time.time()
x = vdf.corr(method = "pearson", show = False)
print("Pearson, time: {0}".format(time.time() - start_time))
start_time = time.time()
x = vdf.corr(method = "kendall", show = False)
print("Kendall, time: {0}".format(time.time() - start_time))

Pearson, time: 0.03376507759094238

Kendall, time: 2.1013760566711426

As you can see, a Kendall Correlation Matrix is noticeably slower (around 100 times more than Pearson) because of its time complexity. Keep this in mind when using methods on larger datasets.

7. Limit the number of elements in a plot¶

Graphics are a powerful way to understand data, but graphs can be difficult to parse if it has too many elements. Let's draw a multi-histogram where one column is categorical with thousands of categories.

In [42]:

vdf.hist(["name", "survived"])

Out[42]:

<AxesSubplot:xlabel='"name"', ylabel='Density'>

VerticaPy will try to draw it, but it could take time with a large dataset. Worse, it might be completely incomprehensible. Instead, we should try to create graphics with as few categories as possible.

In [43]:

vdf.hist(["pclass", "survived"])

Out[43]:

<AxesSubplot:xlabel='"pclass"', ylabel='Density'>

Try to always check the cardinality of your variables before creating graphics.

In [44]:

vdf.nunique()

Out[44]:

	approx_unique
"pclass"	3.0
"survived"	2.0
"name"	1233.0
"sex"	2.0
"age"	96.0
"sibsp"	7.0
"parch"	8.0
"ticket"	888.0
"fare"	275.0
"cabin"	181.0
"embarked"	3.0
"boat"	26.0
"body"	118.0
"home.dest"	355.0

Rows: 1-14 | Columns: 2

8. Filter unneeded data¶

Filtering should be the first action you perform when you prepare your data. Proper filtering will help you avoid unnecessary computation and therefore drastically improve the performance of every method you call. While the performance impact isn't as important for small datasets, when working with large ones, you'll always want to filter your data in some way.

In the following example, our goal is to analyze the passengers on the Titanic that didn't have a lifeboat. Since we aren't concerned at all with passengers with a lifeboat, we can simply filter them out of the dataset and move on from there.

In [45]:

vdf.filter("boat IS NOT NULL")

795 elements were filtered.

Out[45]:

	123 pclass Int	123 survived Int	Abc Varchar(164)	Abc sex Varchar(20)	123 age Numeric(6,3)	123 sibsp Int	123 parch Int	Abc ticket Varchar(36)	123 fare Numeric(10,5)	Abc cabin Varchar(30)	Abc embarked Varchar(20)	Abc boat Varchar(100)	123 body Int	Abc Varchar(100)
1	1	0		male	36.0	0	0	13050	75.2417	C6	C	A	[null]
2	1	0		male	[null]	0	0	PC 17600	30.6958	[null]	C	14	[null]
3	1	1		female	29.0	0	0	24160	211.3375	B5	S	2	[null]
4	1	1		male	0.92	1	2	113781	151.55	C22 C26	S	11	[null]
5	1	1		male	48.0	0	0	19952	26.55	E12	S	3	[null]
6	1	1		female	63.0	1	0	13502	77.9583	D7	S	10	[null]
7	1	1		female	53.0	2	0	11769	51.4792	C101	S	D	[null]
8	1	1		female	18.0	1	0	PC 17757	227.525	C62 C64	C	4	[null]
9	1	1		female	24.0	0	0	PC 17477	69.3	B35	C	9	[null]
10	1	1		male	80.0	0	0	27042	30.0	A23	S	B	[null]
11	1	1		female	50.0	0	1	PC 17558	247.5208	B58 B60	C	6	[null]
12	1	1		female	32.0	0	0	11813	76.2917	D15	C	8	[null]
13	1	1		male	37.0	1	1	11751	52.5542	D35	S	5	[null]
14	1	1		female	47.0	1	1	11751	52.5542	D35	S	5	[null]
15	1	1		male	26.0	0	0	111369	30.0	C148	C	5	[null]
16	1	1		female	42.0	0	0	PC 17757	227.525	[null]	C	4	[null]
17	1	1		female	29.0	0	0	PC 17483	221.7792	C97	S	8	[null]
18	1	1		male	25.0	1	0	11967	91.0792	B49	C	7	[null]
19	1	1		female	19.0	1	0	11967	91.0792	B49	C	7	[null]
20	1	1		female	35.0	0	0	PC 17760	135.6333	C99	S	8	[null]
21	1	1		male	28.0	0	0	110564	26.55	C52	S	D	[null]
22	1	1		male	40.0	0	0	112277	31.0	A31	C	7	[null]
23	1	1		female	30.0	0	0	36928	164.8667	C7	S	8	[null]
24	1	1		female	58.0	0	0	113783	26.55	C103	S	8	[null]
25	1	1		female	45.0	0	0	PC 17608	262.375	[null]	C	4	[null]
26	1	1		female	22.0	0	1	113505	55.0	E33	S	6	[null]
27	1	1		female	44.0	0	0	PC 17610	27.7208	B4	C	6	[null]
28	1	1		female	59.0	2	0	11769	51.4792	C101	S	D	[null]
29	1	1		female	60.0	0	0	11813	76.2917	D15	C	8	[null]
30	1	1		female	41.0	0	0	16966	134.5	E40	C	3	[null]
31	1	1		male	42.0	0	0	PC 17476	26.2875	E24	S	5	[null]
32	1	1		female	53.0	0	0	PC 17606	27.4458	[null]	C	6	[null]
33	1	1		male	36.0	0	1	PC 17755	512.3292	B51 B53 B55	C	3	[null]
34	1	1		female	58.0	0	1	PC 17755	512.3292	B51 B53 B55	C	3	[null]
35	1	1		male	11.0	1	2	113760	120.0	B96 B98	S	4	[null]
36	1	1		female	14.0	1	2	113760	120.0	B96 B98	S	4	[null]
37	1	1		male	36.0	1	2	113760	120.0	B96 B98	S	C	[null]
38	1	1		female	36.0	1	2	113760	120.0	B96 B98	S	4	[null]
39	1	1		female	[null]	0	0	17770	27.7208	[null]	C	5	[null]
40	1	1		female	76.0	1	0	19877	78.85	C46	S	6	[null]
41	1	1		female	47.0	1	0	W.E.P. 5734	61.175	E31	S	4	[null]
42	1	1		male	27.0	1	0	113806	53.1	E8	S	5	[null]
43	1	1		female	33.0	1	0	113806	53.1	E8	S	5	[null]
44	1	1		female	36.0	0	0	PC 17608	262.375	B61	C	4	[null]
45	1	1		female	30.0	0	0	110152	86.5	B77	S	8	[null]
46	1	1		male	45.0	0	0	PC 17594	29.7	A9	C	7	[null]
47	1	1		female	[null]	0	1	113505	55.0	E33	S	6	[null]
48	1	1		female	26.0	1	0	13508	136.7792	C89	C	4	[null]
49	1	1		female	22.0	0	0	113781	151.55	[null]	S	11	[null]
50	1	1		female	39.0	1	1	PC 17756	83.1583	E49	C	14	[null]
51	1	1		female	64.0	0	2	PC 17756	83.1583	E45	C	14	[null]
52	1	1		female	55.0	2	0	11770	25.7	C101	S	2	[null]
53	1	1		female	36.0	0	2	WE/P 5735	71.0	B22	S	7	[null]
54	1	1		female	64.0	1	1	112901	26.55	B26	S	7	[null]
55	1	1		female	38.0	1	0	PC 17599	71.2833	C85	C	4	[null]
56	1	1		male	51.0	0	0	113055	26.55	E17	S	5 9	[null]
57	1	1		male	27.0	0	0	113804	30.5	[null]	S	3	[null]
58	1	1		female	33.0	0	0	113781	151.55	[null]	S	8	[null]
59	1	1		female	27.0	1	2	F.C. 12750	52.0	B71	S	3	[null]
60	1	1		male	31.0	1	0	17474	57.0	B20	S	3	[null]
61	1	1		female	17.0	1	0	17474	57.0	B20	S	3	[null]
62	1	1		male	53.0	1	1	33638	81.8583	A34	S	13	[null]
63	1	1		male	4.0	0	2	33638	81.8583	A34	S	5	[null]
64	1	1		female	54.0	1	1	33638	81.8583	A34	S	5	[null]
65	1	1		female	27.0	1	1	PC 17558	247.5208	B58 B60	C	6	[null]
66	1	1		female	48.0	1	0	PC 17761	106.425	C86	C	2	[null]
67	1	1		female	23.0	0	1	11767	83.1583	C54	C	7	[null]
68	1	1		female	38.0	0	0	PC 17757	227.525	C45	C	4	[null]
69	1	1		female	54.0	1	0	36947	78.2667	D20	C	4	[null]
70	1	1		female	[null]	0	0	PC 17598	31.6833	[null]	S	7	[null]
71	1	1		female	[null]	0	0	17421	110.8833	[null]	C	4	[null]
72	1	1		female	24.0	3	2	19950	263.0	C23 C25 C27	S	10	[null]
73	1	1		female	28.0	3	2	19950	263.0	C23 C25 C27	S	10	[null]
74	1	1		female	23.0	3	2	19950	263.0	C23 C25 C27	S	10	[null]
75	1	1		female	60.0	1	4	19950	263.0	C23 C25 C27	S	10	[null]
76	1	1		female	30.0	0	0	PC 17485	56.9292	E36	C	1	[null]
77	1	1		male	50.0	2	0	PC 17611	133.65	[null]	S	5	[null]
78	1	1		male	43.0	1	0	17765	27.7208	D40	C	5	[null]
79	1	1		female	[null]	1	0	PC 17611	133.65	[null]	S	5	[null]
80	1	1		female	22.0	0	2	13568	49.5	B39	C	5	[null]
81	1	1		male	60.0	1	1	13567	79.2	B41	C	5	[null]
82	1	1		female	48.0	1	1	13567	79.2	B41	C	5	[null]
83	1	1		female	35.0	1	0	113803	53.1	C123	S	D	[null]
84	1	1		female	35.0	0	0	113503	211.5	C130	C	4	[null]
85	1	1		female	22.0	0	1	112378	59.4	[null]	C	7	[null]
86	1	1		female	45.0	0	1	112378	59.4	[null]	C	7	[null]
87	1	1		male	49.0	1	0	17453	89.1042	C92	C	5	[null]
88	1	1		female	[null]	1	0	17453	89.1042	C92	C	5	[null]
89	1	1		male	53.0	0	0	113780	28.5	C51	C	B	[null]
90	1	1		female	19.0	0	0	112053	30.0	B42	S	3	[null]
91	1	1		female	58.0	0	1	PC 17582	153.4625	C125	S	3	[null]
92	1	1		male	23.0	0	1	PC 17759	63.3583	D10 D12	C	7	[null]
93	1	1		female	45.0	0	1	PC 17759	63.3583	D10 D12	C	7	[null]
94	1	1		male	25.0	1	0	11765	55.4417	E50	C	5	[null]
95	1	1		female	25.0	1	0	11765	55.4417	E50	C	5	[null]
96	1	1		male	48.0	1	0	PC 17572	76.7292	D33	C	3	[null]
97	1	1		female	49.0	1	0	PC 17572	76.7292	D33	C	3	[null]
98	1	1		female	35.0	1	0	36973	83.475	C83	S	D	[null]
99	1	1		male	27.0	0	0	PC 17572	76.7292	D49	C	3	[null]
100	1	1		male	[null]	0	0	16988	30.0	D45	S	3	[null]

Rows: 1-100 of 439 | Columns: 14

9. Filter unneeded columns¶

Again, you should always try to cut down your dataset to the essential columns, but writing an explicit command to exclude columns can be cumbersome. Another way to do this is by simply dropping the columns with the 'drop' method.

In [46]:

vdf.drop(["name", "body"])

Out[46]:

	123 pclass Int	123 survived Int	Abc sex Varchar(20)	123 age Numeric(6,3)	123 sibsp Int	123 parch Int	Abc ticket Varchar(36)	123 fare Numeric(10,5)	Abc cabin Varchar(30)	Abc embarked Varchar(20)	Abc boat Varchar(100)	Abc Varchar(100)
1	1	0	male	36.0	0	0	13050	75.2417	C6	C	A
2	1	0	male	[null]	0	0	PC 17600	30.6958	[null]	C	14
3	1	1	female	29.0	0	0	24160	211.3375	B5	S	2
4	1	1	male	0.92	1	2	113781	151.55	C22 C26	S	11
5	1	1	male	48.0	0	0	19952	26.55	E12	S	3
6	1	1	female	63.0	1	0	13502	77.9583	D7	S	10
7	1	1	female	53.0	2	0	11769	51.4792	C101	S	D
8	1	1	female	18.0	1	0	PC 17757	227.525	C62 C64	C	4
9	1	1	female	24.0	0	0	PC 17477	69.3	B35	C	9
10	1	1	male	80.0	0	0	27042	30.0	A23	S	B
11	1	1	female	50.0	0	1	PC 17558	247.5208	B58 B60	C	6
12	1	1	female	32.0	0	0	11813	76.2917	D15	C	8
13	1	1	male	37.0	1	1	11751	52.5542	D35	S	5
14	1	1	female	47.0	1	1	11751	52.5542	D35	S	5
15	1	1	male	26.0	0	0	111369	30.0	C148	C	5
16	1	1	female	42.0	0	0	PC 17757	227.525	[null]	C	4
17	1	1	female	29.0	0	0	PC 17483	221.7792	C97	S	8
18	1	1	male	25.0	1	0	11967	91.0792	B49	C	7
19	1	1	female	19.0	1	0	11967	91.0792	B49	C	7
20	1	1	female	35.0	0	0	PC 17760	135.6333	C99	S	8
21	1	1	male	28.0	0	0	110564	26.55	C52	S	D
22	1	1	male	40.0	0	0	112277	31.0	A31	C	7
23	1	1	female	30.0	0	0	36928	164.8667	C7	S	8
24	1	1	female	58.0	0	0	113783	26.55	C103	S	8
25	1	1	female	45.0	0	0	PC 17608	262.375	[null]	C	4
26	1	1	female	22.0	0	1	113505	55.0	E33	S	6
27	1	1	female	44.0	0	0	PC 17610	27.7208	B4	C	6
28	1	1	female	59.0	2	0	11769	51.4792	C101	S	D
29	1	1	female	60.0	0	0	11813	76.2917	D15	C	8
30	1	1	female	41.0	0	0	16966	134.5	E40	C	3
31	1	1	male	42.0	0	0	PC 17476	26.2875	E24	S	5
32	1	1	female	53.0	0	0	PC 17606	27.4458	[null]	C	6
33	1	1	male	36.0	0	1	PC 17755	512.3292	B51 B53 B55	C	3
34	1	1	female	58.0	0	1	PC 17755	512.3292	B51 B53 B55	C	3
35	1	1	male	11.0	1	2	113760	120.0	B96 B98	S	4
36	1	1	female	14.0	1	2	113760	120.0	B96 B98	S	4
37	1	1	male	36.0	1	2	113760	120.0	B96 B98	S	C
38	1	1	female	36.0	1	2	113760	120.0	B96 B98	S	4
39	1	1	female	[null]	0	0	17770	27.7208	[null]	C	5
40	1	1	female	76.0	1	0	19877	78.85	C46	S	6
41	1	1	female	47.0	1	0	W.E.P. 5734	61.175	E31	S	4
42	1	1	male	27.0	1	0	113806	53.1	E8	S	5
43	1	1	female	33.0	1	0	113806	53.1	E8	S	5
44	1	1	female	36.0	0	0	PC 17608	262.375	B61	C	4
45	1	1	female	30.0	0	0	110152	86.5	B77	S	8
46	1	1	male	45.0	0	0	PC 17594	29.7	A9	C	7
47	1	1	female	[null]	0	1	113505	55.0	E33	S	6
48	1	1	female	26.0	1	0	13508	136.7792	C89	C	4
49	1	1	female	22.0	0	0	113781	151.55	[null]	S	11
50	1	1	female	39.0	1	1	PC 17756	83.1583	E49	C	14
51	1	1	female	64.0	0	2	PC 17756	83.1583	E45	C	14
52	1	1	female	55.0	2	0	11770	25.7	C101	S	2
53	1	1	female	36.0	0	2	WE/P 5735	71.0	B22	S	7
54	1	1	female	64.0	1	1	112901	26.55	B26	S	7
55	1	1	female	38.0	1	0	PC 17599	71.2833	C85	C	4
56	1	1	male	51.0	0	0	113055	26.55	E17	S	5 9
57	1	1	male	27.0	0	0	113804	30.5	[null]	S	3
58	1	1	female	33.0	0	0	113781	151.55	[null]	S	8
59	1	1	female	27.0	1	2	F.C. 12750	52.0	B71	S	3
60	1	1	male	31.0	1	0	17474	57.0	B20	S	3
61	1	1	female	17.0	1	0	17474	57.0	B20	S	3
62	1	1	male	53.0	1	1	33638	81.8583	A34	S	13
63	1	1	male	4.0	0	2	33638	81.8583	A34	S	5
64	1	1	female	54.0	1	1	33638	81.8583	A34	S	5
65	1	1	female	27.0	1	1	PC 17558	247.5208	B58 B60	C	6
66	1	1	female	48.0	1	0	PC 17761	106.425	C86	C	2
67	1	1	female	23.0	0	1	11767	83.1583	C54	C	7
68	1	1	female	38.0	0	0	PC 17757	227.525	C45	C	4
69	1	1	female	54.0	1	0	36947	78.2667	D20	C	4
70	1	1	female	[null]	0	0	PC 17598	31.6833	[null]	S	7
71	1	1	female	[null]	0	0	17421	110.8833	[null]	C	4
72	1	1	female	24.0	3	2	19950	263.0	C23 C25 C27	S	10
73	1	1	female	28.0	3	2	19950	263.0	C23 C25 C27	S	10
74	1	1	female	23.0	3	2	19950	263.0	C23 C25 C27	S	10
75	1	1	female	60.0	1	4	19950	263.0	C23 C25 C27	S	10
76	1	1	female	30.0	0	0	PC 17485	56.9292	E36	C	1
77	1	1	male	50.0	2	0	PC 17611	133.65	[null]	S	5
78	1	1	male	43.0	1	0	17765	27.7208	D40	C	5
79	1	1	female	[null]	1	0	PC 17611	133.65	[null]	S	5
80	1	1	female	22.0	0	2	13568	49.5	B39	C	5
81	1	1	male	60.0	1	1	13567	79.2	B41	C	5
82	1	1	female	48.0	1	1	13567	79.2	B41	C	5
83	1	1	female	35.0	1	0	113803	53.1	C123	S	D
84	1	1	female	35.0	0	0	113503	211.5	C130	C	4
85	1	1	female	22.0	0	1	112378	59.4	[null]	C	7
86	1	1	female	45.0	0	1	112378	59.4	[null]	C	7
87	1	1	male	49.0	1	0	17453	89.1042	C92	C	5
88	1	1	female	[null]	1	0	17453	89.1042	C92	C	5
89	1	1	male	53.0	0	0	113780	28.5	C51	C	B
90	1	1	female	19.0	0	0	112053	30.0	B42	S	3
91	1	1	female	58.0	0	1	PC 17582	153.4625	C125	S	3
92	1	1	male	23.0	0	1	PC 17759	63.3583	D10 D12	C	7
93	1	1	female	45.0	0	1	PC 17759	63.3583	D10 D12	C	7
94	1	1	male	25.0	1	0	11765	55.4417	E50	C	5
95	1	1	female	25.0	1	0	11765	55.4417	E50	C	5
96	1	1	male	48.0	1	0	PC 17572	76.7292	D33	C	3
97	1	1	female	49.0	1	0	PC 17572	76.7292	D33	C	3
98	1	1	female	35.0	1	0	36973	83.475	C83	S	D
99	1	1	male	27.0	0	0	PC 17572	76.7292	D49	C	3
100	1	1	male	[null]	0	0	16988	30.0	D45	S	3

Rows: 1-100 of 439 | Columns: 12

By using the 'drop' method, VerticaPy will simply exclude the specified columns from the SELECT query during SQL code generation.

