### *NTV-pandas : A tabular analyzer and a semantic, compact and reversible converter*
<img src="https://loco-philippe.github.io/ES/ntv_pandas.png" alt="ntv-pandas" align="middle" style="height:80px;">
For more information, see the [user guide](https://loco-philippe.github.io/ntv-pandas/docs/user_guide.html) or the [github repository](https://github.com/loco-philippe/ntv-pandas).
NTV-pandas is referenced in the [pandas ecosystem](https://pandas.pydata.org/community/ecosystem.html).
# Why a NTV-pandas converter ?
pandas provide IO converters but limitations are present:
- the multidimensional structure is not taken into account,
- the converters are not always reversible (conversion round trip),
- the converters take into account few data types,
- external data types (e.g. TableSchema types) are not included.
pandas does not have a tool for analyzing tabular structures and detecting integrity errors
## main features
The converter integrates:
- interfaces with Xarray, scipp, JSON,
- all the pandas `dtype` and the data-type associated to a JSON representation,
- an always reversible conversion,
- an identification of tabular and multidimensional structure,
- a full compatibility with [Table Schema specification](http://dataprotocols.org/json-table-schema/#field-types-and-formats).
The NTV-pandas converter uses the [semantic NTV format](https://loco-philippe.github.io/ES/JSON%20semantic%20format%20(JSON-NTV).htm)
to include a large set of data types in a JSON representation.
The NTV-pandas analyzer uses the [TAB-analysis](https://github.com/loco-philippe/tab-analysis/blob/main/README.md) tool to analyze and measure the relationships between Fields in DataFrame and the [TAB-dataset](https://github.com/loco-philippe/tab-dataset/blob/main/README.md) to identify integrity errors ([example](https://github.com/loco-philippe/ntv-pandas/tree/main/example#readme)).
The multidimensional converter uses the [NTV-numpy](https://github.com/loco-philippe/ntv-numpy/blob/main/README.md) multidimensional format and interfaces.
NTV-pandas was developped originally in the [NTV project](https://github.com/loco-philippe/NTV)
## multidimensional converter example
In the example below, a Dataframe is converted to Xarray and scipp.
The DataFrame resulting from these conversions are identical to the initial DataFrame (reversibility).
```python
In [1]: import pandas as pd
import ntv_pandas as npd
In [2]: fruits = {'plants': ['fruit', 'fruit', 'fruit', 'fruit', 'vegetable', 'vegetable', 'vegetable', 'vegetable'],
'plts': ['fr', 'fr', 'fr', 'fr', 've', 've', 've', 've'],
'quantity': ['1 kg', '10 kg', '1 kg', '10 kg', '1 kg', '10 kg', '1 kg', '10 kg'],
'product': ['apple', 'apple', 'orange', 'orange', 'peppers', 'peppers', 'carrot', 'carrot'],
'price': [1, 10, 2, 20, 1.5, 15, 1.5, 20],
'price level': ['low', 'low', 'high', 'high', 'low', 'low', 'high', 'high'],
'group': ['fruit 1', 'fruit 10', 'fruit 1', 'veget', 'veget', 'veget', 'veget', 'veget'],
'id': [1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008],
'supplier': ["sup1", "sup1", "sup1", "sup2", "sup2", "sup2", "sup2", "sup1"],
'location': ["fr", "gb", "es", "ch", "gb", "fr", "es", "ch"],
'valid': ["ok", "ok", "ok", "ok", "ok", "ok", "ok", "ok"]}
