Python Developer Guide - Kinetica Docs

The following guide provides step-by-step instructions to get started writing Python applications using Kinetica. This guide demonstrates only a small set of the available API. A detailed description of the complete interface is available under Python API Reference.

Prerequisites

The prerequisites for running the match graph example are listed below:

Python API Installation

When using this include, precede it with the following heading:

Python API Installation

Depending on the target operating system, a Python virtual environment may need to be installed first:

Python Virtual Environment

The native Kinetica Python API is accessible through the following means:

PyPI
Git

Python Virtual Environment

A Python virtual environment is necessary to install in an operating environment where Python is externally managed.

Install a Python virtual environment:
```
python3 -m venv .venv
```
Activate the Python virtual environment:
```
source .venv/bin/activate
```

PyPI

Install the API:
```
pip3 install gpudb
```
Test the installation:
```
python3 -c "import gpudb;print('Import Successful')"
```
If Import Successful is displayed, the API has been installed as is ready for use.

Git

In the desired directory, run the following, but be sure to replace <kinetica-version> with the name of the installed Kinetica version, e.g., v7.2:
```
git clone -b release/<kinetica-version> --single-branch https://github.com/kineticadb/kinetica-api-python.git
```
Change directory into the newly downloaded repository:
```
cd kinetica-api-python
```
In the root directory of the unzipped repository, install the Kinetica API:
```
sudo pip3 install .
```
Test the installation (Python3 is necessary for running the API example):
```
python3 examples/example.py
```

Data File

The tutorial script references the taxi_trip_data.csv data file, mentioned in the Prerequisites, in the current local directory, by default. This directory can specified as a parameter when running the script.

Connecting to the Database

To interact with Kinetica, you must first instantiate an object of the GPUdb class while providing the connection URL, username, & password to the database server. For more details on connecting to the database, see Connecting via API.

Connect to the Database

kdb = gpudb.GPUdb(host = args.url, username = args.username, password = args.password)

Creating a Schema

All tables & views must be created within a schema. So, the first step will be to create a schema to contain all of the tables & views created within this tutorial.

Create Tutorial Schema

schema = "tutorial_python"
kdb.create_schema(schema)

Creating a Type

Before a table can be created and any data can be loaded into it, a type needs to be defined. The type is the set of column definitions for the table, structured as a list of lists, where each contained list corresponds to a single column definition. Each column comprises at least two values: a column name (always the first value) and a base type (always the second value). Any subsequent values are column properties. You can also use the GPUdbColumnProperty object in addition to literal string values to help define a column. For this tutorial, three types will be used to create three corresponding tables:

A base table containing a sample of NYC taxi cab ride data
A lookup table containing sample cab vendor information (created in the example below)
A 1-to-1 relation table containing payment information for a subset of the cab-ride transactions

The definition of the type for the vendor table follows:

Define a Type

vendor_columns = [
    # column types and properties can be listed as strings
    ["vendor_id", "string", "char4", "primary_key"],
    ["vendor_name", "string", "char64"],
    ["phone", "string", "char16", "nullable"],
    ["email", "string", "char64", "nullable"],
    ["hq_street", "string", "char64"],
    # column properties can also be listed using the GPUdbColumnProperty
    # object
    [
        "hq_city",
        "string",
        gpudb.GPUdbColumnProperty.CHAR8,
        gpudb.GPUdbColumnProperty.DICT
    ],
    [
        "hq_state",
        "string",
        gpudb.GPUdbColumnProperty.CHAR8,
        gpudb.GPUdbColumnProperty.DICT
    ],
    ["hq_zip", "int"],
    ["num_emps", "int"],
    ["num_cabs", "int"]
]

While the type is just defined here, it will be created implicitly when first used to create a table, shown below.

Creating a Table

After you’ve defined a type list, you can create a table using the GPUdbTable interface, passing in the type, the name of the table, any table options, and a handle to the database connection.

