chore: import upstream snapshot with attribution
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wehub-resource-sync
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/*
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* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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using Python.Runtime;
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using QuantConnect.Data;
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using QuantConnect.Indicators;
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using QuantConnect.Util;
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using System;
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using System.Collections;
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using System.Collections.Generic;
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using System.Linq;
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namespace QuantConnect.Python
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{
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/// <summary>
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/// Collection of methods that converts lists of objects in pandas.DataFrame
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/// </summary>
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public partial class PandasConverter
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{
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private static dynamic _pandas;
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private static PyObject _concat;
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/// <summary>
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/// Initializes the <see cref="PandasConverter"/> class
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/// </summary>
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static PandasConverter()
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{
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using (Py.GIL())
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{
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var pandas = Py.Import("pandas");
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_pandas = pandas;
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// keep it so we don't need to ask for it each time
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_concat = pandas.GetAttr("concat");
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}
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}
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/// <summary>
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/// Converts an enumerable of <see cref="Slice"/> in a pandas.DataFrame
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/// </summary>
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/// <param name="data">Enumerable of <see cref="Slice"/></param>
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/// <param name="flatten">Whether to flatten collections into rows and columns</param>
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/// <param name="dataType">Optional type of bars to add to the data frame
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/// If true, the base data items time will be ignored and only the base data collection time will be used in the index</param>
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/// <returns><see cref="PyObject"/> containing a pandas.DataFrame</returns>
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public PyObject GetDataFrame(IEnumerable<Slice> data, bool flatten = false, Type dataType = null)
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{
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var generator = new DataFrameGenerator(data, flatten, dataType);
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return generator.GenerateDataFrame();
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}
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/// <summary>
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/// Converts an enumerable of <see cref="IBaseData"/> in a pandas.DataFrame
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/// </summary>
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/// <param name="data">Enumerable of <see cref="Slice"/></param>
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/// <param name="symbolOnlyIndex">Whether to make the index only the symbol, without time or any other index levels</param>
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/// <param name="forceMultiValueSymbol">Useful when the data contains points for multiple symbols.
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/// If false and <paramref name="symbolOnlyIndex"/> is true, it will assume there is a single point for each symbol,
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/// and will apply performance improvements for the data frame generation.</param>
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/// <param name="flatten">Whether to flatten collections into rows and columns</param>
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/// <returns><see cref="PyObject"/> containing a pandas.DataFrame</returns>
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/// <remarks>Helper method for testing</remarks>
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public PyObject GetDataFrame<T>(IEnumerable<T> data, bool symbolOnlyIndex = false, bool forceMultiValueSymbol = false, bool flatten = false)
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where T : ISymbolProvider
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{
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var generator = new DataFrameGenerator<T>(data, flatten);
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return generator.GenerateDataFrame(
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// Use 2 instead of maxLevels for backwards compatibility
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levels: symbolOnlyIndex ? 1 : 2,
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sort: false,
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symbolOnlyIndex: symbolOnlyIndex,
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forceMultiValueSymbol: forceMultiValueSymbol);
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}
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/// <summary>
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/// Converts a dictionary with a list of <see cref="IndicatorDataPoint"/> in a pandas.DataFrame
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/// </summary>
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/// <param name="data">Dictionary with a list of <see cref="IndicatorDataPoint"/></param>
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/// <param name="extraData">Optional dynamic properties to include in the DataFrame.</param>
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/// <returns><see cref="PyObject"/> containing a pandas.DataFrame</returns>
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public PyObject GetIndicatorDataFrame(IEnumerable<KeyValuePair<string, List<IndicatorDataPoint>>> data, IEnumerable<KeyValuePair<string, List<(DateTime, object)>>> extraData = null)
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{
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using (Py.GIL())
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{
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using var pyDict = new PyDict();
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foreach (var kvp in data)
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{
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AddSeriesToPyDict(kvp.Key, kvp.Value, pyDict);
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}
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if (extraData != null)
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{
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foreach (var kvp in extraData)
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{
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AddDynamicSeriesToPyDict(kvp.Key, kvp.Value, pyDict);
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}
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}
