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polakowo
GitHub Repository: polakowo/vectorbt
 Path: blob/master/tests/notebooks/base.ipynb
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Kernel: Python 3

base

import vectorbt as vbt

from vectorbt.base import column_grouper, array_wrapper, combine_fns, index_fns, indexing, reshape_fns

import numpy as np
import pandas as pd
from datetime import datetime
from numba import njit
import itertools

v1 = 0
a1 = np.array([1])
a2 = np.array([1, 2, 3])
a3 = np.array([[1, 2, 3]])
a4 = np.array([[1], [2], [3]])
a5 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
sr_none = pd.Series([1])
print(sr_none)
sr1 = pd.Series([1], index=pd.Index(['x1'], name='i1'), name='a1')
print(sr1)
sr2 = pd.Series([1, 2, 3], index=pd.Index(['x2', 'y2', 'z2'], name='i2'), name='a2')
print(sr2)
df_none = pd.DataFrame([[1]])
print(df_none)
df1 = pd.DataFrame(
    [[1]], 
    index=pd.Index(['x3'], name='i3'), 
    columns=pd.Index(['a3'], name='c3'))
print(df1)
df2 = pd.DataFrame(
    [[1], [2], [3]], 
    index=pd.Index(['x4', 'y4', 'z4'], name='i4'), 
    columns=pd.Index(['a4'], name='c4'))
print(df2)
df3 = pd.DataFrame(
    [[1, 2, 3]], 
    index=pd.Index(['x5'], name='i5'), 
    columns=pd.Index(['a5', 'b5', 'c5'], name='c5'))
print(df3)
df4 = pd.DataFrame(
    [[1, 2, 3], [4, 5, 6], [7, 8, 9]], 
    index=pd.Index(['x6', 'y6', 'z6'], name='i6'), 
    columns=pd.Index(['a6', 'b6', 'c6'], name='c6'))
print(df4)

multi_i = pd.MultiIndex.from_arrays([['x7', 'y7', 'z7'], ['x8', 'y8', 'z8']], names=['i7', 'i8']) 
multi_c = pd.MultiIndex.from_arrays([['a7', 'b7', 'c7'], ['a8', 'b8', 'c8']], names=['c7', 'c8'])
df5 = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=multi_i, columns=multi_c)
print(df5)
0 1 dtype: int64 i1 x1 1 Name: a1, dtype: int64 i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 0 0 1 c3 a3 i3 x3 1 c4 a4 i4 x4 1 y4 2 z4 3 c5 a5 b5 c5 i5 x5 1 2 3 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 c7 a7 b7 c7 c8 a8 b8 c8 i7 i8 x7 x8 1 2 3 y7 y8 4 5 6 z7 z8 7 8 9

column_grouper

some_columns = pd.MultiIndex.from_arrays([
    [1, 1, 1, 1, 0, 0, 0, 0],
    [3, 3, 2, 2, 1, 1, 0, 0],
    [7, 6, 5, 4, 3, 2, 1, 0]
], names=['first', 'second', 'third'])

print(column_grouper.group_by_to_index(some_columns, group_by=0))
print(column_grouper.group_by_to_index(some_columns, group_by='first'))
print(column_grouper.group_by_to_index(some_columns, group_by=[0, 1]))
print(column_grouper.group_by_to_index(some_columns, group_by=['first', 'second']))
print(column_grouper.group_by_to_index(some_columns, group_by=np.array([3, 2, 1, 1, 1, 0, 0, 0])))
print(column_grouper.group_by_to_index(some_columns, group_by=pd.Index([3, 2, 1, 1, 1, 0, 0, 0], name='fourth')))
Index([1, 1, 1, 1, 0, 0, 0, 0], dtype='int64', name='first') Index([1, 1, 1, 1, 0, 0, 0, 0], dtype='int64', name='first') MultiIndex([(1, 3), (1, 3), (1, 2), (1, 2), (0, 1), (0, 1), (0, 0), (0, 0)], names=['first', 'second']) MultiIndex([(1, 3), (1, 3), (1, 2), (1, 2), (0, 1), (0, 1), (0, 0), (0, 0)], names=['first', 'second']) Index([3, 2, 1, 1, 1, 0, 0, 0], dtype='int64') Index([3, 2, 1, 1, 1, 0, 0, 0], dtype='int64', name='fourth') 
# group_arr comes always from 0 to n, also keeps order
print(column_grouper.get_groups_and_index(some_columns, 0))
print(column_grouper.get_groups_and_index(some_columns, [0, 1]))
print(column_grouper.get_groups_and_index(some_columns, np.array([3, 2, 1, 1, 1, 0, 0, 0])))
(array([0, 0, 0, 0, 1, 1, 1, 1]), Index([1, 0], dtype='int64', name='first')) (array([0, 0, 1, 1, 2, 2, 3, 3]), MultiIndex([(1, 3), (1, 2), (0, 1), (0, 0)], names=['first', 'second'])) (array([0, 1, 2, 2, 2, 3, 3, 3]), Index([3, 2, 1, 0], dtype='int64')) 
print(column_grouper.get_group_lens_nb(np.array([0, 0, 0, 0, 1, 1, 1, 1])))
print(column_grouper.get_group_lens_nb(np.array([0, 1])))
print(column_grouper.get_group_lens_nb(np.array([0, 0])))
print(column_grouper.get_group_lens_nb(np.array([0])))
print(column_grouper.get_group_lens_nb(np.array([])))
[4 4] [1 1] [2] [1] [] 
print(column_grouper.ColumnGrouper(sr2.to_frame().columns, group_by=np.array([0])).group_by)
print(column_grouper.ColumnGrouper(sr2.to_frame().columns, group_by=np.array([0])).get_groups_and_columns())
print(column_grouper.ColumnGrouper(sr2.to_frame().columns, group_by=np.array([0])).get_groups())
print(column_grouper.ColumnGrouper(sr2.to_frame().columns, group_by=np.array([0])).get_columns())
print(column_grouper.ColumnGrouper(sr2.to_frame().columns, group_by=np.array([0])).get_group_lens())
print(column_grouper.ColumnGrouper(sr2.to_frame().columns, group_by=np.array([0])).get_group_start_idxs())
print(column_grouper.ColumnGrouper(sr2.to_frame().columns, group_by=np.array([0])).get_group_end_idxs())
Index([0], dtype='int64') (array([0]), Index([0], dtype='int64')) [0] Index([0], dtype='int64') [1] [0] [1] 
print(column_grouper.ColumnGrouper(df4.columns, group_by=np.array([0, 0, 1])).group_by)
print(column_grouper.ColumnGrouper(df4.columns, group_by=np.array([0, 0, 1])).get_groups_and_columns())
print(column_grouper.ColumnGrouper(df4.columns, group_by=np.array([0, 0, 1])).get_groups())
print(column_grouper.ColumnGrouper(df4.columns, group_by=np.array([0, 0, 1])).get_columns())
print(column_grouper.ColumnGrouper(df4.columns, group_by=np.array([0, 0, 1])).get_group_lens())
print(column_grouper.ColumnGrouper(df4.columns, group_by=np.array([0, 0, 1])).get_group_start_idxs())
print(column_grouper.ColumnGrouper(df4.columns, group_by=np.array([0, 0, 1])).get_group_end_idxs())
Index([0, 0, 1], dtype='int64') (array([0, 0, 1]), Index([0, 1], dtype='int64')) [0 0 1] Index([0, 1], dtype='int64') [2 1] [0 2] [2 3]

array_wrapper

sr2_wrapper = array_wrapper.ArrayWrapper.from_obj(sr2)
df4_wrapper = array_wrapper.ArrayWrapper.from_obj(df4)

sr2_wrapper_co = sr2_wrapper.copy(column_only_select=True)
df4_wrapper_co = df4_wrapper.copy(column_only_select=True)

sr2_grouped_wrapper = sr2_wrapper.copy(group_by=np.array([0]))
df4_grouped_wrapper = df4_wrapper.copy(group_by=np.array([0, 0, 1]))

sr2_grouped_wrapper_co = sr2_grouped_wrapper.copy(column_only_select=True)
df4_grouped_wrapper_co = df4_grouped_wrapper.copy(column_only_select=True)

# test indexing
print(sr2_wrapper.indexing_func_meta(lambda x: x.iloc[:2])[1:])
print(df4_wrapper.indexing_func_meta(lambda x: x.iloc[0, :2])[1:])
print(df4_wrapper.indexing_func_meta(lambda x: x.iloc[:2, 0])[1:])
print(df4_wrapper.indexing_func_meta(lambda x: x.iloc[:2, [0]])[1:])
print(df4_wrapper.indexing_func_meta(lambda x: x.iloc[:2, :2])[1:])
(array([0, 1]), 0, 0) (0, array([0, 1]), array([0, 1])) (array([0, 1]), 0, 0) (array([0, 1]), array([0]), array([0])) (array([0, 1]), array([0, 1]), array([0, 1])) 
print(df4_wrapper_co.indexing_func_meta(lambda x: x.iloc[0])[1:])
print(df4_wrapper_co.indexing_func_meta(lambda x: x.iloc[[0]])[1:])
print(df4_wrapper_co.indexing_func_meta(lambda x: x.iloc[:2])[1:])
(array([0, 1, 2]), 0, 0) (array([0, 1, 2]), array([0]), array([0])) (array([0, 1, 2]), array([0, 1]), array([0, 1])) 
print(sr2_grouped_wrapper.indexing_func_meta(lambda x: x.iloc[:2])[1:])
print(df4_grouped_wrapper.indexing_func_meta(lambda x: x.iloc[:2, 0])[1:])
print(df4_grouped_wrapper.indexing_func_meta(lambda x: x.iloc[:2, 1])[1:])
print(df4_grouped_wrapper.indexing_func_meta(lambda x: x.iloc[:2, [1]])[1:])
print(df4_grouped_wrapper.indexing_func_meta(lambda x: x.iloc[:2, :2])[1:])
(array([0, 1]), 0, 0) (array([0, 1]), 0, array([0, 1])) (array([0, 1]), 1, 2) (array([0, 1]), array([1]), array([2])) (array([0, 1]), array([0, 1]), array([0, 1, 2])) 
print(df4_grouped_wrapper_co.indexing_func_meta(lambda x: x.iloc[0])[1:])
print(df4_grouped_wrapper_co.indexing_func_meta(lambda x: x.iloc[1])[1:])
print(df4_grouped_wrapper_co.indexing_func_meta(lambda x: x.iloc[[1]])[1:])
print(df4_grouped_wrapper_co.indexing_func_meta(lambda x: x.iloc[:2])[1:])
(array([0, 1, 2]), 0, array([0, 1])) (array([0, 1, 2]), 1, 2) (array([0, 1, 2]), array([1]), array([2])) (array([0, 1, 2]), array([0, 1]), array([0, 1, 2])) 
print(sr2_wrapper.iloc[:2].index)
print(sr2_wrapper.iloc[:2].columns)
print(sr2_wrapper.iloc[:2].ndim)

print(df4_wrapper.iloc[0, :2].index)
print(df4_wrapper.iloc[0, :2].columns)
print(df4_wrapper.iloc[0, :2].ndim)

print(df4_wrapper.iloc[:2, 0].index)
print(df4_wrapper.iloc[:2, 0].columns)
print(df4_wrapper.iloc[:2, 0].ndim)

print(df4_wrapper.iloc[:2, [0]].index)
print(df4_wrapper.iloc[:2, [0]].columns)
print(df4_wrapper.iloc[:2, [0]].ndim)

print(df4_wrapper.iloc[:2, :2].index)
print(df4_wrapper.iloc[:2, :2].columns)
print(df4_wrapper.iloc[:2, :2].ndim)
Index(['x2', 'y2'], dtype='object', name='i2') Index(['a2'], dtype='object') 1 Index(['a6', 'b6'], dtype='object', name='c6') Index(['x6'], dtype='object', name='i6') 1 Index(['x6', 'y6'], dtype='object', name='i6') Index(['a6'], dtype='object', name='c6') 1 Index(['x6', 'y6'], dtype='object', name='i6') Index(['a6'], dtype='object', name='c6') 2 Index(['x6', 'y6'], dtype='object', name='i6') Index(['a6', 'b6'], dtype='object', name='c6') 2 
print(df4_wrapper_co.iloc[0].index)
print(df4_wrapper_co.iloc[0].columns)
print(df4_wrapper_co.iloc[0].ndim)

print(df4_wrapper_co.iloc[[0]].index)
print(df4_wrapper_co.iloc[[0]].columns)
print(df4_wrapper_co.iloc[[0]].ndim)

print(df4_wrapper_co.iloc[:2].index)
print(df4_wrapper_co.iloc[:2].columns)
print(df4_wrapper_co.iloc[:2].ndim)
Index(['x6', 'y6', 'z6'], dtype='object', name='i6') Index(['a6'], dtype='object', name='c6') 1 Index(['x6', 'y6', 'z6'], dtype='object', name='i6') Index(['a6'], dtype='object', name='c6') 2 Index(['x6', 'y6', 'z6'], dtype='object', name='i6') Index(['a6', 'b6'], dtype='object', name='c6') 2 
print(sr2_grouped_wrapper.iloc[:2].index)
print(sr2_grouped_wrapper.iloc[:2].columns)
print(sr2_grouped_wrapper.iloc[:2].ndim)
print(sr2_grouped_wrapper.iloc[:2].grouped_ndim)
print(sr2_grouped_wrapper.iloc[:2].grouper.group_by)

print(df4_grouped_wrapper.iloc[:2, 0].index)
print(df4_grouped_wrapper.iloc[:2, 0].columns)
print(df4_grouped_wrapper.iloc[:2, 0].ndim)
print(df4_grouped_wrapper.iloc[:2, 0].grouped_ndim)
print(df4_grouped_wrapper.iloc[:2, 0].grouper.group_by)

print(df4_grouped_wrapper.iloc[:2, 1].index)
print(df4_grouped_wrapper.iloc[:2, 1].columns)
print(df4_grouped_wrapper.iloc[:2, 1].ndim)
print(df4_grouped_wrapper.iloc[:2, 1].grouped_ndim)
print(df4_grouped_wrapper.iloc[:2, 1].grouper.group_by)

print(df4_grouped_wrapper.iloc[:2, [1]].index)
print(df4_grouped_wrapper.iloc[:2, [1]].columns)
print(df4_grouped_wrapper.iloc[:2, [1]].ndim)
print(df4_grouped_wrapper.iloc[:2, [1]].grouped_ndim)
print(df4_grouped_wrapper.iloc[:2, [1]].grouper.group_by)

