Getting started

To use our library, simply install it via pip. This command will install the library along with the default engines pandas and polars:

pip install calista

If you require support for another engines such as Snowflake, Spark, or BigQuery, use the following command and replace EngineName with the name of your desired engine:

pip install calista[EngineName]

Example: if you want to have Snowflake and Spark, use the following command:

pip install calista[snowflake, spark]

To study the quality of your data with the Calista library, you have several engines at your disposal. Here’s how to use them.

How to specify an engine and load data

Pandas

Pandas is a powerful Python library primarily used for data manipulation and analysis. Its key features include data structures like DataFrame and Series, which facilitate handling structured data effectively.

Load a table with Calista:

from calista import CalistaEngine

table = CalistaEngine(engine="pandas") \
.load_from_path(<path_to_your_file>, file_format=<your_file_format>)

Polars

Polars is a fast and efficient Rust library for data manipulation and analysis, with bindings available for Python. It offers similar functionalities to Pandas, such as DataFrame and Series structures, but with a focus on high-performance computing. It is optimized for large datasets, multithreading, and lazy evaluation.

Load a table with Calista:

from calista import CalistaEngine

table = CalistaEngine(engine="polars") \
.load_from_path(<path_to_your_file>, file_format=<your_file_format>)

Spark

Spark is a distributed computing framework designed for processing big data tasks. The key advantages include its speed, fault tolerance, and support for various data sources and processing engines. It excels in iterative processing and real-time analytics, making it suitable for a wide range of big data applications.

Install Calista with the spark engine:

pip install calista[spark]

Load a table with Calista:

from calista import CalistaEngine

table = CalistaEngine(engine="spark") \
.load_from_path(<path_to_your_file>, file_format=<your_file_format>)

For the previous engines, you can also use the following functions to load your Calista table from an existing dataframe or a dictionary.

calista.table.CalistaEngine.load_from_dataframe()

calista.table.CalistaEngine.load_from_dict()

Snowflake

As this engine is developed in Snowpark, before computing a rule, a configuration must be defined to connect to the Snowflake data warehouse. Snowflake is a cloud-based data warehousing platform designed for storing, processing, and analyzing large volumes of data. It offers a scalable and elastic architecture, allowing users to efficiently manage data across multiple clouds.

Install Calista with the snowflake engine:

pip install calista[snowflake]

Load a table with Calista:

from calista import CalistaEngine

config = {
     "credentials": {
         "account": <account-identifier>,
         "user": <user-name>,
         "password": <password>,
     }
 }
 table = CalistaEngine(engine="snowflake", config=config) \
     .load_from_database(database=<your_database_name>, schema=<your_schema_name>, table=<your_table_name>)

BigQuery

As this engine is developed in SQL, before computing a rule, a configuration must be defined to connect to the BigQuery data warehouse. BigQuery is a fully managed, serverless data warehouse provided by Google Cloud Platform. It’s designed for storing and analyzing large datasets using SQL queries, with scalable compute and storage resources.

Install Calista with the BigQuery engine:

pip install calista[bigquery]

Load a table with Calista:

from calista import CalistaEngine

connection_string = f'bigquery://<my-project>/<my-dataset>'
credentials_path='<path_to_credentials>.json'
config = {
    'connection_string': connection_string,
    'credentials_path': credentials_path
    }
table = CalistaEngine(engine="bigquery", config=config).load_from_database(table=<your_table_name>)

How to compute metrics

You can create your own rules by chaining several Calista’s functions with these operators :
& | ~

from calista import functions as func

my_rule = func.is_iban(col_name="IBAN") & func.is_float("SALAIRE") | ~func.is_iban(col_name="ADRESSE_IP_V4")
print(table.analyze(rule_name=<your_rule_name>, rule=my_rule))

rule_name : your_rule_name
total_row_count : 100
valid_row_count : 100
valid_row_count_pct : 100.0
timestamp : 2024-05-06 16:19:13.221048

You can also compute several rules at the same time

from calista import functions as func

rules = {
"check_iban_quality": func.is_iban("IBAN"),
"check_CDI_ID_are_integer": func.is_integer("CDI") & func.is_integer("ID"),
"check_email_quality": func.is_email("EMAIL"),
}
print(table.analyze_rules(rules))

[
Metrics(
       rule='check_iban_quality',
       total_row_count=100,
       valid_row_count=90,
       valid_row_count_pct=90.0,
       timestamp='2024-05-07 11:37:34.038035'
  ),
  Metrics(
      rule='check_CDI_ID_are_integer',
      total_row_count=100,
      valid_row_count=98,
      valid_row_count_pct=98.0,
      timestamp='2024-05-07 11:37:34.038035'),
  Metrics(
      rule='check_email_quality',
      total_row_count=100,
      valid_row_count=92,
      valid_row_count_pct=92.0,
      timestamp='2024-05-07 11:37:34.038035')
]

