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Change FullName to PersonFullName #740

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merged 6 commits into from Mar 29, 2021
Merged

Change FullName to PersonFullName #740

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4d34571
rename in py files
jeff-hernandez Mar 25, 2021
464e3e8
lint fix
jeff-hernandez Mar 25, 2021
b5365c1
update release notes
jeff-hernandez Mar 25, 2021
205aaff
note breaking change
jeff-hernandez Mar 29, 2021
1e63034
update docs
jeff-hernandez Mar 29, 2021
8881c6a
Merge branch 'main' into person_full_name
jeff-hernandez Mar 29, 2021
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6 changes: 3 additions & 3 deletions README.md
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Expand Up @@ -50,7 +50,7 @@ df = pd.read_csv("https://api.featurelabs.com/datasets/online-retail-logs-2018-0
df.ww.init(name='retail', make_index=True, index='order_product_id')
df.ww.set_types(logical_types={
'quantity': 'Integer',
'customer_name': 'FullName',
'customer_name': 'PersonFullName',
'country': 'Categorical',
'order_id': 'Categorical'
})
Expand All @@ -59,15 +59,15 @@ df.ww

```
Physical Type Logical Type Semantic Tag(s)
Column
Column
order_product_id Int64 Integer ['index']
order_id category Categorical ['category']
product_id category Categorical ['category']
description string NaturalLanguage []
quantity Int64 Integer ['numeric']
order_date datetime64[ns] Datetime []
unit_price float64 Double ['numeric']
customer_name string FullName []
customer_name string PersonFullName []
country category Categorical ['category']
total float64 Double ['numeric']
cancelled boolean Boolean []
Expand Down
2 changes: 1 addition & 1 deletion docs/source/api_reference.rst
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Expand Up @@ -118,12 +118,12 @@ Logical Types
Double
EmailAddress
Filepath
FullName
Integer
IPAddress
LatLong
NaturalLanguage
Ordinal
PersonFullName
PhoneNumber
PostalCode
SubRegionCode
Expand Down
16 changes: 8 additions & 8 deletions docs/source/guides/understanding_types_and_tags.ipynb
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Expand Up @@ -171,7 +171,7 @@
"logical_types = {\n",
" 'integers': 'Integer',\n",
" 'bools': 'Boolean',\n",
" 'names': 'FullName'\n",
" 'names': 'PersonFullName'\n",
"}\n",
"\n",
"df.ww.init(logical_types=logical_types)\n",
Expand All @@ -192,7 +192,7 @@
"outputs": [],
"source": [
"logical_types = {\n",
" 'names': 'FullName'\n",
" 'names': 'PersonFullName'\n",
"}\n",
"df.ww.init(logical_types=logical_types)\n",
"df.ww"
Expand All @@ -202,7 +202,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"With that input, you get the same results. Woodwork used the `FullName` logical type you assigned to the `names` column and then correctly inferred the logical types for the `integers` and `bools` columns.\n",
"With that input, you get the same results. Woodwork used the `PersonFullName` logical type you assigned to the `names` column and then correctly inferred the logical types for the `integers` and `bools` columns.\n",
"\n",
"Next, look at what happens if we do not specify any logical types."
]
Expand All @@ -221,9 +221,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case, Woodwork correctly inferred type for the `integers` and `bools` columns, but failed to recognize the `names` column should have a logical type of `FullName`. In situations like this, Woodwork provides users the ability to change the logical type.\n",
"In this case, Woodwork correctly inferred type for the `integers` and `bools` columns, but failed to recognize the `names` column should have a logical type of `PersonFullName`. In situations like this, Woodwork provides users the ability to change the logical type.\n",
"\n",
"Update the logical type of the `names` column to be `FullName`."
"Update the logical type of the `names` column to be `PersonFullName`."
]
},
{
Expand All @@ -232,17 +232,17 @@
"metadata": {},
"outputs": [],
"source": [
"df.ww.set_types(logical_types={'names': 'FullName'})\n",
"df.ww.set_types(logical_types={'names': 'PersonFullName'})\n",
"df.ww"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you look carefully at the output, you can see that several things happened to the `names` column. First, the correct `FullName` logical type has been applied. Second, the physical type of the column has changed from `category` to `string` to match the standard physical type for the `FullName` logical type. Finally, the standard tag of `category` that was previously set for the `names` column has been removed because it no longer applies.\n",
"If you look carefully at the output, you can see that several things happened to the `names` column. First, the correct `PersonFullName` logical type has been applied. Second, the physical type of the column has changed from `category` to `string` to match the standard physical type for the `PersonFullName` logical type. Finally, the standard tag of `category` that was previously set for the `names` column has been removed because it no longer applies.\n",
"\n",
"When setting the LogicalType for a column, the type can be specified by passing a string representing the camel-case name of the LogicalType class as you have done in previous examples. Alternatively, you can pass the class directly instead of a string or the snake-case name of the string. All of these would be valid values to use for setting the FullName Logical type: `FullName`, `\"FullName\"` or `\"full_name\"`. \n",
"When setting the LogicalType for a column, the type can be specified by passing a string representing the camel-case name of the LogicalType class as you have done in previous examples. Alternatively, you can pass the class directly instead of a string or the snake-case name of the string. All of these would be valid values to use for setting the PersonFullName Logical type: `PersonFullName`, `\"PersonFullName\"` or `\"person_full_name\"`. \n",
"\n",
"Note—in order to use the class name, first you have to import the class."