print(df4_grouped_wrapper.iloc[:2, :2].index)
print(df4_grouped_wrapper.iloc[:2, :2].columns)
print(df4_grouped_wrapper.iloc[:2, :2].ndim)
print(df4_grouped_wrapper.iloc[:2, :2].grouped_ndim)
print(df4_grouped_wrapper.iloc[:2, :2].grouper.group_by)
Index(['x2', 'y2'], dtype='object', name='i2') Index(['a2'], dtype='object') 1 1 Index([0], dtype='int64') Index(['x6', 'y6'], dtype='object', name='i6') Index(['a6', 'b6'], dtype='object', name='c6') 2 1 Index([0, 0], dtype='int64') Index(['x6', 'y6'], dtype='object', name='i6') Index(['c6'], dtype='object', name='c6') 1 1 Index([1], dtype='int64') Index(['x6', 'y6'], dtype='object', name='i6') Index(['c6'], dtype='object', name='c6') 2 2 Index([1], dtype='int64') Index(['x6', 'y6'], dtype='object', name='i6') Index(['a6', 'b6', 'c6'], dtype='object', name='c6') 2 2 Index([0, 0, 1], dtype='int64') 
print(df4_grouped_wrapper_co.iloc[0].index)
print(df4_grouped_wrapper_co.iloc[0].columns)
print(df4_grouped_wrapper_co.iloc[0].ndim)
print(df4_grouped_wrapper_co.iloc[0].grouped_ndim)
print(df4_grouped_wrapper_co.iloc[0].grouper.group_by)

print(df4_grouped_wrapper_co.iloc[1].index)
print(df4_grouped_wrapper_co.iloc[1].columns)
print(df4_grouped_wrapper_co.iloc[1].ndim)
print(df4_grouped_wrapper_co.iloc[1].grouped_ndim)
print(df4_grouped_wrapper_co.iloc[1].grouper.group_by)

print(df4_grouped_wrapper_co.iloc[[1]].index)
print(df4_grouped_wrapper_co.iloc[[1]].columns)
print(df4_grouped_wrapper_co.iloc[[1]].ndim)
print(df4_grouped_wrapper_co.iloc[[1]].grouped_ndim)
print(df4_grouped_wrapper_co.iloc[[1]].grouper.group_by)

print(df4_grouped_wrapper_co.iloc[:2].index)
print(df4_grouped_wrapper_co.iloc[:2].columns)
print(df4_grouped_wrapper_co.iloc[:2].ndim)
print(df4_grouped_wrapper_co.iloc[:2].grouped_ndim)
print(df4_grouped_wrapper_co.iloc[:2].grouper.group_by)
Index(['x6', 'y6', 'z6'], dtype='object', name='i6') Index(['a6', 'b6'], dtype='object', name='c6') 2 1 Index([0, 0], dtype='int64') Index(['x6', 'y6', 'z6'], dtype='object', name='i6') Index(['c6'], dtype='object', name='c6') 1 1 Index([1], dtype='int64') Index(['x6', 'y6', 'z6'], dtype='object', name='i6') Index(['c6'], dtype='object', name='c6') 2 2 Index([1], dtype='int64') Index(['x6', 'y6', 'z6'], dtype='object', name='i6') Index(['a6', 'b6', 'c6'], dtype='object', name='c6') 2 2 Index([0, 0, 1], dtype='int64') 
big_df = pd.DataFrame(np.empty((1000, 1000)))

big_df_wrapper = array_wrapper.ArrayWrapper.from_obj(big_df)
big_df_wrapper_co = big_df_wrapper.copy(column_only_select=True)
big_df_grouped_wrapper = df4_wrapper.copy(group_by=np.array([0, 0, 1]))
big_df_grouped_wrapper_co = big_df_grouped_wrapper.copy(column_only_select=True)

%timeit big_df_wrapper.iloc[:, 0]
%timeit big_df_wrapper.iloc[:, :]

%timeit big_df_wrapper_co.iloc[0]
%timeit big_df_wrapper_co.iloc[:]

%timeit big_df_grouped_wrapper.iloc[:, 0]
%timeit big_df_grouped_wrapper.iloc[:, :]

%timeit big_df_grouped_wrapper_co.iloc[0]
%timeit big_df_grouped_wrapper_co.iloc[:]
759 µs ± 10.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) 680 µs ± 15.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) 569 µs ± 8.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) 579 µs ± 6.89 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) 839 µs ± 4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) 748 µs ± 7.08 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) 648 µs ± 6.35 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) 658 µs ± 4.24 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) 
print(df4_grouped_wrapper_co.wrap(np.array([[1, 2], [3, 4], [5, 6]])))
print(df4_grouped_wrapper_co.wrap_reduced(np.array([1, 2])))

print(df4_grouped_wrapper_co.wrap(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), group_by=False))
print(df4_grouped_wrapper_co.wrap_reduced(np.array([1, 2, 3]), group_by=False))
0 1 i6 x6 1 2 y6 3 4 z6 5 6 0 1 1 2 dtype: int64 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 c6 a6 1 b6 2 c6 3 dtype: int64 
print(df4_grouped_wrapper_co.iloc[0].wrap(np.array([1, 2, 3])))
print(df4_grouped_wrapper_co.iloc[0].wrap_reduced(np.array([1])))

print(df4_grouped_wrapper_co.iloc[0].wrap(np.array([[1, 2], [3, 4], [5, 6]]), group_by=False))
print(df4_grouped_wrapper_co.iloc[0].wrap_reduced(np.array([1, 2]), group_by=False))
i6 x6 1 y6 2 z6 3 dtype: int64 1 c6 a6 b6 i6 x6 1 2 y6 3 4 z6 5 6 c6 a6 1 b6 2 dtype: int64 
print(df4_grouped_wrapper_co.iloc[[0]].wrap(np.array([1, 2, 3])))
print(df4_grouped_wrapper_co.iloc[[0]].wrap_reduced(np.array([1])))

print(df4_grouped_wrapper_co.iloc[[0]].wrap(np.array([[1, 2], [3, 4], [5, 6]]), group_by=False))
print(df4_grouped_wrapper_co.iloc[[0]].wrap_reduced(np.array([1, 2]), group_by=False))
0 i6 x6 1 y6 2 z6 3 0 1 dtype: int64 c6 a6 b6 i6 x6 1 2 y6 3 4 z6 5 6 c6 a6 1 b6 2 dtype: int64 
print(df4_grouped_wrapper_co.iloc[1].wrap(np.array([1, 2, 3])))
print(df4_grouped_wrapper_co.iloc[1].wrap_reduced(np.array([1])))

print(df4_grouped_wrapper_co.iloc[1].wrap(np.array([1, 2, 3]), group_by=False))
print(df4_grouped_wrapper_co.iloc[1].wrap_reduced(np.array([1]), group_by=False))
i6 x6 1 y6 2 z6 3 Name: 1, dtype: int64 1 i6 x6 1 y6 2 z6 3 Name: c6, dtype: int64 1 
print(df4_grouped_wrapper_co.iloc[[1]].wrap(np.array([1, 2, 3])))
print(df4_grouped_wrapper_co.iloc[[1]].wrap_reduced(np.array([1])))

print(df4_grouped_wrapper_co.iloc[[1]].wrap(np.array([1, 2, 3]), group_by=False))
print(df4_grouped_wrapper_co.iloc[[1]].wrap_reduced(np.array([1]), group_by=False))
1 i6 x6 1 y6 2 z6 3 1 1 dtype: int64 c6 c6 i6 x6 1 y6 2 z6 3 c6 c6 1 dtype: int64

index_fns

i1 = index_fns.index_from_values([0.1, 0.2], name='a')
i2 = index_fns.index_from_values(np.tile(np.arange(1, 4)[:, None][:, None], (1, 3, 3)), name='b')
i3 = index_fns.index_from_values(np.random.uniform(size=(3, 3, 3)), name='c')

print(i1)
print(i2)
print(i3)
Index([0.1, 0.2], dtype='float64', name='a') Index([1, 2, 3], dtype='int64', name='b') Index(['array_0', 'array_1', 'array_2'], dtype='object', name='c') 
print(index_fns.repeat_index(i2, 3))
print(index_fns.repeat_index(multi_i, 3))
Index([1, 1, 1, 2, 2, 2, 3, 3, 3], dtype='int64', name='b') MultiIndex([('x7', 'x8'), ('x7', 'x8'), ('x7', 'x8'), ('y7', 'y8'), ('y7', 'y8'), ('y7', 'y8'), ('z7', 'z8'), ('z7', 'z8'), ('z7', 'z8')], names=['i7', 'i8']) 
print(index_fns.tile_index(i2, 3))
print(index_fns.tile_index(multi_i, 3))
Index([1, 2, 3, 1, 2, 3, 1, 2, 3], dtype='int64', name='b') MultiIndex([('x7', 'x8'), ('y7', 'y8'), ('z7', 'z8'), ('x7', 'x8'), ('y7', 'y8'), ('z7', 'z8'), ('x7', 'x8'), ('y7', 'y8'), ('z7', 'z8')], names=['i7', 'i8']) 
i23 = index_fns.stack_indexes((i2, i3))
i32 = index_fns.stack_indexes((i3, i2))

print(i23)
print(i32)

print(index_fns.stack_indexes((multi_i, multi_i), drop_duplicates=False))
print(index_fns.stack_indexes((multi_i, multi_i), drop_duplicates=True))
print(index_fns.stack_indexes(([0, 1], ['a', 'b']), drop_redundant=False))
print(index_fns.stack_indexes(([0, 1], ['a', 'b']), drop_redundant=True))
print(index_fns.stack_indexes((pd.Index([0, 1], name='test_name'), ['a', 'b']), drop_redundant=True))
print(index_fns.stack_indexes((['a', 'a'], ['a', 'b']), drop_redundant=True))
print(index_fns.stack_indexes((pd.Index(['a', 'a'], name='test_name'), ['a', 'b']), drop_redundant=True))
MultiIndex([(1, 'array_0'), (2, 'array_1'), (3, 'array_2')], names=['b', 'c']) MultiIndex([('array_0', 1), ('array_1', 2), ('array_2', 3)], names=['c', 'b']) MultiIndex([('x7', 'x8', 'x7', 'x8'), ('y7', 'y8', 'y7', 'y8'), ('z7', 'z8', 'z7', 'z8')], names=['i7', 'i8', 'i7', 'i8']) MultiIndex([('x7', 'x8'), ('y7', 'y8'), ('z7', 'z8')], names=['i7', 'i8']) MultiIndex([(0, 'a'), (1, 'b')], ) Index(['a', 'b'], dtype='object') MultiIndex([(0, 'a'), (1, 'b')], names=['test_name', None]) Index(['a', 'b'], dtype='object') MultiIndex([('a', 'a'), ('a', 'b')], names=['test_name', None]) 
print(index_fns.combine_indexes((pd.Index([1]), pd.Index([2, 3])), drop_duplicates=False))
print(index_fns.combine_indexes((pd.Index([1]), pd.Index([2, 3])), drop_duplicates=True))
print(index_fns.combine_indexes((pd.Index([1, 2]), pd.Index([3])), drop_duplicates=False))
print(index_fns.combine_indexes((pd.Index([1, 2]), pd.Index([3])), drop_duplicates=True))
print(index_fns.combine_indexes((i1, i2))) # combine_fns uses stack
print(index_fns.combine_indexes((i2, i3)))
print(index_fns.combine_indexes((i23, i23)))
Index([2, 3], dtype='int64') Index([2, 3], dtype='int64') Index([1, 2], dtype='int64') Index([1, 2], dtype='int64') MultiIndex([(0.1, 1), (0.1, 2), (0.1, 3), (0.2, 1), (0.2, 2), (0.2, 3)], names=['a', 'b']) MultiIndex([(1, 'array_0'), (1, 'array_1'), (1, 'array_2'), (2, 'array_0'), (2, 'array_1'), (2, 'array_2'), (3, 'array_0'), (3, 'array_1'), (3, 'array_2')], names=['b', 'c']) MultiIndex([(1, 'array_0', 1, 'array_0'), (1, 'array_0', 2, 'array_1'), (1, 'array_0', 3, 'array_2'), (2, 'array_1', 1, 'array_0'), (2, 'array_1', 2, 'array_1'), (2, 'array_1', 3, 'array_2'), (3, 'array_2', 1, 'array_0'), (3, 'array_2', 2, 'array_1'), (3, 'array_2', 3, 'array_2')], names=['b', 'c', 'b', 'c']) 
print(index_fns.drop_levels(multi_i, 'i10'))
print(index_fns.drop_levels(multi_i, ['i7', 'i8']))
MultiIndex([('x7', 'x8'), ('y7', 'y8'), ('z7', 'z8')], names=['i7', 'i8']) MultiIndex([('x7', 'x8'), ('y7', 'y8'), ('z7', 'z8')], names=['i7', 'i8']) 
print(index_fns.rename_levels(pd.Index([1, 2, 3], name='i'), {'i': 'f'}))
print(index_fns.rename_levels(multi_i, {'i7': 'f7', 'i8': 'f8'}))
Index([1, 2, 3], dtype='int64', name='f') MultiIndex([('x7', 'x8'), ('y7', 'y8'), ('z7', 'z8')], names=['f7', 'f8']) 
print(index_fns.select_levels(multi_i, 'i7'))
print(index_fns.select_levels(multi_i, ['i7']))
print(index_fns.select_levels(multi_i, ['i7', 'i8']))
Index(['x7', 'y7', 'z7'], dtype='object', name='i7') MultiIndex([('x7',), ('y7',), ('z7',)], names=['i7']) MultiIndex([('x7', 'x8'), ('y7', 'y8'), ('z7', 'z8')], names=['i7', 'i8']) 
print(index_fns.drop_redundant_levels(pd.Index(['a', 'a']))) # ignores levels with single element
print(index_fns.drop_redundant_levels(pd.Index(['a', 'a'], name='hi')))
print(index_fns.drop_redundant_levels(pd.MultiIndex.from_arrays([['a', 'a'], ['b', 'b']], names=['hi', 'hi2'])))
print(index_fns.drop_redundant_levels(pd.MultiIndex.from_arrays([['a', 'b'], ['a', 'b']], names=['hi', 'hi2'])))
print(index_fns.drop_redundant_levels(pd.MultiIndex.from_arrays([[0, 1], ['a', 'b']], names=[None, 'hi2']))) # ignores 0-to-n
print(index_fns.drop_redundant_levels(pd.MultiIndex.from_arrays([[0, 2], ['a', 'b']], names=[None, 'hi2']))) # legit
print(index_fns.drop_redundant_levels(pd.MultiIndex.from_arrays([[0, 1], ['a', 'b']], names=['hi', 'hi2']))) # legit (w/ name)
Index(['a', 'a'], dtype='object') Index(['a', 'a'], dtype='object', name='hi') MultiIndex([('a', 'b'), ('a', 'b')], names=['hi', 'hi2']) MultiIndex([('a', 'a'), ('b', 'b')], names=['hi', 'hi2']) Index(['a', 'b'], dtype='object', name='hi2') MultiIndex([(0, 'a'), (2, 'b')], names=[None, 'hi2']) MultiIndex([(0, 'a'), (1, 'b')], names=['hi', 'hi2']) 
print(index_fns.drop_duplicate_levels(pd.MultiIndex.from_arrays(
    [[1, 2, 3], [1, 2, 3]], names=['a', 'a'])))
print(index_fns.drop_duplicate_levels(pd.MultiIndex.from_tuples(
    [(0, 1, 2, 1), ('a', 'b', 'c', 'b')], names=['x', 'y', 'z', 'y']), keep='last'))
print(index_fns.drop_duplicate_levels(pd.MultiIndex.from_tuples(
    [(0, 1, 2, 1), ('a', 'b', 'c', 'b')], names=['x', 'y', 'z', 'y']), keep='first'))
Index([1, 2, 3], dtype='int64', name='a') MultiIndex([( 0, 2, 1), ('a', 'c', 'b')], names=['x', 'z', 'y']) MultiIndex([( 0, 1, 2), ('a', 'b', 'c')], names=['x', 'y', 'z']) 
multi_c1 = pd.MultiIndex.from_arrays([['a8', 'b8']], names=['c8'])
multi_c2 = pd.MultiIndex.from_arrays([['a7', 'a7', 'c7', 'c7'], ['a8', 'b8', 'a8', 'b8']], names=['c7', 'c8'])