How to get enhanced data

You have the possibility to get your enhanced data by applying a rule:

from calista import functions as func

print(table.apply_rule(rule_name="check_iban_quality", rule=func.is_iban("IBAN"))[['IBAN', 'check_iban_quality']])

                           IBAN  check_iban_quality
 FR4756356801990924110246661                True
 FR9152927592715361970259533                True
 FR6098743347361131022029548                True
 FR2371478023732554095214206                True
 FR0330875910858658779613722                True
..                          ...                 ...
FR1773393443400319003480793                True
FR0228768854412051157590266                True
FR5869598054756805717971833                True
FR6634213649058126775820977                True
                       None               False

You can also do the same with a list of rules:

from calista import functions as func

rules = {
"check_iban_quality": func.is_iban("IBAN"),
"check_email_quality": func.is_email("EMAIL"),
}
print(table.apply_rules(rules)[['IBAN', 'check_iban_quality', 'EMAIL', 'check_email_quality']])

                          IBAN  check_iban_quality                          EMAIL  check_email_quality
 FR4756356801990924110246661                True  aristidesgordillo@example.net                 True
 FR9152927592715361970259533                True     oceane.leclercq@orange.com                 True
 FR6098743347361131022029548                True        elodie.morel@icloud.com                 True
 FR2371478023732554095214206                True          therese04@example.com                 True
 FR0330875910858658779613722                True      bertrand.dijoux@yahoo.com                 True
..                          ...                 ...                            ...                  ...
FR1773393443400319003480793                True         eugene.munoz@yahoo.com                 True
FR0228768854412051157590266                True                           None                False
FR5869598054756805717971833                True            aaron50@example.net                 True
FR6634213649058126775820977                True         lucie.allard@gmail.com                 True
                       None               False     alexandria.petit@yahoo.com                 True

If you want to retrieve the data not validating your rule for some further analysis, it is possible:

from calista import functions as func

my_rule = func.is_iban("IBAN")
print(table.get_invalid_rows(rule=my_rule))

          NOM      PRENOM SEXE DATE_ENTREE  CDI  IBAN  ...    CDD                          EMAIL             TELEPHONE   SALAIRE DEVISE   ID
Chevalier    Adélaïde    M        None  1.0  None  ...  False  adelaide.chevalier@icloud.com  +33 (0)3 52 49 21 25  39630.16    GPB   11
    Petit  Antoinette    F  2016-04-04  1.0  None  ...  False     antoinette.petit@gmail.com  +33 (0)3 63 22 80 94  48302.80    EUR   29
    Gomez     Antoine    M  2015-04-04  0.0  None  ...   True     miguel-angel83@example.com  +33 (0)6 30 22 34 32  53213.86    EUR   32
   Lebrun      Xavier    M  2018-01-20  1.0  None  ...  False       xavier.lebrun@orange.com                  None  51289.21    EUR   48
  Ferrand     Chantal    M  2002-01-26  NaN  None  ...   True     chantal.ferrand@orange.com        06 82 99 40 77  89947.60    EUR   55
Lemonnier    Éléonore    M  2011-12-22  1.0  None  ...  False  eleonore.lemonnier@orange.com            0329984138  58303.00    EUR   60
    Dupré    Frédéric    F  2022-07-21  0.0  None  ...   True       frederic.dupre@gmail.com            0385249100  53914.36    EUR   63
  Étienne    Nathalie    F  2008-07-17  0.0  None  ...   True     nathalie.etienne@gmail.com  +33 (0)3 51 82 62 52  48394.97    EUR   65
  Roussel         Luc    F  2013-11-27  0.0  None  ...   None          luc.roussel@gmail.com                  None  47089.29    EUR   79
    Petit  Alexandria    F  2003-11-18  0.0  None  ...   True     alexandria.petit@yahoo.com                  None  82053.90    EUR  100

Data filtering and aggregation

Sometimes you need to check a rule on a subset of a dataset and not the entire dataset. With calista, before checking a rule, you have the possibility to filter data on which you want to apply it. To do so, you can use following CalistaTable methods:

where | filter

from calista import functions as func

my_rule = func.is_iban(col_name="IBAN") & func.is_float("SALAIRE") | ~func.is_iban(col_name="ADRESSE_IP_V4")
print(table.where(func.column_lt_column(col_left="DATE_ENTREE", col_right="DATE_SORTIE")).analyze(rule_name=<your_rule_name>, rule=my_rule))

rule_name : your_rule_name
total_row_count : 69
valid_row_count : 69
valid_row_count_pct : 100.0
timestamp : 2024-05-06 16:19:13.221048