Create a Table

table_vendor_obj = gpudb.GPUdbTable(
    _type = vendor_columns,
    name = table_vendor,
    options = {"is_replicated": "true"},
    db = kdb
)

To pass in table creation options, you can either specify a Python map, as above, or you can use the GPUdbTableOptions object instead:

Use GPUdbTableOptions Instead of Raw Map for Options

creation_options = gpudb.GPUdbTableOptions.default().is_replicated(True)

At this point in the tutorial, a payment and taxi_trip_data type and table are also created.

To acquire a GPUdbTable object for a table that already exists, pass in a _type of None:

Get a Handle to an Existing Table

table_vendor_obj = gpudb.GPUdbTable(_type=None, name=table_vendor, db=kdb)

Inserting Data

You can insert single records or multiple records using the insert_records method. As mentioned previously, the number and order of values must match the column declarations found in the type creation step. In Python, you can pass a value of None for null values, but the column must be declared nullable when creating the type schema.

The following methods are not the only way to insert records, but they are the simplest. There is a convenience class called GPUdbIngestor and an automatic multi-head capability available through GPUdbTable, which facilitate inserting records into a table in batches; see Distributed Ingest for details.

Key/Value Record

In this example, a dictionary object will be used to map key/value pairs to table column values.

Insert by Dictionary

# Create ordered dictionary for keys & values of record
payment_datum = collections.OrderedDict()
payment_datum["payment_id"] = 189
payment_datum["payment_type"] = "No Charge"
payment_datum["credit_type"] = None
payment_datum["payment_timestamp"] = None
payment_datum["fare_amount"] = 6.5
payment_datum["surcharge"] = 0
payment_datum["mta_tax"] = 0.6
payment_datum["tip_amount"] = 0
payment_datum["tolls_amount"] = 0
payment_datum["total_amount"] = 7.1

# Insert the record into the table
table_payment_obj.insert_records(payment_datum)

In-Line Record

In this example, records will be defined in-line and added to a list. They can either be inserted as a single list of lists, as is done here, or as individual lists, one per record.

Insert by List

# Create a list of in-line records. The order of the values must match the
# column order in the type
vendor_records = [
    ["VTS", "Vine Taxi Service", "9998880001", "admin@vtstaxi.com",
     "26 Summit St.", "Flushing", "NY", 11354, 450, 400],
    ["YCAB", "Yes Cab", "7895444321", None, "97 Edgemont St.", "Brooklyn",
     "NY", 11223, 445, 425],
    ["NYC", "New York City Cabs", None, "support@nyc-taxis.com",
     "9669 East Bayport St.", "Bronx", "NY", 10453, 505, 500],
    ["DDS", "Dependable Driver Service", None, None,
        "8554 North Homestead St.", "Bronx", "NY", 10472, 200, 124],
    ["CMT", "Crazy Manhattan Taxi", "9778896500",
     "admin@crazymanhattantaxi.com", "950 4th Road Suite 78", "Brooklyn",
     "NY", 11210, 500, 468],
    ["TNY", "Taxi New York", None, None, "725 Squaw Creek St.", "Bronx",
     "NY", 10458, 315, 305],
    ["NYMT", "New York Metro Taxi", None, None, "4 East Jennings St.",
     "Brooklyn", "NY", 11228, 166, 150],
    ["5BTC", "Five Boroughs Taxi Co.", "4566541278", "mgmt@5btc.com",
     "9128 Lantern Street", "Brooklyn", "NY", 11229, 193, 175]
]

# Insert the records into the Vendor table
table_vendor_obj.insert_records(vendor_records)

CSV

CSV and other data files can be loaded into Kinetica via KiFS using two lines of code:

Upload the local file to KiFS using upload_files, passing the KiFS path to upload to and the bytes of the file to upload.
Insert the data from the uploaded file in KiFS using insert_records_from_files, passing the name of the table to load data into and the KiFS path to the source file.

