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groupby_test.py
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groupby_test.py
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from collections import defaultdict
from numpy import nan
import numpy as np
from pandas import *
import pandas._libs.lib as tseries
import pandas.core.groupby as gp
import pandas.util.testing as tm
from pandas.compat import range
reload(gp)
"""
k = 1000
values = np.random.randn(8 * k)
key1 = np.array(['foo', 'bar', 'baz', 'bar', 'foo', 'baz', 'bar', 'baz'] * k,
dtype=object)
key2 = np.array(['b', 'b', 'b', 'b', 'a', 'a', 'a', 'a' ] * k,
dtype=object)
shape, labels, idicts = gp.labelize(key1, key2)
print(tseries.group_labels(key1))
# print(shape)
# print(labels)
# print(idicts)
result = tseries.group_aggregate(values, labels, shape)
print(tseries.groupby_indices(key2))
df = DataFrame({'key1' : key1,
'key2' : key2,
'v1' : values,
'v2' : values})
k1 = df['key1']
k2 = df['key2']
# del df['key1']
# del df['key2']
# r2 = gp.multi_groupby(df, np.sum, k1, k2)
# print(result)
gen = gp.generate_groups(df['v1'], labels, shape, axis=1,
factory=DataFrame)
res = defaultdict(dict)
for a, gen1 in gen:
for b, group in gen1:
print(a, b)
print(group)
# res[b][a] = group['values'].sum()
res[b][a] = group.sum()
res = DataFrame(res)
grouped = df.groupby(['key1', 'key2'])
"""
# data = {'A' : [0, 0, 0, 0, 1, 1, 1, 1, 1, 1., nan, nan],
# 'B' : ['A', 'B'] * 6,
# 'C' : np.random.randn(12)}
# df = DataFrame(data)
# df['C'][2:10:2] = nan
# single column
# grouped = df.drop(['B'], axis=1).groupby('A')
# exp = {}
# for cat, group in grouped:
# exp[cat] = group['C'].sum()
# exp = DataFrame({'C' : exp})
# result = grouped.sum()
# grouped = df.groupby(['A', 'B'])
# expd = {}
# for cat1, cat2, group in grouped:
# expd.setdefault(cat1, {})[cat2] = group['C'].sum()
# exp = DataFrame(expd).T.stack()
# result = grouped.sum()['C']
# print('wanted')
# print(exp)
# print('got')
# print(result)
# tm.N = 10000
# mapping = {'A': 0, 'C': 1, 'B': 0, 'D': 1}
# tf = lambda x: x - x.mean()
# df = tm.makeTimeDataFrame()
# ts = df['A']
# # grouped = df.groupby(lambda x: x.strftime('%m/%y'))
# grouped = df.groupby(mapping, axis=1)
# groupedT = df.T.groupby(mapping, axis=0)
# r1 = groupedT.transform(tf).T
# r2 = grouped.transform(tf)
# fillit = lambda x: x.fillna(method='pad')
# f = lambda x: x
# transformed = df.groupby(lambda x: x.strftime('%m/%y')).transform(lambda
# x: x)
# def ohlc(group):
# return Series([group[0], group.max(), group.min(), group[-1]],
# index=['open', 'high', 'low', 'close'])
# grouper = [lambda x: x.year, lambda x: x.month]
# dr = DateRange('1/1/2000', '1/1/2002')
# ts = Series(np.random.randn(len(dr)), index=dr)
# import string
# k = 20
# n = 1000
# keys = list(string.letters[:k])
# df = DataFrame({'A' : np.tile(keys, n),
# 'B' : np.repeat(keys[:k/2], n * 2),
# 'C' : np.random.randn(k * n)})
# def f():
# for x in df.groupby(['A', 'B']):
# pass
a = np.arange(100).repeat(100)
b = np.tile(np.arange(100), 100)
index = MultiIndex.from_arrays([a, b])
s = Series(np.random.randn(len(index)), index)
df = DataFrame({'A': s})
df['B'] = df.index.get_level_values(0)
df['C'] = df.index.get_level_values(1)
def f():
for x in df.groupby(['B', 'B']):
pass