When you need to aggregate the data before checking a rule, you can also do it:

from calista import functions as func

my_rule = func.mean_le_value(col_name="SALAIRE", value=63500)
print(table.group_by("SEXE").analyze(rule_name='rule_after_groupby', rule=my_rule))

rule_name : rule_after_groupby
total_row_count : 2
valid_row_count : 1
valid_row_count_pct : 50.0
timestamp : 2024-05-06 16:19:13.221048

After checking a rule on an aggregated data, you may need to get the granular data for some further analysis:

from calista import functions as func

my_rule = func.mean_le_value(col_name="SALAIRE", value=63500)
print(table.group_by("SEXE").get_invalid_rows(my_rule, granular=True))

          NOM      PRENOM  SEXE DATE_ENTREE  CDI                         IBAN  ...                          EMAIL             TELEPHONE     SALAIRE DEVISE   ID  MEAN_SALAIRE
     David      Benoît     F  2017-11-08  1.0  FR4756356801990924110246661  ...  aristidesgordillo@example.net       +34624 93 56 97   54088.900    EUR    1  63797.860051
     Guyot     Richard     F  2003-02-01  1.0  FR2371478023732554095214206  ...          therese04@example.com         +39 051102083   46860.500    EUR    4  63797.860051
     Payet      Sophie     F  2019-08-20  1.0       GB04CWQU49139432843509  ...         sophie.payet@yahoo.com     +33 5 49 39 05 83         NaN    EUR    6  63797.860051
      Huet        Noël  None  2004-11-17  1.0  FR1981073760101001813753760  ...           noel.huet@orange.com     +33 1 79 16 57 57   62250.610    EUR    7           NaN
 Letellier        None  None  1999-12-04  1.0  FR6906093250967318491811332  ...   paulette.letellier@yahoo.com        05 87 79 23 68   99577.630    EUR    8           NaN
      Blin   Joséphine     F  2016-06-14  0.0  FR5424174388864165764478788  ...      josephine.blin@icloud.com     +33 3 88 09 43 69   48686.030    EUR    9  63797.860051
   Clément    Augustin     F  2011-11-12  1.0  FR4824529266739098177591337  ...    augustin.clement@icloud.com             037554761   72859.530    EUR   10  63797.860051
    Roche      Julien     F  2004-06-24  1.0  FR1860468059025110302957190  ...        julien.roche@orange.com  +33 (0)2 56 20 68 92   80623.670    EUR   12  63797.860051
  Rivière      Océane     F  2003-03-09  1.0  FR4833873535424706528489623  ...      oceane.riviere@icloud.com            0442359528   59771.080    EUR   13  63797.860051
  Fouquet       Élise     F  2002-06-05  1.0  FR1114003618928576373627316  ...            aimee46@example.com  +33 (0)5 35 95 73 53  112954.080    EUR   14  63797.860051
     Mahe        None     F  2019-11-03  0.0  FR7482236532283013826453918  ...         arthur.mahe@icloud.com  +33 (0)2 36 96 67 27         NaN   None   15  63797.860051
    Dupré    Clémence     F  2001-08-13  1.0  FR9163588265500172623591721  ...      clemence.dupre@orange.com            0179258124   58653.670    USD   16  63797.860051
   Techer      Robert     F  2007-11-06  1.0  FR2825426228635025209800528  ...   donairemarianela@example.net     +33 6 58 26 29 74   80798.330    EUR   17  63797.860051
    Louis        None     F  2013-05-18  1.0  FR4871649149129350095943291  ...           leon.louis@gmail.com     +33 3 52 61 76 92   49108.943    EUR   27  63797.860051
   Lebrun     William     F  2011-12-07  1.0  FR7362594136933819144154596  ...       william.lebrun@yahoo.com  +33 (0)3 56 50 10 84   55611.920    EUR   28  63797.860051
    Petit  Antoinette     F  2016-04-04  1.0                         None  ...     antoinette.petit@gmail.com  +33 (0)3 63 22 80 94   48302.800    EUR   29  63797.860051
  Prévost    Zacharie     F  2010-03-16  1.0  FR2322908186377673922107573  ...     zacharie.prevost@gmail.com            0493937492   63204.870    EUR   30  63797.860051
   Gérard    Éléonore     F  2015-02-10  1.0  FR4580038492174663200778602  ...                           None            0386807149   53604.880    EUR   31  63797.860051
   Hebert      Olivie     F  2016-12-14  0.0  FR1406325108155800285047085  ...       luzfiguerola@example.com        04 23 64 91 43   56225.940    EUR   33  63797.860051
     Mary        None     F  2014-03-16  0.0  IT35I6458656215473634264620  ...         camille.mary@yahoo.com        +34821 990 687   78936.210    EUR   35  63797.860051