index_fns.align_index_to(multi_c1, multi_c2)
array([0, 1, 0, 1])
print(index_fns.pick_levels(multi_c, required_levels=[], optional_levels=[]))
print(index_fns.pick_levels(multi_c, required_levels=['c8'], optional_levels=[]))
print(index_fns.pick_levels(multi_c, required_levels=['c8'], optional_levels=[]))
print(index_fns.pick_levels(multi_c, required_levels=['c7', 'c8'], optional_levels=[]))
print(index_fns.pick_levels(multi_c, required_levels=['c8', None], optional_levels=[]))
print(index_fns.pick_levels(multi_c, required_levels=[None, None], optional_levels=[]))
print(index_fns.pick_levels(multi_c, required_levels=[None], optional_levels=['c8']))
print(index_fns.pick_levels(multi_c, required_levels=['c8'], optional_levels=[None]))
print(index_fns.pick_levels(multi_c, required_levels=[], optional_levels=['c7', 'c8']))
([], []) ([1], []) ([1], []) ([0, 1], []) ([1, 0], []) ([0, 1], []) ([0], [1]) ([1], [None]) ([], [0, 1])

reshape_fns

print(reshape_fns.soft_to_ndim(a2, 1))
print(reshape_fns.soft_to_ndim(sr2, 1))
print(reshape_fns.soft_to_ndim(df2, 1))
print(reshape_fns.soft_to_ndim(df4, 1)) # cannot -> do nothing
print(reshape_fns.soft_to_ndim(a2, 2))
print(reshape_fns.soft_to_ndim(sr2, 2))
print(reshape_fns.soft_to_ndim(df2, 2))
[1 2 3] i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 i4 x4 1 y4 2 z4 3 Name: a4, dtype: int64 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 [[1] [2] [3]] a2 i2 x2 1 y2 2 z2 3 c4 a4 i4 x4 1 y4 2 z4 3 
print(reshape_fns.to_1d(None))
print(reshape_fns.to_1d(v1))
print(reshape_fns.to_1d(a1))
print(reshape_fns.to_1d(a2))
print(reshape_fns.to_1d(sr1))
print(reshape_fns.to_1d(sr2))
print(reshape_fns.to_1d(df1))
print(reshape_fns.to_1d(df2))
[None] [0] [1] [1 2 3] i1 x1 1 Name: a1, dtype: int64 i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 i3 x3 1 Name: a3, dtype: int64 i4 x4 1 y4 2 z4 3 Name: a4, dtype: int64 
print(reshape_fns.to_2d(None))
print(reshape_fns.to_2d(v1))
print(reshape_fns.to_2d(a1))
print(reshape_fns.to_2d(a2))
print(reshape_fns.to_2d(sr1))
print(reshape_fns.to_2d(sr2))
print(reshape_fns.to_2d(sr2, expand_axis=0))
[[None]] [[0]] [[1]] [[1] [2] [3]] a1 i1 x1 1 a2 i2 x2 1 y2 2 z2 3 i2 x2 y2 z2 0 1 2 3 
print(reshape_fns.repeat(v1, 3, axis=0))
print(reshape_fns.repeat(a1, 3, axis=0))
print(reshape_fns.repeat(a2, 3, axis=0))
print(reshape_fns.repeat(a3, 3, axis=0))
print(reshape_fns.repeat(a4, 3, axis=0))
print(reshape_fns.repeat(a5, 3, axis=0))
print(reshape_fns.repeat(sr_none, 3, axis=0))
print(reshape_fns.repeat(sr1, 3, axis=0))
print(reshape_fns.repeat(sr2, 3, axis=0))
print(reshape_fns.repeat(df_none, 3, axis=0))
print(reshape_fns.repeat(df1, 3, axis=0))
print(reshape_fns.repeat(df2, 3, axis=0))
print(reshape_fns.repeat(df3, 3, axis=0))
print(reshape_fns.repeat(df4, 3, axis=0))
[0 0 0] [1 1 1] [1 1 1 2 2 2 3 3 3] [[1 2 3] [1 2 3] [1 2 3]] [[1] [1] [1] [2] [2] [2] [3] [3] [3]] [[1 2 3] [1 2 3] [1 2 3] [4 5 6] [4 5 6] [4 5 6] [7 8 9] [7 8 9] [7 8 9]] 0 1 1 1 2 1 dtype: int64 i1 x1 1 x1 1 x1 1 Name: a1, dtype: int64 i2 x2 1 x2 1 x2 1 y2 2 y2 2 y2 2 z2 3 z2 3 z2 3 Name: a2, dtype: int64 0 0 1 1 1 2 1 c3 a3 i3 x3 1 x3 1 x3 1 c4 a4 i4 x4 1 x4 1 x4 1 y4 2 y4 2 y4 2 z4 3 z4 3 z4 3 c5 a5 b5 c5 i5 x5 1 2 3 x5 1 2 3 x5 1 2 3 c6 a6 b6 c6 i6 x6 1 2 3 x6 1 2 3 x6 1 2 3 y6 4 5 6 y6 4 5 6 y6 4 5 6 z6 7 8 9 z6 7 8 9 z6 7 8 9 
print(reshape_fns.repeat(v1, 3, axis=1))
print(reshape_fns.repeat(a1, 3, axis=1))
print(reshape_fns.repeat(a2, 3, axis=1))
print(reshape_fns.repeat(a3, 3, axis=1))
print(reshape_fns.repeat(a4, 3, axis=1))
print(reshape_fns.repeat(a5, 3, axis=1))
print(reshape_fns.repeat(sr_none, 3, axis=1))
print(reshape_fns.repeat(sr1, 3, axis=1))
print(reshape_fns.repeat(sr2, 3, axis=1))
print(reshape_fns.repeat(df_none, 3, axis=1))
print(reshape_fns.repeat(df1, 3, axis=1))
print(reshape_fns.repeat(df2, 3, axis=1))
print(reshape_fns.repeat(df3, 3, axis=1))
print(reshape_fns.repeat(df4, 3, axis=1))
[[0 0 0]] [[1 1 1]] [[1 1 1] [2 2 2] [3 3 3]] [[1 1 1 2 2 2 3 3 3]] [[1 1 1] [2 2 2] [3 3 3]] [[1 1 1 2 2 2 3 3 3] [4 4 4 5 5 5 6 6 6] [7 7 7 8 8 8 9 9 9]] 0 1 2 0 1 1 1 a1 a1 a1 i1 x1 1 1 1 a2 a2 a2 i2 x2 1 1 1 y2 2 2 2 z2 3 3 3 0 1 2 0 1 1 1 c3 a3 a3 a3 i3 x3 1 1 1 c4 a4 a4 a4 i4 x4 1 1 1 y4 2 2 2 z4 3 3 3 c5 a5 a5 a5 b5 b5 b5 c5 c5 c5 i5 x5 1 1 1 2 2 2 3 3 3 c6 a6 a6 a6 b6 b6 b6 c6 c6 c6 i6 x6 1 1 1 2 2 2 3 3 3 y6 4 4 4 5 5 5 6 6 6 z6 7 7 7 8 8 8 9 9 9 
print(reshape_fns.tile(v1, 3, axis=0))
print(reshape_fns.tile(a1, 3, axis=0))
print(reshape_fns.tile(a2, 3, axis=0))
print(reshape_fns.tile(a3, 3, axis=0))
print(reshape_fns.tile(a4, 3, axis=0))
print(reshape_fns.tile(a5, 3, axis=0))
print(reshape_fns.tile(sr_none, 3, axis=0))
print(reshape_fns.tile(sr1, 3, axis=0))
print(reshape_fns.tile(sr2, 3, axis=0))
print(reshape_fns.tile(df_none, 3, axis=0))
print(reshape_fns.tile(df1, 3, axis=0))
print(reshape_fns.tile(df2, 3, axis=0))
print(reshape_fns.tile(df3, 3, axis=0))
print(reshape_fns.tile(df4, 3, axis=0))
[0 0 0] [1 1 1] [1 2 3 1 2 3 1 2 3] [[1 2 3] [1 2 3] [1 2 3]] [[1] [2] [3] [1] [2] [3] [1] [2] [3]] [[1 2 3] [4 5 6] [7 8 9] [1 2 3] [4 5 6] [7 8 9] [1 2 3] [4 5 6] [7 8 9]] 0 1 1 1 2 1 dtype: int64 i1 x1 1 x1 1 x1 1 Name: a1, dtype: int64 i2 x2 1 y2 2 z2 3 x2 1 y2 2 z2 3 x2 1 y2 2 z2 3 Name: a2, dtype: int64 0 0 1 1 1 2 1 c3 a3 i3 x3 1 x3 1 x3 1 c4 a4 i4 x4 1 y4 2 z4 3 x4 1 y4 2 z4 3 x4 1 y4 2 z4 3 c5 a5 b5 c5 i5 x5 1 2 3 x5 1 2 3 x5 1 2 3 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 x6 1 2 3 y6 4 5 6 z6 7 8 9 x6 1 2 3 y6 4 5 6 z6 7 8 9 
print(reshape_fns.tile(v1, 3, axis=1))
print(reshape_fns.tile(a1, 3, axis=1))
print(reshape_fns.tile(a2, 3, axis=1))
print(reshape_fns.tile(a3, 3, axis=1))
print(reshape_fns.tile(a4, 3, axis=1))
print(reshape_fns.tile(a5, 3, axis=1))
print(reshape_fns.tile(sr_none, 3, axis=1))
print(reshape_fns.tile(sr1, 3, axis=1))
print(reshape_fns.tile(sr2, 3, axis=1))
print(reshape_fns.tile(df_none, 3, axis=1))
print(reshape_fns.tile(df1, 3, axis=1))
print(reshape_fns.tile(df2, 3, axis=1))
print(reshape_fns.tile(df3, 3, axis=1))
print(reshape_fns.tile(df4, 3, axis=1))
[[0 0 0]] [[1 1 1]] [[1 1 1] [2 2 2] [3 3 3]] [[1 2 3 1 2 3 1 2 3]] [[1 1 1] [2 2 2] [3 3 3]] [[1 2 3 1 2 3 1 2 3] [4 5 6 4 5 6 4 5 6] [7 8 9 7 8 9 7 8 9]] 0 1 2 0 1 1 1 a1 a1 a1 i1 x1 1 1 1 a2 a2 a2 i2 x2 1 1 1 y2 2 2 2 z2 3 3 3 0 1 2 0 1 1 1 c3 a3 a3 a3 i3 x3 1 1 1 c4 a4 a4 a4 i4 x4 1 1 1 y4 2 2 2 z4 3 3 3 c5 a5 b5 c5 a5 b5 c5 a5 b5 c5 i5 x5 1 2 3 1 2 3 1 2 3 c6 a6 b6 c6 a6 b6 c6 a6 b6 c6 i6 x6 1 2 3 1 2 3 1 2 3 y6 4 5 6 4 5 6 4 5 6 z6 7 8 9 7 8 9 7 8 9 
# Change broadcasting rules globally
vbt.settings.broadcasting['index_from'] = 'stack' # default is 'strict'
vbt.settings.broadcasting['columns_from'] = 'stack'

print(vbt.settings.broadcasting)
Config({ "align_index": false, "align_columns": true, "index_from": "stack", "columns_from": "stack", "ignore_sr_names": true, "drop_duplicates": true, "keep": "last", "drop_redundant": true, "ignore_default": true }) 
# Broadcasting arrays
args = [
    ('v1', v1),
    ('a1', a1),
    ('a2', a2),
    ('a3', a3),
    ('a4', a4),
    ('a5', a5)
]
arg_combs = list(itertools.combinations_with_replacement(args, 2))