In [47]:

print(vdf.current_relation())

(
   SELECT
     * 
   FROM
 (
   SELECT
     "pclass",
     "survived",
     "sex",
     "age",
     "sibsp",
     "parch",
     "ticket",
     "fare",
     "cabin",
     "embarked",
     "boat",
     "home.dest" 
   FROM
 "public"."titanic") 
VERTICAPY_SUBTABLE WHERE (boat IS NOT NULL)) 
VERTICAPY_SUBTABLE

10. Maximize your ressources¶

You might encounter datasets with hundreds of columns. These datasets can be resource intensive because you have to compute many aggregations at the same time. VerticaPy allows you to control the number of queries you'll send to the system, allowing for some useful optimizations.

Let's generate a large dataset and see what we can do to handle it.

In [52]:

from verticapy.datasets import gen_dataset
features_ranges = {}
for i in range(600):
    features_ranges[f"x{i}"] = {"type": float, "range": [0, 1]}
vp.drop("test_dataset", method = "table")
vdf = gen_dataset(features_ranges, nrows = 10000).to_db("test_dataset", 
                                                        relation_type = "table", 
                                                        inplace = True)
vdf

Out[52]:

	123 x0 Float	123 x1 Float	123 x2 Float	123 x3 Float	123 x4 Float	123 x5 Float	123 x6 Float	123 x7 Float	123 x8 Float	123 x9 Float	123 x10 Float	123 x11 Float	123 x12 Float	123 x13 Float	123 x14 Float	123 x15 Float	123 x16 Float	123 x17 Float	123 x18 Float	123 x19 Float	123 x20 Float	123 x21 Float	123 x22 Float	123 x23 Float	123 x24 Float	...	123 x575 Float	123 x576 Float	123 x577 Float	123 x578 Float	123 x579 Float	123 x580 Float	123 x581 Float	123 x582 Float	123 x583 Float	123 x584 Float	123 x585 Float	123 x586 Float	123 x587 Float	123 x588 Float	123 x589 Float	123 x590 Float	123 x591 Float	123 x592 Float	123 x593 Float	123 x594 Float	123 x595 Float	123 x596 Float	123 x597 Float	123 x598 Float	123 x599 Float
1	5.20595349371433e-05	0.485537143424153	0.310573806287721	0.17678599152714	0.878996989224106	0.672955710673705	0.427016830304638	0.973969946848229	0.0463997314218432	0.815124413464218	0.391868985490873	0.881440195720643	0.927366451360285	0.972796163288876	0.438081233995035	0.899093058193102	0.270564198726788	0.828351575415581	0.670606910251081	0.00691566616296768	0.244990032864735	0.237273046979681	0.593805334996432	0.418030732544139	0.754198654321954	...	0.167198063107207	0.66410061926581	0.591387247433886	0.801861852407455	0.160147324670106	0.394959231372923	0.574036001926288	0.367323393234983	0.12169961235486	0.319393459474668	0.644115259405226	0.404665975831449	0.228299217764288	0.893671463243663	0.653764561284333	0.0432208550628275	0.867323723621666	0.161540731554851	0.501341378316283	0.934534269152209	0.546305241528898	0.360079299192876	0.205802466021851	0.738606488797814	0.974907765863463
2	9.12342220544815e-05	0.590735004516318	0.882021387806162	0.807899940526113	0.711216947296634	0.188597822096199	0.0914702252484858	0.0544094850774854	0.145819653989747	0.491020490648225	0.564146702410653	0.479361131088808	0.0683218501508236	0.170866159722209	0.998306306544691	0.353908515535295	0.657991246320307	0.950230355840176	0.597807428101078	0.955734903225675	0.8572982782498	0.35656450339593	0.792389542330056	0.778561599552631	0.957219142001122	...	0.517275118036196	0.78418490383774	0.464827378746122	0.225700930459425	0.979388368315995	0.519117088522762	0.250407279236242	0.102565104607493	0.229686575941741	0.114929591305554	0.426856120815501	0.501485610380769	0.556145877111703	0.334615163039416	0.623225529445335	0.270954991923645	0.145561676472425	0.531705247005448	0.303247437812388	0.885837420355529	0.167087592883036	0.242697077337652	0.599079611944035	0.747869855258614	0.142291998490691
3	0.000313950935378671	0.971825310494751	0.25097096292302	0.184859347296879	0.390021583763883	0.848451353842393	0.717313979519531	0.218293828656897	0.919568052748218	0.0344389842357486	0.428135209018365	0.876995357684791	0.27770495088771	0.168209461728111	0.873780781868845	0.567112604621798	0.976665391819552	0.673653927864507	0.205331231001765	0.585696727968752	0.0154113282915205	0.353451657108963	0.467424136819318	0.846257669152692	0.817340336041525	...	0.67054178006947	0.540544708259404	0.917200654977933	0.728609589394182	0.597091436386108	0.197245578747243	0.107135031139478	0.0679437993094325	0.789403797360137	0.777199340285733	0.767102502984926	0.410148833645508	0.699540945002809	0.965898974332958	0.161106799496338	0.976732995826751	0.995254686335102	0.61504055862315	0.0883637766819447	0.586336502805352	0.913205028045923	0.00139876455068588	0.582190479850397	0.121553729521111	0.834747690008953
4	0.000348165864124894	0.514557993272319	0.11887997132726	0.295070218853652	0.665599208325148	0.298725477885455	0.68120707408525	0.800706213572994	0.263220171676949	0.354912436101586	0.114155641989782	0.37006649048999	0.808830021880567	0.53140323003754	0.94215980428271	0.911958450218663	0.298196947900578	0.347267954843119	0.812091138912365	0.23330820677802	0.934122515143827	0.924393917899579	0.480800931807607	0.0956339514814317	0.156290424754843	...	0.768978404812515	0.956076709553599	0.0353506035171449	0.482680244836956	0.720112280687317	0.40887110074982	0.119812757940963	0.571051431354135	0.596502817468718	0.247205943567678	0.984226516680792	0.0681454793084413	0.920964878750965	0.108473145635799	0.956578589975834	0.0854171137325466	0.985457987058908	0.707029990386218	0.709613722283393	0.771255206782371	0.72974056028761	0.932461752556264	0.315052018268034	0.723329573636875	0.242409084457904
5	0.000543929636478424	0.430594844510779	0.45327917044051	0.361475860700011	0.947569382376969	0.318890801863745	0.324479850940406	0.85119098960422	0.442027089186013	0.419261903269216	0.0473281478043646	0.680384134873748	0.260326553368941	0.395267856074497	0.375534699298441	0.456479738699272	0.687495809979737	0.172390855615959	0.896469952305779	0.110564883798361	0.157864786684513	0.907979461597279	0.482405613409355	0.621053696144372	0.0376831751782447	...	0.568237458355725	0.139524054713547	0.20069858757779	0.0393721011932939	0.704097861424088	0.886494970181957	0.407103452598676	0.604970705928281	0.454309214837849	0.854147390928119	0.446033544372767	0.280742262955755	0.774687433615327	0.464250945486128	0.121843478642404	0.00450773374177516	0.250076497904956	0.677564952522516	0.345467021688819	0.961186888394877	0.0738108425866812	0.656103662680835	0.723669653059915	0.717909814557061	0.663673594594002
6	0.00065303361043334	0.704251058632508	0.467781809857115	0.264956458704546	0.395995118888095	0.497432985110208	0.201243643881753	0.642188858240843	0.0634401268325746	0.0744246849790215	0.445458367699757	0.314175287261605	0.703889897326007	0.322278953623027	0.893158378778026	0.360803448362276	0.094298658426851	0.308781487168744	0.530132415471599	0.792136463336647	0.364130642730743	0.0571534272748977	0.773166512837633	0.997822631383315	0.648393482901156	...	0.0140404445119202	0.742923176847398	0.258339375257492	0.552029890706763	0.683688709046692	0.95612878468819	0.349498979514465	0.0754324006848037	0.311314723221585	0.436544922180474	0.141199418576434	0.402274329215288	0.544963134918362	0.487752554705366	0.460722686257213	0.868628986412659	0.081838998477906	0.853526944061741	0.41887966892682	0.240077747963369	0.618126435671002	0.170666457619518	0.207266625482589	0.447347375331447	0.745776536874473
7	0.000682923709973693	0.644610994961113	0.616245283046737	0.123426298610866	0.443946395535022	0.643657223321497	0.441787774208933	0.150944351218641	0.559415593510494	0.605078289285302	0.315637140069157	0.154103143606335	0.134863581275567	0.848456078441814	0.453098441939801	0.838772770715877	0.872466262429953	0.227294779382646	0.727198318811134	0.975423069670796	0.937626459402964	0.496109237428755	0.269249163800851	0.0692040659487247	0.379938506986946	...	0.037696061655879	0.775120094651356	0.636968676932156	0.86673281993717	0.808848927961662	0.332977320067585	0.085852179909125	0.37671291991137	0.896897500148043	0.995074050500989	0.88267021602951	0.801379155600443	0.64041000767611	0.255462269065902	0.301744208205491	0.243042844347656	0.261499479878694	0.171056173043326	0.360516448505223	0.708982574520633	0.421358312247321	0.214519023662433	0.444327210076153	0.437608614098281	0.838648703182116
8	0.000793067505583167	0.253356415778399	0.415544093353674	0.7858901550062	0.518841414479539	0.58124653249979	0.838872970547527	0.253339023096487	0.537777259014547	0.511636992683634	0.876401922665536	0.330910145072266	0.837250211276114	0.891292826039717	0.0300761514808983	0.492607116000727	0.677193257724866	0.932157736970112	0.135663843713701	0.0830265819095075	0.538005317561328	0.943019419442862	0.0444187875837088	0.731323141837493	0.396121923113242	...	0.873294182820246	0.955755655420944	0.0719545548781753	0.231276272563264	0.522659169510007	0.603492273483425	0.505635055946186	0.503558372845873	0.668043007608503	0.93298056605272	0.569968913914636	0.178924583597109	0.906137820100412	0.683470293879509	0.937676642322913	0.155517283128574	0.561612573219463	0.939914074027911	0.979315676027909	0.770145743153989	0.991599828703329	0.574154407950118	0.987718650372699	0.860681550344452	0.111758790444583
9	0.000808189157396555	0.617995445150882	0.696658476255834	0.911908351117745	0.0746490750461817	0.0270547145046294	0.778803712688386	0.830991541268304	0.522100063739344	0.621482615824789	0.369138852693141	0.104004176333547	0.229563752422109	0.14387673069723	0.080871112877503	0.733459386974573	0.613935362081975	0.601380746113136	0.84580114739947	0.575360710499808	0.999191816197708	0.881340791704133	0.0409915717318654	0.602274929638952	0.533451740629971	...	0.504118523327634	0.892249741125852	0.465759584447369	0.100535372737795	0.305523607414216	0.898552039405331	0.0169904972426593	0.233689826447517	0.62591705750674	0.607556416653097	0.187945291632786	0.52122471225448	0.838821975514293	0.904034097446129	0.523557051550597	0.73128763679415	0.403599898330867	0.00886141718365252	0.378119049360976	0.880755959777161	0.755399168236181	0.957178877666593	0.474744056118652	0.380020038690418	0.911325834225863
10	0.000822716159746051	0.303757929708809	0.674965204903856	0.985240588197485	0.448494721669704	0.285802850499749	0.162648947909474	0.700317578390241	0.335482957074419	0.806018033064902	0.0216339391190559	0.0320320068858564	0.485301839886233	0.0921966573223472	0.368745758431032	0.396297462750226	0.696527161635458	0.130280503071845	0.570096999173984	0.78883409849368	0.908887054305524	0.297445093281567	0.255620962474495	0.991246218094602	0.430116546107456	...	0.827182898530737	0.60562601312995	0.829281496116892	0.0895934132859111	0.0667535986285657	0.800689417868853	0.964669033652171	0.271402568323538	0.171136529650539	0.360468459781259	0.947657118318602	0.875000069616362	0.647767289076	0.818007030757144	0.159016718389466	0.00837163557298481	0.471277346136048	0.89308297378011	0.187736437655985	0.340545450104401	0.876182594336569	0.743140260688961	0.837466723285615	0.0150935670826584	0.494878334226087
11	0.000979060772806406	0.439807241782546	0.626514463685453	0.0704199536703527	0.313154702074826	0.350070314481854	0.404403449269012	0.308721815003082	0.576431647641584	0.069539297837764	0.581332214409485	0.835125667275861	0.444976998027414	0.728649721946567	0.550182907609269	0.0309040464926511	0.077445977833122	0.336784617509693	0.491119947517291	0.627216346329078	0.615304162492976	0.590732817072421	0.617980676004663	0.661433363566175	0.592870745575055	...	0.989247507182881	0.606482609407976	0.451556464657187	0.915223909541965	0.344954282743856	0.338236053241417	0.277097255922854	0.0497198225930333	0.376942702569067	0.595391295151785	0.390230144606903	0.746718790847808	0.757885016966611	0.573431629687548	0.305864129215479	0.825817208969966	0.252157980576158	0.574568703304976	0.121699206763878	0.257007281994447	0.412975249113515	0.0925130390096456	0.77945464826189	0.564663758967072	0.506397417979315
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87	0.00848092022351921	0.222356282873079	0.29351053875871	0.197558671468869	0.195558101404458	0.602790989680216	0.813749768771231	0.0774901276454329	0.781322402879596	0.279261991614476	0.869432922219858	0.73684075800702	0.374639065703377	0.972875922452658	0.882515258854255	0.81845522695221	0.406602134695277	0.44088463159278	0.162957599153742	0.6679845105391	0.846003010170534	0.611140089342371	0.988292069174349	0.493441754253581	0.0526971917133778	...	0.718595174606889	0.53255936363712	0.753517876612023	0.421683304477483	0.635499072493985	0.0179416493047029	0.0656900003086776	0.333390511805192	0.292213526787236	0.927701366366819	0.981569889932871	0.747983646579087	0.989795314380899	0.650222146883607	0.508151067653671	0.0499754971824586	0.579670913983136	0.789158394094557	0.0798017394263297	0.0955222737975419	0.31137506198138	0.260038833832368	0.555723301367834	0.164155967300758	0.313900409033522
88	0.00851803249679506	0.529020482907072	0.69007230643183	0.454014534363523	0.219051675405353	0.151521884137765	0.853487001499161	0.639228207524866	0.457068851450458	0.521847967989743	0.126012114342302	0.00032291654497385	0.200706353643909	0.849997863406315	0.325347895501181	0.547759938752279	0.20888876891695	0.753733812365681	0.237308284500614	0.278668432263657	0.854778473963961	0.827688863035291	0.809503264958039	0.319368686992675	0.0587892103940248	...	0.818787793861702	0.62223787419498	0.240042709279805	0.203716139541939	0.297722151502967	0.80036340537481	0.0113077347632498	0.743841422721744	0.373102647485211	0.694920117966831	0.0461184016894549	0.0825039790943265	0.594297927571461	0.439089559949934	0.676635204581544	0.811122391140088	0.808245287509635	0.344671705504879	0.202105782227591	0.277317958185449	0.235388107132167	0.310788674978539	0.488032161723822	0.925330803496763	0.613092022249475
89	0.00885382643900812	0.875200126087293	0.982419854030013	0.204924191115424	0.412543873535469	0.522105760406703	0.576879120664671	0.659603044623509	0.634677389403805	0.624296225141734	0.756125740706921	0.0454057171009481	0.447754234541208	0.376160613261163	0.790130971232429	0.322863636305556	0.686073221731931	0.896026724018157	0.908665336668491	0.536640047095716	0.270938848610967	0.464944191742688	0.95084630465135	0.86090446007438	0.488551297690719	...	0.754612621618435	0.676015567500144	0.637408159906045	0.897257248405367	0.718231537146494	0.535554763395339	0.961939284112304	0.473900581942871	0.81944497092627	0.481163071934134	0.0627267865929753	0.267334070289508	0.941490361699834	0.366474045207724	0.287446354050189	0.698967295698822	0.629469709238037	0.616491289110854	0.992155366810039	0.342704983893782	0.627644948894158	0.679178569233045	0.681971471756697	0.486125104362145	0.789176452672109
90	0.00909327482804656	0.128973981831223	0.585047417320311	0.961765482323244	0.319019561400637	0.869328566361219	0.621508100070059	0.434283448616043	0.785585893085226	0.33180279051885	0.344007721869275	0.596239636885002	0.461911451537162	0.21808414789848	0.723105450393632	0.0964482720009983	0.0366877815686166	0.328646128065884	0.63519585528411	0.00530118169263005	0.624044875847176	0.90113859763369	0.880166816525161	0.594454572768882	0.570577051257715	...	0.137337640160695	0.0659334463998675	0.352443094132468	0.0889705712907016	0.345827830955386	0.112868036841974	0.268827310297638	0.667821195907891	0.180004886118695	0.117464266950265	0.522005806211382	0.341894189361483	0.00285228621214628	0.84081483958289	0.565816243644804	0.611177121289074	0.277431181631982	0.901691494509578	0.782857570797205	0.184909287374467	0.505605682730675	0.624057317618281	0.420271919807419	0.231838702922687	0.267497834283859
91	0.00924704596400261	0.147553584072739	0.508063717279583	0.0280664165038615	0.889239056035876	0.319859089097008	0.501303313067183	0.0080767769832164	0.674704367760569	0.0536098938900977	0.588253404013813	0.600049520842731	0.159225630341098	0.688707916764542	0.0628255733754486	0.260248673148453	0.76441271812655	0.641464220127091	0.852677256334573	0.735715687973425	0.413087195949629	0.151502442779019	0.213725346140563	0.570855492958799	0.79311825032346	...	0.172636086586863	0.813442215090618	0.148976140189916	0.923411395167932	0.975955854170024	0.64114390569739	0.146189942024648	0.191325946012512	0.847241767216474	0.478967381408438	0.7584393681027	0.722657972481102	0.597211310639977	0.230858143419027	0.490496747428551	0.0957626651506871	0.0134728618431836	0.815792707493529	0.428366154665127	0.693225861294195	0.122978422790766	0.157362345606089	0.733569935429841	0.768718275241554	0.226525850361213
92	0.00946435495279729	0.824762779287994	0.34288594732061	0.164894312852994	0.405423355987296	0.85442597954534	0.123315159929916	0.672841741004959	0.128158869920298	0.766058131819591	0.897360787261277	0.830298717366531	0.490808403119445	0.684051705058664	0.0883453129790723	0.157781578367576	0.470300262561068	0.0115521510597318	0.399971244856715	0.860135365277529	0.697626510169357	0.074061332270503	0.414312256034464	0.31688860594295	0.915141411591321	...	0.556628802558407	0.53928406490013	0.822887854417786	0.708682701922953	0.808626908110455	0.460458771325648	0.427666814997792	0.542982555460185	0.522565717576072	0.444793164497241	0.636407538317144	0.241194425150752	0.239957341225818	0.103442708030343	0.990831021452323	0.928504563868046	0.370891367085278	0.689060932258144	0.0426494430284947	0.364038313506171	0.394912627991289	0.0560588038060814	0.233480089111254	0.106471472419798	0.0113272164016962
93	0.00946715474128723	0.975761613575742	0.230816222727299	0.61213770089671	0.46798948594369	0.232269518543035	0.118327436735854	0.562298651784658	0.593704466242343	0.728178257821128	0.131951568415388	0.365087065612897	0.715448807692155	0.73199857538566	0.463408313691616	0.816567642847076	0.126444064313546	0.148065130924806	0.44316343893297	0.0763151512946934	0.284235691884533	0.742862357292324	0.470074087148532	0.522779753897339	0.198329889215529	...	0.0281978989951313	0.326949591748416	0.802360552828759	0.57794566755183	0.867350146872923	0.264165885047987	0.563569794176146	0.297489050077274	0.0122422354761511	0.953188708750531	0.951445504557341	0.316069049062207	0.357745043933392	0.396531745791435	0.979395239148289	0.835883058840409	0.110199433518574	0.42473488394171	0.185772756114602	0.856243706541136	0.916559862205759	0.390161706600338	0.0822050855495036	0.643564132740721	0.935100894887
94	0.00961066340096295	0.227654562797397	0.834150770911947	0.981298056896776	0.899406693177298	0.0208894666284323	0.751334445551038	0.880268483189866	0.545818760758266	0.0479917649645358	0.673765507293865	0.282674903282896	0.281462321989238	0.239131658570841	0.00358489039354026	0.336417121114209	0.0970285041257739	0.514011580962688	0.356756030814722	0.36862086574547	0.122383130481467	0.668651483487338	0.792449047323316	0.65835903887637	0.375716412439942	...	0.89136874768883	0.318790392717347	0.354550857329741	0.748397490475327	0.369620862882584	0.302043829346076	0.298142929794267	0.331247650552541	0.897372888401151	0.626443963265046	0.508510891115293	0.552126254420727	0.368940379237756	0.68098700302653	0.988300702068955	0.56815616087988	0.896302396198735	0.877572525758296	0.00254485569894314	0.556959882844239	0.707723527448252	0.610067375004292	0.393116349820048	0.502664403291419	0.818634700961411
95	0.00962293264456093	0.0935416608117521	0.310996977146715	0.316919288598001	0.848661946831271	0.377989716129377	0.236697892425582	0.152540335198864	0.996203216724098	0.578429543878883	0.933971300721169	0.166959035443142	0.312051014043391	0.521615908714011	0.685657419729978	0.259214965160936	0.653513116994873	0.746125767473131	0.645119206281379	0.993007513927296	0.569327380042523	0.0146142092999071	0.179485923843458	0.717313044937328	0.362865807721391	...	0.488936898065731	0.916940055321902	0.304699439555407	0.65645625651814	0.805591923883185	0.952860370045528	0.235715942457318	0.0355088151991367	0.125833974452689	0.637661880813539	0.870334678096697	0.862889683572575	0.102916307281703	0.513829084811732	0.140632378635928	0.49310958548449	0.331888370681554	0.0984242493286729	0.306810255628079	0.458505577407777	0.629166439175606	0.993265094934031	0.947291647549719	0.816449363017455	0.783945296891034
96	0.00965966377407312	0.857680878834799	0.618562259478495	0.635788084706292	0.635648869909346	0.911934737116098	0.31212760694325	0.241023585898802	0.150937253376469	0.0972512166481465	0.109026144258678	0.531022537266836	0.596848331158981	0.539019796298817	0.661908267764375	0.561519115930423	0.370649161282927	0.250235031126067	0.527783554280177	0.790883158100769	0.615695554530248	0.380817835684866	0.620204798644409	0.558063487056643	0.975162257906049	...	0.583026437321678	0.533838965231553	0.506023583002388	0.596368263708428	0.507013481808826	0.656014763284475	0.0596953327767551	0.938907453790307	0.272512170020491	0.843718450749293	0.52405965141952	0.0485840237233788	0.586388068273664	0.114708211272955	0.633130878675729	0.0397592925000936	0.298787088133395	0.732442778302357	0.353591227671131	0.400159884477034	0.197048142086715	0.343612517695874	0.559787548612803	0.817266907310113	0.783271288964897
97	0.00972658186219633	0.692959072766826	0.0486342338845134	0.783238548086956	0.160770263755694	0.5298073918093	0.93237027246505	0.547930711181834	0.986777379410341	0.698071001563221	0.983585368841887	0.738127237418666	0.520279544172809	0.185567562934011	0.437042931094766	0.607841359684244	0.582788614556193	0.65520309843123	0.464414179790765	0.25547610828653	0.527615664992481	0.984283751808107	0.738952532177791	0.139970470918342	0.719137626932934	...	0.760172463487834	0.555020049447194	0.204106678720564	0.322754994500428	0.310017777141184	0.785715121775866	0.269620047416538	0.663912531454116	0.853543356060982	0.66764345834963	0.801393907051533	0.631188481813297	0.67289001098834	0.914906073594466	0.457231434527785	0.346458432497457	0.397624600445852	0.631416477728635	0.827926503727213	0.968088918132707	0.511030979687348	0.577628522878513	0.19390199938789	0.255057224771008	0.899868222884834
98	0.00985606969334185	0.667693499941379	0.931646558223292	0.689672989305109	0.200394773622975	0.0672223132569343	0.0913151344284415	0.895141348708421	0.44139987975359	0.723404487362131	0.0973884623963386	0.148741463664919	0.447361073922366	0.896779636386782	0.003855150192976	0.756954731652513	0.714324184227735	0.989547198405489	0.13273108471185	0.657627671724185	0.316266902722418	0.653663638746366	0.49544447963126	0.190211180597544	0.520473257405683	...	0.569248368730769	0.677836274728179	0.450819859048352	0.723862473387271	0.863013072870672	0.509118453366682	0.218852573307231	0.0714990610722452	0.0981308096088469	0.045087018981576	0.289558774093166	0.0309762973338366	0.118349929805845	0.220141991274431	0.955611336510628	0.939826196292415	0.115448526805267	0.951977925375104	0.854286523535848	0.097131050657481	0.749721223954111	0.950510227121413	0.569369619945064	0.397779510123655	0.0303781612310559
99	0.0101318615488708	0.662880232790485	0.122057115659118	0.740471428958699	0.935664381599054	0.679875571746379	0.338068253593519	0.686561811482534	0.469989400822669	0.179946760646999	0.633446159306914	0.579075969057158	0.173217358300462	0.860771061154082	0.00376510107889771	0.756900643231347	0.759280733531341	0.606717823306099	0.715507024899125	0.633010727819055	0.417071977863088	0.579058602685109	0.271589594660327	0.165139456978068	0.637616327730939	...	0.219777278136462	0.975613878574222	0.000115433475002646	0.00662260083481669	0.361153847305104	0.179624821292236	0.242538495222107	0.540843163384125	0.976923468289897	0.980032498715445	0.316799078835174	0.169800930656493	0.286071908427402	0.0811776891350746	0.487389508867636	0.979992950335145	0.116272666025907	0.83669591974467	0.928404540289193	0.711673312820494	0.397445381619036	0.7378370387014	0.207517934497446	0.922513144090772	0.870160201331601
100	0.0103061466943473	0.110453602625057	0.552420568885282	0.672776134684682	0.18031755881384	0.053813717328012	0.710754769854248	0.579055867623538	0.691381671698764	0.727991050574929	0.553623089566827	0.0668222503736615	0.950091493781656	0.182638361817226	0.665211591869593	0.793377450900152	0.717621367191896	0.508317811880261	0.016812197631225	0.170683601871133	0.164249985478818	0.685464148642495	0.889310171129182	0.00224538566544652	0.157580882310867	...	0.384168792981654	0.280128390528262	0.537432349519804	0.333254691911861	0.0851389255840331	0.645773645956069	0.0350734158419073	0.367705639451742	0.442660799715668	0.0404679160565138	0.483725734753534	0.549227622337639	0.31340526137501	0.477974840672687	0.135994026437402	0.0823690549004823	0.258681894512847	0.838722283951938	0.218102322425693	0.89637154713273	0.509305321378633	0.474014234496281	0.212072994327173	0.482526635052636	0.977601603372023