Insert from File

kdb.upload_files("/data/" + CSV_FILE, open(csv_path, "rb").read())
kdb.insert_records_from_files(table_taxi, ["kifs://data/" + CSV_FILE])

The KiFS directory must already exist before uploading a file into it. The create_directory method can be used to create this directory.

Retrieving Data

Once the table is populated with data, the data can be retrieved using binary encoding or JSON encoding. Binary encoding is more efficient than JSON encoding. The example below uses the GPUdbTable.get_records method and binary encoding:

Retrieve Records (Binary Encoding via GPUdbTable class)

# Retrieve no more than 10 records from the Payment table using the
# GPUdbTable interface with binary encoding
print("{:>10s} {:<12s} {:<11s} {:<17s} {:<11s} {:<9s} {:<7s} {:<10s} {:<12s} {:<12s}".format(
        "Payment ID", "Payment Type", "Credit Type", "Payment Timestamp", "Fare Amount",
        "Surcharge", "MTA Tax", "Tip Amount", "Tolls Amount", "Total Amount"
))
print("{:=>10s} {:=<12s} {:=<11s} {:=<17s} {:=<11s} {:=<9s} {:=<7s} {:=<10s} {:=<12s} {:=<12s}".format(
        "", "", "", "", "", "", "", "", "", ""
))
for record in table_payment_obj.get_records(
    offset = 0,
    limit = 10,
    encoding = "binary",
    options = {"sort_by": "payment_id"}
):
    print("{payment_id:>10d} {payment_type:<12s} {credit_type:<11s} {payment_timestamp:>17} " \
        "{fare_amount:11.2f} {surcharge:9.2f} {mta_tax:7.2f} {tip_amount:10.2f} " \
        "{tolls_amount:12.2f} {total_amount:12.2f}".format(
            payment_id=record["payment_id"],
            payment_type=(record["payment_type"] or ""),
            credit_type=(record["credit_type"] or ""),
            payment_timestamp=(record["payment_timestamp"] or ""),
            fare_amount=record["fare_amount"],
            surcharge=record["surcharge"],
            mta_tax=record["mta_tax"],
            tip_amount=record["tip_amount"],
            tolls_amount=record["tolls_amount"],
            total_amount=record["total_amount"]
    ))

This example demonstrates JSON encoding record retrieval, using the GPUdb.get_records method:

Retrieve Records (JSON Encoding via GPUdb class)

# Retrieve all records from the Vendor table using the GPUdb interface with JSON encoding
print("{:<9s} {:<26s} {:<11s} {:<29s} {:<24s} {:<8s} {:<8s} {:<6s} {:<11s} {:<6s}".format(
        "Vendor ID", "Vendor Name", "Phone", "Email", "HQ Street",
        "HQ City", "HQ State", "HQ Zip", "# Employees", "# Cabs"
))
print("{:=<9s} {:=<26s} {:=<11s} {:=<29s} {:=<24s} {:=<8s} {:=<8s} {:=<6s} {:=<11s} {:=<6s}".format(
        "", "", "", "", "", "", "", "", "", ""
))
vendor_records_gpudb = kdb.get_records(
    table_name = table_vendor,
    offset = 0,
    limit = gpudb.GPUdb.END_OF_SET,
    encoding = "json",
    options = {"sort_by": "vendor_id"}
)["records_json"]
for record in vendor_records_gpudb:
    rec = json.loads(record)
    rec["phone"] = rec["phone"] or ""
    rec["email"] = rec["email"] or ""
    print("{vendor_id:<9s} {vendor_name:<26s} {phone:<11} {email:<29s} {hq_street:<24s} " \
        "{hq_city:<8s} {hq_state:<8s} {hq_zip:<6d} {num_emps:11d} {num_cabs:6d}".format(**rec))

For large tables, the data can be easily be retrieved in smaller blocks by using the offset and limit parameters. The returned response also contains the type (column definitions) of the results.

Updating Records

Using any GPUdbTable object, you can update records via the update_records method.

