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test_convert_to.py
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# -*- coding: utf-8 -*-
from datetime import datetime
import pytest
import pytz
import collections
from collections import OrderedDict, defaultdict
import numpy as np
from pandas import compat
from pandas.compat import long
from pandas import (DataFrame, Series, MultiIndex, Timestamp,
date_range)
import pandas.util.testing as tm
from pandas.tests.frame.common import TestData
class TestDataFrameConvertTo(TestData):
def test_to_dict_timestamp(self):
# GH11247
# split/records producing np.datetime64 rather than Timestamps
# on datetime64[ns] dtypes only
tsmp = Timestamp('20130101')
test_data = DataFrame({'A': [tsmp, tsmp], 'B': [tsmp, tsmp]})
test_data_mixed = DataFrame({'A': [tsmp, tsmp], 'B': [1, 2]})
expected_records = [{'A': tsmp, 'B': tsmp},
{'A': tsmp, 'B': tsmp}]
expected_records_mixed = [{'A': tsmp, 'B': 1},
{'A': tsmp, 'B': 2}]
assert (test_data.to_dict(orient='records') ==
expected_records)
assert (test_data_mixed.to_dict(orient='records') ==
expected_records_mixed)
expected_series = {
'A': Series([tsmp, tsmp], name='A'),
'B': Series([tsmp, tsmp], name='B'),
}
expected_series_mixed = {
'A': Series([tsmp, tsmp], name='A'),
'B': Series([1, 2], name='B'),
}
tm.assert_dict_equal(test_data.to_dict(orient='series'),
expected_series)
tm.assert_dict_equal(test_data_mixed.to_dict(orient='series'),
expected_series_mixed)
expected_split = {
'index': [0, 1],
'data': [[tsmp, tsmp],
[tsmp, tsmp]],
'columns': ['A', 'B']
}
expected_split_mixed = {
'index': [0, 1],
'data': [[tsmp, 1],
[tsmp, 2]],
'columns': ['A', 'B']
}
tm.assert_dict_equal(test_data.to_dict(orient='split'),
expected_split)
tm.assert_dict_equal(test_data_mixed.to_dict(orient='split'),
expected_split_mixed)
def test_to_dict_index_not_unique_with_index_orient(self):
# GH22801
# Data loss when indexes are not unique. Raise ValueError.
df = DataFrame({'a': [1, 2], 'b': [0.5, 0.75]}, index=['A', 'A'])
pytest.raises(ValueError, df.to_dict, orient='index')
def test_to_dict_invalid_orient(self):
df = DataFrame({'A': [0, 1]})
pytest.raises(ValueError, df.to_dict, orient='xinvalid')
def test_to_records_dt64(self):
df = DataFrame([["one", "two", "three"],
["four", "five", "six"]],
index=date_range("2012-01-01", "2012-01-02"))
# convert_datetime64 defaults to None
expected = df.index.values[0]
result = df.to_records()['index'][0]
assert expected == result
# check for FutureWarning if convert_datetime64=False is passed
with tm.assert_produces_warning(FutureWarning):
expected = df.index.values[0]
result = df.to_records(convert_datetime64=False)['index'][0]
assert expected == result
# check for FutureWarning if convert_datetime64=True is passed
with tm.assert_produces_warning(FutureWarning):
expected = df.index[0]
result = df.to_records(convert_datetime64=True)['index'][0]
assert expected == result
def test_to_records_with_multindex(self):
# GH3189
index = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
data = np.zeros((8, 4))
df = DataFrame(data, index=index)
r = df.to_records(index=True)['level_0']
assert 'bar' in r
assert 'one' not in r
def test_to_records_with_Mapping_type(self):
import email
from email.parser import Parser
compat.Mapping.register(email.message.Message)
headers = Parser().parsestr('From: <[email protected]>\n'
'To: <[email protected]>\n'
'Subject: Test message\n'
'\n'
'Body would go here\n')
frame = DataFrame.from_records([headers])
all(x in frame for x in ['Type', 'Subject', 'From'])
def test_to_records_floats(self):
df = DataFrame(np.random.rand(10, 10))
df.to_records()
def test_to_records_index_name(self):
df = DataFrame(np.random.randn(3, 3))