Rows: 1-100 | Columns: 600

To see what is happening when you compute aggregations, turn on the SQL code generation and turn off the cache.

In [58]:

vp.set_option("sql_on", True)
vp.set_option("cache", False)

When you compute aggregations, VerticaPy allows you to send multiple queries iteratively or at the same time. To see this in action, let's compute the average of each dataset column.

You can see that sending one big query is quite resource expensive:

In [55]:

display(vdf.avg(ncols_block = 600))

Computing the different aggregations.

SELECT
AVG("x0"),
AVG("x1"),
AVG("x2"),
AVG("x3"),
AVG("x4"),
AVG("x5"),
AVG("x6"),
AVG("x7"),
AVG("x8"),
AVG("x9"),
AVG("x10"),
AVG("x11"),
AVG("x12"),
AVG("x13"),
AVG("x14"),
AVG("x15"),
AVG("x16"),
AVG("x17"),
AVG("x18"),
AVG("x19"),
AVG("x20"),
AVG("x21"),
AVG("x22"),
AVG("x23"),
AVG("x24"),
AVG("x25"),
AVG("x26"),
AVG("x27"),
AVG("x28"),
AVG("x29"),
AVG("x30"),
AVG("x31"),
AVG("x32"),
AVG("x33"),
AVG("x34"),
AVG("x35"),
AVG("x36"),
AVG("x37"),
AVG("x38"),
AVG("x39"),
AVG("x40"),
AVG("x41"),
AVG("x42"),
AVG("x43"),
AVG("x44"),
AVG("x45"),
AVG("x46"),
AVG("x47"),
AVG("x48"),
AVG("x49"),
AVG("x50"),
AVG("x51"),
AVG("x52"),
AVG("x53"),
AVG("x54"),
AVG("x55"),
AVG("x56"),
AVG("x57"),
AVG("x58"),
AVG("x59"),
AVG("x60"),
AVG("x61"),
AVG("x62"),
AVG("x63"),
AVG("x64"),
AVG("x65"),
AVG("x66"),
AVG("x67"),
AVG("x68"),
AVG("x69"),
AVG("x70"),
AVG("x71"),
AVG("x72"),
AVG("x73"),
AVG("x74"),
AVG("x75"),
AVG("x76"),
AVG("x77"),
AVG("x78"),
AVG("x79"),
AVG("x80"),
AVG("x81"),
AVG("x82"),
AVG("x83"),
AVG("x84"),
AVG("x85"),
AVG("x86"),
AVG("x87"),
AVG("x88"),
AVG("x89"),
AVG("x90"),
AVG("x91"),
AVG("x92"),
AVG("x93"),
AVG("x94"),
AVG("x95"),
AVG("x96"),
AVG("x97"),
AVG("x98"),
AVG("x99"),
AVG("x100"),
AVG("x101"),
AVG("x102"),
AVG("x103"),
AVG("x104"),
AVG("x105"),
AVG("x106"),
AVG("x107"),
AVG("x108"),
AVG("x109"),
AVG("x110"),
AVG("x111"),
AVG("x112"),
AVG("x113"),
AVG("x114"),
AVG("x115"),
AVG("x116"),
AVG("x117"),
AVG("x118"),
AVG("x119"),
AVG("x120"),
AVG("x121"),
AVG("x122"),
AVG("x123"),
AVG("x124"),
AVG("x125"),
AVG("x126"),
AVG("x127"),
AVG("x128"),
AVG("x129"),
AVG("x130"),
AVG("x131"),
AVG("x132"),
AVG("x133"),
AVG("x134"),
AVG("x135"),
AVG("x136"),
AVG("x137"),
AVG("x138"),
AVG("x139"),
AVG("x140"),
AVG("x141"),
AVG("x142"),
AVG("x143"),
AVG("x144"),
AVG("x145"),
AVG("x146"),
AVG("x147"),
AVG("x148"),
AVG("x149"),
AVG("x150"),
AVG("x151"),
AVG("x152"),
AVG("x153"),
AVG("x154"),
AVG("x155"),
AVG("x156"),
AVG("x157"),
AVG("x158"),
AVG("x159"),
AVG("x160"),
AVG("x161"),
AVG("x162"),
AVG("x163"),
AVG("x164"),
AVG("x165"),
AVG("x166"),
AVG("x167"),
AVG("x168"),
AVG("x169"),
AVG("x170"),
AVG("x171"),
AVG("x172"),
AVG("x173"),
AVG("x174"),
AVG("x175"),
AVG("x176"),
AVG("x177"),
AVG("x178"),
AVG("x179"),
AVG("x180"),
AVG("x181"),
AVG("x182"),
AVG("x183"),
AVG("x184"),
AVG("x185"),
AVG("x186"),
AVG("x187"),
AVG("x188"),
AVG("x189"),
AVG("x190"),
AVG("x191"),
AVG("x192"),
AVG("x193"),
AVG("x194"),
AVG("x195"),
AVG("x196"),
AVG("x197"),
AVG("x198"),
AVG("x199"),
AVG("x200"),
AVG("x201"),
AVG("x202"),
AVG("x203"),
AVG("x204"),
AVG("x205"),
AVG("x206"),
AVG("x207"),
AVG("x208"),
AVG("x209"),
AVG("x210"),
AVG("x211"),
AVG("x212"),
AVG("x213"),
AVG("x214"),
AVG("x215"),
AVG("x216"),
AVG("x217"),
AVG("x218"),
AVG("x219"),
AVG("x220"),
AVG("x221"),
AVG("x222"),
AVG("x223"),
AVG("x224"),
AVG("x225"),
AVG("x226"),
AVG("x227"),
AVG("x228"),
AVG("x229"),
AVG("x230"),
AVG("x231"),
AVG("x232"),
AVG("x233"),
AVG("x234"),
AVG("x235"),
AVG("x236"),
AVG("x237"),
AVG("x238"),
AVG("x239"),
AVG("x240"),
AVG("x241"),
AVG("x242"),
AVG("x243"),
AVG("x244"),
AVG("x245"),
AVG("x246"),
AVG("x247"),
AVG("x248"),
AVG("x249"),
AVG("x250"),
AVG("x251"),
AVG("x252"),
AVG("x253"),
AVG("x254"),
AVG("x255"),
AVG("x256"),
AVG("x257"),
AVG("x258"),
AVG("x259"),
AVG("x260"),
AVG("x261"),
AVG("x262"),
AVG("x263"),
AVG("x264"),
AVG("x265"),
AVG("x266"),
AVG("x267"),
AVG("x268"),
AVG("x269"),
AVG("x270"),
AVG("x271"),
AVG("x272"),
AVG("x273"),
AVG("x274"),
AVG("x275"),
AVG("x276"),
AVG("x277"),
AVG("x278"),
AVG("x279"),
AVG("x280"),
AVG("x281"),
AVG("x282"),
AVG("x283"),
AVG("x284"),
AVG("x285"),
AVG("x286"),
AVG("x287"),
AVG("x288"),
AVG("x289"),
AVG("x290"),
AVG("x291"),
AVG("x292"),
AVG("x293"),
AVG("x294"),
AVG("x295"),
AVG("x296"),
AVG("x297"),
AVG("x298"),
AVG("x299"),
AVG("x300"),
AVG("x301"),
AVG("x302"),
AVG("x303"),
AVG("x304"),
AVG("x305"),
AVG("x306"),
AVG("x307"),
AVG("x308"),
AVG("x309"),
AVG("x310"),
AVG("x311"),
AVG("x312"),
AVG("x313"),
AVG("x314"),
AVG("x315"),
AVG("x316"),
AVG("x317"),
AVG("x318"),
AVG("x319"),
AVG("x320"),
AVG("x321"),
AVG("x322"),
AVG("x323"),
AVG("x324"),
AVG("x325"),
AVG("x326"),
AVG("x327"),
AVG("x328"),
AVG("x329"),
AVG("x330"),
AVG("x331"),
AVG("x332"),
AVG("x333"),
AVG("x334"),
AVG("x335"),
AVG("x336"),
AVG("x337"),
AVG("x338"),
AVG("x339"),
AVG("x340"),
AVG("x341"),
AVG("x342"),
AVG("x343"),
AVG("x344"),
AVG("x345"),
AVG("x346"),
AVG("x347"),
AVG("x348"),
AVG("x349"),
AVG("x350"),
AVG("x351"),
AVG("x352"),
AVG("x353"),
AVG("x354"),
AVG("x355"),
AVG("x356"),
AVG("x357"),
AVG("x358"),
AVG("x359"),
AVG("x360"),
AVG("x361"),
AVG("x362"),
AVG("x363"),
AVG("x364"),
AVG("x365"),
AVG("x366"),
AVG("x367"),
AVG("x368"),
AVG("x369"),
AVG("x370"),
AVG("x371"),
AVG("x372"),
AVG("x373"),
AVG("x374"),
AVG("x375"),
AVG("x376"),
AVG("x377"),
AVG("x378"),
AVG("x379"),
AVG("x380"),
AVG("x381"),
AVG("x382"),
AVG("x383"),
AVG("x384"),
AVG("x385"),
AVG("x386"),
AVG("x387"),
AVG("x388"),
AVG("x389"),
AVG("x390"),
AVG("x391"),
AVG("x392"),
AVG("x393"),
AVG("x394"),
AVG("x395"),
AVG("x396"),
AVG("x397"),
AVG("x398"),
AVG("x399"),
AVG("x400"),
AVG("x401"),
AVG("x402"),
AVG("x403"),
AVG("x404"),
AVG("x405"),
AVG("x406"),
AVG("x407"),
AVG("x408"),
AVG("x409"),
AVG("x410"),
AVG("x411"),
AVG("x412"),
AVG("x413"),
AVG("x414"),
AVG("x415"),
AVG("x416"),
AVG("x417"),
AVG("x418"),
AVG("x419"),
AVG("x420"),
AVG("x421"),
AVG("x422"),
AVG("x423"),
AVG("x424"),
AVG("x425"),
AVG("x426"),
AVG("x427"),
AVG("x428"),
AVG("x429"),
AVG("x430"),
AVG("x431"),
AVG("x432"),
AVG("x433"),
AVG("x434"),
AVG("x435"),
AVG("x436"),
AVG("x437"),
AVG("x438"),
AVG("x439"),
AVG("x440"),
AVG("x441"),
AVG("x442"),
AVG("x443"),
AVG("x444"),
AVG("x445"),
AVG("x446"),
AVG("x447"),
AVG("x448"),
AVG("x449"),
AVG("x450"),
AVG("x451"),
AVG("x452"),
AVG("x453"),
AVG("x454"),
AVG("x455"),
AVG("x456"),
AVG("x457"),
AVG("x458"),
AVG("x459"),
AVG("x460"),
AVG("x461"),
AVG("x462"),
AVG("x463"),
AVG("x464"),
AVG("x465"),
AVG("x466"),
AVG("x467"),
AVG("x468"),
AVG("x469"),
AVG("x470"),
AVG("x471"),
AVG("x472"),
AVG("x473"),
AVG("x474"),
AVG("x475"),
AVG("x476"),
AVG("x477"),
AVG("x478"),
AVG("x479"),
AVG("x480"),
AVG("x481"),
AVG("x482"),
AVG("x483"),
AVG("x484"),
AVG("x485"),
AVG("x486"),
AVG("x487"),
AVG("x488"),
AVG("x489"),
AVG("x490"),
AVG("x491"),
AVG("x492"),
AVG("x493"),
AVG("x494"),
AVG("x495"),
AVG("x496"),
AVG("x497"),
AVG("x498"),
AVG("x499"),
AVG("x500"),
AVG("x501"),
AVG("x502"),
AVG("x503"),
AVG("x504"),
AVG("x505"),
AVG("x506"),
AVG("x507"),
AVG("x508"),
AVG("x509"),
AVG("x510"),
AVG("x511"),
AVG("x512"),
AVG("x513"),
AVG("x514"),
AVG("x515"),
AVG("x516"),
AVG("x517"),
AVG("x518"),
AVG("x519"),
AVG("x520"),
AVG("x521"),
AVG("x522"),
AVG("x523"),
AVG("x524"),
AVG("x525"),
AVG("x526"),
AVG("x527"),
AVG("x528"),
AVG("x529"),
AVG("x530"),
AVG("x531"),
AVG("x532"),
AVG("x533"),
AVG("x534"),
AVG("x535"),
AVG("x536"),
AVG("x537"),
AVG("x538"),
AVG("x539"),
AVG("x540"),
AVG("x541"),
AVG("x542"),
AVG("x543"),
AVG("x544"),
AVG("x545"),
AVG("x546"),
AVG("x547"),
AVG("x548"),
AVG("x549"),
AVG("x550"),
AVG("x551"),
AVG("x552"),
AVG("x553"),
AVG("x554"),
AVG("x555"),
AVG("x556"),
AVG("x557"),
AVG("x558"),
AVG("x559"),
AVG("x560"),
AVG("x561"),
AVG("x562"),
AVG("x563"),
AVG("x564"),
AVG("x565"),
AVG("x566"),
AVG("x567"),
AVG("x568"),
AVG("x569"),
AVG("x570"),
AVG("x571"),
AVG("x572"),
AVG("x573"),
AVG("x574"),
AVG("x575"),
AVG("x576"),
AVG("x577"),
AVG("x578"),
AVG("x579"),
AVG("x580"),
AVG("x581"),
AVG("x582"),
AVG("x583"),
AVG("x584"),
AVG("x585"),
AVG("x586"),
AVG("x587"),
AVG("x588"),
AVG("x589"),
AVG("x590"),
AVG("x591"),
AVG("x592"),
AVG("x593"),
AVG("x594"),
AVG("x595"),
AVG("x596"),
AVG("x597"),
AVG("x598"),
AVG("x599")
FROM
"public"."test_dataset" LIMIT 1

	avg
"x0"	0.495328640506021
"x1"	0.498410496117291
"x2"	0.499796020665369
"x3"	0.500288217322272
"x4"	0.501549792736745
"x5"	0.502030357531738
"x6"	0.498203794788383
"x7"	0.499528634219361
"x8"	0.498655800491967
"x9"	0.501991024077614
"x10"	0.499470449560788
"x11"	0.503527408929076
"x12"	0.494434265249921
"x13"	0.500999432951491
"x14"	0.499102837571758
"x15"	0.499115148377954
"x16"	0.498147533271462
"x17"	0.503055292108236
"x18"	0.498873396961717
"x19"	0.500234201267036
"x20"	0.500846420762525
"x21"	0.497544945688546
"x22"	0.499816283349297
"x23"	0.49421561504358
"x24"	0.498501294320636
"x25"	0.49881612469398
"x26"	0.500256177358888
"x27"	0.497643181022117
"x28"	0.497196014247974
"x29"	0.496317569292686
"x30"	0.499120011079405
"x31"	0.504665919276001
"x32"	0.497500529544335
"x33"	0.501751783046429
"x34"	0.495941181869968
"x35"	0.493580291184946
"x36"	0.502128273874149
"x37"	0.500820784376212
"x38"	0.498700643126364
"x39"	0.504414001272083
"x40"	0.50297581018405
"x41"	0.498589111867477
"x42"	0.493329039907991
"x43"	0.495365951688774
"x44"	0.501398265630705
"x45"	0.497031733239675
"x46"	0.49858817340259
"x47"	0.499666033335752
"x48"	0.497840583433164
"x49"	0.497329473723052
"x50"	0.49669896126797
"x51"	0.496988497646665
"x52"	0.495283583330153
"x53"	0.503541071587382
"x54"	0.502496378843393
"x55"	0.506106085637049
"x56"	0.503235346138873
"x57"	0.502128457865585
"x58"	0.501718418996432
"x59"	0.501931357378722
"x60"	0.492535700597335
"x61"	0.496213648770773
"x62"	0.501711242381227
"x63"	0.49593384194742
"x64"	0.501410126678646
"x65"	0.496181363479933
"x66"	0.502847261195513
"x67"	0.499017013058043
"x68"	0.500901301923464
"x69"	0.502686372651509
"x70"	0.503725287213479
"x71"	0.49858107758367
"x72"	0.500332314733882
"x73"	0.503799533855729
"x74"	0.497731596026267
"x75"	0.500254210987454
"x76"	0.503705967230629
"x77"	0.497663569217268
"x78"	0.498292489934526
"x79"	0.504205192289595
"x80"	0.501272203026921
"x81"	0.498586322333827
"x82"	0.501165568577242
"x83"	0.507249641807377
"x84"	0.499038335806946
"x85"	0.500589053839631
"x86"	0.504052388792532
"x87"	0.497889207095001
"x88"	0.500446273626364
"x89"	0.499201328990585
"x90"	0.502505042524263
"x91"	0.498666686973721
"x92"	0.4980780772798
"x93"	0.498206184870098
"x94"	0.501078245462058
"x95"	0.502400306417863
"x96"	0.496301139098872
"x97"	0.499954794859467
"x98"	0.495530540163047
"x99"	0.499518119273777
"x100"	0.502472493177536
"x101"	0.504902573059266
"x102"	0.498767855446343
"x103"	0.498763861700776
"x104"	0.494740242705843
"x105"	0.50120537653435
"x106"	0.500558270047256
"x107"	0.501785635916004
"x108"	0.500023144892836
"x109"	0.500689867577143
"x110"	0.500623461510986
"x111"	0.502040224120254
"x112"	0.49922121064926
"x113"	0.499335735470266
"x114"	0.502046788542392
"x115"	0.498266887489939
"x116"	0.502299062704365
"x117"	0.499504777825647
"x118"	0.498147775378544
"x119"	0.500105008480838
"x120"	0.500926059096353
"x121"	0.498799051559274
"x122"	0.498231966562965
"x123"	0.502014504623017
"x124"	0.496966086774343
"x125"	0.497726782605075
"x126"	0.501800164589402
"x127"	0.504625166970142
"x128"	0.494857996223634
"x129"	0.504154442140413
"x130"	0.495419377293601
"x131"	0.495755362548213
"x132"	0.499762857829197
"x133"	0.498344265133003
"x134"	0.504667159711616
