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# FastPivot A basic but fast reconstruction of pandas.pivot_table Contains two functions (see documentation below): fastpivot.pivot_table fastpivot.pivot_sparse # Installation pip install fastpivot Latest version: 0.1.11 # Documentation ~~~text def fastpivot.pivot_table(df, index, columns, values, aggfunc='mean', fill_value=None, dropna=True, dropna_idxcol=True): """ A limited, but hopefully fast implementation of pivot table. Tends to be faster than pandas.pivot_table when resulting pivot table is sparse. The main limitation is that you must include index, columns, values and you must aggregate. You also must aggregate by a list of preconstructed functions: For numerical values (np.float64 or np.int64), you can aggregate by any of ['sum', 'mean', 'std', 'max', 'min', 'count', 'median', 'nunique'] For other values, you can aggregate by any of ['count', 'nunique'] The arguments and return format mimic pandas very closely, with a few small differences: 1) Occasionaly the ordering of the columns will be different, such as when passing a list of aggfuncs with a single value column 2) When passing values of type np.int64, values of type np.float64 will be returned. Pandas returns np.int64 in some cases and np.float64 in others. 3) When passing multi index or column, pandas constructs the cartesion product space, whereas this pivot constructs the subspace of the product space where the tuples exist in the passed dataframe. 4) The following arguments are not supported here: margins, margins_name, observed. Generally on a dataframe with many rows and many distinct values in the passed index and column, the performance of this pivot_table function beats pandas significantly (see benchmarks) On a dataframe with many rows but few distinct values in the passed index and column, the speed of this pivot_table tends to be roughly on par with pandas, and in some cases can actually be slower. Arguments: df (required): pandas dataframe index (required): string or list, name(s) of column(s) that you want to become the index of the pivot table. columns (required): string or list, name(s) of column(s) that contains as values the columns of the pivot table. values (required): string or list, name(s) of column(s) that contains as values the values of the pivot table. aggfunc (default 'mean'): string, list, or dict, name of aggregation function. must be on implemented list above. if aggfunc is a dict, the format must be as in the following example: values = ['column_name1', 'column_name2', 'column_name3'] aggfunc = {'column_name1': 'mean', 'column_name2': 'median', 'column_name3': ['count', 'nunique']} fill_value (default None): scalar, value to replace missing values with in the pivot table. dropna (default True): bool, if True rows and columns that are entirely NaN values will be dropped. dropna_idxcol (default True): bool, if True rows where the passed index or column contain NaNs will be dropped. if False, NaN will be given its own index or column when appropriate. Returns: pivot_df: pandas dataframe """ def fastpivot.pivot_sparse(df, index, columns, values, fill_value=None, dropna_idxcol=True): """ Uses scipy.sparse.coo_matrix to construct a pivot table. This uses less memory and is faster in most cases when the resulting pivot_table will be sparse. Aggregates by sum. Less functionality overall, but efficient for its usecase. Arguments: df (required): pandas dataframe index (required): string or list, name(s) of column(s) that you want to become the index of the pivot table. columns (required): string or list, name(s) of column(s) that contains as values the columns of the pivot table. values (required): string, name of column that contains as values the values of the pivot table. fill_value (default None): scalar, value to replace missing values with in the pivot table. dropna_idxcol (default True): bool, if True rows where the passed index or column contain NaNs will be dropped. if False, NaN will be given its own index or column when appropriate. Returns: pivot_df: pandas dataframe """ ~~~ # Example ~~~text import pandas as pd import numpy as np import time from fastpivot import pivot_table, pivot_sparse N_ROWS = 100000 N_COLS = 1000 N_IDX = 1000 NAME_IDX = 'pivot_idx' NAME_COL = 'pivot_col' NAME_VALUE = 'value' col1 = ['idx{}'.format(x) for x in np.random.randint(0, N_IDX, size=N_ROWS)] col2 = ['col{}'.format(x) for x in np.random.randint(0, N_COLS, size=N_ROWS)] col3 = [x for x in np.random.normal(size=N_ROWS)] data = np.transpose([col1, col2, col3]) df = pd.DataFrame(data, columns=[NAME_IDX, NAME_COL, NAME_VALUE], index=range(len(data))) df[NAME_VALUE] = df[NAME_VALUE].astype(np.float64) print(df) msg = 'fastpivot.pivot_table' tick = time.perf_counter() pivot_fast = pivot_table(df, index=NAME_IDX, columns=NAME_COL, values=NAME_VALUE, fill_value=0.0, aggfunc='sum') print(msg, time.perf_counter() - tick) print(pivot_fast) msg = 'pandas.pivot_table' tick = time.perf_counter() pivot_pandas = df.pivot_table(index=NAME_IDX, columns=NAME_COL, values=NAME_VALUE, fill_value=0.0, aggfunc='sum') print(msg, time.perf_counter() - tick) print(pivot_pandas) msg = 'fastpivot.pivot_sparse' tick = time.perf_counter() pivot_sparse_df = pivot_sparse(df, index=NAME_IDX, columns=NAME_COL, values=NAME_VALUE, fill_value=0.0) print(msg, time.perf_counter() - tick) print(pivot_sparse_df) ~~~ # Benchmarks Computed using AMD Ryzen 5 2600 CPU and 16 GB RAM. Time is in denoted in seconds. NaN indicates that the function is not capable of doing the computation. OOM indicates that the function ran out of memory. N_ROWS denotes the number of rows in the input dataframe. N_COLS denotes the number of distinct values in the column which is passed as the columns argument. N_IDX denotes the number of distinct values in the column which is passed as the index argument. For multicol and multiidx, both columns for the columns or index argument have N_COLS or N_IDX distinct values respectively. All examples use dtype np.float64 and fill_value = 0. All arguments except for df, index, column, values, aggfunc, and fill_value are default. ~~~text Test 1: N_ROWS = 100000 N_COLS = 1000 N_IDX = 1000 fastpivot.pivot_table pandas.pivot_table fastpivot.pivot_sparse sum 0.060811 0.242244 0.067733 mean 0.047007 0.219768 NaN std 0.062170 0.041423 NaN max 0.042843 0.223578 NaN min 0.041605 0.225407 NaN count 0.037413 0.223480 NaN nunique 0.080111 0.258028 NaN median 0.095170 0.216555 NaN multicol sum 0.793830 17.408360 4.243026 multiidx sum 0.978207 4.674374 0.307516 Winner: fastpivot.pivot_table Test 2: N_ROWS = 1000000 N_COLS = 10 N_IDX = 10 fastpivot.pivot_table pandas.pivot_table fastpivot.pivot_sparse sum 0.317188 0.116396 0.317928 mean 0.267151 0.102374 NaN std 0.275409 0.101728 NaN max 0.266737 0.118855 NaN min 0.271199 0.113987 NaN count 0.279253 0.115915 NaN nunique