Update Records

# Update the e-mail of, and add two employees and one cab to, the DDS vendor
table_vendor_obj.update_records(
    expressions = ["vendor_id = 'DDS'"],
    new_values_maps = {
        "email": "'management@ddstaxico.com'",
        "num_emps": "num_emps + 2",
        "num_cabs": "num_cabs + 1"
    },
    options = {"use_expressions_in_new_values_maps":"true"}
)

Deleting Records

You can delete records using the delete_records method. This method allows you to specify multiple filter expressions—note that each expression is used to delete records independently from the others (i.e., a record only needs to meet any one expression’s criteria to be deleted from the table). Set delete_all_records to True in the options map to delete all records in the table.

Delete Records

# Delete payment 189
delete_expr = ["payment_id = 189"]
table_payment_obj.delete_records(expressions=delete_expr)

Alter Table

Some properties can be altered or added after table creation, using alter_table, including indexes and dictionary encoding.

Indexes

Using the alter_table method, you can create indexes on columns using the create_index action paired with a column name.

Index Columns

# Add column indexes on:
#   - payment table, fare_amount (for query-chaining filter example)
#   - taxi table, passenger_count (for filter-by-range example)
table_payment_obj.alter_table(action="create_index", value="fare_amount")
table_taxi_obj.alter_table(action="create_index", value="passenger_count")

Dictionary Encoding

Applying dictionary encoding via alter_table involves adding a new property to a column.

Dictionary Encode Column

# Apply dictionary encoding to the vendor_id column
at_resp = table_taxi_obj.alter_table(
    action = "change_column",
    value = "vendor_id",
    options = {"column_properties": "char4,dict"}
)

To add a new property, all existing column properties must be listed along with any new properties.

Filters

Filters are an easy way to reduce larger tables into more concise views using expressions.

Count Value-Filtered Records

# Filter for only payments with no corresponding payment type, returning the
# count of records found; allow Kinetica to assign a random name to the view
f1_count = table_payment_obj.filter(expression="IS_NULL(payment_type)").size()

Count Using Filter Chaining; Name the Backing Views

# Using GPUdbTable query chaining, filter null payment type records with a fare amount greater than 8
f2_count = table_payment_obj.filter(
    view_name = view_example1, expression = "IS_NULL(payment_type)"
).filter(
    view_name = view_example2, expression = "fare_amount > 8"
).size()

Count List-Filtered Records

# Filter by list where vendor ID is either NYC or YCAB
f3_count = table_taxi_obj.filter_by_list(
    view_name = view_example3, column_values_map = {"vendor_id": ["NYC", "YCAB"]}
).size()

Count Range-Filtered Records

# Filter by range trip with passenger count between 1 and 3
f4_count = table_taxi_obj.filter_by_range(
    view_name = view_example4, column_name = "passenger_count", lower_bound = 1, upper_bound = 3
).size()

Aggregates

Kinetica supports various aggregate and group-by queries, which group and aggregate your data to return counts and useful statistics.

Calculate Count/Min/Mean/Max

# Aggregate count, min, mean, and max on the trip distance
a1_resp = table_taxi_obj.aggregate_statistics(
    column_name = "trip_distance",
    stats = "count,min,max,mean"
)["stats"]

print("Statistics of values in the trip_distance column:")
print("\tCount: {count:5.0f}\n\tMin:   {min:5.2f}\n\tMean:  {mean:5.2f}\n\tMax:   {max:5.2f}\n" \
    "".format(**a1_resp))

Retrieve Unique Values

# Find unique taxi vendor IDs
a2_resp = table_taxi_obj.aggregate_unique(
    column_name = "vendor_id",
    offset = 0,
    limit = gpudb.GPUdb.END_OF_SET,
    encoding = "json"
)["data"]["vendor_id"]

print("Unique vendor IDs in the taxi trip table:")
for vendor in a2_resp:
    print("\t* {}".format(vendor))