df_fruits = pd.DataFrame(fruits)
df_fruits.npd.analysis(distr=True).partitions() # return the list of partitions (a partition is a list of dimensions)
Out[2]:
[['plants', 'quantity', 'price level'],
['quantity', 'price level', 'supplier'],
['plants', 'location'],
['quantity', 'product'],
['supplier', 'location'],
['id']]
In [3]: kwargs = {'dims':['product', 'quantity'], 'datagroup': False, 'ntv_type': False, 'json_name': False}
xd_fruits = df_fruits.npd.to_xarray(**kwargs)
xd_fruits
Out[3]:
<xarray.Dataset> Size: 976B
Dimensions: (product: 4, quantity: 2)
Coordinates:
* product (product) <U7 112B 'apple' 'carrot' 'orange' 'peppers'
* quantity (quantity) <U5 40B '1 kg' '10 kg'
plants (product) <U9 144B 'fruit' 'vegetable' 'fruit' 'vegetable'
plts (product) <U2 32B 'fr' 've' 'fr' 've'
price level (product) <U4 64B 'low' 'high' 'high' 'low'
valid <U2 8B 'ok'
Data variables:
group (product, quantity) <U8 256B 'fruit 1' 'fruit 10' ... 'veget'
id (product, quantity) int64 64B 1001 1002 1007 ... 1004 1005 1006
location (product, quantity) <U2 64B 'fr' 'gb' 'es' ... 'ch' 'gb' 'fr'
price (product, quantity) float64 64B 1.0 10.0 1.5 ... 20.0 1.5 15.0
supplier (product, quantity) <U4 128B 'sup1' 'sup1' ... 'sup2' 'sup2'
In [4]: sc_fruits = df_fruits.npd.to_scipp(**kwargs)
sc_fruits
Out[4]:
<scipp.Dataset>
Dimensions: Sizes[product:4, quantity:2, ]
Coordinates:
* plants string [dimensionless] (product) ["fruit", "vegetable", "fruit", "vegetable"]
* plts string [dimensionless] (product) ["fr", "ve", "fr", "ve"]
* price level string [dimensionless] (product) ["low", "high", "high", "low"]
* product string [dimensionless] (product) ["apple", "carrot", "orange", "peppers"]
* quantity string [dimensionless] (quantity) ["1 kg", "10 kg"]
* valid string [dimensionless] () "ok"
Data:
group string [dimensionless] (product, quantity) ["fruit 1", "fruit 10", ..., "veget", "veget"]
id int64 [dimensionless] (product, quantity) [1001, 1002, ..., 1005, 1006]
location string [dimensionless] (product, quantity) ["fr", "gb", ..., "gb", "fr"]
price float64 [dimensionless] (product, quantity) [1, 10, ..., 1.5, 15]
supplier string [dimensionless] (product, quantity) ["sup1", "sup1", ..., "sup2", "sup2"]
```
Reversibility:
```python
In [5]: df_fruits_xd = npd.from_xarray(xd_fruits, **kwargs)
df_fruits_xd_sort = df_fruits_xd.reset_index()[list(df_fruits.columns)].sort_values(list(df_fruits.columns)).reset_index(drop=True)
df_fruits_sort = df_fruits.sort_values(list(df_fruits.columns)).reset_index(drop=True)
df_fruits_xd_sort.equals(df_fruits_sort)
Out[5]:
True
In [6]: df_fruits_sc = npd.from_scipp(sc_fruits, **kwargs)
df_fruits_sc_sort = df_fruits_sc.reset_index()[list(df_fruits.columns)].sort_values(list(df_fruits.columns)).reset_index(drop=True)
df_fruits_sort = df_fruits.sort_values(list(df_fruits.columns)).reset_index(drop=True)
df_fruits_sc_sort.equals(df_fruits_sort)
Out[6]:
True
```
## JSON converter example
In the example below, a DataFrame with multiple data types is converted to JSON (first to NTV format and then to Table Schema format).
The DataFrame resulting from these JSON conversions are identical to the initial DataFrame (reversibility).
With the existing JSON interface, these conversions are not possible.