for (n1, arg1), (n2, arg2) in arg_combs:
    print(arg1)
    print(arg2)
    print("================")
    arg1, arg2 = reshape_fns.broadcast(arg1, arg2)
    print(arg1)
    print(arg2)
    print()
0 0 ================ 0 0 0 [1] ================ [0] [1] 0 [1 2 3] ================ [0 0 0] [1 2 3] 0 [[1 2 3]] ================ [[0 0 0]] [[1 2 3]] 0 [[1] [2] [3]] ================ [[0] [0] [0]] [[1] [2] [3]] 0 [[1 2 3] [4 5 6] [7 8 9]] ================ [[0 0 0] [0 0 0] [0 0 0]] [[1 2 3] [4 5 6] [7 8 9]] [1] [1] ================ [1] [1] [1] [1 2 3] ================ [1 1 1] [1 2 3] [1] [[1 2 3]] ================ [[1 1 1]] [[1 2 3]] [1] [[1] [2] [3]] ================ [[1] [1] [1]] [[1] [2] [3]] [1] [[1 2 3] [4 5 6] [7 8 9]] ================ [[1 1 1] [1 1 1] [1 1 1]] [[1 2 3] [4 5 6] [7 8 9]] [1 2 3] [1 2 3] ================ [1 2 3] [1 2 3] [1 2 3] [[1 2 3]] ================ [[1 2 3]] [[1 2 3]] [1 2 3] [[1] [2] [3]] ================ [[1 2 3] [1 2 3] [1 2 3]] [[1 1 1] [2 2 2] [3 3 3]] [1 2 3] [[1 2 3] [4 5 6] [7 8 9]] ================ [[1 2 3] [1 2 3] [1 2 3]] [[1 2 3] [4 5 6] [7 8 9]] [[1 2 3]] [[1 2 3]] ================ [[1 2 3]] [[1 2 3]] [[1 2 3]] [[1] [2] [3]] ================ [[1 2 3] [1 2 3] [1 2 3]] [[1 1 1] [2 2 2] [3 3 3]] [[1 2 3]] [[1 2 3] [4 5 6] [7 8 9]] ================ [[1 2 3] [1 2 3] [1 2 3]] [[1 2 3] [4 5 6] [7 8 9]] [[1] [2] [3]] [[1] [2] [3]] ================ [[1] [2] [3]] [[1] [2] [3]] [[1] [2] [3]] [[1 2 3] [4 5 6] [7 8 9]] ================ [[1 1 1] [2 2 2] [3 3 3]] [[1 2 3] [4 5 6] [7 8 9]] [[1 2 3] [4 5 6] [7 8 9]] [[1 2 3] [4 5 6] [7 8 9]] ================ [[1 2 3] [4 5 6] [7 8 9]] [[1 2 3] [4 5 6] [7 8 9]] 
# Broadcasting series
args = [
    ('sr_none', sr_none),
    ('sr1', sr1),
    ('sr2', sr2)
]
arg_combs = list(itertools.combinations_with_replacement(args, 2))

for (n1, arg1), (n2, arg2) in arg_combs:
    print(n1 + '+' + n2)
    print(arg1)
    print(arg2)
    print("================")
    arg1, arg2 = reshape_fns.broadcast(arg1, arg2)
    print(arg1)
    print(arg2)
    print()
sr_none+sr_none 0 1 dtype: int64 0 1 dtype: int64 ================ 0 1 dtype: int64 0 1 dtype: int64 sr_none+sr1 0 1 dtype: int64 i1 x1 1 Name: a1, dtype: int64 ================ i1 x1 1 dtype: int64 i1 x1 1 dtype: int64 sr_none+sr2 0 1 dtype: int64 i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 ================ i2 x2 1 y2 1 z2 1 dtype: int64 i2 x2 1 y2 2 z2 3 dtype: int64 sr1+sr1 i1 x1 1 Name: a1, dtype: int64 i1 x1 1 Name: a1, dtype: int64 ================ i1 x1 1 Name: a1, dtype: int64 i1 x1 1 Name: a1, dtype: int64 sr1+sr2 i1 x1 1 Name: a1, dtype: int64 i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 ================ i1 i2 x1 x2 1 y2 1 z2 1 dtype: int64 i1 i2 x1 x2 1 y2 2 z2 3 dtype: int64 sr2+sr2 i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 ================ i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 
# Broadcasting arrays and series
a_args = [
    ('v1', v1),
    ('a1', a1),
    ('a2', a2),
    ('a3', a3),
    ('a4', a4),
    ('a5', a5)
]
sr_args = [
    ('sr_none', sr_none),
    ('sr1', sr1),
    ('sr2', sr2)
]
arg_combs = list(itertools.product(a_args, sr_args))

for (n1, arg1), (n2, arg2) in arg_combs:
    print(n1 + '+' + n2)
    print(arg1)
    print(arg2)
    print("================")
    arg1, arg2 = reshape_fns.broadcast(arg1, arg2)
    print(arg1)
    print(arg2)
    print()
v1+sr_none 0 0 1 dtype: int64 ================ 0 0 dtype: int64 0 1 dtype: int64 v1+sr1 0 i1 x1 1 Name: a1, dtype: int64 ================ i1 x1 0 Name: a1, dtype: int64 i1 x1 1 Name: a1, dtype: int64 v1+sr2 0 i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 ================ i2 x2 0 y2 0 z2 0 Name: a2, dtype: int64 i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 a1+sr_none [1] 0 1 dtype: int64 ================ 0 1 dtype: int64 0 1 dtype: int64 a1+sr1 [1] i1 x1 1 Name: a1, dtype: int64 ================ i1 x1 1 Name: a1, dtype: int64 i1 x1 1 Name: a1, dtype: int64 a1+sr2 [1] i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 ================ i2 x2 1 y2 1 z2 1 Name: a2, dtype: int64 i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 a2+sr_none [1 2 3] 0 1 dtype: int64 ================ 0 1 1 2 2 3 dtype: int64 0 1 1 1 2 1 dtype: int64 a2+sr1 [1 2 3] i1 x1 1 Name: a1, dtype: int64 ================ i1 x1 1 x1 2 x1 3 Name: a1, dtype: int64 i1 x1 1 x1 1 x1 1 Name: a1, dtype: int64 a2+sr2 [1 2 3] i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 ================ i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 a3+sr_none [[1 2 3]] 0 1 dtype: int64 ================ 0 1 2 0 1 2 3 0 1 2 0 1 1 1 a3+sr1 [[1 2 3]] i1 x1 1 Name: a1, dtype: int64 ================ a1 a1 a1 i1 x1 1 2 3 a1 a1 a1 i1 x1 1 1 1 a3+sr2 [[1 2 3]] i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 ================ a2 a2 a2 i2 x2 1 2 3 y2 1 2 3 z2 1 2 3 a2 a2 a2 i2 x2 1 1 1 y2 2 2 2 z2 3 3 3 a4+sr_none [[1] [2] [3]] 0 1 dtype: int64 ================ 0 0 1 1 2 2 3 0 0 1 1 1 2 1 a4+sr1 [[1] [2] [3]] i1 x1 1 Name: a1, dtype: int64 ================ a1 i1 x1 1 x1 2 x1 3 a1 i1 x1 1 x1 1 x1 1 a4+sr2 [[1] [2] [3]] i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 ================ a2 i2 x2 1 y2 2 z2 3 a2 i2 x2 1 y2 2 z2 3 a5+sr_none [[1 2 3] [4 5 6] [7 8 9]] 0 1 dtype: int64 ================ 0 1 2 0 1 2 3 1 4 5 6 2 7 8 9 0 1 2 0 1 1 1 1 1 1 1 2 1 1 1 a5+sr1 [[1 2 3] [4 5 6] [7 8 9]] i1 x1 1 Name: a1, dtype: int64 ================ a1 a1 a1 i1 x1 1 2 3 x1 4 5 6 x1 7 8 9 a1 a1 a1 i1 x1 1 1 1 x1 1 1 1 x1 1 1 1 a5+sr2 [[1 2 3] [4 5 6] [7 8 9]] i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 ================ a2 a2 a2 i2 x2 1 2 3 y2 4 5 6 z2 7 8 9 a2 a2 a2 i2 x2 1 1 1 y2 2 2 2 z2 3 3 3 
# Broadcasting dataframes
args = [
    ('df_none', df_none),
    ('df1', df1),
    ('df2', df2),
    ('df3', df3),
    ('df4', df4)
]
arg_combs = list(itertools.combinations_with_replacement(args, 2))

for (n1, arg1), (n2, arg2) in arg_combs:
    print(n1 + '+' + n2)
    print(arg1)
    print(arg2)
    print("================")
    arg1, arg2 = reshape_fns.broadcast(arg1, arg2)
    print(arg1)
    print(arg2)
    print()
df_none+df_none 0 0 1 0 0 1 ================ 0 0 1 0 0 1 df_none+df1 0 0 1 c3 a3 i3 x3 1 ================ c3 a3 i3 x3 1 c3 a3 i3 x3 1 df_none+df2 0 0 1 c4 a4 i4 x4 1 y4 2 z4 3 ================ c4 a4 i4 x4 1 y4 1 z4 1 c4 a4 i4 x4 1 y4 2 z4 3 df_none+df3 0 0 1 c5 a5 b5 c5 i5 x5 1 2 3 ================ c5 a5 b5 c5 i5 x5 1 1 1 c5 a5 b5 c5 i5 x5 1 2 3 df_none+df4 0 0 1 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 ================ c6 a6 b6 c6 i6 x6 1 1 1 y6 1 1 1 z6 1 1 1 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 df1+df1 c3 a3 i3 x3 1 c3 a3 i3 x3 1 ================ c3 a3 i3 x3 1 c3 a3 i3 x3 1 df1+df2 c3 a3 i3 x3 1 c4 a4 i4 x4 1 y4 2 z4 3 ================ c3 a3 c4 a4 i3 i4 x3 x4 1 y4 1 z4 1 c3 a3 c4 a4 i3 i4 x3 x4 1 y4 2 z4 3 df1+df3 c3 a3 i3 x3 1 c5 a5 b5 c5 i5 x5 1 2 3 ================ c3 a3 c5 a5 b5 c5 i3 i5 x3 x5 1 1 1 c3 a3 c5 a5 b5 c5 i3 i5 x3 x5 1 2 3 df1+df4 c3 a3 i3 x3 1 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 ================ c3 a3 c6 a6 b6 c6 i3 i6 x3 x6 1 1 1 y6 1 1 1 z6 1 1 1 c3 a3 c6 a6 b6 c6 i3 i6 x3 x6 1 2 3 y6 4 5 6 z6 7 8 9 df2+df2 c4 a4 i4 x4 1 y4 2 z4 3 c4 a4 i4 x4 1 y4 2 z4 3 ================ c4 a4 i4 x4 1 y4 2 z4 3 c4 a4 i4 x4 1 y4 2 z4 3 df2+df3 c4 a4 i4 x4 1 y4 2 z4 3 c5 a5 b5 c5 i5 x5 1 2 3 ================ c4 a4 c5 a5 b5 c5 i4 i5 x4 x5 1 1 1 y4 x5 2 2 2 z4 x5 3 3 3 c4 a4 c5 a5 b5 c5 i4 i5 x4 x5 1 2 3 y4 x5 1 2 3 z4 x5 1 2 3 df2+df4 c4 a4 i4 x4 1 y4 2 z4 3 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 ================ c4 a4 c6 a6 b6 c6 i4 i6 x4 x6 1 1 1 y4 y6 2 2 2 z4 z6 3 3 3 c4 a4 c6 a6 b6 c6 i4 i6 x4 x6 1 2 3 y4 y6 4 5 6 z4 z6 7 8 9 df3+df3 c5 a5 b5 c5 i5 x5 1 2 3 c5 a5 b5 c5 i5 x5 1 2 3 ================ c5 a5 b5 c5 i5 x5 1 2 3 c5 a5 b5 c5 i5 x5 1 2 3 df3+df4 c5 a5 b5 c5 i5 x5 1 2 3 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 ================ c5 a5 b5 c5 c6 a6 b6 c6 i5 i6 x5 x6 1 2 3 y6 1 2 3 z6 1 2 3 c5 a5 b5 c5 c6 a6 b6 c6 i5 i6 x5 x6 1 2 3 y6 4 5 6 z6 7 8 9 df4+df4 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 ================ c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 
# Broadcasting arrays and dataframes
a_args = [
    ('v1', v1),
    ('a1', a1),
    ('a2', a2),
    ('a3', a3),
    ('a4', a4),
    ('a5', a5)
]
sr_args = [
    ('df_none', df_none),
    ('df1', df1),
    ('df2', df2),
    ('df3', df3),
    ('df4', df4)
]
arg_combs = list(itertools.product(a_args, sr_args))