"x135"	0.500426490560034
"x136"	0.495148637985485
"x137"	0.499247576732608
"x138"	0.500246977145318
"x139"	0.498025120325852
"x140"	0.49665202575603
"x141"	0.50157614965036
"x142"	0.494219642718253
"x143"	0.501118474691175
"x144"	0.498214634110243
"x145"	0.497676468651718
"x146"	0.4999434025286
"x147"	0.50381975648317
"x148"	0.499791994529241
"x149"	0.501425083181378
"x150"	0.503124807463679
"x151"	0.503843662469694
"x152"	0.499438914951705
"x153"	0.499690357795637
"x154"	0.492923485315032
"x155"	0.498921156159765
"x156"	0.505832323109452
"x157"	0.50427568726968
"x158"	0.501047079447447
"x159"	0.501144096319587
"x160"	0.500279258508724
"x161"	0.501948064998072
"x162"	0.500822011614218
"x163"	0.497227232640726
"x164"	0.495996041991841
"x165"	0.501643313851114
"x166"	0.493981937356992
"x167"	0.501158596251789
"x168"	0.492721518449881
"x169"	0.502087166458927
"x170"	0.496132122005289
"x171"	0.50004740234639
"x172"	0.507995243759337
"x173"	0.495010604015226
"x174"	0.504332425329136
"x175"	0.500722138965363
"x176"	0.501477807205217
"x177"	0.503222832425241
"x178"	0.496611627437011
"x179"	0.501386782278749
"x180"	0.496568095402792
"x181"	0.49866127618046
"x182"	0.499939231952606
"x183"	0.49839989884852
"x184"	0.49844872907605
"x185"	0.499527396393754
"x186"	0.505675627022749
"x187"	0.502176277364884
"x188"	0.50282409973233
"x189"	0.501910180421011
"x190"	0.499091349298926
"x191"	0.503729833525093
"x192"	0.501052054715296
"x193"	0.497996389698493
"x194"	0.501086532175215
"x195"	0.497719087828533
"x196"	0.50029780225656
"x197"	0.497283518747846
"x198"	0.50029253732739
"x199"	0.503720246048248
"x200"	0.50173565008766
"x201"	0.505880159354582
"x202"	0.495958490648027
"x203"	0.501755221227929
"x204"	0.501195751817548
"x205"	0.502214450883982
"x206"	0.500531362394965
"x207"	0.50205923199188
"x208"	0.50295131285931
"x209"	0.502243809388275
"x210"	0.507529762405949
"x211"	0.504731268971018
"x212"	0.496389128514612
"x213"	0.501199241986056
"x214"	0.49963519236967
"x215"	0.503726938822726
"x216"	0.500616870596819
"x217"	0.499451167386491
"x218"	0.505073839902342
"x219"	0.495555917869695
"x220"	0.503535190937086
"x221"	0.500010514657409
"x222"	0.501485859632539
"x223"	0.501452482858882
"x224"	0.496859017808642
"x225"	0.499720605799975
"x226"	0.49902702385562
"x227"	0.493869393159961
"x228"	0.500975813526777
"x229"	0.503612502819882
"x230"	0.500296765768202
"x231"	0.501570096647646
"x232"	0.501653867180506
"x233"	0.495997469444037
"x234"	0.500007144648512
"x235"	0.501950742057967
"x236"	0.501136892649159
"x237"	0.497207203832921
"x238"	0.497366881357762
"x239"	0.503939381418563
"x240"	0.500307605826342
"x241"	0.500278400719655
"x242"	0.496130419079564
"x243"	0.498876399969449
"x244"	0.498236415238259
"x245"	0.505986941487947
"x246"	0.499224769213772
"x247"	0.501577037782432
"x248"	0.502718629168347
"x249"	0.497431639110297
"x250"	0.497242679194268
"x251"	0.500110229029763
"x252"	0.499876112394035
"x253"	0.500209626919613
"x254"	0.498006003626087
"x255"	0.501028609297518
"x256"	0.497476164073031
"x257"	0.49678638799903
"x258"	0.503028783134325
"x259"	0.500678621253278
"x260"	0.501899834921793
"x261"	0.49799142391663
"x262"	0.502971393630072
"x263"	0.503649817536864
"x264"	0.501297247359576
"x265"	0.498187974785664
"x266"	0.500154159696493
"x267"	0.498174033115013
"x268"	0.502259716476523
"x269"	0.500892450533505
"x270"	0.49974688360109
"x271"	0.503252161878953
"x272"	0.500539697517548
"x273"	0.504436443216214
"x274"	0.502837233404955
"x275"	0.500573441805621
"x276"	0.502325369855715
"x277"	0.502028237938345
"x278"	0.499332443716889
"x279"	0.502182460590545
"x280"	0.496923384860391
"x281"	0.502459710514196
"x282"	0.494431597842555
"x283"	0.498645268598385
"x284"	0.496526186001836
"x285"	0.500086312099337
"x286"	0.501093746714317
"x287"	0.499139699267014
"x288"	0.496876103397319
"x289"	0.501409091628436
"x290"	0.49791826228518
"x291"	0.498047744078701
"x292"	0.503011588848778
"x293"	0.494279091265984
"x294"	0.50145187503472
"x295"	0.500172946690372
"x296"	0.497979902947624
"x297"	0.492805902662501
"x298"	0.499126493594469
"x299"	0.498135944902035
"x300"	0.497372175921849
"x301"	0.499937077994528
"x302"	0.498308722825442
"x303"	0.49533371287086
"x304"	0.498127515137498
"x305"	0.498703532381309
"x306"	0.499237531865598
"x307"	0.497270844759117
"x308"	0.498935120696947
"x309"	0.505050902428967
"x310"	0.494586903066631
"x311"	0.501683675867925
"x312"	0.498886407341203
"x313"	0.502651175533538
"x314"	0.502610984513233
"x315"	0.499020783808059
"x316"	0.497362622722238
"x317"	0.497419736533822
"x318"	0.499328824524721
"x319"	0.499461412384966
"x320"	0.496635588650592
"x321"	0.50164939600958
"x322"	0.495475489443797
"x323"	0.504439370975946
"x324"	0.492955837118463
"x325"	0.50213546285443
"x326"	0.501837605465343
"x327"	0.505604026965867
"x328"	0.501586500711599
"x329"	0.495532248499035
"x330"	0.497800878073298
"x331"	0.499882971630897
"x332"	0.500076033881283
"x333"	0.495062761539174
"x334"	0.496449061025702
"x335"	0.504990343881771
"x336"	0.500045601881808
"x337"	0.501849232240906
"x338"	0.496911254709517
"x339"	0.504145932986075
"x340"	0.498204576935223
"x341"	0.499988833210897
"x342"	0.501985481519345
"x343"	0.501642016830575
"x344"	0.50208484286801
"x345"	0.501863940183376
"x346"	0.504740945380134
"x347"	0.494465954249515
"x348"	0.501264774960466
"x349"	0.503402628929587
"x350"	0.497616449450352
"x351"	0.498765651370934
"x352"	0.4989254358629
"x353"	0.50183583012505
"x354"	0.496825986894267
"x355"	0.498098364283633
"x356"	0.501767813966167
"x357"	0.498895416097227
"x358"	0.501967324887938
"x359"	0.502795286852052
"x360"	0.499872073752759
"x361"	0.498643346011383
"x362"	0.495098243173491
"x363"	0.498941021885141
"x364"	0.502219445838453
"x365"	0.502615964374412
"x366"	0.505264188127546
"x367"	0.500322223420395
"x368"	0.496345346435811
"x369"	0.504413213725598
"x370"	0.500707079809252
"x371"	0.497611740602506
"x372"	0.496615198885486
"x373"	0.500618592508254
"x374"	0.496569180697575
"x375"	0.504701023442182
"x376"	0.500255297750491
"x377"	0.498582437623548
"x378"	0.501286069167382
"x379"	0.503165722717438
"x380"	0.500378197866748
"x381"	0.503250720378035
"x382"	0.497218099547946
"x383"	0.501221833311347
"x384"	0.498849412409216
"x385"	0.501107512276433
"x386"	0.50047080468561
"x387"	0.498652378063696
"x388"	0.500216451354371
"x389"	0.504704176720278
"x390"	0.496354370877845
"x391"	0.503721972145187
"x392"	0.497143805629993
"x393"	0.50330190141215
"x394"	0.49937959353358
"x395"	0.498948919450445
"x396"	0.500168919996172
"x397"	0.502578267350467
"x398"	0.506101156181982
"x399"	0.503158229905623
"x400"	0.498680288048205
"x401"	0.501676260473486
"x402"	0.493510792666185
"x403"	0.499031297577242
"x404"	0.506920870206016
"x405"	0.502246720495657
"x406"	0.498294070439227
"x407"	0.501426641905122
"x408"	0.500076049703243
"x409"	0.498691384791536
"x410"	0.498929796277476
"x411"	0.501708653254574
"x412"	0.497889006545767
"x413"	0.504986315847584
"x414"	0.50249145781952
"x415"	0.498517542092968
"x416"	0.502209066824149
"x417"	0.50141426213542
"x418"	0.499224397290451
"x419"	0.502355230133631
"x420"	0.495981889346498
"x421"	0.498214948748122
"x422"	0.503087869197689
"x423"	0.503969026273629
"x424"	0.502452665825631
"x425"	0.497048816487961
"x426"	0.495624019407202
"x427"	0.499317652632738
"x428"	0.500914652247867
"x429"	0.499057770106616
"x430"	0.501268080979818
"x431"	0.498147857934493
"x432"	0.499914153201319
"x433"	0.495786726674158
"x434"	0.501682487738878
"x435"	0.496457202862645
"x436"	0.508543896326539
"x437"	0.501061310318136
"x438"	0.502894218938588
"x439"	0.502274609185918
"x440"	0.502498467199877
"x441"	0.501282249764865
"x442"	0.497290669186227
"x443"	0.501401792088733
"x444"	0.501240313977888
"x445"	0.497915219181264
"x446"	0.505064357985207
"x447"	0.502044832450291
"x448"	0.501214603475481
"x449"	0.500469672076427
"x450"	0.499569192343717
"x451"	0.504505589536461
"x452"	0.502298843947589
"x453"	0.497023923462909
"x454"	0.505475943069602
"x455"	0.495570238801069
"x456"	0.502516490759049
"x457"	0.50001143220698
"x458"	0.50056624143261
"x459"	0.500763978589862
"x460"	0.503351329483651
"x461"	0.495937412253953
"x462"	0.502993626758666
"x463"	0.50376565954613
"x464"	0.503578705062647
"x465"	0.499874845132907
"x466"	0.49710460913037
"x467"	0.500236035677022
"x468"	0.504071040146146
"x469"	0.502116545564379
"x470"	0.4970413170774
"x471"	0.497961502568331
"x472"	0.498185959877749
"x473"	0.500172177500324
"x474"	0.49990327054779
"x475"	0.505754457844445
"x476"	0.506156544381194
"x477"	0.502520981100039
"x478"	0.498271713060327
"x479"	0.499427241228428
"x480"	0.501416352765658
"x481"	0.497885700659943
"x482"	0.496553136685677
"x483"	0.499256669682357
"x484"	0.499976025144919
"x485"	0.502863525254768
"x486"	0.502828509843699
"x487"	0.497056779966387
"x488"	0.498057491645007
"x489"	0.499423123476515
"x490"	0.495550263123354
"x491"	0.496354107240075
"x492"	0.500804190374236
"x493"	0.502665761463973
"x494"	0.49879279964203
"x495"	0.50084074535016
"x496"	0.49858958128395
"x497"	0.498756470166892
"x498"	0.49870276355634
"x499"	0.49877461641808
"x500"	0.495528407560149
"x501"	0.499308988225553
"x502"	0.496989637391805
"x503"	0.497052493863879
"x504"	0.502207657987811
"x505"	0.497782883539819
"x506"	0.499157336530695
"x507"	0.499570190191874
"x508"	0.501303651056089
"x509"	0.501275910730497
"x510"	0.50475483049925
"x511"	0.49744029519686
"x512"	0.50021479274095
"x513"	0.502164487606823
"x514"	0.502737027523154
"x515"	0.505453019243362
"x516"	0.505072508157813
"x517"	0.497371969039645
"x518"	0.499832538922224
"x519"	0.500174262844981
"x520"	0.502035293567949
"x521"	0.49500378929202
"x522"	0.494185061220149
"x523"	0.497007539583836
"x524"	0.500462249073479
"x525"	0.501112031996949
"x526"	0.50448007855895
"x527"	0.500180586358835
"x528"	0.502937457214855
"x529"	0.495785512156505
"x530"	0.496734989838139
"x531"	0.49939898383636
"x532"	0.498502123921341
"x533"	0.504866269736877
"x534"	0.494965157273132
"x535"	0.495690308914194
"x536"	0.498892336926609
"x537"	0.501628264661017
"x538"	0.503607030711207
"x539"	0.498502264841949
"x540"	0.497903406035737
"x541"	0.49658684479869
"x542"	0.501395893062139
"x543"	0.499979672330362
"x544"	0.499520966207422
"x545"	0.496572526952415
"x546"	0.499305491672084
"x547"	0.49969866590458
"x548"	0.503476167464792
"x549"	0.495446928177774
"x550"	0.505830715886923
"x551"	0.495616547828494
"x552"	0.502230194019736
"x553"	0.503146120540239
"x554"	0.500894720273721
"x555"	0.500924644357059
"x556"	0.499140572330775
"x557"	0.500774686140707
"x558"	0.498897955308785
"x559"	0.50650362433407
"x560"	0.503777572448854
"x561"	0.4953408519377
"x562"	0.501732075663935
"x563"	0.501186506923474
"x564"	0.497129871141445
"x565"	0.506832370619941
"x566"	0.502918937394116
"x567"	0.497088979613851
"x568"	0.495986605457426
"x569"	0.502320092254644
"x570"	0.494138077245164
"x571"	0.497765557831619
"x572"	0.499874999663862
"x573"	0.497969851170666
"x574"	0.502105102461041
"x575"	0.498697688769549
"x576"	0.495100045574782
"x577"	0.502766133953678
"x578"	0.496227313589025
"x579"	0.49932494115436
"x580"	0.501537018946954
"x581"	0.495884116988792
"x582"	0.499084989017015
"x583"	0.494322689776961
"x584"	0.501757184752147
"x585"	0.504229674753267
"x586"	0.497673269462539
"x587"	0.503275601157756
"x588"	0.498003026509518
"x589"	0.497905468545761
"x590"	0.498104806767451
"x591"	0.497433767945156
"x592"	0.49946368073097
"x593"	0.502132979064411
"x594"	0.503633827587846
"x595"	0.497655095852725
"x596"	0.495839959227457
"x597"	0.502938682934688
"x598"	0.500326397570572
"x599"	0.501134085538425