]
Expand Down
4 changes: 2 additions & 2 deletions docs/source/guides/using_woodwork_with_dask_and_koalas.ipynb
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Expand Up @@ -86,7 +86,7 @@
" 'quantity': 'Integer',\n",
" 'order_date': 'Datetime',\n",
" 'unit_price': 'Double',\n",
" 'customer_name': 'FullName',\n",
" 'customer_name': 'PersonFullName',\n",
" 'country': 'Categorical',\n",
" 'total': 'Double',\n",
" 'cancelled': 'Boolean',\n",
Expand Down Expand Up @@ -210,7 +210,7 @@
" 'quantity': 'Integer',\n",
" 'order_date': 'Datetime',\n",
" 'unit_price': 'Double',\n",
" 'customer_name': 'FullName',\n",
" 'customer_name': 'PersonFullName',\n",
" 'country': 'Categorical',\n",
" 'total': 'Double',\n",
" 'cancelled': 'Boolean',\n",
Expand Down
4 changes: 3 additions & 1 deletion docs/source/release_notes.rst
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Expand Up @@ -6,15 +6,17 @@ Release Notes
* Enhancements
* Fixes
* Changes
* Rename ``FullName`` logical type to ``PersonFullName`` (:pr:`740`)
* Rename ``ZIPCode`` logical type to ``PostalCode`` (:pr:`741`)
* Documentation Changes
* Testing Changes

Thanks to the following people for contributing to this release:
:user:`thehomebrewnerd`
:user:`jeff-hernandez`, :user:`thehomebrewnerd`

**Breaking Changes**
* The ``ZIPCode`` logical type has been renamed to ``PostalCode``
* The ``FullName`` logical type has been renamed to ``PersonFullName``

**v0.1.0 March 22, 2021**
* Enhancements
Expand Down
6 changes: 3 additions & 3 deletions docs/source/start.ipynb
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Expand Up @@ -106,7 +106,7 @@
"metadata": {},
"source": [
"## Updating Logical Types\n",
"If the initial inference was not to our liking, the logical type can be changed to a more appropriate value. Let's change some of the columns to a different logical type to illustrate this process. In this case, set the logical type for the `order_product_id` and `country` columns to be `Categorical` and set `customer_name` to have a logical type of `FullName`."
"If the initial inference was not to our liking, the logical type can be changed to a more appropriate value. Let's change some of the columns to a different logical type to illustrate this process. In this case, set the logical type for the `order_product_id` and `country` columns to be `Categorical` and set `customer_name` to have a logical type of `PersonFullName`."
]
},
{
Expand All @@ -116,7 +116,7 @@
"outputs": [],
"source": [
"df.ww.set_types(logical_types={\n",
" 'customer_name': 'FullName',\n",
" 'customer_name': 'PersonFullName',\n",
" 'country': 'Categorical',\n",
" 'order_id': 'Categorical'\n",
"})\n",
Expand Down Expand Up @@ -492,7 +492,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
"version": "3.8.8"
}
},
"nbformat": 4,
Expand Down
2 changes: 1 addition & 1 deletion woodwork/logical_types.py
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Expand Up @@ -156,7 +156,7 @@ class Filepath(LogicalType):
primary_dtype = 'string'


class FullName(LogicalType):
class PersonFullName(LogicalType):
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"""Represents Logical Types that may contain first, middle and last names,
including honorifics and suffixes.