df.index.name = 'X'
rs = df.to_records()
assert 'X' in rs.dtype.fields
df = DataFrame(np.random.randn(3, 3))
rs = df.to_records()
assert 'index' in rs.dtype.fields
df.index = MultiIndex.from_tuples([('a', 'x'), ('a', 'y'), ('b', 'z')])
df.index.names = ['A', None]
rs = df.to_records()
assert 'level_0' in rs.dtype.fields
def test_to_records_with_unicode_index(self):
# GH13172
# unicode_literals conflict with to_records
result = DataFrame([{u'a': u'x', u'b': 'y'}]).set_index(u'a') \
.to_records()
expected = np.rec.array([('x', 'y')], dtype=[('a', 'O'), ('b', 'O')])
tm.assert_almost_equal(result, expected)
def test_to_records_with_unicode_column_names(self):
# xref issue: https://github.com/numpy/numpy/issues/2407
# Issue #11879. to_records used to raise an exception when used
# with column names containing non-ascii characters in Python 2
result = DataFrame(data={u"accented_name_é": [1.0]}).to_records()
# Note that numpy allows for unicode field names but dtypes need
# to be specified using dictionary instead of list of tuples.
expected = np.rec.array(
[(0, 1.0)],
dtype={"names": ["index", u"accented_name_é"],
"formats": ['=i8', '=f8']}
)
tm.assert_almost_equal(result, expected)
def test_to_records_with_categorical(self):
# GH8626
# dict creation
df = DataFrame({'A': list('abc')}, dtype='category')
expected = Series(list('abc'), dtype='category', name='A')
tm.assert_series_equal(df['A'], expected)
# list-like creation
df = DataFrame(list('abc'), dtype='category')
expected = Series(list('abc'), dtype='category', name=0)
tm.assert_series_equal(df[0], expected)
# to record array
# this coerces
result = df.to_records()
expected = np.rec.array([(0, 'a'), (1, 'b'), (2, 'c')],
dtype=[('index', '=i8'), ('0', 'O')])
tm.assert_almost_equal(result, expected)
@pytest.mark.parametrize('mapping', [
dict,
collections.defaultdict(list),
collections.OrderedDict])
def test_to_dict(self, mapping):
test_data = {
'A': {'1': 1, '2': 2},
'B': {'1': '1', '2': '2', '3': '3'},
}
# GH16122
recons_data = DataFrame(test_data).to_dict(into=mapping)
for k, v in compat.iteritems(test_data):
for k2, v2 in compat.iteritems(v):
assert (v2 == recons_data[k][k2])
recons_data = DataFrame(test_data).to_dict("l", mapping)
for k, v in compat.iteritems(test_data):
for k2, v2 in compat.iteritems(v):
assert (v2 == recons_data[k][int(k2) - 1])
recons_data = DataFrame(test_data).to_dict("s", mapping)
for k, v in compat.iteritems(test_data):
for k2, v2 in compat.iteritems(v):
assert (v2 == recons_data[k][k2])
recons_data = DataFrame(test_data).to_dict("sp", mapping)
expected_split = {'columns': ['A', 'B'], 'index': ['1', '2', '3'],
'data': [[1.0, '1'], [2.0, '2'], [np.nan, '3']]}
tm.assert_dict_equal(recons_data, expected_split)
recons_data = DataFrame(test_data).to_dict("r", mapping)
expected_records = [{'A': 1.0, 'B': '1'},
{'A': 2.0, 'B': '2'},
{'A': np.nan, 'B': '3'}]
assert isinstance(recons_data, list)
assert (len(recons_data) == 3)
for l, r in zip(recons_data, expected_records):
tm.assert_dict_equal(l, r)
# GH10844
recons_data = DataFrame(test_data).to_dict("i")
for k, v in compat.iteritems(test_data):
for k2, v2 in compat.iteritems(v):
assert (v2 == recons_data[k2][k])
df = DataFrame(test_data)
df['duped'] = df[df.columns[0]]
recons_data = df.to_dict("i")
comp_data = test_data.copy()
comp_data['duped'] = comp_data[df.columns[0]]
for k, v in compat.iteritems(comp_data):
for k2, v2 in compat.iteritems(v):
assert (v2 == recons_data[k2][k])
@pytest.mark.parametrize('mapping', [
list,
collections.defaultdict,
[]])
def test_to_dict_errors(self, mapping):
# GH16122
df = DataFrame(np.random.randn(3, 3))
with pytest.raises(TypeError):
df.to_dict(into=mapping)
def test_to_dict_not_unique_warning(self):