Rows: 1-600 | Columns: 2

By using blocks of 100 columns, you reduce your impact on the system.

In [56]:

display(vdf.avg(ncols_block = 100))

Computing the different aggregations.

SELECT
AVG("x0"),
AVG("x1"),
AVG("x2"),
AVG("x3"),
AVG("x4"),
AVG("x5"),
AVG("x6"),
AVG("x7"),
AVG("x8"),
AVG("x9"),
AVG("x10"),
AVG("x11"),
AVG("x12"),
AVG("x13"),
AVG("x14"),
AVG("x15"),
AVG("x16"),
AVG("x17"),
AVG("x18"),
AVG("x19"),
AVG("x20"),
AVG("x21"),
AVG("x22"),
AVG("x23"),
AVG("x24"),
AVG("x25"),
AVG("x26"),
AVG("x27"),
AVG("x28"),
AVG("x29"),
AVG("x30"),
AVG("x31"),
AVG("x32"),
AVG("x33"),
AVG("x34"),
AVG("x35"),
AVG("x36"),
AVG("x37"),
AVG("x38"),
AVG("x39"),
AVG("x40"),
AVG("x41"),
AVG("x42"),
AVG("x43"),
AVG("x44"),
AVG("x45"),
AVG("x46"),
AVG("x47"),
AVG("x48"),
AVG("x49"),
AVG("x50"),
AVG("x51"),
AVG("x52"),
AVG("x53"),
AVG("x54"),
AVG("x55"),
AVG("x56"),
AVG("x57"),
AVG("x58"),
AVG("x59"),
AVG("x60"),
AVG("x61"),
AVG("x62"),
AVG("x63"),
AVG("x64"),
AVG("x65"),
AVG("x66"),
AVG("x67"),
AVG("x68"),
AVG("x69"),
AVG("x70"),
AVG("x71"),
AVG("x72"),
AVG("x73"),
AVG("x74"),
AVG("x75"),
AVG("x76"),
AVG("x77"),
AVG("x78"),
AVG("x79"),
AVG("x80"),
AVG("x81"),
AVG("x82"),
AVG("x83"),
AVG("x84"),
AVG("x85"),
AVG("x86"),
AVG("x87"),
AVG("x88"),
AVG("x89"),
AVG("x90"),
AVG("x91"),
AVG("x92"),
AVG("x93"),
AVG("x94"),
AVG("x95"),
AVG("x96"),
AVG("x97"),
AVG("x98"),
AVG("x99")
FROM
"public"."test_dataset" LIMIT 1

Computing the different aggregations.

SELECT
AVG("x100"),
AVG("x101"),
AVG("x102"),
AVG("x103"),
AVG("x104"),
AVG("x105"),
AVG("x106"),
AVG("x107"),
AVG("x108"),
AVG("x109"),
AVG("x110"),
AVG("x111"),
AVG("x112"),
AVG("x113"),
AVG("x114"),
AVG("x115"),
AVG("x116"),
AVG("x117"),
AVG("x118"),
AVG("x119"),
AVG("x120"),
AVG("x121"),
AVG("x122"),
AVG("x123"),
AVG("x124"),
AVG("x125"),
AVG("x126"),
AVG("x127"),
AVG("x128"),
AVG("x129"),
AVG("x130"),
AVG("x131"),
AVG("x132"),
AVG("x133"),
AVG("x134"),
AVG("x135"),
AVG("x136"),
AVG("x137"),
AVG("x138"),
AVG("x139"),
AVG("x140"),
AVG("x141"),
AVG("x142"),
AVG("x143"),
AVG("x144"),
AVG("x145"),
AVG("x146"),
AVG("x147"),
AVG("x148"),
AVG("x149"),
AVG("x150"),
AVG("x151"),
AVG("x152"),
AVG("x153"),
AVG("x154"),
AVG("x155"),
AVG("x156"),
AVG("x157"),
AVG("x158"),
AVG("x159"),
AVG("x160"),
AVG("x161"),
AVG("x162"),
AVG("x163"),
AVG("x164"),
AVG("x165"),
AVG("x166"),
AVG("x167"),
AVG("x168"),
AVG("x169"),
AVG("x170"),
AVG("x171"),
AVG("x172"),
AVG("x173"),
AVG("x174"),
AVG("x175"),
AVG("x176"),
AVG("x177"),
AVG("x178"),
AVG("x179"),
AVG("x180"),
AVG("x181"),
AVG("x182"),
AVG("x183"),
AVG("x184"),
AVG("x185"),
AVG("x186"),
AVG("x187"),
AVG("x188"),
AVG("x189"),
AVG("x190"),
AVG("x191"),
AVG("x192"),
AVG("x193"),
AVG("x194"),
AVG("x195"),
AVG("x196"),
AVG("x197"),
AVG("x198"),
AVG("x199")
FROM
"public"."test_dataset" LIMIT 1

Computing the different aggregations.

SELECT
AVG("x200"),
AVG("x201"),
AVG("x202"),
AVG("x203"),
AVG("x204"),
AVG("x205"),
AVG("x206"),
AVG("x207"),
AVG("x208"),
AVG("x209"),
AVG("x210"),
AVG("x211"),
AVG("x212"),
AVG("x213"),
AVG("x214"),
AVG("x215"),
AVG("x216"),
AVG("x217"),
AVG("x218"),
AVG("x219"),
AVG("x220"),
AVG("x221"),
AVG("x222"),
AVG("x223"),
AVG("x224"),
AVG("x225"),
AVG("x226"),
AVG("x227"),
AVG("x228"),
AVG("x229"),
AVG("x230"),
AVG("x231"),
AVG("x232"),
AVG("x233"),
AVG("x234"),
AVG("x235"),
AVG("x236"),
AVG("x237"),
AVG("x238"),
AVG("x239"),
AVG("x240"),
AVG("x241"),
AVG("x242"),
AVG("x243"),
AVG("x244"),
AVG("x245"),
AVG("x246"),
AVG("x247"),
AVG("x248"),
AVG("x249"),
AVG("x250"),
AVG("x251"),
AVG("x252"),
AVG("x253"),
AVG("x254"),
AVG("x255"),
AVG("x256"),
AVG("x257"),
AVG("x258"),
AVG("x259"),
AVG("x260"),
AVG("x261"),
AVG("x262"),
AVG("x263"),
AVG("x264"),
AVG("x265"),
AVG("x266"),
AVG("x267"),
AVG("x268"),
AVG("x269"),
AVG("x270"),
AVG("x271"),
AVG("x272"),
AVG("x273"),
AVG("x274"),
AVG("x275"),
AVG("x276"),
AVG("x277"),
AVG("x278"),
AVG("x279"),
AVG("x280"),
AVG("x281"),
AVG("x282"),
AVG("x283"),
AVG("x284"),
AVG("x285"),
AVG("x286"),
AVG("x287"),
AVG("x288"),
AVG("x289"),
AVG("x290"),
AVG("x291"),
AVG("x292"),
AVG("x293"),
AVG("x294"),
AVG("x295"),
AVG("x296"),
AVG("x297"),
AVG("x298"),
AVG("x299")
FROM
"public"."test_dataset" LIMIT 1

Computing the different aggregations.

SELECT
AVG("x300"),
AVG("x301"),
AVG("x302"),
AVG("x303"),
AVG("x304"),
AVG("x305"),
AVG("x306"),
AVG("x307"),
AVG("x308"),
AVG("x309"),
AVG("x310"),
AVG("x311"),
AVG("x312"),
AVG("x313"),
AVG("x314"),
AVG("x315"),
AVG("x316"),
AVG("x317"),
AVG("x318"),
AVG("x319"),
AVG("x320"),
AVG("x321"),
AVG("x322"),
AVG("x323"),
AVG("x324"),
AVG("x325"),
AVG("x326"),
AVG("x327"),
AVG("x328"),
AVG("x329"),
AVG("x330"),
AVG("x331"),
AVG("x332"),
AVG("x333"),
AVG("x334"),
AVG("x335"),
AVG("x336"),
AVG("x337"),
AVG("x338"),
AVG("x339"),
AVG("x340"),
AVG("x341"),
AVG("x342"),
AVG("x343"),
AVG("x344"),
AVG("x345"),
AVG("x346"),
AVG("x347"),
AVG("x348"),
AVG("x349"),
AVG("x350"),
AVG("x351"),
AVG("x352"),
AVG("x353"),
AVG("x354"),
AVG("x355"),
AVG("x356"),
AVG("x357"),
AVG("x358"),
AVG("x359"),
AVG("x360"),
AVG("x361"),
AVG("x362"),
AVG("x363"),
AVG("x364"),
AVG("x365"),
AVG("x366"),
AVG("x367"),
AVG("x368"),
AVG("x369"),
AVG("x370"),
AVG("x371"),
AVG("x372"),
AVG("x373"),
AVG("x374"),
AVG("x375"),
AVG("x376"),
AVG("x377"),
AVG("x378"),
AVG("x379"),
AVG("x380"),
AVG("x381"),
AVG("x382"),
AVG("x383"),
AVG("x384"),
AVG("x385"),
AVG("x386"),
AVG("x387"),
AVG("x388"),
AVG("x389"),
AVG("x390"),
AVG("x391"),
AVG("x392"),
AVG("x393"),
AVG("x394"),
AVG("x395"),
AVG("x396"),
AVG("x397"),
AVG("x398"),
AVG("x399")
FROM
"public"."test_dataset" LIMIT 1

Computing the different aggregations.