```python
In [1]: from shapely.geometry import Point
from datetime import date
import pandas as pd
import ntv_pandas as npd
In [2]: data = {'index': [100, 200, 300, 400, 500],
'dates::date': [date(1964,1,1), date(1985,2,5), date(2022,1,21), date(1964,1,1), date(1985,2,5)],
'value': [10, 10, 20, 20, 30],
'value32': pd.Series([12, 12, 22, 22, 32], dtype='int32'),
'res': [10, 20, 30, 10, 20],
'coord::point': [Point(1,2), Point(3,4), Point(5,6), Point(7,8), Point(3,4)],
'names': pd.Series(['john', 'eric', 'judith', 'mila', 'hector'], dtype='string'),
'unique': True }
In [3]: df = pd.DataFrame(data).set_index('index')
df.index.name = None
In [4]: df
Out[4]: dates::date value value32 res coord::point names unique
100 1964-01-01 10 12 10 POINT (1 2) john True
200 1985-02-05 10 12 20 POINT (3 4) eric True
300 2022-01-21 20 22 30 POINT (5 6) judith True
400 1964-01-01 20 22 10 POINT (7 8) mila True
500 1985-02-05 30 32 20 POINT (3 4) hector True
```
JSON-NTV representation:
```python
In [5]: df_to_json = df.npd.to_json()
pprint(df_to_json, compact=True, width=120, sort_dicts=False)
Out[5]: {':tab': {'index': [100, 200, 300, 400, 500],
'dates::date': ['1964-01-01', '1985-02-05', '2022-01-21', '1964-01-01', '1985-02-05'],
'value': [10, 10, 20, 20, 30],
'value32::int32': [12, 12, 22, 22, 32],
'res': [10, 20, 30, 10, 20],
'coord::point': [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [7.0, 8.0], [3.0, 4.0]],
'names::string': ['john', 'eric', 'judith', 'mila', 'hector'],
'unique': True}}
```
Reversibility:
```python
In [6]: print(npd.read_json(df_to_json).equals(df))
Out[6]: True
```
Table Schema representation:
```python
In [7]: df_to_table = df.npd.to_json(table=True)
pprint(df_to_table['data'][0], sort_dicts=False)
Out[7]: {'index': 100,
'dates': '1964-01-01',
'value': 10,
'value32': 12,
'res': 10,
'coord': [1.0, 2.0],
'names': 'john',
'unique': True}
In [8]: pprint(df_to_table['schema'], sort_dicts=False)
Out[8]: {'fields': [{'name': 'index', 'type': 'integer'},
{'name': 'dates', 'type': 'date'},
{'name': 'value', 'type': 'integer'},
{'name': 'value32', 'type': 'integer', 'format': 'int32'},
{'name': 'res', 'type': 'integer'},
{'name': 'coord', 'type': 'geopoint', 'format': 'array'},
{'name': 'names', 'type': 'string'},
{'name': 'unique', 'type': 'boolean'}],
'primaryKey': ['index'],
'pandas_version': '1.4.0'}
```
Reversibility:
```python
In [9]: print(npd.read_json(df_to_table).equals(df))
Out[9]: True
```
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"description": "### *NTV-pandas : A tabular analyzer and a semantic, compact and reversible converter*\r\n\r\n<img src=\"https://loco-philippe.github.io/ES/ntv_pandas.png\" alt=\"ntv-pandas\" align=\"middle\" style=\"height:80px;\">\r\n\r\nFor more information, see the [user guide](https://loco-philippe.github.io/ntv-pandas/docs/user_guide.html) or the [github repository](https://github.com/loco-philippe/ntv-pandas).\r\n\r\nNTV-pandas is referenced in the [pandas ecosystem](https://pandas.pydata.org/community/ecosystem.html).\r\n\r\n# Why a NTV-pandas converter ?