Expand Down
4 changes: 2 additions & 2 deletions woodwork/tests/accessor/test_statistics.py
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Expand Up @@ -10,12 +10,12 @@
Double,
EmailAddress,
Filepath,
FullName,
Integer,
IPAddress,
LatLong,
NaturalLanguage,
Ordinal,
PersonFullName,
PhoneNumber,
PostalCode,
SubRegionCode,
Expand Down Expand Up @@ -229,7 +229,7 @@ def test_describe_accessor_method(describe_df):
formatted_datetime_ltypes = [Datetime(datetime_format='%Y~%m~%d')]
timedelta_ltypes = [Timedelta]
numeric_ltypes = [Double, Integer]
natural_language_ltypes = [EmailAddress, Filepath, FullName, IPAddress,
natural_language_ltypes = [EmailAddress, Filepath, PersonFullName, IPAddress,
PhoneNumber, URL]
latlong_ltypes = [LatLong]

Expand Down
46 changes: 23 additions & 23 deletions woodwork/tests/accessor/test_table_accessor.py
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Expand Up @@ -23,12 +23,12 @@
Double,
EmailAddress,
Filepath,
FullName,
Integer,
IPAddress,
LatLong,
NaturalLanguage,
Ordinal,
PersonFullName,
PhoneNumber,
PostalCode,
SubRegionCode
Expand Down Expand Up @@ -667,7 +667,7 @@ def test_sets_string_dtype_on_init():

logical_types = [
Filepath,
FullName,
PersonFullName,
IPAddress,
NaturalLanguage,
PhoneNumber,
Expand Down Expand Up @@ -1085,7 +1085,7 @@ def test_accessor_with_falsy_column_names(falsy_names_df):

def test_get_invalid_schema_message(sample_df):
schema_df = sample_df.copy()
schema_df.ww.init(name='test_schema', index='id', logical_types={'id': 'Double', 'full_name': 'FullName'})
schema_df.ww.init(name='test_schema', index='id', logical_types={'id': 'Double', 'full_name': 'PersonFullName'})
schema = schema_df.ww.schema

assert _get_invalid_schema_message(schema_df, schema) is None
Expand Down Expand Up @@ -1131,7 +1131,7 @@ def test_get_invalid_schema_message_index_checks(sample_df):
pytest.xfail('Index validation not performed for Dask or Koalas DataFrames')

schema_df = sample_df.copy()
schema_df.ww.init(name='test_schema', index='id', logical_types={'id': 'Double', 'full_name': 'FullName'})
schema_df.ww.init(name='test_schema', index='id', logical_types={'id': 'Double', 'full_name': 'PersonFullName'})
schema = schema_df.ww.schema

different_underlying_index_df = schema_df.copy()
Expand Down Expand Up @@ -1274,7 +1274,7 @@ def test_get_subset_df_with_schema(sample_df):
schema_df.ww.init(time_index='signup_date',
index='id',
name='df_name',
logical_types={'full_name': FullName,
logical_types={'full_name': PersonFullName,
'email': EmailAddress,
'phone_number': PhoneNumber,
'age': Double,
Expand Down Expand Up @@ -1323,7 +1323,7 @@ def test_get_subset_df_use_dataframe_order(sample_df):

def test_select_ltypes_no_match_and_all(sample_df):
schema_df = sample_df.copy()
schema_df.ww.init(logical_types={'full_name': FullName,
schema_df.ww.init(logical_types={'full_name': PersonFullName,
'email': EmailAddress,
'phone_number': PhoneNumber,
'age': Double,
Expand All @@ -1342,50 +1342,50 @@ def test_select_ltypes_no_match_and_all(sample_df):

def test_select_ltypes_strings(sample_df):
schema_df = sample_df.copy()
schema_df.ww.init(logical_types={'full_name': FullName,
schema_df.ww.init(logical_types={'full_name': PersonFullName,
'email': EmailAddress,
'phone_number': PhoneNumber,
'age': Double,
'signup_date': Datetime,
})

df_multiple_ltypes = schema_df.ww.select(['FullName', 'email_address', 'double', 'Boolean', 'datetime'])
df_multiple_ltypes = schema_df.ww.select(['PersonFullName', 'email_address', 'double', 'Boolean', 'datetime'])
assert len(df_multiple_ltypes.columns) == 5
assert 'phone_number' not in df_multiple_ltypes.columns
assert 'id' not in df_multiple_ltypes.columns

df_single_ltype = schema_df.ww.select('full_name')
df_single_ltype = schema_df.ww.select('person_full_name')
assert set(df_single_ltype.columns) == {'full_name'}


def test_select_ltypes_objects(sample_df):
schema_df = sample_df.copy()
schema_df.ww.init(logical_types={'full_name': FullName,
schema_df.ww.init(logical_types={'full_name': PersonFullName,