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return MakeIndicatorDataFrame(pyDict);
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}
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}
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/// <summary>
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/// Converts a dictionary with a list of <see cref="IndicatorDataPoint"/> in a pandas.DataFrame
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/// </summary>
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/// <param name="data"><see cref="PyObject"/> that should be a dictionary (convertible to PyDict) of string to list of <see cref="IndicatorDataPoint"/></param>
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/// <returns><see cref="PyObject"/> containing a pandas.DataFrame</returns>
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public PyObject GetIndicatorDataFrame(PyObject data)
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{
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using (Py.GIL())
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{
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using var inputPythonType = data.GetPythonType();
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var inputTypeStr = inputPythonType.ToString();
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var targetTypeStr = nameof(PyDict);
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PyObject currentKvp = null;
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try
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{
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using var pyDictData = new PyDict(data);
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using var seriesPyDict = new PyDict();
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targetTypeStr = $"{nameof(String)}: {nameof(List<IndicatorDataPoint>)}";
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foreach (var kvp in pyDictData.Items())
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{
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currentKvp = kvp;
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AddSeriesToPyDict(kvp[0].As<string>(), kvp[1].As<List<IndicatorDataPoint>>(), seriesPyDict);
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}
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return MakeIndicatorDataFrame(seriesPyDict);
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}
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catch (Exception e)
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{
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if (currentKvp != null)
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{
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inputTypeStr = $"{currentKvp[0].GetPythonType()}: {currentKvp[1].GetPythonType()}";
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}
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throw new ArgumentException(Messages.PandasConverter.ConvertToDictionaryFailed(inputTypeStr, targetTypeStr, e.Message), e);
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}
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}
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}
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/// <summary>
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/// Returns a string that represent the current object
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/// </summary>
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/// <returns></returns>
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public override string ToString()
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{
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if (_pandas == null)
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{
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return Messages.PandasConverter.PandasModuleNotImported;
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}
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using (Py.GIL())
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{
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return _pandas.Repr();
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}
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}
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/// <summary>
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/// Concatenates multiple data frames
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/// </summary>
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/// <param name="dataFrames">The data frames to concatenate</param>
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/// <param name="keys">
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/// Optional new keys for a new multi-index level that would be added
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/// to index each individual data frame in the resulting one
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/// </param>
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/// <param name="names">The optional names of the new index level (and the existing ones if they need to be changed)</param>
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/// <param name="sort">Whether to sort the resulting data frame</param>
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/// <param name="dropna">Whether to drop columns containing NA values only (Nan, None, etc)</param>
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/// <returns>A new data frame result from concatenating the input</returns>
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public static PyObject ConcatDataFrames<T>(IEnumerable<PyObject> dataFrames, IEnumerable<T> keys, IEnumerable<string> names,
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bool sort = true, bool dropna = true)
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{
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using (Py.GIL())
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{
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using var pyDataFrames = dataFrames.ToPyListUnSafe();
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if (pyDataFrames.Length() == 0)
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{
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return _pandas.DataFrame();
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}
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using var kwargs = Py.kw("sort", sort);
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PyList pyKeys = null;
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PyList pyNames = null;
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try
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{
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if (keys != null && names != null)
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{
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pyNames = names.ToPyListUnSafe();
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pyKeys = ConvertConcatKeys(keys);
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using var pyFalse = false.ToPython();
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kwargs.SetItem("keys", pyKeys);
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kwargs.SetItem("names", pyNames);
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kwargs.SetItem("copy", pyFalse);
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}
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var result = _concat.Invoke(new[] { pyDataFrames }, kwargs);
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// Drop columns with only NaN or None values
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if (dropna)
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{
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using var dropnaKwargs = Py.kw("axis", 1, "inplace", true, "how", "all");
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result.GetAttr("dropna").Invoke(Array.Empty<PyObject>(), dropnaKwargs);
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}
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return result;
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}
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finally
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{
