A basic but fast reconstruction of pandas.pivot_table
Contains two functions (see documentation below):
fastpivot.pivot_table
fastpivot.pivot_sparse
pip install fastpivot
Latest version: 0.1.13
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
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
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)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.
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 0.306899 0.267046 NaN
median 0.289684 0.135484 NaN
multicol sum 0.847213 0.160205 0.831495
multiidx sum 0.822259 0.150282 0.883725
Winner: pandas.pivot_table
Test 3:
N_ROWS = 2000000
N_COLS = 1000
N_IDX = 50000
fastpivot.pivot_table pandas.pivot_table fastpivot.pivot_sparse
sum 1.178474 3.889266 0.844055
mean 1.608712 3.877892 NaN
std 1.892230 1.196348 NaN
max 1.295431 3.850022 NaN
min 1.288993 3.868061 NaN
count 1.117461 3.870997 NaN
nunique 3.148521 4.536493 NaN
median 3.951064 3.971369 NaN
multicol sum OOM OOM 38.984358
multiidx sum OOM OOM 12.011507
Winners: fastpivot.pivot_sparse (when applicable), fastpivot.pivot_table
Test 4:
N_ROWS = 1000000
N_COLS = 1000
N_IDX = 100
fastpivot.pivot_table pandas.pivot_table fastpivot.pivot_sparse
sum 0.344388 0.303900 0.364910
mean 0.281136 0.292918 NaN
std 0.293342 0.161475 NaN
max 0.284871 0.296082 NaN
min 0.280373 0.285619 NaN
count 0.274774 0.269245 NaN
nunique 0.397173 0.500805 NaN
median 0.356431 0.303541 NaN
multicol sum 3.395306 89.057448 29.051697
multiidx sum 0.737902 1.263041 0.946133
Winner: fastpivot.pivot_table
Test 5:
N_ROWS = 1000000
N_COLS = 1000
N_IDX = 10000
fastpivot.pivot_table pandas.pivot_table fastpivot.pivot_sparse
sum 0.425536 1.233995 0.392080
mean 0.480641 1.189147 NaN
std 0.607801 0.488366 NaN
max 0.412936 1.213504 NaN
min 0.414011 1.186172 NaN
count 0.379848 1.199575 NaN
nunique 1.043115 1.473638 NaN
median 1.177022 1.258532 NaN
multicol sum OOM OOM 30.198564
multiidx sum OOM OOM 4.907232
Winners: fastpivot.pivot_sparse (when applicable), fastpivot.pivot_table
pandas.pivot_table is highly flexible and optimized for input with a large number of rows but few distinct values in the index and column, so that the resulting pivot table is small and each value results from aggregating a large number of values in the original dataframe.
fastpivot.pivot_table is reasonably flexible (though not as flexible as pandas) and outperforms pandas when there are a large number of distinct values in the index and column. It outperforms pandas especially for multi column and mutli index input.
fastpivot.pivot_sparse is very limited but faster and much more memory efficient than pandas when there is an extremely large number of distinct values in the index and column, so that the resulting dataframe is extremely sparse.