\r\n\r\npandas provide IO converters but limitations are present:\r\n\r\n- the multidimensional structure is not taken into account,\r\n- the converters are not always reversible (conversion round trip),\r\n- the converters take into account few data types,\r\n- external data types (e.g. TableSchema types) are not included.\r\n\r\npandas does not have a tool for analyzing tabular structures and detecting integrity errors\r\n\r\n## main features\r\n\r\nThe converter integrates:\r\n\r\n- interfaces with Xarray, scipp, JSON,\r\n- all the pandas `dtype` and the data-type associated to a JSON representation,\r\n- an always reversible conversion,\r\n- an identification of tabular and multidimensional structure,\r\n- a full compatibility with [Table Schema specification](http://dataprotocols.org/json-table-schema/#field-types-and-formats).\r\n\r\nThe NTV-pandas converter uses the [semantic NTV format](https://loco-philippe.github.io/ES/JSON%20semantic%20format%20(JSON-NTV).htm)\r\nto include a large set of data types in a JSON representation.\r\n\r\nThe NTV-pandas analyzer uses the [TAB-analysis](https://github.com/loco-philippe/tab-analysis/blob/main/README.md) tool to analyze and measure the relationships between Fields in DataFrame and the [TAB-dataset](https://github.com/loco-philippe/tab-dataset/blob/main/README.md) to identify integrity errors ([example](https://github.com/loco-philippe/ntv-pandas/tree/main/example#readme)).\r\n\r\nThe multidimensional converter uses the [NTV-numpy](https://github.com/loco-philippe/ntv-numpy/blob/main/README.md) multidimensional format and interfaces.\r\n\r\nNTV-pandas was developped originally in the [NTV project](https://github.com/loco-philippe/NTV)\r\n\r\n## multidimensional converter example\r\n\r\nIn the example below, a Dataframe is converted to Xarray and scipp.\r\n\r\nThe DataFrame resulting from these conversions are identical to the initial DataFrame (reversibility).\r\n\r\n```python\r\nIn [1]: import pandas as pd\r\n import ntv_pandas as npd\r\n\r\nIn [2]: fruits = {'plants': ['fruit', 'fruit', 'fruit', 'fruit', 'vegetable', 'vegetable', 'vegetable', 'vegetable'],\r\n 'plts': ['fr', 'fr', 'fr', 'fr', 've', 've', 've', 've'],\r\n 'quantity': ['1 kg', '10 kg', '1 kg', '10 kg', '1 kg', '10 kg', '1 kg', '10 kg'],\r\n 'product': ['apple', 'apple', 'orange', 'orange', 'peppers', 'peppers', 'carrot', 'carrot'],\r\n 'price': [1, 10, 2, 20, 1.5, 15, 1.5, 20],\r\n 'price level': ['low', 'low', 'high', 'high', 'low', 'low', 'high', 'high'],\r\n 'group': ['fruit 1', 'fruit 10', 'fruit 1', 'veget', 'veget', 'veget', 'veget', 'veget'],\r\n 'id': [1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008],\r\n 'supplier': [\"sup1\", \"sup1\", \"sup1\", \"sup2\", \"sup2\", \"sup2\", \"sup2\", \"sup1\"],\r\n 'location': [\"fr\", \"gb\", \"es\", \"ch\", \"gb\", \"fr\", \"es\", \"ch\"],\r\n 'valid': [\"ok\", \"ok\", \"ok\", \"ok\", \"ok\", \"ok\", \"ok\", \"ok\"]}\r\n df_fruits = pd.DataFrame(fruits)\r\n df_fruits.npd.analysis(distr=True).partitions() # return the list of partitions (a partition is a list of dimensions)\r\nOut[2]:\r\n [['plants', 'quantity', 