pandas-ml-common 0.2.0

Augment pandas DataFrame with methods for machine learning

The pandas ml common module

This module holds all common extensions and utilities for the pandas ml quant stack.

• easy joining of data frames with multi indexes

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

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

df1.unique_level_columns(0)



['A', 'B']



df1.add_multi_index('Z', axis=1)

• data splitting, sampling and folding (aka cross validation)

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)

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)

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

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])

• serialisation utils

from pandas_ml_common import serializeb, deserializeb



deserializeb(serializeb(np.array([1, 2, 3])))

array([1, 2, 3])

• re-scalings

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.])