Introduction to PyKX

Explore PyKX basics by covering atomic types and collection types, generating pickaxe objects from Python data via k dot two q, and performing indexing, slicing, and read/write csv operations.

Open pykx basics notebook, import numpy, pandas, and pi as ke, then create atomic types (float, int, boolean, symbol, timestamp, date) and collection types (vectors, lists, dictionaries, tables) with exercises.

Transform Python data into pick types using cx dot two q for dictionaries, pandas dataframes, and pi arrow arrays, and generate random data with picaxe functions for tables and vectors.

Learn indexing and slicing in Pike objects, retrieving specific items from vectors, lists, and tables, including last elements with negative indices and selecting a single column.

Learn to load csv data into Pike from csv files using pandas read_csv or Pyqt, storing it in a PK table, and write csv output with kqueue write.csv.

Explore advanced indexing and slicing of Pike objects, use a pandas-like API for data manipulation, apply custom and inbuilt functions, and compare PyKX to pandas on large crypto data.

Learn to use PyKX's pandas-like API to index and slice a minute-by-minute bitcoin dataset and extract the close price. Compute mean, min, max, and median on peak vectors.

Apply custom functions to pick objects with dot apply method, use PyKX native functions for max, min, average, median, and sorting, and compare performance with pandas on bitcoin and ethereum.

Learn to create and explore PyKX tables, inspect shape and dtypes, rename columns, drop duplicates, and perform indexing, filtering, sorting, grouping, and as-of merges.

Develop PyKX table construction from data and CSVs, examine shape, columns, dtypes; add a date column from timestamps; and interrogate with at, get, all, any, including renaming and dropping duplicates.

Explore PyKX indexing, slicing, and filtering with loc and iloc, perform date and volume filters, sort by close prices, and group by date to compute daily totals.

Compute mean and median for numeric columns in a bitcoin table, find the max of the volume, and apply operations with table.egg. Use a 10-point moving average to reveal trends.

Explore as of joins and merges in pykx to combine time series data on a timestamp key, using exact or nearest preceding matches for cross-table data like trades and quotes.

Query tables in PyKX by applying filters, sorts, and groupings to retrieve relevant rows and columns, using q sql, cx.q.sql, and ipc for external data.

Learn to query tables with qSQL in PyKX by loading data into Tbtc, creating a date column, and using select, where, group by, and average to analyze prices.

Query the bitcoin table using standard SQL in Pike. Learn to connect via IPC to an external process on port 5000 and run selective SQL queries.

Explore time series analytics with PyKX, including date and time types, retrieving current timestamps, and operations. Learn about X bar bucketing and window joins to analyze data across time contexts.

Learn time series analysis with PyKX by importing bitcoin data, creating a dates column from timestamps, and manipulating date and time types, including plotting price over time with matplotlib.

Apply x bar to floor prices to the nearest lower multiple, creating smooth bins for visualization and testing, then use window joins on trades and quotes for time series analysis.