'price level'],\r\n ['quantity', 'price level', 'supplier'],\r\n ['plants', 'location'],\r\n ['quantity', 'product'],\r\n ['supplier', 'location'],\r\n ['id']]\r\n\r\nIn [3]: kwargs = {'dims':['product', 'quantity'], 'datagroup': False, 'ntv_type': False, 'json_name': False}\r\n xd_fruits = df_fruits.npd.to_xarray(**kwargs)\r\n xd_fruits\r\nOut[3]:\r\n <xarray.Dataset> Size: 976B\r\n Dimensions: (product: 4, quantity: 2)\r\n Coordinates:\r\n * product (product) <U7 112B 'apple' 'carrot' 'orange' 'peppers'\r\n * quantity (quantity) <U5 40B '1 kg' '10 kg'\r\n plants (product) <U9 144B 'fruit' 'vegetable' 'fruit' 'vegetable'\r\n plts (product) <U2 32B 'fr' 've' 'fr' 've'\r\n price level (product) <U4 64B 'low' 'high' 'high' 'low'\r\n valid <U2 8B 'ok'\r\n Data variables:\r\n group (product, quantity) <U8 256B 'fruit 1' 'fruit 10' ... 'veget'\r\n id (product, quantity) int64 64B 1001 1002 1007 ... 1004 1005 1006\r\n location (product, quantity) <U2 64B 'fr' 'gb' 'es' ... 'ch' 'gb' 'fr'\r\n price (product, quantity) float64 64B 1.0 10.0 1.5 ... 20.0 1.5 15.0\r\n supplier (product, quantity) <U4 128B 'sup1' 'sup1' ... 'sup2' 'sup2'\r\n\r\nIn [4]: sc_fruits = df_fruits.npd.to_scipp(**kwargs)\r\n sc_fruits\r\nOut[4]:\r\n <scipp.Dataset>\r\n Dimensions: Sizes[product:4, quantity:2, ]\r\n Coordinates:\r\n * plants string [dimensionless] (product) [\"fruit\", \"vegetable\", \"fruit\", \"vegetable\"]\r\n * plts string [dimensionless] (product) [\"fr\", \"ve\", \"fr\", \"ve\"]\r\n * price level string [dimensionless] (product) [\"low\", \"high\", \"high\", \"low\"]\r\n * product string [dimensionless] (product) [\"apple\", \"carrot\", \"orange\", \"peppers\"]\r\n * quantity string [dimensionless] (quantity) [\"1 kg\", \"10 kg\"]\r\n * valid string [dimensionless] () \"ok\"\r\n Data:\r\n group string [dimensionless] (product, quantity) [\"fruit 1\", \"fruit 10\", ..., \"veget\", \"veget\"]\r\n id int64 [dimensionless] (product, quantity) [1001, 1002, ..., 1005, 1006]\r\n location string [dimensionless] (product, quantity) [\"fr\", \"gb\", ..., \"gb\", \"fr\"]\r\n price float64 [dimensionless] (product, quantity) [1, 10, ..., 1.5, 15]\r\n supplier string [dimensionless] (product, quantity) [\"sup1\", \"sup1\", ..., \"sup2\", \"sup2\"]\r\n```\r\n\r\nReversibility:\r\n\r\n```python\r\nIn [5]: df_fruits_xd = npd.from_xarray(xd_fruits, **kwargs)\r\n df_fruits_xd_sort = df_fruits_xd.reset_index()[list(df_fruits.columns)].sort_values(list(df_fruits.columns)).reset_index(drop=True)\r\n df_fruits_sort = df_fruits.sort_values(list(df_fruits.columns)).reset_index(drop=True)\r\n df_fruits_xd_sort.equals(df_fruits_sort)\r\nOut[5]:\r\n True\r\n\r\nIn [6]: df_fruits_sc = npd.from_scipp(sc_fruits, **kwargs)\r\n df_fruits_sc_sort = df_fruits_sc.reset_index()[list(df_fruits.columns)].sort_values(list(df_fruits.columns)).reset_index(drop=True)\r\n df_fruits_sort = df_fruits.sort_values(list(df_fruits.columns)).reset_index(drop=True)\r\n df_fruits_sc_sort.equals(df_fruits_sort)\r\nOut[6]:\r\n True\r\n```\r\n\r\n## JSON converter example\r\n\r\nIn the example below, a DataFrame with multiple data types is converted to JSON (first to NTV format and then to Table Schema format).