SELECT
AVG("x400"),
AVG("x401"),
AVG("x402"),
AVG("x403"),
AVG("x404"),
AVG("x405"),
AVG("x406"),
AVG("x407"),
AVG("x408"),
AVG("x409"),
AVG("x410"),
AVG("x411"),
AVG("x412"),
AVG("x413"),
AVG("x414"),
AVG("x415"),
AVG("x416"),
AVG("x417"),
AVG("x418"),
AVG("x419"),
AVG("x420"),
AVG("x421"),
AVG("x422"),
AVG("x423"),
AVG("x424"),
AVG("x425"),
AVG("x426"),
AVG("x427"),
AVG("x428"),
AVG("x429"),
AVG("x430"),
AVG("x431"),
AVG("x432"),
AVG("x433"),
AVG("x434"),
AVG("x435"),
AVG("x436"),
AVG("x437"),
AVG("x438"),
AVG("x439"),
AVG("x440"),
AVG("x441"),
AVG("x442"),
AVG("x443"),
AVG("x444"),
AVG("x445"),
AVG("x446"),
AVG("x447"),
AVG("x448"),
AVG("x449"),
AVG("x450"),
AVG("x451"),
AVG("x452"),
AVG("x453"),
AVG("x454"),
AVG("x455"),
AVG("x456"),
AVG("x457"),
AVG("x458"),
AVG("x459"),
AVG("x460"),
AVG("x461"),
AVG("x462"),
AVG("x463"),
AVG("x464"),
AVG("x465"),
AVG("x466"),
AVG("x467"),
AVG("x468"),
AVG("x469"),
AVG("x470"),
AVG("x471"),
AVG("x472"),
AVG("x473"),
AVG("x474"),
AVG("x475"),
AVG("x476"),
AVG("x477"),
AVG("x478"),
AVG("x479"),
AVG("x480"),
AVG("x481"),
AVG("x482"),
AVG("x483"),
AVG("x484"),
AVG("x485"),
AVG("x486"),
AVG("x487"),
AVG("x488"),
AVG("x489"),
AVG("x490"),
AVG("x491"),
AVG("x492"),
AVG("x493"),
AVG("x494"),
AVG("x495"),
AVG("x496"),
AVG("x497"),
AVG("x498"),
AVG("x499")
FROM
"public"."test_dataset" LIMIT 1

Computing the different aggregations.

SELECT
AVG("x500"),
AVG("x501"),
AVG("x502"),
AVG("x503"),
AVG("x504"),
AVG("x505"),
AVG("x506"),
AVG("x507"),
AVG("x508"),
AVG("x509"),
AVG("x510"),
AVG("x511"),
AVG("x512"),
AVG("x513"),
AVG("x514"),
AVG("x515"),
AVG("x516"),
AVG("x517"),
AVG("x518"),
AVG("x519"),
AVG("x520"),
AVG("x521"),
AVG("x522"),
AVG("x523"),
AVG("x524"),
AVG("x525"),
AVG("x526"),
AVG("x527"),
AVG("x528"),
AVG("x529"),
AVG("x530"),
AVG("x531"),
AVG("x532"),
AVG("x533"),
AVG("x534"),
AVG("x535"),
AVG("x536"),
AVG("x537"),
AVG("x538"),
AVG("x539"),
AVG("x540"),
AVG("x541"),
AVG("x542"),
AVG("x543"),
AVG("x544"),
AVG("x545"),
AVG("x546"),
AVG("x547"),
AVG("x548"),
AVG("x549"),
AVG("x550"),
AVG("x551"),
AVG("x552"),
AVG("x553"),
AVG("x554"),
AVG("x555"),
AVG("x556"),
AVG("x557"),
AVG("x558"),
AVG("x559"),
AVG("x560"),
AVG("x561"),
AVG("x562"),
AVG("x563"),
AVG("x564"),
AVG("x565"),
AVG("x566"),
AVG("x567"),
AVG("x568"),
AVG("x569"),
AVG("x570"),
AVG("x571"),
AVG("x572"),
AVG("x573"),
AVG("x574"),
AVG("x575"),
AVG("x576"),
AVG("x577"),
AVG("x578"),
AVG("x579"),
AVG("x580"),
AVG("x581"),
AVG("x582"),
AVG("x583"),
AVG("x584"),
AVG("x585"),
AVG("x586"),
AVG("x587"),
AVG("x588"),
AVG("x589"),
AVG("x590"),
AVG("x591"),
AVG("x592"),
AVG("x593"),
AVG("x594"),
AVG("x595"),
AVG("x596"),
AVG("x597"),
AVG("x598"),
AVG("x599")
FROM
"public"."test_dataset" LIMIT 1

	avg
"x0"	0.495328640506021
"x1"	0.498410496117291
"x2"	0.499796020665369
"x3"	0.500288217322272
"x4"	0.501549792736745
"x5"	0.502030357531738
"x6"	0.498203794788383
"x7"	0.499528634219361
"x8"	0.498655800491967
"x9"	0.501991024077614
"x10"	0.499470449560788
"x11"	0.503527408929076
"x12"	0.494434265249921
"x13"	0.500999432951491
"x14"	0.499102837571758
"x15"	0.499115148377954
"x16"	0.498147533271462
"x17"	0.503055292108236
"x18"	0.498873396961717
"x19"	0.500234201267036
"x20"	0.500846420762525
"x21"	0.497544945688546
"x22"	0.499816283349297
"x23"	0.49421561504358
"x24"	0.498501294320636
"x25"	0.49881612469398
"x26"	0.500256177358888
"x27"	0.497643181022117
"x28"	0.497196014247974
"x29"	0.496317569292686
"x30"	0.499120011079405
"x31"	0.504665919276001
"x32"	0.497500529544335
"x33"	0.501751783046429
"x34"	0.495941181869968
"x35"	0.493580291184946
"x36"	0.502128273874149
"x37"	0.500820784376212
"x38"	0.498700643126364
"x39"	0.504414001272083
"x40"	0.50297581018405
"x41"	0.498589111867477
"x42"	0.493329039907991
"x43"	0.495365951688774
"x44"	0.501398265630705
"x45"	0.497031733239675
"x46"	0.49858817340259
"x47"	0.499666033335752
"x48"	0.497840583433164
"x49"	0.497329473723052
"x50"	0.49669896126797
"x51"	0.496988497646665
"x52"	0.495283583330153
"x53"	0.503541071587382
"x54"	0.502496378843393
"x55"	0.506106085637049
"x56"	0.503235346138873
"x57"	0.502128457865585
"x58"	0.501718418996432
"x59"	0.501931357378722
"x60"	0.492535700597335
"x61"	0.496213648770773
"x62"	0.501711242381227
"x63"	0.49593384194742
"x64"	0.501410126678646
"x65"	0.496181363479933
"x66"	0.502847261195513
"x67"	0.499017013058043
"x68"	0.500901301923464
"x69"	0.502686372651509
"x70"	0.503725287213479
"x71"	0.49858107758367
"x72"	0.500332314733882
"x73"	0.503799533855729
"x74"	0.497731596026267
"x75"	0.500254210987454
"x76"	0.503705967230629
"x77"	0.497663569217268
"x78"	0.498292489934526
"x79"	0.504205192289595
"x80"	0.501272203026921
"x81"	0.498586322333827
"x82"	0.501165568577242
"x83"	0.507249641807377
"x84"	0.499038335806946
"x85"	0.500589053839631
"x86"	0.504052388792532
"x87"	0.497889207095001
"x88"	0.500446273626364
"x89"	0.499201328990585
"x90"	0.502505042524263
"x91"	0.498666686973721
"x92"	0.4980780772798
"x93"	0.498206184870098
"x94"	0.501078245462058
"x95"	0.502400306417863
"x96"	0.496301139098872
"x97"	0.499954794859467
"x98"	0.495530540163047
"x99"	0.499518119273777
"x100"	0.502472493177536
"x101"	0.504902573059266
"x102"	0.498767855446343
"x103"	0.498763861700776
"x104"	0.494740242705843
"x105"	0.50120537653435
"x106"	0.500558270047256
"x107"	0.501785635916004
"x108"	0.500023144892836
"x109"	0.500689867577143
"x110"	0.500623461510986
"x111"	0.502040224120254
"x112"	0.49922121064926
"x113"	0.499335735470266
"x114"	0.502046788542392
"x115"	0.498266887489939
"x116"	0.502299062704365
"x117"	0.499504777825647
"x118"	0.498147775378544
"x119"	0.500105008480838
"x120"	0.500926059096353
"x121"	0.498799051559274
"x122"	0.498231966562965
"x123"	0.502014504623017
"x124"	0.496966086774343
"x125"	0.497726782605075
"x126"	0.501800164589402
"x127"	0.504625166970142
"x128"	0.494857996223634
"x129"	0.504154442140413
"x130"	0.495419377293601
"x131"	0.495755362548213
"x132"	0.499762857829197
"x133"	0.498344265133003
"x134"	0.504667159711616
"x135"	0.500426490560034
"x136"	0.495148637985485
"x137"	0.499247576732608
"x138"	0.500246977145318
"x139"	0.498025120325852
"x140"	0.49665202575603
"x141"	0.50157614965036
"x142"	0.494219642718253
"x143"	0.501118474691175
"x144"	0.498214634110243
"x145"	0.497676468651718
"x146"	0.4999434025286
"x147"	0.50381975648317
"x148"	0.499791994529241
"x149"	0.501425083181378
"x150"	0.503124807463679
"x151"	0.503843662469694
"x152"	0.499438914951705
"x153"	0.499690357795637
"x154"	0.492923485315032
"x155"	0.498921156159765
"x156"	0.505832323109452
"x157"	0.50427568726968
"x158"	0.501047079447447
"x159"	0.501144096319587
"x160"	0.500279258508724
"x161"	0.501948064998072
"x162"	0.500822011614218
"x163"	0.497227232640726
"x164"	0.495996041991841
"x165"	0.501643313851114
"x166"	0.493981937356992
"x167"	0.501158596251789
"x168"	0.492721518449881
"x169"	0.502087166458927
"x170"	0.496132122005289
"x171"	0.50004740234639
"x172"	0.507995243759337
"x173"	0.495010604015226
"x174"	0.504332425329136
"x175"	0.500722138965363
"x176"	0.501477807205217
"x177"	0.503222832425241
"x178"	0.496611627437011
"x179"	0.501386782278749
"x180"	0.496568095402792
"x181"	0.49866127618046
"x182"	0.499939231952606
"x183"	0.49839989884852
"x184"	0.49844872907605
"x185"	0.499527396393754
"x186"	0.505675627022749
"x187"	0.502176277364884
"x188"	0.50282409973233
"x189"	0.501910180421011
"x190"	0.499091349298926
"x191"	0.503729833525093
"x192"	0.501052054715296
"x193"	0.497996389698493
"x194"	0.501086532175215
"x195"	0.497719087828533
"x196"	0.50029780225656
"x197"	0.497283518747846
"x198"	0.50029253732739
"x199"	0.503720246048248
"x200"	0.50173565008766
"x201"	0.505880159354582
"x202"	0.495958490648027
"x203"	0.501755221227929
"x204"	0.501195751817548
"x205"	0.502214450883982
"x206"	0.500531362394965
"x207"	0.50205923199188
"x208"	0.50295131285931
"x209"	0.502243809388275
"x210"	0.507529762405949
"x211"	0.504731268971018
"x212"	0.496389128514612
"x213"	0.501199241986056
"x214"	0.49963519236967
"x215"	0.503726938822726
"x216"	0.500616870596819
"x217"	0.499451167386491
"x218"	0.505073839902342
"x219"	0.495555917869695
"x220"	0.503535190937086
"x221"	0.500010514657409
"x222"	0.501485859632539
"x223"	0.501452482858882
"x224"	0.496859017808642
"x225"	0.499720605799975
"x226"	0.49902702385562
"x227"	0.493869393159961
"x228"	0.500975813526777
"x229"	0.503612502819882
"x230"	0.500296765768202
"x231"	0.501570096647646
"x232"	0.501653867180506
"x233"	0.495997469444037
"x234"	0.500007144648512
"x235"	0.501950742057967
"x236"	0.501136892649159
"x237"	0.497207203832921
"x238"	0.497366881357762
"x239"	0.503939381418563
"x240"	0.500307605826342
"x241"	0.500278400719655
"x242"	0.496130419079564
"x243"	0.498876399969449
"x244"	0.498236415238259
"x245"	0.505986941487947
"x246"	0.499224769213772
"x247"	0.501577037782432
"x248"	0.502718629168347
"x249"	0.497431639110297
"x250"	0.497242679194268
"x251"	0.500110229029763
"x252"	0.499876112394035
"x253"	0.500209626919613
"x254"	0.498006003626087
"x255"	0.501028609297518
"x256"	0.497476164073031
"x257"	0.49678638799903
"x258"	0.503028783134325
"x259"	0.500678621253278
"x260"	0.501899834921793
"x261"	0.49799142391663
"x262"	0.502971393630072
"x263"	0.503649817536864
"x264"	0.501297247359576
"x265"	0.498187974785664
"x266"	0.500154159696493
"x267"	0.498174033115013
"x268"	0.502259716476523
"x269"	0.500892450533505
"x270"	0.49974688360109
"x271"	0.503252161878953
"x272"	0.500539697517548
"x273"	0.504436443216214
"x274"	0.502837233404955
"x275"	0.500573441805621
"x276"	0.502325369855715
"x277"	0.502028237938345
"x278"	0.499332443716889
"x279"	0.502182460590545
"x280"	0.496923384860391
"x281"	0.502459710514196
"x282"	0.494431597842555
"x283"	0.498645268598385
"x284"	0.496526186001836
"x285"	0.500086312099337
"x286"	0.501093746714317
"x287"	0.499139699267014
"x288"	0.496876103397319
"x289"	0.501409091628436
"x290"	0.49791826228518
"x291"	0.498047744078701
"x292"	0.503011588848778
"x293"	0.494279091265984
"x294"	0.50145187503472
"x295"	0.500172946690372
"x296"	0.497979902947624
"x297"	0.492805902662501
"x298"	0.499126493594469
"x299"	0.498135944902035
"x300"	0.497372175921849
"x301"	0.499937077994528
"x302"	0.498308722825442
"x303"	0.49533371287086
"x304"	0.498127515137498
"x305"	0.498703532381309
"x306"	0.499237531865598
"x307"	0.497270844759117
"x308"	0.498935120696947
"x309"	0.505050902428967
"x310"	0.494586903066631
"x311"	0.501683675867925
"x312"	0.498886407341203
"x313"	0.502651175533538
"x314"	0.502610984513233
"x315"	0.499020783808059
"x316"	0.497362622722238
"x317"	0.497419736533822
"x318"	0.499328824524721
"x319"	0.499461412384966
"x320"	0.496635588650592
"x321"	0.50164939600958
"x322"	0.495475489443797
"x323"	0.504439370975946
"x324"	0.492955837118463
"x325"	0.50213546285443
"x326"	0.501837605465343
"x327"	0.505604026965867
"x328"	0.501586500711599
"x329"	0.495532248499035
"x330"	0.497800878073298
"x331"	0.499882971630897
"x332"	0.500076033881283
"x333"	0.495062761539174
"x334"	0.496449061025702
"x335"	0.504990343881771
"x336"	0.500045601881808
"x337"	0.501849232240906
"x338"	0.496911254709517
"x339"	0.504145932986075
"x340"	0.498204576935223
"x341"	0.499988833210897
"x342"	0.501985481519345
"x343"	0.501642016830575
"x344"	0.50208484286801
"x345"	0.501863940183376
"x346"	0.504740945380134
"x347"	0.494465954249515
"x348"	0.501264774960466
"x349"	0.503402628929587
"x350"	0.497616449450352
"x351"	0.498765651370934
"x352"	0.4989254358629
"x353"	0.50183583012505
"x354"	0.496825986894267
"x355"	0.498098364283633
"x356"	0.501767813966167
"x357"	0.498895416097227
"x358"	0.501967324887938
"x359"	0.502795286852052
"x360"	0.499872073752759
"x361"	0.498643346011383
"x362"	0.495098243173491
"x363"	0.498941021885141
"x364"	0.502219445838453
"x365"	0.502615964374412
"x366"	0.505264188127546
"x367"	0.500322223420395
"x368"	0.496345346435811
"x369"	0.504413213725598
"x370"	0.500707079809252
"x371"	0.497611740602506
"x372"	0.496615198885486
"x373"	0.500618592508254
"x374"	0.496569180697575
"x375"	0.504701023442182
"x376"	0.500255297750491
"x377"	0.498582437623548
"x378"	0.501286069167382
"x379"	0.503165722717438
"x380"	0.500378197866748
"x381"	0.503250720378035
"x382"	0.497218099547946
"x383"	0.501221833311347
"x384"	0.498849412409216
"x385"	0.501107512276433
"x386"	0.50047080468561
"x387"	0.498652378063696
"x388"	0.500216451354371
"x389"	0.504704176720278
"x390"	0.496354370877845
"x391"	0.503721972145187
"x392"	0.497143805629993
"x393"	0.50330190141215
"x394"	0.49937959353358
"x395"	0.498948919450445
"x396"	0.500168919996172
"x397"	0.502578267350467
"x398"	0.506101156181982
"x399"	0.503158229905623
"x400"	0.498680288048205
"x401"	0.501676260473486
"x402"	0.493510792666185
"x403"	0.499031297577242
"x404"	0.506920870206016
"x405"	0.502246720495657
"x406"	0.498294070439227
"x407"	0.501426641905122
"x408"	0.500076049703243
"x409"	0.498691384791536
"x410"	0.498929796277476
"x411"	0.501708653254574
"x412"	0.497889006545767
"x413"	0.504986315847584
"x414"	0.50249145781952
"x415"	0.498517542092968
"x416"	0.502209066824149
"x417"	0.50141426213542
"x418"	0.499224397290451
"x419"	0.502355230133631
"x420"	0.495981889346498
"x421"	0.498214948748122
"x422"	0.503087869197689
"x423"	0.503969026273629
"x424"	0.502452665825631
"x425"	0.497048816487961
"x426"	0.495624019407202
"x427"	0.499317652632738
"x428"	0.500914652247867
"x429"	0.499057770106616
"x430"	0.501268080979818
"x431"	0.498147857934493
"x432"	0.499914153201319
"x433"	0.495786726674158
"x434"	0.501682487738878
"x435"	0.496457202862645
"x436"	0.508543896326539
"x437"	0.501061310318136
"x438"	0.502894218938588
"x439"	0.502274609185918
"x440"	0.502498467199877
"x441"	0.501282249764865
"x442"	0.497290669186227
"x443"	0.501401792088733
"x444"	0.501240313977888
"x445"	0.497915219181264
"x446"	0.505064357985207
"x447"	0.502044832450291
"x448"	0.501214603475481
"x449"	0.500469672076427
"x450"	0.499569192343717
"x451"	0.504505589536461
"x452"	0.502298843947589
"x453"	0.497023923462909
"x454"	0.505475943069602
"x455"	0.495570238801069
"x456"	0.502516490759049
"x457"	0.50001143220698
"x458"	0.50056624143261
"x459"	0.500763978589862
"x460"	0.503351329483651
"x461"	0.495937412253953
"x462"	0.502993626758666
"x463"	0.50376565954613
"x464"	0.503578705062647
"x465"	0.499874845132907
"x466"	0.49710460913037
"x467"	0.500236035677022
"x468"	0.504071040146146
"x469"	0.502116545564379
"x470"	0.4970413170774
"x471"	0.497961502568331
"x472"	0.498185959877749
"x473"	0.500172177500324
"x474"	0.49990327054779
"x475"	0.505754457844445
"x476"	0.506156544381194
"x477"	0.502520981100039
"x478"	0.498271713060327
"x479"	0.499427241228428
"x480"	0.501416352765658
"x481"	0.497885700659943
"x482"	0.496553136685677
"x483"	0.499256669682357
"x484"	0.499976025144919
"x485"	0.502863525254768
"x486"	0.502828509843699
"x487"	0.497056779966387
"x488"	0.498057491645007
"x489"	0.499423123476515
"x490"	0.495550263123354
"x491"	0.496354107240075
"x492"	0.500804190374236
"x493"	0.502665761463973
"x494"	0.49879279964203
"x495"	0.50084074535016
"x496"	0.49858958128395
"x497"	0.498756470166892
"x498"	0.49870276355634
"x499"	0.49877461641808
"x500"	0.495528407560149
"x501"	0.499308988225553
"x502"	0.496989637391805
"x503"	0.497052493863879
"x504"	0.502207657987811
"x505"	0.497782883539819
"x506"	0.499157336530695
"x507"	0.499570190191874
"x508"	0.501303651056089
"x509"	0.501275910730497
"x510"	0.50475483049925
"x511"	0.49744029519686
"x512"	0.50021479274095
"x513"	0.502164487606823
"x514"	0.502737027523154
"x515"	0.505453019243362
"x516"	0.505072508157813
"x517"	0.497371969039645
"x518"	0.499832538922224
"x519"	0.500174262844981
"x520"	0.502035293567949
"x521"	0.49500378929202
"x522"	0.494185061220149
"x523"	0.497007539583836
"x524"	0.500462249073479
"x525"	0.501112031996949
"x526"	0.50448007855895
"x527"	0.500180586358835
"x528"	0.502937457214855
"x529"	0.495785512156505
"x530"	0.496734989838139
"x531"	0.49939898383636
"x532"	0.498502123921341
"x533"	0.504866269736877
"x534"	0.494965157273132
"x535"	0.495690308914194
"x536"	0.498892336926609
"x537"	0.501628264661017
"x538"	0.503607030711207
"x539"	0.498502264841949
"x540"	0.497903406035737
"x541"	0.49658684479869
"x542"	0.501395893062139
"x543"	0.499979672330362
"x544"	0.499520966207422
"x545"	0.496572526952415
"x546"	0.499305491672084
"x547"	0.49969866590458
"x548"	0.503476167464792
"x549"	0.495446928177774
"x550"	0.505830715886923
"x551"	0.495616547828494
"x552"	0.502230194019736
"x553"	0.503146120540239
"x554"	0.500894720273721
"x555"	0.500924644357059
"x556"	0.499140572330775
"x557"	0.500774686140707
"x558"	0.498897955308785
"x559"	0.50650362433407
"x560"	0.503777572448854
"x561"	0.4953408519377
"x562"	0.501732075663935
"x563"	0.501186506923474
"x564"	0.497129871141445
"x565"	0.506832370619941
"x566"	0.502918937394116
"x567"	0.497088979613851
"x568"	0.495986605457426
"x569"	0.502320092254644
"x570"	0.494138077245164
"x571"	0.497765557831619
"x572"	0.499874999663862
"x573"	0.497969851170666
"x574"	0.502105102461041
"x575"	0.498697688769549
"x576"	0.495100045574782
"x577"	0.502766133953678
"x578"	0.496227313589025
"x579"	0.49932494115436
"x580"	0.501537018946954
"x581"	0.495884116988792
"x582"	0.499084989017015
"x583"	0.494322689776961
"x584"	0.501757184752147
"x585"	0.504229674753267
"x586"	0.497673269462539
"x587"	0.503275601157756
"x588"	0.498003026509518
"x589"	0.497905468545761
"x590"	0.498104806767451
"x591"	0.497433767945156
"x592"	0.49946368073097
"x593"	0.502132979064411
"x594"	0.503633827587846
"x595"	0.497655095852725
"x596"	0.495839959227457
"x597"	0.502938682934688
"x598"	0.500326397570572
"x599"	0.501134085538425

