pandas-ml-common-0.2.5.tar.gz

## The pandas ml common module This module holds all common extensions and utilities for the pandas ml quant stack. Feel free to study the [examples][ghl1] as well. * easy joining of data frames with multi indexes ```python from pandas_ml_common import pd, np df1 = pd.DataFrame({"a": np.random.random(10), "b": np.random.random(10)}) print(df1.inner_join(df1, prefix_left='A', prefix='B', force_multi_index=True).to_markdown()) ``` | | ('A', 'a') | ('A', 'b') | ('B', 'a') | ('B', 'b') | |---:|-------------:|-------------:|-------------:|-------------:| | 0 | 0.907892 | 0.726913 | 0.907892 | 0.726913 | | 1 | 0.602275 | 0.134278 | 0.602275 | 0.134278 | | 2 | 0.264399 | 0.207429 | 0.264399 | 0.207429 | | 3 | 0.559751 | 0.816759 | 0.559751 | 0.816759 | | 4 | 0.951172 | 0.797524 | 0.951172 | 0.797524 | | 5 | 0.504332 | 0.51996 | 0.504332 | 0.51996 | | 6 | 0.765235 | 0.17908 | 0.765235 | 0.17908 | | 7 | 0.388691 | 0.644103 | 0.388691 | 0.644103 | | 8 | 0.663636 | 0.678879 | 0.663636 | 0.678879 | | 9 | 0.291603 | 0.0164627 | 0.291603 | 0.0164627 | * access columns with regex ```python df4 = pd.DataFrame({"a_22_a": np.random.random(1), "b_21_b": np.random.random(1)}) df4._[r'.*\d+_.'] ``` | | a_22_a | b_21_b | |---:|---------:|----------:| | 0 | 0.22039 | 0.0374084 | * easy access multi level index ```python df1.unique_level_columns(0) ['A', 'B'] df1.add_multi_index('Z', axis=1) ``` * data splitting, sampling and folding (aka cross validation) ```python from pandas_ml_common import Sampler, XYWeight, random_splitter df2 = pd.DataFrame({"c": np.random.random(10)}) sampler = Sampler(XYWeight(df1, df2), splitter=random_splitter(0.5)) for batches in sampler.sample_for_training(): for batch in batches: print(batch) ``` * access to nested numpy arrays in data frame columns (`df._.values`) ```python df3 = pd.DataFrame({"a": [[1, 2], [3, 4], [5, 6]]}) df3._.values array([[1, 2], [3, 4], [5, 6]]) ``` * dynamic method call providing suitable *args and **kwargs (dependency injection) ```python from pandas_ml_common import call_callable_dynamic_args def adder(a, b): return a + b call_callable_dynamic_args(adder, a=12, b=10, c='illegal') 22 ``` * numpy utils ```python from pandas_ml_common import np_nans np_nans((3, 3)) array([[nan, nan, nan], [nan, nan, nan], [nan, nan, nan]]) from pandas_ml_common import temp_seed with temp_seed(42): print(np.random.random(2)) np.random.random(2) [0.37454012 0.95071431] array([0.69373278, 0.69790163]) ``` * serialization utils ```python from pandas_ml_common import serializeb, deserializeb deserializeb(serializeb(np.array([1, 2, 3]))) array([1, 2, 3]) ``` * re-scalings ```python from pandas_ml_common import ReScaler x = np.arange(0, 1, .1) rescaler = ReScaler((0, 1), (5, -5)) rescaler(x) array([ 5., 4., 3., 2., 1., 0., -1., -2., -3., -4.]) ``` [ghl1]: https://github.com/KIC/pandas-ml-quant/tree/0.2.5/pandas-ml-common/./examples/