\r\n\r\nThe DataFrame resulting from these JSON conversions are identical to the initial DataFrame (reversibility).\r\n\r\nWith the existing JSON interface, these conversions are not possible.\r\n\r\n```python\r\nIn [1]: from shapely.geometry import Point\r\n from datetime import date\r\n import pandas as pd\r\n import ntv_pandas as npd\r\n\r\nIn [2]: data = {'index': [100, 200, 300, 400, 500],\r\n 'dates::date': [date(1964,1,1), date(1985,2,5), date(2022,1,21), date(1964,1,1), date(1985,2,5)],\r\n 'value': [10, 10, 20, 20, 30],\r\n 'value32': pd.Series([12, 12, 22, 22, 32], dtype='int32'),\r\n 'res': [10, 20, 30, 10, 20],\r\n 'coord::point': [Point(1,2), Point(3,4), Point(5,6), Point(7,8), Point(3,4)],\r\n 'names': pd.Series(['john', 'eric', 'judith', 'mila', 'hector'], dtype='string'),\r\n 'unique': True }\r\n\r\nIn [3]: df = pd.DataFrame(data).set_index('index')\r\n df.index.name = None\r\n\r\nIn [4]: df\r\nOut[4]: dates::date value value32 res coord::point names unique\r\n 100 1964-01-01 10 12 10 POINT (1 2) john True\r\n 200 1985-02-05 10 12 20 POINT (3 4) eric True\r\n 300 2022-01-21 20 22 30 POINT (5 6) judith True\r\n 400 1964-01-01 20 22 10 POINT (7 8) mila True\r\n 500 1985-02-05 30 32 20 POINT (3 4) hector True\r\n```\r\n\r\nJSON-NTV representation:\r\n\r\n```python\r\nIn [5]: df_to_json = df.npd.to_json()\r\n pprint(df_to_json, compact=True, width=120, sort_dicts=False)\r\nOut[5]: {':tab': {'index': [100, 200, 300, 400, 500],\r\n 'dates::date': ['1964-01-01', '1985-02-05', '2022-01-21', '1964-01-01', '1985-02-05'],\r\n 'value': [10, 10, 20, 20, 30],\r\n 'value32::int32': [12, 12, 22, 22, 32],\r\n 'res': [10, 20, 30, 10, 20],\r\n 'coord::point': [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [7.0, 8.0], [3.0, 4.0]],\r\n 'names::string': ['john', 'eric', 'judith', 'mila', 'hector'],\r\n 'unique': True}}\r\n```\r\n\r\nReversibility:\r\n\r\n```python\r\nIn [6]: print(npd.read_json(df_to_json).equals(df))\r\nOut[6]: True\r\n```\r\n\r\nTable Schema representation:\r\n\r\n```python\r\nIn [7]: df_to_table = df.npd.to_json(table=True)\r\n pprint(df_to_table['data'][0], sort_dicts=False)\r\nOut[7]: {'index': 100,\r\n 'dates': '1964-01-01',\r\n 'value': 10,\r\n 'value32': 12,\r\n 'res': 10,\r\n 'coord': [1.0, 2.0],\r\n 'names': 'john',\r\n 'unique': True}\r\n\r\nIn [8]: pprint(df_to_table['schema'], sort_dicts=False)\r\nOut[8]: {'fields': [{'name': 'index', 'type': 'integer'},\r\n {'name': 'dates', 'type': 'date'},\r\n {'name': 'value', 'type': 'integer'},\r\n {'name': 'value32', 'type': 'integer', 'format': 'int32'},\r\n {'name': 'res', 'type': 'integer'},\r\n {'name': 'coord', 'type': 'geopoint', 'format': 'array'},\r\n {'name': 'names', 'type': 'string'},\r\n {'name': 'unique', 'type': 'boolean'}],\r\n 'primaryKey': ['index'],\r\n 'pandas_version': '1.4.0'}\r\n```\r\n\r\nReversibility:\r\n\r\n```python\r\nIn [9]: print(npd.read_json(df_to_table).equals(df))\r\nOut[9]: True\r\n```\r\n",
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