for (n1, arg1), (n2, arg2) in arg_combs:
    print(n1 + '+' + n2)
    print(arg1)
    print(arg2)
    print("================")
    arg1, arg2 = reshape_fns.broadcast(arg1, arg2)
    print(arg1)
    print(arg2)
    print()
v1+df_none 0 0 0 1 ================ 0 0 0 0 0 1 v1+df1 0 c3 a3 i3 x3 1 ================ c3 a3 i3 x3 0 c3 a3 i3 x3 1 v1+df2 0 c4 a4 i4 x4 1 y4 2 z4 3 ================ c4 a4 i4 x4 0 y4 0 z4 0 c4 a4 i4 x4 1 y4 2 z4 3 v1+df3 0 c5 a5 b5 c5 i5 x5 1 2 3 ================ c5 a5 b5 c5 i5 x5 0 0 0 c5 a5 b5 c5 i5 x5 1 2 3 v1+df4 0 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 ================ c6 a6 b6 c6 i6 x6 0 0 0 y6 0 0 0 z6 0 0 0 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 a1+df_none [1] 0 0 1 ================ 0 0 1 0 0 1 a1+df1 [1] c3 a3 i3 x3 1 ================ c3 a3 i3 x3 1 c3 a3 i3 x3 1 a1+df2 [1] c4 a4 i4 x4 1 y4 2 z4 3 ================ c4 a4 i4 x4 1 y4 1 z4 1 c4 a4 i4 x4 1 y4 2 z4 3 a1+df3 [1] c5 a5 b5 c5 i5 x5 1 2 3 ================ c5 a5 b5 c5 i5 x5 1 1 1 c5 a5 b5 c5 i5 x5 1 2 3 a1+df4 [1] c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 ================ c6 a6 b6 c6 i6 x6 1 1 1 y6 1 1 1 z6 1 1 1 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 a2+df_none [1 2 3] 0 0 1 ================ 0 1 2 0 1 2 3 0 1 2 0 1 1 1 a2+df1 [1 2 3] c3 a3 i3 x3 1 ================ c3 a3 a3 a3 i3 x3 1 2 3 c3 a3 a3 a3 i3 x3 1 1 1 a2+df2 [1 2 3] c4 a4 i4 x4 1 y4 2 z4 3 ================ c4 a4 a4 a4 i4 x4 1 2 3 y4 1 2 3 z4 1 2 3 c4 a4 a4 a4 i4 x4 1 1 1 y4 2 2 2 z4 3 3 3 a2+df3 [1 2 3] c5 a5 b5 c5 i5 x5 1 2 3 ================ c5 a5 b5 c5 i5 x5 1 2 3 c5 a5 b5 c5 i5 x5 1 2 3 a2+df4 [1 2 3] c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 ================ c6 a6 b6 c6 i6 x6 1 2 3 y6 1 2 3 z6 1 2 3 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 a3+df_none [[1 2 3]] 0 0 1 ================ 0 1 2 0 1 2 3 0 1 2 0 1 1 1 a3+df1 [[1 2 3]] c3 a3 i3 x3 1 ================ c3 a3 a3 a3 i3 x3 1 2 3 c3 a3 a3 a3 i3 x3 1 1 1 a3+df2 [[1 2 3]] c4 a4 i4 x4 1 y4 2 z4 3 ================ c4 a4 a4 a4 i4 x4 1 2 3 y4 1 2 3 z4 1 2 3 c4 a4 a4 a4 i4 x4 1 1 1 y4 2 2 2 z4 3 3 3 a3+df3 [[1 2 3]] c5 a5 b5 c5 i5 x5 1 2 3 ================ c5 a5 b5 c5 i5 x5 1 2 3 c5 a5 b5 c5 i5 x5 1 2 3 a3+df4 [[1 2 3]] c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 ================ c6 a6 b6 c6 i6 x6 1 2 3 y6 1 2 3 z6 1 2 3 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 a4+df_none [[1] [2] [3]] 0 0 1 ================ 0 0 1 1 2 2 3 0 0 1 1 1 2 1 a4+df1 [[1] [2] [3]] c3 a3 i3 x3 1 ================ c3 a3 i3 x3 1 x3 2 x3 3 c3 a3 i3 x3 1 x3 1 x3 1 a4+df2 [[1] [2] [3]] c4 a4 i4 x4 1 y4 2 z4 3 ================ c4 a4 i4 x4 1 y4 2 z4 3 c4 a4 i4 x4 1 y4 2 z4 3 a4+df3 [[1] [2] [3]] c5 a5 b5 c5 i5 x5 1 2 3 ================ c5 a5 b5 c5 i5 x5 1 1 1 x5 2 2 2 x5 3 3 3 c5 a5 b5 c5 i5 x5 1 2 3 x5 1 2 3 x5 1 2 3 a4+df4 [[1] [2] [3]] c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 ================ c6 a6 b6 c6 i6 x6 1 1 1 y6 2 2 2 z6 3 3 3 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 a5+df_none [[1 2 3] [4 5 6] [7 8 9]] 0 0 1 ================ 0 1 2 0 1 2 3 1 4 5 6 2 7 8 9 0 1 2 0 1 1 1 1 1 1 1 2 1 1 1 a5+df1 [[1 2 3] [4 5 6] [7 8 9]] c3 a3 i3 x3 1 ================ c3 a3 a3 a3 i3 x3 1 2 3 x3 4 5 6 x3 7 8 9 c3 a3 a3 a3 i3 x3 1 1 1 x3 1 1 1 x3 1 1 1 a5+df2 [[1 2 3] [4 5 6] [7 8 9]] c4 a4 i4 x4 1 y4 2 z4 3 ================ c4 a4 a4 a4 i4 x4 1 2 3 y4 4 5 6 z4 7 8 9 c4 a4 a4 a4 i4 x4 1 1 1 y4 2 2 2 z4 3 3 3 a5+df3 [[1 2 3] [4 5 6] [7 8 9]] c5 a5 b5 c5 i5 x5 1 2 3 ================ c5 a5 b5 c5 i5 x5 1 2 3 x5 4 5 6 x5 7 8 9 c5 a5 b5 c5 i5 x5 1 2 3 x5 1 2 3 x5 1 2 3 a5+df4 [[1 2 3] [4 5 6] [7 8 9]] c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 ================ c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 
# Broadcasting series and dataframes
a_args = [
    ('sr_none', sr_none),
    ('sr1', sr1),
    ('sr2', sr2)
]
sr_args = [
    ('df_none', df_none),
    ('df1', df1),
    ('df2', df2),
    ('df3', df3),
    ('df4', df4)
]
arg_combs = list(itertools.product(a_args, sr_args))

for (n1, arg1), (n2, arg2) in arg_combs:
    print(n1 + '+' + n2)
    print(arg1)
    print(arg2)
    print("================")
    arg1, arg2 = reshape_fns.broadcast(arg1, arg2)
    print(arg1)
    print(arg2)
    print()
sr_none+df_none 0 1 dtype: int64 0 0 1 ================ 0 0 1 0 0 1 sr_none+df1 0 1 dtype: int64 c3 a3 i3 x3 1 ================ c3 a3 i3 x3 1 c3 a3 i3 x3 1 sr_none+df2 0 1 dtype: int64 c4 a4 i4 x4 1 y4 2 z4 3 ================ c4 a4 i4 x4 1 y4 1 z4 1 c4 a4 i4 x4 1 y4 2 z4 3 sr_none+df3 0 1 dtype: int64 c5 a5 b5 c5 i5 x5 1 2 3 ================ c5 a5 b5 c5 i5 x5 1 1 1 c5 a5 b5 c5 i5 x5 1 2 3 sr_none+df4 0 1 dtype: int64 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 ================ c6 a6 b6 c6 i6 x6 1 1 1 y6 1 1 1 z6 1 1 1 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 sr1+df_none i1 x1 1 Name: a1, dtype: int64 0 0 1 ================ 0 i1 x1 1 0 i1 x1 1 sr1+df1 i1 x1 1 Name: a1, dtype: int64 c3 a3 i3 x3 1 ================ c3 a3 i1 i3 x1 x3 1 c3 a3 i1 i3 x1 x3 1 sr1+df2 i1 x1 1 Name: a1, dtype: int64 c4 a4 i4 x4 1 y4 2 z4 3 ================ c4 a4 i1 i4 x1 x4 1 y4 1 z4 1 c4 a4 i1 i4 x1 x4 1 y4 2 z4 3 sr1+df3 i1 x1 1 Name: a1, dtype: int64 c5 a5 b5 c5 i5 x5 1 2 3 ================ c5 a5 b5 c5 i1 i5 x1 x5 1 1 1 c5 a5 b5 c5 i1 i5 x1 x5 1 2 3 sr1+df4 i1 x1 1 Name: a1, dtype: int64 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 ================ c6 a6 b6 c6 i1 i6 x1 x6 1 1 1 y6 1 1 1 z6 1 1 1 c6 a6 b6 c6 i1 i6 x1 x6 1 2 3 y6 4 5 6 z6 7 8 9 sr2+df_none i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 0 0 1 ================ 0 i2 x2 1 y2 2 z2 3 0 i2 x2 1 y2 1 z2 1 sr2+df1 i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 c3 a3 i3 x3 1 ================ c3 a3 i2 i3 x2 x3 1 y2 x3 2 z2 x3 3 c3 a3 i2 i3 x2 x3 1 y2 x3 1 z2 x3 1 sr2+df2 i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 c4 a4 i4 x4 1 y4 2 z4 3 ================ c4 a4 i2 i4 x2 x4 1 y2 y4 2 z2 z4 3 c4 a4 i2 i4 x2 x4 1 y2 y4 2 z2 z4 3 sr2+df3 i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 c5 a5 b5 c5 i5 x5 1 2 3 ================ c5 a5 b5 c5 i2 i5 x2 x5 1 1 1 y2 x5 2 2 2 z2 x5 3 3 3 c5 a5 b5 c5 i2 i5 x2 x5 1 2 3 y2 x5 1 2 3 z2 x5 1 2 3 sr2+df4 i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 ================ c6 a6 b6 c6 i2 i6 x2 x6 1 1 1 y2 y6 2 2 2 z2 z6 3 3 3 c6 a6 b6 c6 i2 i6 x2 x6 1 2 3 y2 y6 4 5 6 z2 z6 7 8 9 
[np.broadcast_to(x, (3, 3)) for x in (0, a1, a2, sr_none, sr1, sr2)]
[array([[0, 0, 0], [0, 0, 0], [0, 0, 0]]), array([[1, 1, 1], [1, 1, 1], [1, 1, 1]]), array([[1, 2, 3], [1, 2, 3], [1, 2, 3]]), array([[1, 1, 1], [1, 1, 1], [1, 1, 1]]), array([[1, 1, 1], [1, 1, 1], [1, 1, 1]]), array([[1, 2, 3], [1, 2, 3], [1, 2, 3]])]
# Broadcasting all at once
for i in reshape_fns.broadcast(
    0, a1, a2, sr_none, sr1, sr2,
    to_shape=(3, 3),
    index_from='stack',
    columns_from='stack'
):
    print(i)
0 1 2 i1 i2 x1 x2 0 0 0 y2 0 0 0 z2 0 0 0 0 1 2 i1 i2 x1 x2 1 1 1 y2 1 1 1 z2 1 1 1 0 1 2 i1 i2 x1 x2 1 2 3 y2 1 2 3 z2 1 2 3 0 1 2 i1 i2 x1 x2 1 1 1 y2 1 1 1 z2 1 1 1 0 1 2 i1 i2 x1 x2 1 1 1 y2 1 1 1 z2 1 1 1 0 1 2 i1 i2 x1 x2 1 1 1 y2 2 2 2 z2 3 3 3 
# Broadcasting all at once
for i in reshape_fns.broadcast(
    v1, a1, a2, a3, a4, a5, sr_none, sr1, sr2, df_none, df1, df2, df3, df4,
    index_from='stack',
    columns_from='stack'
):
    print(i)
c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 0 0 0 y2 x3 y4 x5 y6 0 0 0 z2 x3 z4 x5 z6 0 0 0 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 1 1 1 z2 x3 z4 x5 z6 1 1 1 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 2 3 y2 x3 y4 x5 y6 1 2 3 z2 x3 z4 x5 z6 1 2 3 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 2 3 y2 x3 y4 x5 y6 1 2 3 z2 x3 z4 x5 z6 1 2 3 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 2 2 2 z2 x3 z4 x5 z6 3 3 3 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 2 3 y2 x3 y4 x5 y6 4 5 6 z2 x3 z4 x5 z6 7 8 9 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 1 1 1 z2 x3 z4 x5 z6 1 1 1 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 1 1 1 z2 x3 z4 x5 z6 1 1 1 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 2 2 2 z2 x3 z4 x5 z6 3 3 3 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 1 1 1 z2 x3 z4 x5 z6 1 1 1 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 1 1 1 z2 x3 z4 x5 z6 1 1 1 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 2 2 2 z2 x3 z4 x5 z6 3 3 3 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 2 3 y2 x3 y4 x5 y6 1 2 3 z2 x3 z4 x5 z6 1 2 3 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 2 3 y2 x3 y4 x5 y6 4 5 6 z2 x3 z4 x5 z6 7 8 9 
for i in reshape_fns.broadcast(
    v1, a1, a2, a3, a4, a5, sr_none, sr1, sr2, df_none, df1, df2, df3, df4,
    index_from=None, # use as-is
    columns_from=None
):
    print(i)
c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 0 0 0 y2 x3 y4 x5 y6 0 0 0 z2 x3 z4 x5 z6 0 0 0 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 1 1 1 z2 x3 z4 x5 z6 1 1 1 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 2 3 y2 x3 y4 x5 y6 1 2 3 z2 x3 z4 x5 z6 1 2 3 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 2 3 y2 x3 y4 x5 y6 1 2 3 z2 x3 z4 x5 z6 1 2 3 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 2 2 2 z2 x3 z4 x5 z6 3 3 3 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 2 3 y2 x3 y4 x5 y6 4 5 6 z2 x3 z4 x5 z6 7 8 9 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 1 1 1 z2 x3 z4 x5 z6 1 1 1 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 1 1 1 z2 x3 z4 x5 z6 1 1 1 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 2 2 2 z2 x3 z4 x5 z6 3 3 3 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 1 1 1 z2 x3 z4 x5 z6 1 1 1 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 1 1 1 z2 x3 z4 x5 z6 1 1 1 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 2 2 2 z2 x3 z4 x5 z6 3 3 3 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 2 3 y2 x3 y4 x5 y6 1 2 3 z2 x3 z4 x5 z6 1 2 3 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 2 3 y2 x3 y4 x5 y6 4 5 6 z2 x3 z4 x5 z6 7 8 9 
for i in reshape_fns.broadcast(
    v1, a1, a2, a3, a4, a5, sr_none, sr1, sr2, df_none, df1, df2, df3, df4,
    index_from=-1, # take index from the last dataframe
    columns_from=-1
):
    print(i)
c6 a6 b6 c6 i6 x6 0 0 0 y6 0 0 0 z6 0 0 0 c6 a6 b6 c6 i6 x6 1 1 1 y6 1 1 1 z6 1 1 1 c6 a6 b6 c6 i6 x6 1 2 3 y6 1 2 3 z6 1 2 3 c6 a6 b6 c6 i6 x6 1 2 3 y6 1 2 3 z6 1 2 3 c6 a6 b6 c6 i6 x6 1 1 1 y6 2 2 2 z6 3 3 3 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 c6 a6 b6 c6 i6 x6 1 1 1 y6 1 1 1 z6 1 1 1 c6 a6 b6 c6 i6 x6 1 1 1 y6 1 1 1 z6 1 1 1 c6 a6 b6 c6 i6 x6 1 1 1 y6 2 2 2 z6 3 3 3 c6 a6 b6 c6 i6 x6 1 1 1 y6 1 1 1 z6 1 1 1 c6 a6 b6 c6 i6 x6 1 1 1 y6 1 1 1 z6 1 1 1 c6 a6 b6 c6 i6 x6 1 1 1 y6 2 2 2 z6 3 3 3 c6 a6 b6 c6 i6 x6 1 2 3 y6 1 2 3 z6 1 2 3 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 
for i in reshape_fns.broadcast(
    v1, a1, a2, a3, a4, a5, sr_none, sr1, sr2, df_none, df1, df2, df3, df4,
    index_from=multi_i, # specify manually
    columns_from=multi_c
):
    print(i)
c7 a7 b7 c7 c8 a8 b8 c8 i7 i8 x7 x8 0 0 0 y7 y8 0 0 0 z7 z8 0 0 0 c7 a7 b7 c7 c8 a8 b8 c8 i7 i8 x7 x8 1 1 1 y7 y8 1 1 1 z7 z8 1 1 1 c7 a7 b7 c7 c8 a8 b8 c8 i7 i8 x7 x8 1 2 3 y7 y8 1 2 3 z7 z8 1 2 3 c7 a7 b7 c7 c8 a8 b8 c8 i7 i8 x7 x8 1 2 3 y7 y8 1 2 3 z7 z8 1 2 3 c7 a7 b7 c7 c8 a8 b8 c8 i7 i8 x7 x8 1 1 1 y7 y8 2 2 2 z7 z8 3 3 3 c7 a7 b7 c7 c8 a8 b8 c8 i7 i8 x7 x8 1 2 3 y7 y8 4 5 6 z7 z8 7 8 9 c7 a7 b7 c7 c8 a8 b8 c8 i7 i8 x7 x8 1 1 1 y7 y8 1 1 1 z7 z8 1 1 1 c7 a7 b7 c7 c8 a8 b8 c8 i7 i8 x7 x8 1 1 1 y7 y8 1 1 1 z7 z8 1 1 1 c7 a7 b7 c7 c8 a8 b8 c8 i7 i8 x7 x8 1 1 1 y7 y8 2 2 2 z7 z8 3 3 3 c7 a7 b7 c7 c8 a8 b8 c8 i7 i8 x7 x8 1 1 1 y7 y8 1 1 1 z7 z8 1 1 1 c7 a7 b7 c7 c8 a8 b8 c8 i7 i8 x7 x8 1 1 1 y7 y8 1 1 1 z7 z8 1 1 1 c7 a7 b7 c7 c8 a8 b8 c8 i7 i8 x7 x8 1 1 1 y7 y8 2 2 2 z7 z8 3 3 3 c7 a7 b7 c7 c8 a8 b8 c8 i7 i8 x7 x8 1 2 3 y7 y8 1 2 3 z7 z8 1 2 3 c7 a7 b7 c7 c8 a8 b8 c8 i7 i8 x7 x8 1 2 3 y7 y8 4 5 6 z7 z8 7 8 9 
# Do not clean columns
vbt.settings.broadcasting['drop_duplicates'] = False
vbt.settings.broadcasting['drop_redundant'] = False
vbt.settings.broadcasting['ignore_sr_names'] = False