'email': EmailAddress,
'phone_number': PhoneNumber,
'age': Double,
'signup_date': Datetime,
})

df_multiple_ltypes = schema_df.ww.select([FullName, EmailAddress, Double, Boolean, Datetime])
df_multiple_ltypes = schema_df.ww.select([PersonFullName, EmailAddress, Double, Boolean, Datetime])
assert len(df_multiple_ltypes.columns) == 5
assert 'phone_number' not in df_multiple_ltypes.columns
assert 'id' not in df_multiple_ltypes.columns

df_single_ltype = schema_df.ww.select(FullName)
df_single_ltype = schema_df.ww.select(PersonFullName)
assert len(df_single_ltype.columns) == 1


def test_select_ltypes_mixed(sample_df):
schema_df = sample_df.copy()
schema_df.ww.init(logical_types={'full_name': FullName,
schema_df.ww.init(logical_types={'full_name': PersonFullName,
'email': EmailAddress,
'phone_number': PhoneNumber,
'age': Double,
'signup_date': Datetime,
})

df_mixed_ltypes = schema_df.ww.select(['FullName', 'email_address', Double])
df_mixed_ltypes = schema_df.ww.select(['PersonFullName', 'email_address', Double])
assert len(df_mixed_ltypes.columns) == 3
assert 'phone_number' not in df_mixed_ltypes.columns

Expand All @@ -1395,7 +1395,7 @@ def test_select_ltypes_table(sample_df):
schema_df.ww.init(name='testing',
index='id',
time_index='signup_date',
logical_types={'full_name': FullName,
logical_types={'full_name': PersonFullName,
'email': EmailAddress,
'phone_number': PhoneNumber,
'age': Double,
Expand All @@ -1413,7 +1413,7 @@ def test_select_ltypes_table(sample_df):
assert df_with_indices.ww.index == 'id'
assert df_with_indices.ww.time_index == 'signup_date'

df_values = schema_df.ww.select(['FullName'])
df_values = schema_df.ww.select(['PersonFullName'])
assert df_values.ww.name == schema_df.ww.name
assert df_values.ww.columns['full_name'] == schema_df.ww.columns['full_name']

Expand Down Expand Up @@ -1465,7 +1465,7 @@ def test_select_single_inputs(sample_df):
index='id',
name='df_name',
logical_types={
'full_name': FullName,
'full_name': PersonFullName,
'email': EmailAddress,
'phone_number': PhoneNumber,
'signup_date': Datetime(datetime_format='%Y-%m-%d')
Expand All @@ -1476,7 +1476,7 @@ def test_select_single_inputs(sample_df):
'signup_date': 'date_of_birth'
})

df_ltype_string = schema_df.ww.select('full_name')
df_ltype_string = schema_df.ww.select('person_full_name')
assert len(df_ltype_string.columns) == 1
assert 'full_name' in df_ltype_string.columns

Expand All @@ -1500,7 +1500,7 @@ def test_select_list_inputs(sample_df):
index='id',
name='df_name',
logical_types={
'full_name': FullName,
'full_name': PersonFullName,
'email': EmailAddress,
'phone_number': PhoneNumber,
'signup_date': Datetime(datetime_format='%Y-%m-%d'),
Expand All @@ -1513,14 +1513,14 @@ def test_select_list_inputs(sample_df):
'is_registered': 'category'
})

df_just_strings = schema_df.ww.select(['FullName', 'index', 'tag2', 'boolean'])
df_just_strings = schema_df.ww.select(['PersonFullName', 'index', 'tag2', 'boolean'])
assert len(df_just_strings.columns) == 4
assert 'id' in df_just_strings.columns
assert 'full_name' in df_just_strings.columns
assert 'email' in df_just_strings.columns
assert 'is_registered' in df_just_strings.columns

df_mixed_selectors = schema_df.ww.select([FullName, 'index', 'time_index', Integer])
df_mixed_selectors = schema_df.ww.select([PersonFullName, 'index', 'time_index', Integer])
assert len(df_mixed_selectors.columns) == 4
assert 'id' in df_mixed_selectors.columns
assert 'full_name' in df_mixed_selectors.columns
Expand All @@ -1540,7 +1540,7 @@ def test_select_semantic_tags_no_match(sample_df):
index='id',
name='df_name',
logical_types={
'full_name': FullName,
'full_name': PersonFullName,
'email': EmailAddress,
'phone_number': PhoneNumber,
'signup_date': Datetime(datetime_format='%Y-%m-%d'),
Expand All @@ -1566,7 +1566,7 @@ def test_select_repetitive(sample_df):
index='id',
name='df_name',
logical_types={
'full_name': FullName,
'full_name': PersonFullName,
'email': EmailAddress,
'phone_number': PhoneNumber,
'signup_date': Datetime(datetime_format='%Y-%m-%d'),
Expand Down