# GH16927: When converting to a dict, if a column has a non-unique name
# it will be dropped, throwing a warning.
df = DataFrame([[1, 2, 3]], columns=['a', 'a', 'b'])
with tm.assert_produces_warning(UserWarning):
df.to_dict()
@pytest.mark.parametrize('tz', ['UTC', 'GMT', 'US/Eastern'])
def test_to_records_datetimeindex_with_tz(self, tz):
# GH13937
dr = date_range('2016-01-01', periods=10,
freq='S', tz=tz)
df = DataFrame({'datetime': dr}, index=dr)
expected = df.to_records()
result = df.tz_convert("UTC").to_records()
# both converted to UTC, so they are equal
tm.assert_numpy_array_equal(result, expected)
# orient - orient argument to to_dict function
# item_getter - function for extracting value from
# the resulting dict using column name and index
@pytest.mark.parametrize('orient,item_getter', [
('dict', lambda d, col, idx: d[col][idx]),
('records', lambda d, col, idx: d[idx][col]),
('list', lambda d, col, idx: d[col][idx]),
('split', lambda d, col, idx: d['data'][idx][d['columns'].index(col)]),
('index', lambda d, col, idx: d[idx][col])
])
def test_to_dict_box_scalars(self, orient, item_getter):
# 14216, 23753
# make sure that we are boxing properly
df = DataFrame({'a': [1, 2], 'b': [.1, .2]})
result = df.to_dict(orient=orient)
assert isinstance(item_getter(result, 'a', 0), (int, long))
assert isinstance(item_getter(result, 'b', 0), float)
def test_frame_to_dict_tz(self):
# GH18372 When converting to dict with orient='records' columns of
# datetime that are tz-aware were not converted to required arrays
data = [(datetime(2017, 11, 18, 21, 53, 0, 219225, tzinfo=pytz.utc),),
(datetime(2017, 11, 18, 22, 6, 30, 61810, tzinfo=pytz.utc,),)]
df = DataFrame(list(data), columns=["d", ])
result = df.to_dict(orient='records')
expected = [
{'d': Timestamp('2017-11-18 21:53:00.219225+0000', tz=pytz.utc)},
{'d': Timestamp('2017-11-18 22:06:30.061810+0000', tz=pytz.utc)},
]
tm.assert_dict_equal(result[0], expected[0])
tm.assert_dict_equal(result[1], expected[1])
@pytest.mark.parametrize('into, expected', [
(dict, {0: {'int_col': 1, 'float_col': 1.0},
1: {'int_col': 2, 'float_col': 2.0},
2: {'int_col': 3, 'float_col': 3.0}}),
(OrderedDict, OrderedDict([(0, {'int_col': 1, 'float_col': 1.0}),
(1, {'int_col': 2, 'float_col': 2.0}),
(2, {'int_col': 3, 'float_col': 3.0})])),
(defaultdict(list), defaultdict(list,
{0: {'int_col': 1, 'float_col': 1.0},
1: {'int_col': 2, 'float_col': 2.0},
2: {'int_col': 3, 'float_col': 3.0}}))
])
def test_to_dict_index_dtypes(self, into, expected):
# GH 18580
# When using to_dict(orient='index') on a dataframe with int
# and float columns only the int columns were cast to float
df = DataFrame({'int_col': [1, 2, 3],
'float_col': [1.0, 2.0, 3.0]})
result = df.to_dict(orient='index', into=into)
cols = ['int_col', 'float_col']
result = DataFrame.from_dict(result, orient='index')[cols]
expected = DataFrame.from_dict(expected, orient='index')[cols]
tm.assert_frame_equal(result, expected)