Rows: 1-600 | Columns: 2

You can also send multiple queries at the same time. To do this, you specify the number of 'processes', which can be understood as the number of workers involved in the computation. Each child process will create a DB connection and send its query. In the following example, we will use 6 'processes'.

In [57]:

display(vdf.avg(ncols_block = 100), processes = 6)

Computing the different aggregations.

SELECT
AVG("x0"),
AVG("x1"),
AVG("x2"),
AVG("x3"),
AVG("x4"),
AVG("x5"),
AVG("x6"),
AVG("x7"),
AVG("x8"),
AVG("x9"),
AVG("x10"),
AVG("x11"),
AVG("x12"),
AVG("x13"),
AVG("x14"),
AVG("x15"),
AVG("x16"),
AVG("x17"),
AVG("x18"),
AVG("x19"),
AVG("x20"),
AVG("x21"),
AVG("x22"),
AVG("x23"),
AVG("x24"),
AVG("x25"),
AVG("x26"),
AVG("x27"),
AVG("x28"),
AVG("x29"),
AVG("x30"),
AVG("x31"),
AVG("x32"),
AVG("x33"),
AVG("x34"),
AVG("x35"),
AVG("x36"),
AVG("x37"),
AVG("x38"),
AVG("x39"),
AVG("x40"),
AVG("x41"),
AVG("x42"),
AVG("x43"),
AVG("x44"),
AVG("x45"),
AVG("x46"),
AVG("x47"),
AVG("x48"),
AVG("x49"),
AVG("x50"),
AVG("x51"),
AVG("x52"),
AVG("x53"),
AVG("x54"),
AVG("x55"),
AVG("x56"),
AVG("x57"),
AVG("x58"),
AVG("x59"),
AVG("x60"),
AVG("x61"),
AVG("x62"),
AVG("x63"),
AVG("x64"),
AVG("x65"),
AVG("x66"),
AVG("x67"),
AVG("x68"),
AVG("x69"),
AVG("x70"),
AVG("x71"),
AVG("x72"),
AVG("x73"),
AVG("x74"),
AVG("x75"),
AVG("x76"),
AVG("x77"),
AVG("x78"),
AVG("x79"),
AVG("x80"),
AVG("x81"),
AVG("x82"),
AVG("x83"),
AVG("x84"),
AVG("x85"),
AVG("x86"),
AVG("x87"),
AVG("x88"),
AVG("x89"),
AVG("x90"),
AVG("x91"),
AVG("x92"),
AVG("x93"),
AVG("x94"),
AVG("x95"),
AVG("x96"),
AVG("x97"),
AVG("x98"),
AVG("x99")
FROM
"public"."test_dataset" LIMIT 1

Computing the different aggregations.

SELECT
AVG("x100"),
AVG("x101"),
AVG("x102"),
AVG("x103"),
AVG("x104"),
AVG("x105"),
AVG("x106"),
AVG("x107"),
AVG("x108"),
AVG("x109"),
AVG("x110"),
AVG("x111"),
AVG("x112"),
AVG("x113"),
AVG("x114"),
AVG("x115"),
AVG("x116"),
AVG("x117"),
AVG("x118"),
AVG("x119"),
AVG("x120"),
AVG("x121"),
AVG("x122"),
AVG("x123"),
AVG("x124"),
AVG("x125"),
AVG("x126"),
AVG("x127"),
AVG("x128"),
AVG("x129"),
AVG("x130"),
AVG("x131"),
AVG("x132"),
AVG("x133"),
AVG("x134"),
AVG("x135"),
AVG("x136"),
AVG("x137"),
AVG("x138"),
AVG("x139"),
AVG("x140"),
AVG("x141"),
AVG("x142"),
AVG("x143"),
AVG("x144"),
AVG("x145"),
AVG("x146"),
AVG("x147"),
AVG("x148"),
AVG("x149"),
AVG("x150"),
AVG("x151"),
AVG("x152"),
AVG("x153"),
AVG("x154"),
AVG("x155"),
AVG("x156"),
AVG("x157"),
AVG("x158"),
AVG("x159"),
AVG("x160"),
AVG("x161"),
AVG("x162"),
AVG("x163"),
AVG("x164"),
AVG("x165"),
AVG("x166"),
AVG("x167"),
AVG("x168"),
AVG("x169"),
AVG("x170"),
AVG("x171"),
AVG("x172"),
AVG("x173"),
AVG("x174"),
AVG("x175"),
AVG("x176"),
AVG("x177"),
AVG("x178"),
AVG("x179"),
AVG("x180"),
AVG("x181"),
AVG("x182"),
AVG("x183"),
AVG("x184"),
AVG("x185"),
AVG("x186"),
AVG("x187"),
AVG("x188"),
AVG("x189"),
AVG("x190"),
AVG("x191"),
AVG("x192"),
AVG("x193"),
AVG("x194"),
AVG("x195"),
AVG("x196"),
AVG("x197"),
AVG("x198"),
AVG("x199")
FROM
"public"."test_dataset" LIMIT 1

Computing the different aggregations.

SELECT
AVG("x200"),
AVG("x201"),
AVG("x202"),
AVG("x203"),
AVG("x204"),
AVG("x205"),
AVG("x206"),
AVG("x207"),
AVG("x208"),
AVG("x209"),
AVG("x210"),
AVG("x211"),
AVG("x212"),
AVG("x213"),
AVG("x214"),
AVG("x215"),
AVG("x216"),
AVG("x217"),
AVG("x218"),
AVG("x219"),
AVG("x220"),
AVG("x221"),
AVG("x222"),
AVG("x223"),
AVG("x224"),
AVG("x225"),
AVG("x226"),
AVG("x227"),
AVG("x228"),
AVG("x229"),
AVG("x230"),
AVG("x231"),
AVG("x232"),
AVG("x233"),
AVG("x234"),
AVG("x235"),
AVG("x236"),
AVG("x237"),
AVG("x238"),
AVG("x239"),
AVG("x240"),
AVG("x241"),
AVG("x242"),
AVG("x243"),
AVG("x244"),
AVG("x245"),
AVG("x246"),
AVG("x247"),
AVG("x248"),
AVG("x249"),
AVG("x250"),
AVG("x251"),
AVG("x252"),
AVG("x253"),
AVG("x254"),
AVG("x255"),
AVG("x256"),
AVG("x257"),
AVG("x258"),
AVG("x259"),
AVG("x260"),
AVG("x261"),
AVG("x262"),
AVG("x263"),
AVG("x264"),
AVG("x265"),
AVG("x266"),
AVG("x267"),
AVG("x268"),
AVG("x269"),
AVG("x270"),
AVG("x271"),
AVG("x272"),
AVG("x273"),
AVG("x274"),
AVG("x275"),
AVG("x276"),
AVG("x277"),
AVG("x278"),
AVG("x279"),
AVG("x280"),
AVG("x281"),
AVG("x282"),
AVG("x283"),
AVG("x284"),
AVG("x285"),
AVG("x286"),
AVG("x287"),
AVG("x288"),
AVG("x289"),
AVG("x290"),
AVG("x291"),
AVG("x292"),
AVG("x293"),
AVG("x294"),
AVG("x295"),
AVG("x296"),
AVG("x297"),
AVG("x298"),
AVG("x299")
FROM
"public"."test_dataset" LIMIT 1

Computing the different aggregations.

SELECT
AVG("x300"),
AVG("x301"),
AVG("x302"),
AVG("x303"),
AVG("x304"),
AVG("x305"),
AVG("x306"),
AVG("x307"),
AVG("x308"),
AVG("x309"),
AVG("x310"),
AVG("x311"),
AVG("x312"),
AVG("x313"),
AVG("x314"),
AVG("x315"),
AVG("x316"),
AVG("x317"),
AVG("x318"),
AVG("x319"),
AVG("x320"),
AVG("x321"),
AVG("x322"),
AVG("x323"),
AVG("x324"),
AVG("x325"),
AVG("x326"),
AVG("x327"),
AVG("x328"),
AVG("x329"),
AVG("x330"),
AVG("x331"),
AVG("x332"),
AVG("x333"),
AVG("x334"),
AVG("x335"),
AVG("x336"),
AVG("x337"),
AVG("x338"),
AVG("x339"),
AVG("x340"),
AVG("x341"),
AVG("x342"),
AVG("x343"),
AVG("x344"),
AVG("x345"),
AVG("x346"),
AVG("x347"),
AVG("x348"),
AVG("x349"),
AVG("x350"),
AVG("x351"),
AVG("x352"),
AVG("x353"),
AVG("x354"),
AVG("x355"),
AVG("x356"),
AVG("x357"),
AVG("x358"),
AVG("x359"),
AVG("x360"),
AVG("x361"),
AVG("x362"),
AVG("x363"),
AVG("x364"),
AVG("x365"),
AVG("x366"),
AVG("x367"),
AVG("x368"),
AVG("x369"),
AVG("x370"),
AVG("x371"),
AVG("x372"),
AVG("x373"),
AVG("x374"),
AVG("x375"),
AVG("x376"),
AVG("x377"),
AVG("x378"),
AVG("x379"),
AVG("x380"),
AVG("x381"),
AVG("x382"),
AVG("x383"),
AVG("x384"),
AVG("x385"),
AVG("x386"),
AVG("x387"),
AVG("x388"),
AVG("x389"),
AVG("x390"),
AVG("x391"),
AVG("x392"),
AVG("x393"),
AVG("x394"),
AVG("x395"),
AVG("x396"),
AVG("x397"),
AVG("x398"),
AVG("x399")
FROM
"public"."test_dataset" LIMIT 1

Computing the different aggregations.

SELECT
AVG("x400"),
AVG("x401"),
AVG("x402"),
AVG("x403"),
AVG("x404"),
AVG("x405"),
AVG("x406"),
AVG("x407"),
AVG("x408"),
AVG("x409"),
AVG("x410"),
AVG("x411"),
AVG("x412"),
AVG("x413"),
AVG("x414"),
AVG("x415"),
AVG("x416"),
AVG("x417"),
AVG("x418"),
AVG("x419"),
AVG("x420"),
AVG("x421"),
AVG("x422"),
AVG("x423"),
AVG("x424"),
AVG("x425"),
AVG("x426"),
AVG("x427"),
AVG("x428"),
AVG("x429"),
AVG("x430"),
AVG("x431"),
AVG("x432"),
AVG("x433"),
AVG("x434"),
AVG("x435"),
AVG("x436"),
AVG("x437"),
AVG("x438"),
AVG("x439"),
AVG("x440"),
AVG("x441"),
AVG("x442"),
AVG("x443"),
AVG("x444"),
AVG("x445"),
AVG("x446"),
AVG("x447"),
AVG("x448"),
AVG("x449"),
AVG("x450"),
AVG("x451"),
AVG("x452"),
AVG("x453"),
AVG("x454"),
AVG("x455"),
AVG("x456"),
AVG("x457"),
AVG("x458"),
AVG("x459"),
AVG("x460"),
AVG("x461"),
AVG("x462"),
AVG("x463"),
AVG("x464"),
AVG("x465"),
AVG("x466"),
AVG("x467"),
AVG("x468"),
AVG("x469"),
AVG("x470"),
AVG("x471"),
AVG("x472"),
AVG("x473"),
AVG("x474"),
AVG("x475"),
AVG("x476"),
AVG("x477"),
AVG("x478"),
AVG("x479"),
AVG("x480"),
AVG("x481"),
AVG("x482"),
AVG("x483"),
AVG("x484"),
AVG("x485"),
AVG("x486"),
AVG("x487"),
AVG("x488"),
AVG("x489"),
AVG("x490"),
AVG("x491"),
AVG("x492"),
AVG("x493"),
AVG("x494"),
AVG("x495"),
AVG("x496"),
AVG("x497"),
AVG("x498"),
AVG("x499")
FROM
"public"."test_dataset" LIMIT 1

Computing the different aggregations.