for i in reshape_fns.broadcast(
    v1, a1, a2, a3, a4, a5, sr_none, sr1, sr2, df_none, df1, df2, df3, df4,
    index_from='stack', # stack but do not clean
    columns_from='stack'
):
    print(i)
    
vbt.settings.broadcasting.reset()
a1 a2 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 0 0 0 y2 x3 y4 x5 y6 0 0 0 z2 x3 z4 x5 z6 0 0 0 a1 a2 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 1 1 1 z2 x3 z4 x5 z6 1 1 1 a1 a2 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 2 3 y2 x3 y4 x5 y6 1 2 3 z2 x3 z4 x5 z6 1 2 3 a1 a2 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 2 3 y2 x3 y4 x5 y6 1 2 3 z2 x3 z4 x5 z6 1 2 3 a1 a2 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 2 2 2 z2 x3 z4 x5 z6 3 3 3 a1 a2 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 2 3 y2 x3 y4 x5 y6 4 5 6 z2 x3 z4 x5 z6 7 8 9 a1 a2 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 1 1 1 z2 x3 z4 x5 z6 1 1 1 a1 a2 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 1 1 1 z2 x3 z4 x5 z6 1 1 1 a1 a2 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 2 2 2 z2 x3 z4 x5 z6 3 3 3 a1 a2 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 1 1 1 z2 x3 z4 x5 z6 1 1 1 a1 a2 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 1 1 1 z2 x3 z4 x5 z6 1 1 1 a1 a2 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 1 1 y2 x3 y4 x5 y6 2 2 2 z2 x3 z4 x5 z6 3 3 3 a1 a2 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 2 3 y2 x3 y4 x5 y6 1 2 3 z2 x3 z4 x5 z6 1 2 3 a1 a2 c3 a3 c4 a4 c5 a5 b5 c5 c6 a6 b6 c6 i1 i2 i3 i4 i5 i6 x1 x2 x3 x4 x5 x6 1 2 3 y2 x3 y4 x5 y6 4 5 6 z2 x3 z4 x5 z6 7 8 9 
big_a = np.empty((1000, 1000))

print(reshape_fns.broadcast(np.empty((1,)), big_a)[0].flags)
%timeit reshape_fns.broadcast(np.empty((1,)), big_a)

print(reshape_fns.broadcast(np.empty((1,)), big_a, require_kwargs={'requirements': 'W'})[0].flags)
%timeit reshape_fns.broadcast(np.empty((1,)), big_a, require_kwargs={'requirements': 'W'})

print(reshape_fns.broadcast(np.empty((1,)), big_a, require_kwargs={'requirements': 'C'})[0].flags)
%timeit reshape_fns.broadcast(np.empty((1,)), big_a, require_kwargs={'requirements': 'C'})

print(reshape_fns.broadcast(np.empty((1,)), big_a, require_kwargs={'requirements': 'F'})[0].flags)
%timeit reshape_fns.broadcast(np.empty((1,)), big_a, require_kwargs={'requirements': 'F'})
C_CONTIGUOUS : False F_CONTIGUOUS : False OWNDATA : False WRITEABLE : False ALIGNED : True WRITEBACKIFCOPY : False UPDATEIFCOPY : False 19.1 µs ± 471 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : True WRITEABLE : True ALIGNED : True WRITEBACKIFCOPY : False UPDATEIFCOPY : False 596 µs ± 10.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : True WRITEABLE : True ALIGNED : True WRITEBACKIFCOPY : False UPDATEIFCOPY : False 181 µs ± 11.4 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) C_CONTIGUOUS : False F_CONTIGUOUS : True OWNDATA : True WRITEABLE : True ALIGNED : True WRITEBACKIFCOPY : False UPDATEIFCOPY : False 1.26 ms ± 35.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) 
print(reshape_fns.broadcast(v1, df4, to_pd=False))
print(reshape_fns.broadcast(v1, df4, to_pd=True))
(array([[0, 0, 0], [0, 0, 0], [0, 0, 0]]), array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])) (c6 a6 b6 c6 i6 x6 0 0 0 y6 0 0 0 z6 0 0 0, c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9) 
# One-side broadcasting, default behaviour is copying index/columns from the second argument
print(reshape_fns.broadcast_to(sr1, sr1))
print(reshape_fns.broadcast_to(sr1, sr2))
print(reshape_fns.broadcast_to(sr1, df1))
print(reshape_fns.broadcast_to(sr1, df2))
print(reshape_fns.broadcast_to(sr1, df3))
print(reshape_fns.broadcast_to(sr1, df4))
i1 x1 1 Name: a1, dtype: int64 i2 x2 1 y2 1 z2 1 Name: a2, dtype: int64 c3 a3 i3 x3 1 c4 a4 i4 x4 1 y4 1 z4 1 c5 a5 b5 c5 i5 x5 1 1 1 c6 a6 b6 c6 i6 x6 1 1 1 y6 1 1 1 z6 1 1 1 
# Broadcasting first element to be an array out of the second argument
print(reshape_fns.broadcast_to_array_of(0.1, v1))
print(reshape_fns.broadcast_to_array_of([0.1], v1))
print(reshape_fns.broadcast_to_array_of([0.1, 0.2], v1))
[0.1] [0.1] [0.1 0.2] 
print(reshape_fns.broadcast_to_array_of(0.1, sr2))
print(reshape_fns.broadcast_to_array_of([0.1], sr2))
print(reshape_fns.broadcast_to_array_of([0.1, 0.2], sr2))
print(reshape_fns.broadcast_to_array_of([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], sr2))
[[0.1 0.1 0.1]] [[0.1 0.1 0.1]] [[0.1 0.1 0.1] [0.2 0.2 0.2]] [[0.1 0.2 0.3] [0.4 0.5 0.6]] 
print(reshape_fns.broadcast_to_array_of(0.1, df2))
print(reshape_fns.broadcast_to_array_of([0.1], df2))
print(reshape_fns.broadcast_to_array_of([0.1, 0.2], df2))
print(reshape_fns.broadcast_to_array_of([[[0.1], [0.2], [0.3]], [[0.4], [0.5], [0.6]]], df2))
[[[0.1] [0.1] [0.1]]] [[[0.1] [0.1] [0.1]]] [[[0.1] [0.1] [0.1]] [[0.2] [0.2] [0.2]]] [[[0.1] [0.2] [0.3]] [[0.4] [0.5] [0.6]]] 
print(reshape_fns.broadcast_to_array_of(0.1, np.empty((2, 2, 2)))) # works even for ndim > 2
[[[[0.1 0.1] [0.1 0.1]] [[0.1 0.1] [0.1 0.1]]]] 
print(reshape_fns.broadcast_to_axis_of(10, np.empty((2,)), 0))
print(reshape_fns.broadcast_to_axis_of(10, np.empty((2,)), 1))
print(reshape_fns.broadcast_to_axis_of(10, np.empty((2, 3)), 0))
print(reshape_fns.broadcast_to_axis_of(10, np.empty((2, 3)), 1))
print(reshape_fns.broadcast_to_axis_of(10, np.empty((2, 3)), 2))
[10 10] 10 [10 10] [10 10 10] 10 
i = pd.MultiIndex.from_arrays([[1, 1, 2, 2], [3, 4, 3, 4], ['a', 'b', 'c', 'd']])
sr = pd.Series([1, 2, 3, 4], index=i)
print(reshape_fns.unstack_to_array(sr))
[[[ 1. nan nan nan] [nan 2. nan nan]] [[nan nan 3. nan] [nan nan nan 4.]]] 
print(reshape_fns.make_symmetric(sr1))
print(reshape_fns.make_symmetric(sr2))
print(reshape_fns.make_symmetric(df1))
print(reshape_fns.make_symmetric(df2))
print(reshape_fns.make_symmetric(df3))
print(reshape_fns.make_symmetric(df4))
print(reshape_fns.make_symmetric(df5))
print(reshape_fns.make_symmetric(pd.Series([1, 2, 3], name='yo'), sort=False))
('i1', None) a1 x1 (i1, None) a1 NaN 1.0 x1 1.0 NaN ('i2', None) a2 x2 y2 z2 (i2, None) a2 NaN 1.0 2.0 3.0 x2 1.0 NaN NaN NaN y2 2.0 NaN NaN NaN z2 3.0 NaN NaN NaN ('i3', 'c3') a3 x3 (i3, c3) a3 NaN 1.0 x3 1.0 NaN ('i4', 'c4') a4 x4 y4 z4 (i4, c4) a4 NaN 1.0 2.0 3.0 x4 1.0 NaN NaN NaN y4 2.0 NaN NaN NaN z4 3.0 NaN NaN NaN ('i5', 'c5') a5 b5 c5 x5 (i5, c5) a5 NaN NaN NaN 1.0 b5 NaN NaN NaN 2.0 c5 NaN NaN NaN 3.0 x5 1.0 2.0 3.0 NaN ('i6', 'c6') a6 b6 c6 x6 y6 z6 (i6, c6) a6 NaN NaN NaN 1.0 4.0 7.0 b6 NaN NaN NaN 2.0 5.0 8.0 c6 NaN NaN NaN 3.0 6.0 9.0 x6 1.0 2.0 3.0 NaN NaN NaN y6 4.0 5.0 6.0 NaN NaN NaN z6 7.0 8.0 9.0 NaN NaN NaN ('i7', 'c7') a7 b7 c7 x7 y7 z7 ('i8', 'c8') a8 b8 c8 x8 y8 z8 (i7, c7) (i8, c8) a7 a8 NaN NaN NaN 1.0 4.0 7.0 b7 b8 NaN NaN NaN 2.0 5.0 8.0 c7 c8 NaN NaN NaN 3.0 6.0 9.0 x7 x8 1.0 2.0 3.0 NaN NaN NaN y7 y8 4.0 5.0 6.0 NaN NaN NaN z7 z8 7.0 8.0 9.0 NaN NaN NaN 0 1 2 yo 0 NaN NaN NaN 1.0 1 NaN NaN NaN 2.0 2 NaN NaN NaN 3.0 yo 1.0 2.0 3.0 NaN 
print(reshape_fns.unstack_to_df(df5.iloc[0]))
print(reshape_fns.unstack_to_df(sr, index_levels=0, column_levels=1))
print(reshape_fns.unstack_to_df(sr, index_levels=(0, 1), column_levels=2))
print(reshape_fns.unstack_to_df(sr, index_levels=0, column_levels=1, symmetric=True).columns)
c8 a8 b8 c8 c7 a7 1.0 NaN NaN b7 NaN 2.0 NaN c7 NaN NaN 3.0 3 4 1 1.0 2.0 2 3.0 4.0 a b c d 1 3 1.0 NaN NaN NaN 4 NaN 2.0 NaN NaN 2 3 NaN NaN 3.0 NaN 4 NaN NaN NaN 4.0 Index([1, 2, 3, 4], dtype='int64')

indexing

PandasIndexer = indexing.PandasIndexer
ParamIndexer = indexing.build_param_indexer(['param1', 'param2', 'tuple'])

class H(PandasIndexer, ParamIndexer):
    def __init__(self, a, param1_mapper, param2_mapper, tuple_mapper):
        self.a = a
        
        self._param1_mapper = param1_mapper
        self._param2_mapper = param2_mapper
        self._tuple_mapper = tuple_mapper
        
        PandasIndexer.__init__(self, my_kw='PandasIndexer')
        ParamIndexer.__init__(self, [param1_mapper, param2_mapper, tuple_mapper], my_kw='ParamIndexer')
        
    def indexing_func(self, pd_indexing_func, my_kw=None): 
        # As soon as you call iloc etc., performs it on each dataframe and mapper and returns a new class instance
        print(my_kw)
        param1_mapper = indexing.indexing_on_mapper(self._param1_mapper, self.a, pd_indexing_func)
        param2_mapper = indexing.indexing_on_mapper(self._param2_mapper, self.a, pd_indexing_func)
        tuple_mapper = indexing.indexing_on_mapper(self._tuple_mapper, self.a, pd_indexing_func)
        return H(pd_indexing_func(self.a), param1_mapper, param2_mapper, tuple_mapper)
        