Report Number of Records per Group

# Find number of trips per vendor
a3_resp = table_taxi_obj.aggregate_group_by(
    column_names = ["vendor_id", "COUNT(vendor_id)"],
    offset = 0,
    limit = gpudb.GPUdb.END_OF_SET,
    options = {"sort_by": "key"}
)["data"]

print("Trips per vendor:")
for vendor in zip(a3_resp["vendor_id"], a3_resp["COUNT(vendor_id)"]):
    print("\t{:<6s} {:3d}".format(vendor[0] + ":", vendor[1]))

Generate Histogram

# Create a histogram for the different groups of passenger counts
a4_resp = table_taxi_obj.aggregate_histogram(
    column_name = "passenger_count",
    start = 1,
    end = 6,
    interval = 1
)["counts"]

print("Passenger count groups by size:")
print("{:<10s} {:<11s}".format("Passengers", "Total Trips"))
print("{:=<10s} {:=<11s}".format("", ""))

for histo_group in zip([1, 2, 3, 4, '>5'], a4_resp):
    print("{:>10} {:11.0f}".format(*histo_group))

Joins

Joins allow you to link multiple tables together, along their relations, retrieving associated information from any or all of them. Tables can only be joined if they’re sharded similarly or replicated. All join types can be performed using the create_join_table method. This method is static and is not called with a corresponding GPUdbTable object. An inner join returns only records that have matching values in both tables.

Inner Join

# Retrieve payment information for rides having more than three passengers
gpudb.GPUdbTable.create_join_table(
    join_table_name = join_table_inner,
    table_names = [
        schema + ".taxi_trip_data as t",
        schema + ".payment as p"
    ],
    column_names = [
        "t.payment_id",
        "payment_type",
        "total_amount",
        "passenger_count",
        "vendor_id",
        "trip_distance"
    ],
    expressions = [
        "t.payment_id = p.payment_id",
        "passenger_count > 3"
    ],
    db = kdb
)

A left join returns all of the records an inner join does, but additionally, for each record in the table on the left side of the join that has no match along the relation to a record in the table on the right side of the join, a corresponding record will be returned with “left-side” columns populated with the “left-side” record data and the “right-side” columns populated with nulls. Note the usage of left join in the given expression.

Left Join

# Retrieve cab ride transactions and the full name of the associated vendor
# (if available--blank if vendor name is unknown) for transactions with
# associated payment data, sorting by increasing values of transaction ID.
gpudb.GPUdbTable.create_join_table(
    join_table_name = join_table_left,
    table_names = [
        schema + ".taxi_trip_data as t",
        schema + ".vendor as v"
    ],
    column_names = [
        "transaction_id",
        "pickup_datetime",
        "trip_distance",
        "t.vendor_id",
        "vendor_name"
    ],
    expressions = [
        "LEFT JOIN t, v ON (t.vendor_id = v.vendor_id)",
        "payment_id <> 0"
    ],
    db = kdb
)

Full outer joins require both tables to be replicated or joined on their shard keys. Set merges that perform deduplication of records, like Union Distinct, Intersect, and Except also need to use replicated tables to ensure the correct results, so a replicated version of the taxi (taxi_trip_data_replicated) table is created at this point in the tutorial using create_projection.

Create Replicated Table for Full Outer Join

kdb.create_projection(
    table_name = table_taxi,
    projection_name = table_taxi_replicated,
    column_names = [
        "transaction_id",
        "payment_id",
        "vendor_id",
        "pickup_datetime",
        "dropoff_datetime",
        "passenger_count",
        "trip_distance",
        "pickup_longitude",
        "pickup_latitude",
        "dropoff_longitude",
        "dropoff_latitude"
    ],
    options = {"is_replicated": "true"}
)

A full outer join returns all of the records a left join does, but additionally, for each record in the table on the right side of the join that has no match along the relation to a record in the table on the left side of the join, a corresponding record will be returned with “right-side” columns populated with the “right-side” record data and the “left-side” columns populated with nulls.