SELECT
AVG("x500"),
AVG("x501"),
AVG("x502"),
AVG("x503"),
AVG("x504"),
AVG("x505"),
AVG("x506"),
AVG("x507"),
AVG("x508"),
AVG("x509"),
AVG("x510"),
AVG("x511"),
AVG("x512"),
AVG("x513"),
AVG("x514"),
AVG("x515"),
AVG("x516"),
AVG("x517"),
AVG("x518"),
AVG("x519"),
AVG("x520"),
AVG("x521"),
AVG("x522"),
AVG("x523"),
AVG("x524"),
AVG("x525"),
AVG("x526"),
AVG("x527"),
AVG("x528"),
AVG("x529"),
AVG("x530"),
AVG("x531"),
AVG("x532"),
AVG("x533"),
AVG("x534"),
AVG("x535"),
AVG("x536"),
AVG("x537"),
AVG("x538"),
AVG("x539"),
AVG("x540"),
AVG("x541"),
AVG("x542"),
AVG("x543"),
AVG("x544"),
AVG("x545"),
AVG("x546"),
AVG("x547"),
AVG("x548"),
AVG("x549"),
AVG("x550"),
AVG("x551"),
AVG("x552"),
AVG("x553"),
AVG("x554"),
AVG("x555"),
AVG("x556"),
AVG("x557"),
AVG("x558"),
AVG("x559"),
AVG("x560"),
AVG("x561"),
AVG("x562"),
AVG("x563"),
AVG("x564"),
AVG("x565"),
AVG("x566"),
AVG("x567"),
AVG("x568"),
AVG("x569"),
AVG("x570"),
AVG("x571"),
AVG("x572"),
AVG("x573"),
AVG("x574"),
AVG("x575"),
AVG("x576"),
AVG("x577"),
AVG("x578"),
AVG("x579"),
AVG("x580"),
AVG("x581"),
AVG("x582"),
AVG("x583"),
AVG("x584"),
AVG("x585"),
AVG("x586"),
AVG("x587"),
AVG("x588"),
AVG("x589"),
AVG("x590"),
AVG("x591"),
AVG("x592"),
AVG("x593"),
AVG("x594"),
AVG("x595"),
AVG("x596"),
AVG("x597"),
AVG("x598"),
AVG("x599")
FROM
"public"."test_dataset" LIMIT 1

	avg
"x0"	0.495328640506021
"x1"	0.498410496117291
"x2"	0.499796020665369
"x3"	0.500288217322272
"x4"	0.501549792736745
"x5"	0.502030357531738
"x6"	0.498203794788383
"x7"	0.499528634219361
"x8"	0.498655800491967
"x9"	0.501991024077614
"x10"	0.499470449560788
"x11"	0.503527408929076
"x12"	0.494434265249921
"x13"	0.500999432951491
"x14"	0.499102837571758
"x15"	0.499115148377954
"x16"	0.498147533271462
"x17"	0.503055292108236
"x18"	0.498873396961717
"x19"	0.500234201267036
"x20"	0.500846420762525
"x21"	0.497544945688546
"x22"	0.499816283349297
"x23"	0.49421561504358
"x24"	0.498501294320636
"x25"	0.49881612469398
"x26"	0.500256177358888
"x27"	0.497643181022117
"x28"	0.497196014247974
"x29"	0.496317569292686
"x30"	0.499120011079405
"x31"	0.504665919276001
"x32"	0.497500529544335
"x33"	0.501751783046429
"x34"	0.495941181869968
"x35"	0.493580291184946
"x36"	0.502128273874149
"x37"	0.500820784376212
"x38"	0.498700643126364
"x39"	0.504414001272083
"x40"	0.50297581018405
"x41"	0.498589111867477
"x42"	0.493329039907991
"x43"	0.495365951688774
"x44"	0.501398265630705
"x45"	0.497031733239675
"x46"	0.49858817340259
"x47"	0.499666033335752
"x48"	0.497840583433164
"x49"	0.497329473723052
"x50"	0.49669896126797
"x51"	0.496988497646665
"x52"	0.495283583330153
"x53"	0.503541071587382
"x54"	0.502496378843393
"x55"	0.506106085637049
"x56"	0.503235346138873
"x57"	0.502128457865585
"x58"	0.501718418996432
"x59"	0.501931357378722
"x60"	0.492535700597335
"x61"	0.496213648770773
"x62"	0.501711242381227
"x63"	0.49593384194742
"x64"	0.501410126678646
"x65"	0.496181363479933
"x66"	0.502847261195513
"x67"	0.499017013058043
"x68"	0.500901301923464
"x69"	0.502686372651509
"x70"	0.503725287213479
"x71"	0.49858107758367
"x72"	0.500332314733882
"x73"	0.503799533855729
"x74"	0.497731596026267
"x75"	0.500254210987454
"x76"	0.503705967230629
"x77"	0.497663569217268
"x78"	0.498292489934526
"x79"	0.504205192289595
"x80"	0.501272203026921
"x81"	0.498586322333827
"x82"	0.501165568577242
"x83"	0.507249641807377
"x84"	0.499038335806946
"x85"	0.500589053839631
"x86"	0.504052388792532
"x87"	0.497889207095001
"x88"	0.500446273626364
"x89"	0.499201328990585
"x90"	0.502505042524263
"x91"	0.498666686973721
"x92"	0.4980780772798
"x93"	0.498206184870098
"x94"	0.501078245462058
"x95"	0.502400306417863
"x96"	0.496301139098872
"x97"	0.499954794859467
"x98"	0.495530540163047
"x99"	0.499518119273777
"x100"	0.502472493177536
"x101"	0.504902573059266
"x102"	0.498767855446343
"x103"	0.498763861700776
"x104"	0.494740242705843
"x105"	0.50120537653435
"x106"	0.500558270047256
"x107"	0.501785635916004
"x108"	0.500023144892836
"x109"	0.500689867577143
"x110"	0.500623461510986
"x111"	0.502040224120254
"x112"	0.49922121064926
"x113"	0.499335735470266
"x114"	0.502046788542392
"x115"	0.498266887489939
"x116"	0.502299062704365
"x117"	0.499504777825647
"x118"	0.498147775378544
"x119"	0.500105008480838
"x120"	0.500926059096353
"x121"	0.498799051559274
"x122"	0.498231966562965
"x123"	0.502014504623017
"x124"	0.496966086774343
"x125"	0.497726782605075
"x126"	0.501800164589402
"x127"	0.504625166970142
"x128"	0.494857996223634
"x129"	0.504154442140413
"x130"	0.495419377293601
"x131"	0.495755362548213
"x132"	0.499762857829197
"x133"	0.498344265133003
"x134"	0.504667159711616
"x135"	0.500426490560034
"x136"	0.495148637985485
"x137"	0.499247576732608
"x138"	0.500246977145318
"x139"	0.498025120325852
"x140"	0.49665202575603
"x141"	0.50157614965036
"x142"	0.494219642718253
"x143"	0.501118474691175
"x144"	0.498214634110243
"x145"	0.497676468651718
"x146"	0.4999434025286
"x147"	0.50381975648317
"x148"	0.499791994529241
"x149"	0.501425083181378
"x150"	0.503124807463679
"x151"	0.503843662469694
"x152"	0.499438914951705
"x153"	0.499690357795637
"x154"	0.492923485315032
"x155"	0.498921156159765
"x156"	0.505832323109452
"x157"	0.50427568726968
"x158"	0.501047079447447
"x159"	0.501144096319587
"x160"	0.500279258508724
"x161"	0.501948064998072
"x162"	0.500822011614218
"x163"	0.497227232640726
"x164"	0.495996041991841
"x165"	0.501643313851114
"x166"	0.493981937356992
"x167"	0.501158596251789
"x168"	0.492721518449881
"x169"	0.502087166458927
"x170"	0.496132122005289
"x171"	0.50004740234639
"x172"	0.507995243759337
"x173"	0.495010604015226
"x174"	0.504332425329136
"x175"	0.500722138965363
"x176"	0.501477807205217
"x177"	0.503222832425241
"x178"	0.496611627437011
"x179"	0.501386782278749
"x180"	0.496568095402792
"x181"	0.49866127618046
"x182"	0.499939231952606
"x183"	0.49839989884852
"x184"	0.49844872907605
"x185"	0.499527396393754
"x186"	0.505675627022749
"x187"	0.502176277364884
"x188"	0.50282409973233
"x189"	0.501910180421011
"x190"	0.499091349298926
"x191"	0.503729833525093
"x192"	0.501052054715296
"x193"	0.497996389698493
"x194"	0.501086532175215
"x195"	0.497719087828533
"x196"	0.50029780225656
"x197"	0.497283518747846
"x198"	0.50029253732739
"x199"	0.503720246048248
"x200"	0.50173565008766
"x201"	0.505880159354582
"x202"	0.495958490648027
"x203"	0.501755221227929
"x204"	0.501195751817548
"x205"	0.502214450883982
"x206"	0.500531362394965
"x207"	0.50205923199188
"x208"	0.50295131285931
"x209"	0.502243809388275
"x210"	0.507529762405949
"x211"	0.504731268971018
"x212"	0.496389128514612
"x213"	0.501199241986056
"x214"	0.49963519236967
"x215"	0.503726938822726
"x216"	0.500616870596819
"x217"	0.499451167386491
"x218"	0.505073839902342
"x219"	0.495555917869695
"x220"	0.503535190937086
"x221"	0.500010514657409
"x222"	0.501485859632539
"x223"	0.501452482858882
"x224"	0.496859017808642
"x225"	0.499720605799975
"x226"	0.49902702385562
"x227"	0.493869393159961
"x228"	0.500975813526777
"x229"	0.503612502819882
"x230"	0.500296765768202
"x231"	0.501570096647646
"x232"	0.501653867180506
"x233"	0.495997469444037
"x234"	0.500007144648512
"x235"	0.501950742057967
"x236"	0.501136892649159
"x237"	0.497207203832921
"x238"	0.497366881357762
"x239"	0.503939381418563
"x240"	0.500307605826342
"x241"	0.500278400719655
"x242"	0.496130419079564
"x243"	0.498876399969449
"x244"	0.498236415238259
"x245"	0.505986941487947
"x246"	0.499224769213772
"x247"	0.501577037782432
"x248"	0.502718629168347
"x249"	0.497431639110297
"x250"	0.497242679194268
"x251"	0.500110229029763
"x252"	0.499876112394035
"x253"	0.500209626919613
"x254"	0.498006003626087
"x255"	0.501028609297518
"x256"	0.497476164073031
"x257"	0.49678638799903
"x258"	0.503028783134325
"x259"	0.500678621253278
"x260"	0.501899834921793
"x261"	0.49799142391663
"x262"	0.502971393630072
"x263"	0.503649817536864
"x264"	0.501297247359576
"x265"	0.498187974785664
"x266"	0.500154159696493
"x267"	0.498174033115013
"x268"	0.502259716476523
"x269"	0.500892450533505
"x270"	0.49974688360109
"x271"	0.503252161878953
"x272"	0.500539697517548
"x273"	0.504436443216214
"x274"	0.502837233404955
"x275"	0.500573441805621
"x276"	0.502325369855715
"x277"	0.502028237938345
"x278"	0.499332443716889
"x279"	0.502182460590545
"x280"	0.496923384860391
"x281"	0.502459710514196
"x282"	0.494431597842555
"x283"	0.498645268598385
"x284"	0.496526186001836
"x285"	0.500086312099337
"x286"	0.501093746714317
"x287"	0.499139699267014
"x288"	0.496876103397319
"x289"	0.501409091628436
"x290"	0.49791826228518
"x291"	0.498047744078701
"x292"	0.503011588848778
"x293"	0.494279091265984
"x294"	0.50145187503472
"x295"	0.500172946690372
"x296"	0.497979902947624
"x297"	0.492805902662501
"x298"	0.499126493594469
"x299"	0.498135944902035
"x300"	0.497372175921849
"x301"	0.499937077994528
"x302"	0.498308722825442
"x303"	0.49533371287086
"x304"	0.498127515137498
"x305"	0.498703532381309
"x306"	0.499237531865598
"x307"	0.497270844759117
"x308"	0.498935120696947
"x309"	0.505050902428967
"x310"	0.494586903066631
"x311"	0.501683675867925
"x312"	0.498886407341203
"x313"	0.502651175533538
"x314"	0.502610984513233
"x315"	0.499020783808059
"x316"	0.497362622722238
"x317"	0.497419736533822
"x318"	0.499328824524721
"x319"	0.499461412384966
"x320"	0.496635588650592
"x321"	0.50164939600958
"x322"	0.495475489443797
"x323"	0.504439370975946
"x324"	0.492955837118463
"x325"	0.50213546285443
"x326"	0.501837605465343
"x327"	0.505604026965867
"x328"	0.501586500711599
"x329"	0.495532248499035
"x330"	0.497800878073298
"x331"	0.499882971630897
"x332"	0.500076033881283
"x333"	0.495062761539174
"x334"	0.496449061025702
"x335"	0.504990343881771
"x336"	0.500045601881808
"x337"	0.501849232240906
"x338"	0.496911254709517
"x339"	0.504145932986075
"x340"	0.498204576935223
"x341"	0.499988833210897
"x342"	0.501985481519345
"x343"	0.501642016830575
"x344"	0.50208484286801
"x345"	0.501863940183376
"x346"	0.504740945380134
"x347"	0.494465954249515
"x348"	0.501264774960466
"x349"	0.503402628929587
"x350"	0.497616449450352
"x351"	0.498765651370934
"x352"	0.4989254358629
"x353"	0.50183583012505
"x354"	0.496825986894267
"x355"	0.498098364283633
"x356"	0.501767813966167
"x357"	0.498895416097227
"x358"	0.501967324887938
"x359"	0.502795286852052
"x360"	0.499872073752759
"x361"	0.498643346011383
"x362"	0.495098243173491
"x363"	0.498941021885141
"x364"	0.502219445838453
"x365"	0.502615964374412
"x366"	0.505264188127546
"x367"	0.500322223420395
"x368"	0.496345346435811
"x369"	0.504413213725598
"x370"	0.500707079809252
"x371"	0.497611740602506
"x372"	0.496615198885486
"x373"	0.500618592508254
"x374"	0.496569180697575
"x375"	0.504701023442182
"x376"	0.500255297750491
"x377"	0.498582437623548
"x378"	0.501286069167382
"x379"	0.503165722717438
"x380"	0.500378197866748
"x381"	0.503250720378035
"x382"	0.497218099547946
"x383"	0.501221833311347
"x384"	0.498849412409216
"x385"	0.501107512276433
"x386"	0.50047080468561
"x387"	0.498652378063696
"x388"	0.500216451354371
"x389"	0.504704176720278
"x390"	0.496354370877845
"x391"	0.503721972145187
"x392"	0.497143805629993
"x393"	0.50330190141215
"x394"	0.49937959353358
"x395"	0.498948919450445
"x396"	0.500168919996172
"x397"	0.502578267350467
"x398"	0.506101156181982
"x399"	0.503158229905623
"x400"	0.498680288048205
"x401"	0.501676260473486
"x402"	0.493510792666185
"x403"	0.499031297577242
"x404"	0.506920870206016
"x405"	0.502246720495657
"x406"	0.498294070439227
"x407"	0.501426641905122
"x408"	0.500076049703243
"x409"	0.498691384791536
"x410"	0.498929796277476
"x411"	0.501708653254574
"x412"	0.497889006545767
"x413"	0.504986315847584
"x414"	0.50249145781952
"x415"	0.498517542092968
"x416"	0.502209066824149
"x417"	0.50141426213542
"x418"	0.499224397290451
"x419"	0.502355230133631
"x420"	0.495981889346498
"x421"	0.498214948748122
"x422"	0.503087869197689
"x423"	0.503969026273629
"x424"	0.502452665825631
"x425"	0.497048816487961
"x426"	0.495624019407202
"x427"	0.499317652632738
"x428"	0.500914652247867
"x429"	0.499057770106616
"x430"	0.501268080979818
"x431"	0.498147857934493
"x432"	0.499914153201319
"x433"	0.495786726674158
"x434"	0.501682487738878
"x435"	0.496457202862645
"x436"	0.508543896326539
"x437"	0.501061310318136
"x438"	0.502894218938588
"x439"	0.502274609185918
"x440"	0.502498467199877
"x441"	0.501282249764865
"x442"	0.497290669186227
"x443"	0.501401792088733
"x444"	0.501240313977888
"x445"	0.497915219181264
"x446"	0.505064357985207
"x447"	0.502044832450291
"x448"	0.501214603475481
"x449"	0.500469672076427
"x450"	0.499569192343717
"x451"	0.504505589536461
"x452"	0.502298843947589
"x453"	0.497023923462909
"x454"	0.505475943069602
"x455"	0.495570238801069
"x456"	0.502516490759049
"x457"	0.50001143220698
"x458"	0.50056624143261
"x459"	0.500763978589862
"x460"	0.503351329483651
"x461"	0.495937412253953
"x462"	0.502993626758666
"x463"	0.50376565954613
"x464"	0.503578705062647
"x465"	0.499874845132907
"x466"	0.49710460913037
"x467"	0.500236035677022
"x468"	0.504071040146146
"x469"	0.502116545564379
"x470"	0.4970413170774
"x471"	0.497961502568331
"x472"	0.498185959877749
"x473"	0.500172177500324
"x474"	0.49990327054779
"x475"	0.505754457844445
"x476"	0.506156544381194
"x477"	0.502520981100039
"x478"	0.498271713060327
"x479"	0.499427241228428
"x480"	0.501416352765658
"x481"	0.497885700659943
"x482"	0.496553136685677
"x483"	0.499256669682357
"x484"	0.499976025144919
"x485"	0.502863525254768
"x486"	0.502828509843699
"x487"	0.497056779966387
"x488"	0.498057491645007
"x489"	0.499423123476515
"x490"	0.495550263123354
"x491"	0.496354107240075
"x492"	0.500804190374236
"x493"	0.502665761463973
"x494"	0.49879279964203
"x495"	0.50084074535016
"x496"	0.49858958128395
"x497"	0.498756470166892
"x498"	0.49870276355634
"x499"	0.49877461641808
"x500"	0.495528407560149
"x501"	0.499308988225553
"x502"	0.496989637391805
"x503"	0.497052493863879
"x504"	0.502207657987811
"x505"	0.497782883539819
"x506"	0.499157336530695
"x507"	0.499570190191874
"x508"	0.501303651056089
"x509"	0.501275910730497
"x510"	0.50475483049925
"x511"	0.49744029519686
"x512"	0.50021479274095
"x513"	0.502164487606823
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Rows: 1-600 | Columns: 2

You should always measure the impact you'll have on the system. Sometimes it is better to send multiple queries iteratively or in parallel rather than one big query. The optimal method depends on the use-case.