    @classmethod
    def run(cls, a, params1, params2, level_names=('p1', 'p2')):
        a = reshape_fns.to_2d(a)
        # Build column hierarchy
        params1_idx = pd.Index(params1, name=level_names[0])
        params2_idx = pd.Index(params2, name=level_names[1])
        params_idx = index_fns.stack_indexes((params1_idx, params2_idx))
        new_columns = index_fns.combine_indexes((params_idx, a.columns))
        
        # Build mappers
        param1_mapper = np.repeat(params1, len(a.columns))
        param1_mapper = pd.Series(param1_mapper, index=new_columns, name=params1_idx.name)
        
        param2_mapper = np.repeat(params2, len(a.columns))
        param2_mapper = pd.Series(param2_mapper, index=new_columns, name=params2_idx.name)
        
        tuple_mapper = list(zip(*list(map(lambda x: x.values, [param1_mapper, param2_mapper]))))
        tuple_mapper = pd.Series(tuple_mapper, index=new_columns, name=(params1_idx.name, params2_idx.name))
        
        # Tile a to match the length of new_columns
        a = array_wrapper.ArrayWrapper(a.index, new_columns, 2).wrap(reshape_fns.tile(a.values, 4, axis=1))
        return cls(a, param1_mapper, param2_mapper, tuple_mapper)
        

# Similate an indicator with two params
h = H.run(df4, [0.1, 0.1, 0.2, 0.2], [0.3, 0.4, 0.5, 0.6])

print(df4)
print(h.a)
print(h._param1_mapper)
print(h._param2_mapper)
print(h._tuple_mapper)
c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 p1 0.1 0.2 p2 0.3 0.4 0.5 0.6 c6 a6 b6 c6 a6 b6 c6 a6 b6 c6 a6 b6 c6 i6 x6 1 2 3 1 2 3 1 2 3 1 2 3 y6 4 5 6 4 5 6 4 5 6 4 5 6 z6 7 8 9 7 8 9 7 8 9 7 8 9 p1 p2 c6 0.1 0.3 a6 0.1 b6 0.1 c6 0.1 0.4 a6 0.1 b6 0.1 c6 0.1 0.2 0.5 a6 0.2 b6 0.2 c6 0.2 0.6 a6 0.2 b6 0.2 c6 0.2 Name: p1, dtype: float64 p1 p2 c6 0.1 0.3 a6 0.3 b6 0.3 c6 0.3 0.4 a6 0.4 b6 0.4 c6 0.4 0.2 0.5 a6 0.5 b6 0.5 c6 0.5 0.6 a6 0.6 b6 0.6 c6 0.6 Name: p2, dtype: float64 p1 p2 c6 0.1 0.3 a6 (0.1, 0.3) b6 (0.1, 0.3) c6 (0.1, 0.3) 0.4 a6 (0.1, 0.4) b6 (0.1, 0.4) c6 (0.1, 0.4) 0.2 0.5 a6 (0.2, 0.5) b6 (0.2, 0.5) c6 (0.2, 0.5) 0.6 a6 (0.2, 0.6) b6 (0.2, 0.6) c6 (0.2, 0.6) Name: (p1, p2), dtype: object 
# Indexing operations are delegated to the underlying dataframes
print(h[(0.1, 0.3, 'a6')].a)
print(h.loc[:, (0.1, 0.3, 'a6'):(0.1, 0.3, 'c6')].a)
print(h.iloc[-2:, -2:].a)
print(h.xs((0.1, 0.3), level=('p1', 'p2'), axis=1).a.columns)
PandasIndexer i6 x6 1 y6 4 z6 7 Name: (0.1, 0.3, a6), dtype: int64 PandasIndexer p1 0.1 p2 0.3 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 PandasIndexer p1 0.2 p2 0.6 c6 b6 c6 i6 y6 5 6 z6 8 9 PandasIndexer Index(['a6', 'b6', 'c6'], dtype='object', name='c6') 
print(h.param1_loc[0.1].a.columns)
print(h.param1_loc[0.1:0.1].a)
print(h.param1_loc[[0.1, 0.1]].a)
ParamIndexer MultiIndex([(0.1, 0.3, 'a6'), (0.1, 0.3, 'b6'), (0.1, 0.3, 'c6'), (0.1, 0.4, 'a6'), (0.1, 0.4, 'b6'), (0.1, 0.4, 'c6')], names=['p1', 'p2', 'c6']) ParamIndexer p1 0.1 p2 0.3 0.4 c6 a6 b6 c6 a6 b6 c6 i6 x6 1 2 3 1 2 3 y6 4 5 6 4 5 6 z6 7 8 9 7 8 9 ParamIndexer p1 0.1 p2 0.3 0.4 0.3 0.4 c6 a6 b6 c6 a6 b6 c6 a6 b6 c6 a6 b6 c6 i6 x6 1 2 3 1 2 3 1 2 3 1 2 3 y6 4 5 6 4 5 6 4 5 6 4 5 6 z6 7 8 9 7 8 9 7 8 9 7 8 9 
print(h.param2_loc[0.3].a)
print(h.param2_loc[0.3:0.3].a)
print(h.param2_loc[[0.3, 0.3]].a.columns)
ParamIndexer p1 0.1 p2 0.3 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 ParamIndexer p1 0.1 p2 0.3 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 ParamIndexer MultiIndex([(0.1, 0.3, 'a6'), (0.1, 0.3, 'b6'), (0.1, 0.3, 'c6'), (0.1, 0.3, 'a6'), (0.1, 0.3, 'b6'), (0.1, 0.3, 'c6')], names=['p1', 'p2', 'c6']) 
print(h.tuple_loc[(0.1, 0.3)].a)
print(h.tuple_loc[(0.1, 0.3):(0.1, 0.3)].a.columns)
print(h.tuple_loc[[(0.1, 0.3), (0.1, 0.3)]].a.columns)
ParamIndexer p1 0.1 p2 0.3 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 ParamIndexer MultiIndex([(0.1, 0.3, 'a6'), (0.1, 0.3, 'b6'), (0.1, 0.3, 'c6')], names=['p1', 'p2', 'c6']) ParamIndexer MultiIndex([(0.1, 0.3, 'a6'), (0.1, 0.3, 'b6'), (0.1, 0.3, 'c6'), (0.1, 0.3, 'a6'), (0.1, 0.3, 'b6'), (0.1, 0.3, 'c6')], names=['p1', 'p2', 'c6'])

combine_fns

vbt.settings.broadcasting['index_from'] = 'stack'
vbt.settings.broadcasting['columns_from'] = 'stack'

print(combine_fns.apply_and_concat_one(3, lambda i, x, a: x + a[i], sr2.values, [10, 20, 30]))
print(combine_fns.apply_and_concat_one_nb(3, njit(lambda i, x, a: x + a[i]), sr2.values, (10, 20, 30)))

print(combine_fns.apply_and_concat_one(3, lambda i, x, a: x + a[i], df4.values, [10, 20, 30]))
print(combine_fns.apply_and_concat_one_nb(3, njit(lambda i, x, a: x + a[i]), df4.values, (10, 20, 30)))
[[11 21 31] [12 22 32] [13 23 33]] [[11 21 31] [12 22 32] [13 23 33]] [[11 12 13 21 22 23 31 32 33] [14 15 16 24 25 26 34 35 36] [17 18 19 27 28 29 37 38 39]] [[11 12 13 21 22 23 31 32 33] [14 15 16 24 25 26 34 35 36] [17 18 19 27 28 29 37 38 39]] 
print(combine_fns.apply_and_concat_multiple(3, lambda i, x, a: (x, x + a[i]), sr2.values, [10, 20, 30]))
print(combine_fns.apply_and_concat_multiple_nb(3, njit(lambda i, x, a: (x, x + a[i])), sr2.values, (10, 20, 30)))

print(combine_fns.apply_and_concat_multiple(3, lambda i, x, a: (x, x + a[i]), df4.values, [10, 20, 30]))
print(combine_fns.apply_and_concat_multiple_nb(3, njit(lambda i, x, a: (x, x + a[i])), df4.values, (10, 20, 30)))
[array([[1, 1, 1], [2, 2, 2], [3, 3, 3]]), array([[11, 21, 31], [12, 22, 32], [13, 23, 33]])] [array([[1, 1, 1], [2, 2, 2], [3, 3, 3]]), array([[11, 21, 31], [12, 22, 32], [13, 23, 33]])] [array([[1, 2, 3, 1, 2, 3, 1, 2, 3], [4, 5, 6, 4, 5, 6, 4, 5, 6], [7, 8, 9, 7, 8, 9, 7, 8, 9]]), array([[11, 12, 13, 21, 22, 23, 31, 32, 33], [14, 15, 16, 24, 25, 26, 34, 35, 36], [17, 18, 19, 27, 28, 29, 37, 38, 39]])] [array([[1, 2, 3, 1, 2, 3, 1, 2, 3], [4, 5, 6, 4, 5, 6, 4, 5, 6], [7, 8, 9, 7, 8, 9, 7, 8, 9]]), array([[11, 12, 13, 21, 22, 23, 31, 32, 33], [14, 15, 16, 24, 25, 26, 34, 35, 36], [17, 18, 19, 27, 28, 29, 37, 38, 39]])] 
print(combine_fns.combine_and_concat(sr2.values, (sr2.values*2, sr2.values*3), lambda x, y, a: x + y + a, 100))
print(combine_fns.combine_and_concat_nb(sr2.values, (sr2.values*2, sr2.values*3), njit(lambda x, y, a: x + y + a), 100))

print(combine_fns.combine_and_concat(df4.values, (df4.values*2, df4.values*3), lambda x, y, a: x + y + a, 100))
print(combine_fns.combine_and_concat_nb(df4.values, (df4.values*2, df4.values*3), njit(lambda x, y, a: x + y + a), 100))
[[103 104] [106 108] [109 112]] [[103 104] [106 108] [109 112]] [[103 106 109 104 108 112] [112 115 118 116 120 124] [121 124 127 128 132 136]] [[103 106 109 104 108 112] [112 115 118 116 120 124] [121 124 127 128 132 136]] 
print(combine_fns.combine_multiple((sr2.values, sr2.values*2, sr2.values*3), lambda x, y, a: x + y + a, 100))
print(combine_fns.combine_multiple_nb((sr2.values, sr2.values*2, sr2.values*3), njit(lambda x, y, a: x + y + a), 100))

print(combine_fns.combine_multiple((df4.values, df4.values*2, df4.values*3), lambda x, y, a: x + y + a, 100))
print(combine_fns.combine_multiple_nb((df4.values, df4.values*2, df4.values*3), njit(lambda x, y, a: x + y + a), 100))
[206 212 218] [206 212 218] [[206 212 218] [224 230 236] [242 248 254]] [[206 212 218] [224 230 236] [242 248 254]]

accessors

print(pd.Series.vbt.empty(5, index=np.arange(10, 15), name='a', fill_value=5))
print(pd.DataFrame.vbt.empty((5, 3), index=np.arange(10, 15), columns=['a', 'b', 'c'], fill_value=5))

print(pd.Series.vbt.empty_like(sr2, fill_value=5))
print(pd.DataFrame.vbt.empty_like(df4, fill_value=5))
10 5 11 5 12 5 13 5 14 5 Name: a, dtype: int64 a b c 10 5 5 5 11 5 5 5 12 5 5 5 13 5 5 5 14 5 5 5 i2 x2 5 y2 5 z2 5 Name: a2, dtype: int64 c6 a6 b6 c6 i6 x6 5 5 5 y6 5 5 5 z6 5 5 5 
print(sr1.vbt.is_series())
print(sr1.vbt.is_frame())
print(df1.vbt.is_series())
print(df2.vbt.is_frame())
True False False True 
print(sr2.vbt.wrapper.index)
print(sr2.vbt.wrapper.columns)
print(df4.vbt.wrapper.index)
print(df4.vbt.wrapper.columns)
Index(['x2', 'y2', 'z2'], dtype='object', name='i2') Index(['a2'], dtype='object') Index(['x6', 'y6', 'z6'], dtype='object', name='i6') Index(['a6', 'b6', 'c6'], dtype='object', name='c6') 
print(df1.vbt.apply_on_index(lambda idx: idx + '_yo', axis=0))
print(df1.vbt.apply_on_index(lambda idx: idx + '_yo', axis=1))
df1_copy = df1.copy()
df1_copy.vbt.apply_on_index(lambda idx: idx + '_yo', axis=0, inplace=True)
print(df1_copy)
df1_copy.vbt.apply_on_index(lambda idx: idx + '_yo', axis=1, inplace=True)
print(df1_copy)
c3 a3 i3 x3_yo 1 c3 a3_yo i3 x3 1 c3 a3 i3 x3_yo 1 c3 a3_yo i3 x3_yo 1 
print(sr2.vbt.to_1d_array())
print(sr2.vbt.to_2d_array())
[1 2 3] [[1] [2] [3]] 
# It will try to return pd.Series
print(sr2.vbt.wrapper.wrap(a2)) # returns sr
print(sr2.vbt.wrapper.wrap(df2.values)) # returns sr
print(sr2.vbt.wrapper.wrap(df2.values, index=df2.index, columns=df2.columns)) # returns sr
print(sr2.vbt.wrapper.wrap(df4.values, columns=df4.columns)) # returns df
print(sr2.vbt.wrapper.wrap(df4.values, index=df4.index, columns=df4.columns)) # returns df
i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64 i4 x4 1 y4 2 z4 3 Name: a4, dtype: int64 c6 a6 b6 c6 i2 x2 1 2 3 y2 4 5 6 z2 7 8 9 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 
# It will try to return pd.DataFrame
print(df2.vbt.wrapper.wrap(a2)) # returns df
print(df2.vbt.wrapper.wrap(sr2.values)) # returns df
print(df2.vbt.wrapper.wrap(df4.values, columns=df4.columns)) # returns df
print(df2.vbt.wrapper.wrap(df4.values, index=df4.index, columns=df4.columns)) # returns df
c4 a4 i4 x4 1 y4 2 z4 3 c4 a4 i4 x4 1 y4 2 z4 3 c6 a6 b6 c6 i4 x4 1 2 3 y4 4 5 6 z4 7 8 9 c6 a6 b6 c6 i6 x6 1 2 3 y6 4 5 6 z6 7 8 9 
print(df4.vbt.tile(2, keys=['a', 'b']))
print(df4.vbt.repeat(2, keys=['a', 'b']))
a b c6 a6 b6 c6 a6 b6 c6 i6 x6 1 2 3 1 2 3 y6 4 5 6 4 5 6 z6 7 8 9 7 8 9 c6 a6 b6 c6 a b a b a b i6 x6 1 1 2 2 3 3 y6 4 4 5 5 6 6 z6 7 7 8 8 9 9 
df10 = pd.DataFrame([[1, 2], [4, 5], [7, 8]], columns=multi_c1)
df20 = pd.DataFrame([[1, 2, 3, 4], [4, 5, 6, 7], [7, 8, 9, 10]], columns=multi_c2)

