["python","pandas","numpy","dataframe","merge"]
Performant cartesian product (CROSS JOIN) with pandas

The contents of this post were originally meant to be a part of Pandas Merging 101, but due to the nature and size of the content required to fully do justice to this topic, it has been moved to its own QnA. Given two simple DataFrames; left = pd.DataFrame({'col1' : ['A', 'B', 'C'], 'col2' : [1, 2, 3]}) right = pd.DataFrame({'col1' : ['X', 'Y', 'Z'], 'col2' : [20, 30, 50]}) left col1 col2 0 A 1 1 B 2 2 C 3 right col1 col2 0 X 20 1 Y 30 2 Z 50 The cross product of these frames can be computed, and will look something like: A 1 X 20 A 1 Y 30 A 1 Z 50 B 2 X 20 B 2 Y 30 B 2 Z 50 C 3 X 20 C 3 Y 30 C 3 Z 50 What is the most performant method of computing this result?
Let's start by establishing a benchmark. The easiest method for solving this is using a temporary "key" column: pandas <= 1.1.X def cartesian_product_basic(left, right): return ( left.assign(key=1).merge(right.assign(key=1), on='key').drop('key', 1)) cartesian_product_basic(left, right) pandas >= 1.2 left.merge(right, how="cross") # implements the technique above col1_x col2_x col1_y col2_y 0 A 1 X 20 1 A 1 Y 30 2 A 1 Z 50 3 B 2 X 20 4 B 2 Y 30 5 B 2 Z 50 6 C 3 X 20 7 C 3 Y 30 8 C 3 Z 50 How this works is that both DataFrames are assigned a temporary "key" column with the same value (say, 1). merge then performs a many-to-many JOIN on "key". While the many-to-many JOIN trick works for reasonably sized DataFrames, you will see relatively lower performance on larger data. A faster implementation will require NumPy. Here are some famous NumPy implementations of 1D cartesian product. We can build on some of these performant solutions to get our desired output. My favourite, however, is @senderle's first implementation. def cartesian_product(*arrays): la = len(arrays) dtype = np.result_type(*arrays) arr = np.empty([len(a) for a in arrays] + [la], dtype=dtype) for i, a in enumerate(np.ix_(*arrays)): arr[...,i] = a return arr.reshape(-1, la) Generalizing: CROSS JOIN on Unique or Non-Unique Indexed DataFrames Disclaimer These solutions are optimised for DataFrames with non-mixed scalar dtypes. If dealing with mixed dtypes, use at your own risk! This trick will work on any kind of DataFrame. We compute the cartesian product of the DataFrames' numeric indices using the aforementioned cartesian_product, use this to reindex the DataFrames, and def cartesian_product_generalized(left, right): la, lb = len(left), len(right) idx = cartesian_product(np.ogrid[:la], np.ogrid[:lb]) return pd.DataFrame( np.column_stack([left.values[idx[:,0]], right.values[idx[:,1]]])) cartesian_product_generalized(left, right) 0 1 2 3 0 A 1 X 20 1 A 1 Y 30 2 A 1 Z 50 3 B 2 X 20 4 B 2 Y 30 5 B 2 Z 50 6 C 3 X 20 7 C 3 Y 30 8 C 3 Z 50 np.array_equal(cartesian_product_generalized(left, right), cartesian_product_basic(left, right)) True And, along similar lines, left2 = left.copy() left2.index = ['s1', 's2', 's1'] right2 = right.copy() right2.index = ['x', 'y', 'y'] left2 col1 col2 s1 A 1 s2 B 2 s1 C 3 right2 col1 col2 x X 20 y Y 30 y Z 50 np.array_equal(cartesian_product_generalized(left, right), cartesian_product_basic(left2, right2)) True This solution can generalise to multiple DataFrames. For example, def cartesian_product_multi(*dfs): idx = cartesian_product(*[np.ogrid[:len(df)] for df in dfs]) return pd.DataFrame( np.column_stack([df.values[idx[:,i]] for i,df in enumerate(dfs)])) cartesian_product_multi(*[left, right, left]).head() 0 1 2 3 4 5 0 A 1 X 20 A 1 1 A 1 X 20 B 2 2 A 1 X 20 C 3 3 A 1 X 20 D 4 4 A 1 Y 30 A 1 Further Simplification A simpler solution not involving @senderle's cartesian_product is possible when dealing with just two DataFrames. Using np.broadcast_arrays, we can achieve almost the same level of performance. def cartesian_product_simplified(left, right): la, lb = len(left), len(right) ia2, ib2 = np.broadcast_arrays(*np.ogrid[:la,:lb]) return pd.DataFrame( np.column_stack([left.values[ia2.ravel()], right.values[ib2.ravel()]])) np.array_equal(cartesian_product_simplified(left, right), cartesian_product_basic(left2, right2)) True Performance Comparison Benchmarking these solutions on some contrived DataFrames with unique indices, we have Do note that timings may vary based on your setup, data, and choice of cartesian_product helper function as applicable. Performance Benchmarking Code This is the timing script. All functions called here are defined above. from timeit import timeit import pandas as pd import matplotlib.pyplot as plt res = pd.DataFrame( index=['cartesian_product_basic', 'cartesian_product_generalized', 'cartesian_product_multi', 'cartesian_product_simplified'], columns=[1, 10, 50, 100, 200, 300, 400, 500, 600, 800, 1000, 2000], dtype=float ) for f in res.index: for c in res.columns: # print(f,c) left2 = pd.concat([left] * c, ignore_index=True) right2 = pd.concat([right] * c, ignore_index=True) stmt = '{}(left2, right2)'.format(f) setp = 'from __main__ import left2, right2, {}'.format(f) res.at[f, c] = timeit(stmt, setp, number=5) ax = res.div(res.min()).T.plot(loglog=True) ax.set_xlabel("N"); ax.set_ylabel("time (relative)"); plt.show() Continue Reading Jump to other topics in Pandas Merging 101 to continue learning: Merging basics - basic types of joins Index-based joins Generalizing to multiple DataFrames Cross join * * you are here