from vbench.benchmark import Benchmark from datetime import datetime common_setup = """from pandas_vb_common import * """ setup = common_setup + """ level1 = np.array([rands(10) for _ in xrange(10)], dtype='O') level2 = np.array([rands(10) for _ in xrange(1000)], dtype='O') label1 = np.arange(10).repeat(1000) label2 = np.tile(np.arange(1000), 10) key1 = np.tile(level1.take(label1), 10) key2 = np.tile(level2.take(label2), 10) shuf = np.arange(100000) random.shuffle(shuf) try: index2 = MultiIndex(levels=[level1, level2], labels=[label1, label2]) index3 = MultiIndex(levels=[np.arange(10), np.arange(100), np.arange(100)], labels=[np.arange(10).repeat(10000), np.tile(np.arange(100).repeat(100), 10), np.tile(np.tile(np.arange(100), 100), 10)]) df_multi = DataFrame(np.random.randn(len(index2), 4), index=index2, columns=['A', 'B', 'C', 'D']) except: # pre-MultiIndex pass try: DataFrame = DataMatrix except: pass df = DataFrame({'data1' : np.random.randn(100000), 'data2' : np.random.randn(100000), 'key1' : key1, 'key2' : key2}) df_key1 = DataFrame(np.random.randn(len(level1), 4), index=level1, columns=['A', 'B', 'C', 'D']) df_key2 = DataFrame(np.random.randn(len(level2), 4), index=level2, columns=['A', 'B', 'C', 'D']) df_shuf = df.reindex(df.index[shuf]) """ #---------------------------------------------------------------------- # DataFrame joins on key join_dataframe_index_single_key_small = \ Benchmark("df.join(df_key1, on='key1')", setup, name='join_dataframe_index_single_key_small') join_dataframe_index_single_key_bigger = \ Benchmark("df.join(df_key2, on='key2')", setup, name='join_dataframe_index_single_key_bigger') join_dataframe_index_single_key_bigger_sort = \ Benchmark("df_shuf.join(df_key2, on='key2', sort=True)", setup, name='join_dataframe_index_single_key_bigger_sort', start_date=datetime(2012, 2, 5)) join_dataframe_index_multi = \ Benchmark("df.join(df_multi, on=['key1', 'key2'])", setup, name='join_dataframe_index_multi', start_date=datetime(2011, 10, 20)) #---------------------------------------------------------------------- # Joins on integer keys setup = common_setup + """ df = DataFrame({'key1': np.tile(np.arange(500).repeat(10), 2), 'key2': np.tile(np.arange(250).repeat(10), 4), 'value': np.random.randn(10000)}) df2 = DataFrame({'key1': np.arange(500), 'value2': randn(500)}) df3 = df[:5000] """ join_dataframe_integer_key = Benchmark("merge(df, df2, on='key1')", setup, start_date=datetime(2011, 10, 20)) join_dataframe_integer_2key = Benchmark("merge(df, df3)", setup, start_date=datetime(2011, 10, 20)) #---------------------------------------------------------------------- # DataFrame joins on index #---------------------------------------------------------------------- # Merges setup = common_setup + """ N = 10000 indices = np.array([rands(10) for _ in xrange(N)], dtype='O') indices2 = np.array([rands(10) for _ in xrange(N)], dtype='O') key = np.tile(indices[:8000], 10) key2 = np.tile(indices2[:8000], 10) left = DataFrame({'key' : key, 'key2':key2, 'value' : np.random.randn(80000)}) right = DataFrame({'key': indices[2000:], 'key2':indices2[2000:], 'value2' : np.random.randn(8000)}) """ merge_2intkey_nosort = Benchmark('merge(left, right, sort=False)', setup, start_date=datetime(2011, 10, 20)) merge_2intkey_sort = Benchmark('merge(left, right, sort=True)', setup, start_date=datetime(2011, 10, 20)) #---------------------------------------------------------------------- # Appending DataFrames setup = common_setup + """ df1 = DataFrame(np.random.randn(10000, 4), columns=['A', 'B', 'C', 'D']) df2 = df1.copy() df2.index = np.arange(10000, 20000) mdf1 = df1.copy() mdf1['obj1'] = 'bar' mdf1['obj2'] = 'bar' mdf1['int1'] = 5 try: mdf1.consolidate(inplace=True) except: pass mdf2 = mdf1.copy() mdf2.index = df2.index """ stmt = "df1.append(df2)" append_frame_single_homogenous = \ Benchmark(stmt, setup, name='append_frame_single_homogenous', ncalls=500, repeat=1) stmt = "mdf1.append(mdf2)" append_frame_single_mixed = Benchmark(stmt, setup, name='append_frame_single_mixed', ncalls=500, repeat=1) #---------------------------------------------------------------------- # data alignment setup = common_setup + """n = 1000000 # indices = Index([rands(10) for _ in xrange(n)]) def sample(values, k): sampler = np.random.permutation(len(values)) return values.take(sampler[:k]) sz = 500000 rng = np.arange(0, 10000000000000, 10000000) stamps = np.datetime64(datetime.now()).view('i8') + rng idx1 = np.sort(sample(stamps, sz)) idx2 = np.sort(sample(stamps, sz)) ts1 = Series(np.random.randn(sz), idx1) ts2 = Series(np.random.randn(sz), idx2) """ stmt = "ts1 + ts2" series_align_int64_index = \ Benchmark(stmt, setup, name="series_align_int64_index", start_date=datetime(2010, 6, 1), logy=True) stmt = "ts1.align(ts2, join='left')" series_align_left_monotonic = \ Benchmark(stmt, setup, name="series_align_left_monotonic", start_date=datetime(2011, 12, 1), logy=True) #---------------------------------------------------------------------- # Concat Series axis=1 setup = common_setup + """ n = 1000 indices = Index([rands(10) for _ in xrange(1000)]) s = Series(n, index=indices) pieces = [s[i:-i] for i in range(1, 10)] pieces = pieces * 50 """ concat_series_axis1 = Benchmark('concat(pieces, axis=1)', setup, start_date=datetime(2012, 2, 27)) setup = common_setup + """ df = DataFrame(randn(5, 4)) """ concat_small_frames = Benchmark('concat([df] * 1000)', setup, start_date=datetime(2012, 1, 1)) #---------------------------------------------------------------------- # Ordered merge setup = common_setup + """ groups = np.array([rands(10) for _ in xrange(10)], dtype='O') left = DataFrame({'group': groups.repeat(5000), 'key' : np.tile(np.arange(0, 10000, 2), 10), 'lvalue': np.random.randn(50000)}) right = DataFrame({'key' : np.arange(10000), 'rvalue' : np.random.randn(10000)}) """ stmt = "ordered_merge(left, right, on='key', left_by='group')"