Full Outer Join

# Retrieve the vendor IDs of known vendors with no recorded cab ride
# transactions, as well as the vendor ID and number of transactions for
# unknown vendors with recorded cab ride transactions
gpudb.GPUdbTable.create_join_table(
    join_table_name = join_table_outer,
    table_names = [
        schema + ".taxi_trip_data_replicated as t",
        schema + ".vendor as v"
    ],
    column_names = [
        "t.vendor_id as vendor_id",
        "v.vendor_id as vendor_id_1"
    ],
    expressions = ["FULL_OUTER JOIN t,v ON ((v.vendor_id = t.vendor_id))"],
    db = kdb
)

Projections

You can create projections using the create_projection method.

Create a Temporary Projection

# Create a projection containing all payments by credit card
table_payment_obj.create_projection(
    projection_name = projection_example1,
    column_names = [
        "payment_id",
        "payment_type",
        "credit_type",
        "payment_timestamp",
        "fare_amount",
        "surcharge",
        "mta_tax",
        "tip_amount",
        "tolls_amount",
        "total_amount"
    ],
    options = {"expression": "payment_type = 'Credit'"}
)

To persist a projection:

Create a Permanent Projection

# Create a persisted table with cab ride transactions greater than 5 miles
# whose trip started during lunch hours
table_taxi_obj.create_projection(
    projection_name = projection_example2,
    column_names = [
        "HOUR(pickup_datetime) as hour_of_day",
        "vendor_id",
        "passenger_count",
        "trip_distance"
    ],
    options = {
        "expression":
            "(HOUR(pickup_datetime) >= 11) AND "
            "(HOUR(pickup_datetime) <= 14) AND "
            "(trip_distance > 5)",
        "persist": "true"
    }
)

Union, Intersect, & Except

Union can be used to combine homogeneous data sets into one larger data set. When calling the create_union method, a mode of union or union_distinct will both combine data sets, but only retain the records that are unique across the chosen columns, removing all duplicates. Using union_all will combine data sets, retaining all records from the source data sets.

Set Union Retaining Duplicates

# Calculate the average number of passengers, as well as the shortest,
# average, and longest trips for all trips in
# each of the two time periods--from April 1st through the 15th, 2015 and
# from April 16th through the 23rd, 2015--and return those two sets of
# statistics in a single result set.
table_taxi_obj.aggregate_group_by(
    column_names = [
        "AVG(passenger_count) as avg_pass_count",
        "AVG(trip_distance) as avg_trip_dist",
        "MIN(trip_distance) as min_trip_dist",
        "MAX(trip_distance) as max_trip_dist"
    ],
    offset = 0,
    limit = gpudb.GPUdb.END_OF_SET,
    options = {
        "expression":
            "(pickup_datetime >= '2015-04-01') AND "
            "(pickup_datetime <= '2015-04-15 23:59:59.999')",
        "result_table": agg_grpby_union_all_src1
    }
)
table_taxi_obj.aggregate_group_by(
    column_names = [
        "AVG(passenger_count) as avg_pass_count",
        "AVG(trip_distance) as avg_trip_dist",
        "MIN(trip_distance) as min_trip_dist",
        "MAX(trip_distance) as max_trip_dist"
    ],
    offset = 0,
    limit = gpudb.GPUdb.END_OF_SET,
    options = {
        "expression":
            "(pickup_datetime >= '2015-04-16') AND "
            "(pickup_datetime <= '2015-04-23 23:59:59.999')",
        "result_table": agg_grpby_union_all_src2
    }
)
gpudb.GPUdbTable.create_union(
    table_name = union_all_table,
    table_names = [
        agg_grpby_union_all_src1,
        agg_grpby_union_all_src2
    ],
    input_column_names = [
        ["'2015-04-01 - 2015-04-15'", "avg_pass_count", "avg_trip_dist", "min_trip_dist", "max_trip_dist"],
        ["'2015-04-16 - 2015-04-23'", "avg_pass_count", "avg_trip_dist", "min_trip_dist", "max_trip_dist"]
    ],
    output_column_names = [
        "pickup_window_range",
        "avg_pass_count",
        "avg_trip",
        "min_trip",
        "max_trip"
    ],
    options = {"mode": "union_all"},
    db = kdb
)

Calling the create_union method with a mode of intersect will perform a set intersection, which will combine data sets but only include the records found in both data sets, removing duplicate result records. Using intersect_all will retain intersecting duplicates from both sets.