print(df10)
print(df20)
print(df10.vbt.align_to(df20))
c8 a8 b8 0 1 2 1 4 5 2 7 8 c7 a7 c7 c8 a8 b8 a8 b8 0 1 2 3 4 1 4 5 6 7 2 7 8 9 10 c7 a7 c7 c8 a8 b8 a8 b8 0 1 2 1 2 1 4 5 4 5 2 7 8 7 8 
print(pd.DataFrame.vbt.broadcast(
    sr2,
    10
))
print(sr2.vbt.broadcast(
    10
))
print(sr2.vbt.broadcast_to(
    df2
))
(i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64, i2 x2 10 y2 10 z2 10 Name: a2, dtype: int64) (i2 x2 1 y2 2 z2 3 Name: a2, dtype: int64, i2 x2 10 y2 10 z2 10 Name: a2, dtype: int64) c4 a4 i4 x4 1 y4 2 z4 3 
print(sr2.vbt.make_symmetric())
print(df2.vbt.make_symmetric())
print(df3.vbt.make_symmetric())
print(df4.vbt.make_symmetric())
('i2', None) a2 x2 y2 z2 (i2, None) a2 NaN 1.0 2.0 3.0 x2 1.0 NaN NaN NaN y2 2.0 NaN NaN NaN z2 3.0 NaN NaN NaN ('i4', 'c4') a4 x4 y4 z4 (i4, c4) a4 NaN 1.0 2.0 3.0 x4 1.0 NaN NaN NaN y4 2.0 NaN NaN NaN z4 3.0 NaN NaN NaN ('i5', 'c5') a5 b5 c5 x5 (i5, c5) a5 NaN NaN NaN 1.0 b5 NaN NaN NaN 2.0 c5 NaN NaN NaN 3.0 x5 1.0 2.0 3.0 NaN ('i6', 'c6') a6 b6 c6 x6 y6 z6 (i6, c6) a6 NaN NaN NaN 1.0 4.0 7.0 b6 NaN NaN NaN 2.0 5.0 8.0 c6 NaN NaN NaN 3.0 6.0 9.0 x6 1.0 2.0 3.0 NaN NaN NaN y6 4.0 5.0 6.0 NaN NaN NaN z6 7.0 8.0 9.0 NaN NaN NaN 
print(df5.iloc[:, 0].vbt.unstack_to_array())
[[ 1. nan nan] [nan 4. nan] [nan nan 7.]] 
print(df5.iloc[:, 0].vbt.unstack_to_df())
i8 x8 y8 z8 i7 x7 1.0 NaN NaN y7 NaN 4.0 NaN z7 NaN NaN 7.0 
print(sr2.vbt.apply(apply_func=lambda x: x ** 2))
print(sr2.vbt.apply(apply_func=lambda x: x ** 2, to_2d=True))
print(df2.vbt.apply(apply_func=lambda x: x ** 2))
i2 x2 1 y2 4 z2 9 Name: a2, dtype: int64 i2 x2 1 y2 4 z2 9 Name: a2, dtype: int64 c4 a4 i4 x4 1 y4 4 z4 9 
print(pd.DataFrame.vbt.concat(sr2, 10, df4, keys=['a', 'b', 'c']))
print(sr2.vbt.concat(10, df4, keys=['a', 'b', 'c']))
a b c c6 a6 b6 c6 a6 b6 c6 a6 b6 c6 i2 i6 x2 x6 1 1 1 10 10 10 1 2 3 y2 y6 2 2 2 10 10 10 4 5 6 z2 z6 3 3 3 10 10 10 7 8 9 a b c c6 a6 b6 c6 a6 b6 c6 a6 b6 c6 i2 i6 x2 x6 1 1 1 10 10 10 1 2 3 y2 y6 2 2 2 10 10 10 4 5 6 z2 z6 3 3 3 10 10 10 7 8 9 
print(sr2.vbt.apply_and_concat(3, sr2.values, 10, apply_func=lambda i, x, y, c, d=1: x + y[i] + c + d, d=100))
print(sr2.vbt.apply_and_concat(3, sr2.values, 10, apply_func=njit(lambda i, x, y, c: x + y[i] + c + 100)))
print(sr2.vbt.apply_and_concat(3, df4.values, 10, apply_func=lambda i, x, y, c, d=1: x + y[:, i] + c + d, d=100))
print(sr2.vbt.apply_and_concat(3, df4.values, 10, apply_func=njit(lambda i, x, y, c: x + y[:, i] + c + 100)))
print(df4.vbt.apply_and_concat(3, df4.values, 10, apply_func=lambda i, x, y, c, d=1: x + y[:, i] + c + d, d=100))
print(df4.vbt.apply_and_concat(
    3, 
    df4.values, 
    10, 
    apply_func=njit(lambda i, x, y, c: x + y[:, i] + c + 100), 
    keys=pd.Index(['a', 'b', 'c'], name='hello')))
apply_idx 0 1 2 i2 x2 112 113 114 y2 113 114 115 z2 114 115 116 apply_idx 0 1 2 i2 x2 112 113 114 y2 113 114 115 z2 114 115 116 apply_idx 0 1 2 i2 x2 112 113 114 y2 116 117 118 z2 120 121 122 apply_idx 0 1 2 i2 x2 112 113 114 y2 116 117 118 z2 120 121 122 apply_idx 0 1 2 c6 a6 b6 c6 a6 b6 c6 a6 b6 c6 i6 x6 112 116 120 113 117 121 114 118 122 y6 115 119 123 116 120 124 117 121 125 z6 118 122 126 119 123 127 120 124 128 hello a b c c6 a6 b6 c6 a6 b6 c6 a6 b6 c6 i6 x6 112 116 120 113 117 121 114 118 122 y6 115 119 123 116 120 124 117 121 125 z6 118 122 126 119 123 127 120 124 128 
print(sr2.vbt.combine(10., combine_func=lambda x, y: x + y))
print(sr2.vbt.combine(10, 100, d=1000, combine_func=lambda x, y, c, d=1: x + y + c + d)) # test args and kwargs
print(sr2.vbt.combine(np.array([10, 20, 30]), combine_func=lambda x, y: x + y))
print(sr2.vbt.combine(np.array([[10, 20, 30]]), combine_func=lambda x, y: x + y))
print(sr2.vbt.combine(sr1, combine_func=lambda x, y: x + y, broadcast_kwargs=dict(index_from='stack')))
print(sr2.vbt.combine(sr2, combine_func=lambda x, y: x + y, broadcast_kwargs=dict(index_from='stack')))
print(sr2.vbt.combine(df2, combine_func=lambda x, y: x + y, broadcast_kwargs=dict(index_from='stack')))
print(sr2.vbt.combine(df3, combine_func=lambda x, y: x + y, broadcast_kwargs=dict(index_from='stack')))
print(sr2.vbt.combine(df4, combine_func=lambda x, y: x + y, broadcast_kwargs=dict(index_from='stack')))
print(sr2.vbt.combine(df5, combine_func=lambda x, y: x + y, broadcast_kwargs=dict(index_from='stack')))
i2 x2 11.0 y2 12.0 z2 13.0 Name: a2, dtype: float64 i2 x2 1111 y2 1112 z2 1113 Name: a2, dtype: int64 i2 x2 11 y2 22 z2 33 Name: a2, dtype: int64 a2 a2 a2 i2 x2 11 21 31 y2 12 22 32 z2 13 23 33 i2 i1 x2 x1 2 y2 x1 3 z2 x1 4 dtype: int64 i2 x2 2 y2 4 z2 6 Name: a2, dtype: int64 c4 a4 i2 i4 x2 x4 2 y2 y4 4 z2 z4 6 c5 a5 b5 c5 i2 i5 x2 x5 2 3 4 y2 x5 3 4 5 z2 x5 4 5 6 c6 a6 b6 c6 i2 i6 x2 x6 2 3 4 y2 y6 6 7 8 z2 z6 10 11 12 c7 a7 b7 c7 c8 a8 b8 c8 i2 i7 i8 x2 x7 x8 2 3 4 y2 y7 y8 6 7 8 z2 z7 z8 10 11 12 
print(sr2.vbt.combine(
    [10, [10, 20, 30], pd.Series([10, 20, 30])],
    10, b=100,
    combine_func=lambda x, y, a, b=1: x + y + a + b, 
    broadcast_kwargs=dict(index_from='stack')))
print(sr2.vbt.combine(
    [10, [10, 20, 30], [[10, 20, 30]], pd.Series([10, 20, 30]), df1, df3],
    10, b=100,
    combine_func=lambda x, y, a, b=1: x + y + a + b, 
    broadcast_kwargs=dict(index_from='stack')))
print(sr2.vbt.combine(
    [10, [10, 20, 30], [[10, 20, 30]], pd.Series([10, 20, 30]), df1, df3],
    10,
    combine_func=njit(lambda x, y, a, b=1: x + y + a + 100), 
    broadcast_kwargs=dict(index_from='stack')))
print(sr2.vbt.combine(
    [10, [10, 20, 30], [[10, 20, 30]], pd.Series([10, 20, 30]), df1, df3],
    10,
    combine_func=njit(lambda x, y, a, b=1: x + y + a + 100), 
    broadcast_kwargs=dict(index_from='stack')))
i2 x2 361 y2 382 z2 403 dtype: int64 c3 a3 c5 a5 b5 c5 i2 i3 i5 x2 x3 x5 703 724 745 y2 x3 x5 714 735 756 z2 x3 x5 725 746 767 c3 a3 c5 a5 b5 c5 i2 i3 i5 x2 x3 x5 703 724 745 y2 x3 x5 714 735 756 z2 x3 x5 725 746 767 c3 a3 c5 a5 b5 c5 i2 i3 i5 x2 x3 x5 703 724 745 y2 x3 x5 714 735 756 z2 x3 x5 725 746 767 
# Test concat=True
print(sr2.vbt.combine(
    [10, [10, 20, 30], pd.Series([10, 20, 30])],
    10, b=100,
    combine_func=lambda x, y, a, b=1: x + y + a + b, 
    concat=True,
    broadcast_kwargs=dict(index_from='stack')))
print(sr2.vbt.combine(
    [10, [10, 20, 30], [[10, 20, 30]], pd.Series([10, 20, 30]), df1, df3],
    10, b=100,
    combine_func=lambda x, y, a, b=1: x + y + a + b, 
    concat=True,
    broadcast_kwargs=dict(index_from='stack')))
print(sr2.vbt.combine(
    [10, [10, 20, 30], [[10, 20, 30]], pd.Series([10, 20, 30]), df1, df3],
    10,
    combine_func=njit(lambda x, y, a, b=1: x + y + a + 100),
    concat=True,
    broadcast_kwargs=dict(index_from='stack')))
print(sr2.vbt.combine(
    [10, [10, 20, 30], [[10, 20, 30]], pd.Series([10, 20, 30]), df1, df3],
    10,
    combine_func=njit(lambda x, y, a, b=1: x + y + a + 100),
    concat=True,
    keys=['a', 'b', 'c', 'd', 'e', 'f'],
    broadcast_kwargs=dict(index_from='stack')))
combine_idx 0 1 2 i2 x2 121 121 121 y2 122 132 132 z2 123 143 143 combine_idx 0 1 2 3 4 \ c3 a3 a3 a3 a3 a3 c5 a5 b5 c5 a5 b5 c5 a5 b5 c5 a5 b5 c5 a5 i2 i3 i5 x2 x3 x5 121 121 121 121 131 141 121 131 141 121 121 121 112 y2 x3 x5 122 122 122 122 132 142 122 132 142 132 132 132 113 z2 x3 x5 123 123 123 123 133 143 123 133 143 143 143 143 114 combine_idx 5 c3 a3 c5 b5 c5 a5 b5 c5 i2 i3 i5 x2 x3 x5 112 112 112 113 114 y2 x3 x5 113 113 113 114 115 z2 x3 x5 114 114 114 115 116 combine_idx 0 1 2 3 4 \ c3 a3 a3 a3 a3 a3 c5 a5 b5 c5 a5 b5 c5 a5 b5 c5 a5 b5 c5 a5 i2 i3 i5 x2 x3 x5 121 121 121 121 131 141 121 131 141 121 121 121 112 y2 x3 x5 122 122 122 122 132 142 122 132 142 132 132 132 113 z2 x3 x5 123 123 123 123 133 143 123 133 143 143 143 143 114 combine_idx 5 c3 a3 c5 b5 c5 a5 b5 c5 i2 i3 i5 x2 x3 x5 112 112 112 113 114 y2 x3 x5 113 113 113 114 115 z2 x3 x5 114 114 114 115 116 a b c d e \ c3 a3 a3 a3 a3 a3 c5 a5 b5 c5 a5 b5 c5 a5 b5 c5 a5 b5 c5 a5 i2 i3 i5 x2 x3 x5 121 121 121 121 131 141 121 131 141 121 121 121 112 y2 x3 x5 122 122 122 122 132 142 122 132 142 132 132 132 113 z2 x3 x5 123 123 123 123 133 143 123 133 143 143 143 143 114 f c3 a3 c5 b5 c5 a5 b5 c5 i2 i3 i5 x2 x3 x5 112 112 112 113 114 y2 x3 x5 113 113 113 114 115 z2 x3 x5 114 114 114 115 116 
# Use magic methods with .vbt to do operations with custom broadcasting
print(pd.Series([1, 2, 3]).vbt + [1, 2, 3])
print(df3.vbt + df4.vbt)  # regular df3 + df4 will return nans
0 2 1 4 2 6 dtype: int64 c5 a5 b5 c5 c6 a6 b6 c6 i5 i6 x5 x6 2 4 6 y6 5 7 9 z6 8 10 12