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pyKeys?.Dispose();
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pyNames?.Dispose();
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}
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}
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}
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public static PyObject ConcatDataFrames(IEnumerable<PyObject> dataFrames, bool sort = true, bool dropna = true)
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{
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return ConcatDataFrames<string>(dataFrames, null, null, sort, dropna);
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}
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/// <summary>
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/// Creates the list of keys required for the pd.concat method, making sure that if the items are enumerables,
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/// they are converted to Python tuples so that they are used as levels for a multi index
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/// </summary>
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private static PyList ConvertConcatKeys(IEnumerable<IEnumerable<object>> keys)
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{
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var keyTuples = keys.Select(x => new PyTuple(x.Select(y => y.ToPython()).ToArray()));
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try
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{
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return keyTuples.ToPyListUnSafe();
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}
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finally
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{
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foreach (var tuple in keyTuples)
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{
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foreach (var x in tuple)
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{
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x.DisposeSafely();
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}
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tuple.DisposeSafely();
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}
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}
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}
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private static PyList ConvertConcatKeys<T>(IEnumerable<T> keys)
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{
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if ((typeof(T).IsAssignableTo(typeof(IEnumerable)) && !typeof(T).IsAssignableTo(typeof(string))))
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{
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return ConvertConcatKeys(keys.Cast<IEnumerable<object>>());
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}
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return keys.ToPyListUnSafe();
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}
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/// <summary>
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/// Creates a series from a list of <see cref="IndicatorDataPoint"/> and adds it to the
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/// <see cref="PyDict"/> as the value of the given <paramref name="key"/>
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/// </summary>
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/// <param name="key">Key to insert in the <see cref="PyDict"/></param>
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/// <param name="points">List of <see cref="IndicatorDataPoint"/> that will make up the resulting series</param>
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/// <param name="pyDict"><see cref="PyDict"/> where the resulting key-value pair will be inserted into</param>
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private void AddSeriesToPyDict(string key, List<IndicatorDataPoint> points, PyDict pyDict)
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{
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var index = new List<DateTime>();
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var values = new List<double>();
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foreach (var point in points)
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{
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if (point.EndTime != default)
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{
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index.Add(point.EndTime);
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values.Add((double)point.Value);
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}
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}
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pyDict.SetItem(key.ToLowerInvariant(), _pandas.Series(values, index));
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}
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/// <summary>
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/// Builds a time‑indexed pandas <see cref="Series"/> from a collection of
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/// heterogeneous data (numbers, enums, strings, etc.) and inserts it into the
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/// specified <see cref="PyDict"/> under the given <paramref name="key"/>.
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/// </summary>
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/// <param name="key">Key to insert in the <see cref="PyDict"/></param>
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/// <param name="entries">A list of tuples whose first item is the timestamp and whose second item is the value associated with that timestamp.</param>
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/// <param name="pyDict"><see cref="PyDict"/> where the resulting key-value pair will be inserted into</param>
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private void AddDynamicSeriesToPyDict(string key, List<(DateTime Timestamp, object Value)> entries, PyDict pyDict)
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{
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var index = new List<DateTime>();
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var values = new List<object>();
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foreach (var (timestamp, value) in entries)
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{
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if (timestamp != default)
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{
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index.Add(timestamp);
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values.Add(value is Enum e ? e.ToString() : value);
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}
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}
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pyDict.SetItem(key.ToLowerInvariant(), _pandas.Series(values, index));
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}
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/// <summary>
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/// Converts a <see cref="PyDict"/> of string to pandas.Series in a pandas.DataFrame
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/// </summary>
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/// <param name="pyDict"><see cref="PyDict"/> of string to pandas.Series</param>
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/// <returns><see cref="PyObject"/> containing a pandas.DataFrame</returns>
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private PyObject MakeIndicatorDataFrame(PyDict pyDict)
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{
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return _pandas.DataFrame(pyDict, columns: pyDict.Keys().Select(x => x.As<string>().ToLowerInvariant()).OrderBy(x => x));
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}
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}
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}
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