Set Intersection

# Retrieve locations (as lat/lon pairs) that were both pick-up and drop-off points
gpudb.GPUdbTable.create_union(
    table_name = union_intersect_table,
    table_names = [
        table_taxi_replicated,
        table_taxi_replicated
    ],
    input_column_names = [
        ["pickup_latitude", "pickup_longitude"],
        ["dropoff_latitude", "dropoff_longitude"]
    ],
    output_column_names = ["latitude", "longitude"],
    options = {"mode": "intersect"},
    db = kdb
)

Calling the create_union method with a mode of except will perform a set except (subtraction), which will return records that appear in the first data set but not the second data set. Note that the data sets on each side of the except will have duplicates removed first, and then the set subtraction will be processed. Using except_all will retain duplicates from the first set.

Set Exception (Subtraction)

# Show vendors that operate before noon, but not after noon: retrieve the
# unique list of IDs of vendors who provided cab rides between midnight
# and noon, and remove from that list the IDs of any vendors who provided
# cab rides between noon and midnight
table_taxi_replicated_obj.create_projection(
    projection_name = projection_except_src1,
    column_names = ["vendor_id"],
    options = {
        "expression":
            "(HOUR(pickup_datetime) >= 0) AND "
            "(HOUR(pickup_datetime) <= 11)"
    }
)
table_taxi_replicated_obj.create_projection(
    projection_name = projection_except_src2,
    column_names = ["vendor_id"],
    options = {
        "expression":
            "(HOUR(pickup_datetime) >= 12) AND "
            "(HOUR(pickup_datetime) <= 23)"
    }
)
gpudb.GPUdbTable.create_union(
    table_name = union_except_table,
    table_names = [
        projection_except_src1,
        projection_except_src2
    ],
    input_column_names = [
        ["vendor_id"],
        ["vendor_id"]
    ],
    output_column_names = ["vendor_id"],
    options = {"mode": "except"},
    db = kdb
)

Download & Run

Included below is a complete example containing all the above requests, the data file, and output.

To run the complete sample, ensure that:

the python_tutorial.py script is in the current directory
the taxi_trip_data.csv file is in the current directory or use the data_dir parameter to specify the local directory containing it

Then, run the following:

Run Example

python python_tutorial.py [--url <kinetica_url>] --username <username> --password <password> [--data_dir <data_file_directory>]

As this script creates a schema and several database objects within it, system admin permission is required to run it.

​Prerequisites

​Python API Installation

​Python API Installation

​Python Virtual Environment

​PyPI

​Git

​Data File

​Connecting to the Database

​Creating a Schema

​Creating a Type

​Creating a Table

​Inserting Data

​Key/Value Record

​In-Line Record

​CSV

​Retrieving Data

​Updating Records

​Deleting Records

​Alter Table

​Indexes

​Dictionary Encoding

​Filters

​Aggregates

​Joins

​Projections

​Union, Intersect, & Except

​Download & Run

Prerequisites

Python API Installation

Python API Installation

Python Virtual Environment

PyPI

Git

Data File

Connecting to the Database

Creating a Schema

Creating a Type

Creating a Table

Inserting Data

Key/Value Record

In-Line Record

CSV

Retrieving Data

Updating Records

Deleting Records

Alter Table

Indexes

Dictionary Encoding

Filters

Aggregates

Joins

Projections

Union, Intersect, & Except

Download & Run