  Foucher   Madeleine     F  2001-04-21  0.0       GB70CHLC19087364645548  ...    madeleine.foucher@gmail.com        04 92 16 69 42   79960.310    EUR   36  63797.860051
 Lefebvre      Marthe     F  2016-01-11  1.0  FR2094934606300706527685700  ...                           None         (271)593-1057   49829.680    EUR   41  63797.860051
  Renault       Aimée     F  2003-09-16  1.0  FR4410756978615230763579217  ...        aimee.renault@gmail.com                  None   54203.700    EUR   42  63797.860051
    Lopez   Anastasie     F  2003-10-29  1.0       GB19QLJX55117284591835  ...     anastasie.lopez@icloud.com            0411710667   78163.420    EUR   43  63797.860051
   Lesage        None     F  2003-05-31  1.0  FR3435152357229347122698563  ...          guy.lesage@orange.com      624-203-5364x491   78951.000   None   44  63797.860051
  Charles       Aimée     F  2014-04-18  0.0  FR2355171053623274635735406  ...                           None  +33 (0)1 73 43 94 50   54464.790    EUR   47  63797.860051
    Jacob      Jeanne     F  2005-02-08  1.0  FR5524474160786140086320486  ...        jeanne.jacob@orange.com        03 65 68 26 54   96146.339    EUR   49  63797.860051
  Raynaud     Suzanne  None  2001-02-25  1.0  FR7049971597282699593917624  ...          ocorbacho@example.net            0577403895   76484.860    EUR   50           NaN
    Leroy        None     F  2020-10-20  0.0  FR2919580775745371762043734  ...      augustin.leroy@icloud.com  +33 (0)2 97 67 16 45   40824.020    EUR   53  63797.860051
Pelletier       Anouk     F  2021-11-16  0.0  FR7985511451054100519654296  ...     anouk.pelletier@icloud.com            0182365953         NaN    EUR   61  63797.860051
    Dupré    Frédéric     F  2022-07-21  0.0                         None  ...       frederic.dupre@gmail.com            0385249100   53914.360    EUR   63  63797.860051
 Martinez      Élodie     F  2010-02-19  0.0  FR6489079921398324785268734  ...      elodie.martinez@yahoo.com            0738787609   56855.360    EUR   64  63797.860051
  Étienne    Nathalie     F  2008-07-17  0.0                         None  ...     nathalie.etienne@gmail.com  +33 (0)3 51 82 62 52   48394.970    EUR   65  63797.860051
    Leleu        Anne     F  1999-08-20  1.0  FR5070832884875175390130427  ...           anne.leleu@yahoo.com                  None   64094.470    GPB   70  63797.860051
  Jourdan        None     F  2003-10-22  0.0  IT19F5525702139130809936165  ...   christiane.jourdan@yahoo.com        02 34 88 09 90   59842.060    EUR   71  63797.860051
    Dumas       Louis  None  2009-12-28  0.0  FR3637964138787947015880922  ...         louis.dumas@orange.com  +33 (0)3 52 06 79 49   89971.720    EUR   77           NaN
   Mendès     Thérèse     F  2011-04-09  1.0  FR1883437207179328287588112  ...       therese.mendes@yahoo.com            0475097898   45948.550    EUR   78  63797.860051
  Roussel         Luc     F  2013-11-27  0.0                         None  ...          luc.roussel@gmail.com                  None   47089.290    EUR   79  63797.860051
   Seguin        None     F  2013-03-20  1.0  FR1395082105509510740229568  ...           olivie84@example.org            0381213701   69461.100    EUR   81  63797.860051
Rodriguez      Maryse     F  2012-07-23  1.0  FR4989854493457317435878643  ...     maryse.rodriguez@gmail.com     +33 2 45 00 67 52   88066.630    EUR   83  63797.860051
    Blanc     Maurice     F        None  1.0  FR9431696256132132586090751  ...       maurice.blanc@orange.com  +33 (0)2 28 12 44 55   53076.710    EUR   84  63797.860051
    Vidal    Adrienne     F  2000-11-06  1.0  FR3432008385462221555786178  ...      adrienne.vidal@icloud.com     +33 4 66 88 36 30  105453.450    EUR   86  63797.860051
     Gros      Émilie     F  1999-08-24  1.0  FR4234262487540294137515029  ...          emilie.gros@yahoo.com          735.792.7071         NaN    EUR   87  63797.860051
    Vidal     Monique     F  2012-07-15  0.0  FR8018260326533128482307609  ...        monique.vidal@yahoo.com            0498508049   35011.190    EUR   91  63797.860051
   Bonnet    Grégoire     F  2008-08-22  0.0  IT67Z3313548660472543022188  ...     gregoire.bonnet@icloud.com  +33 (0)6 11 56 11 35   57602.710    EUR   92  63797.860051
 Guilbert      Hélène     F  2005-05-20  1.0  FR8294004502553798839787408  ...     helene.guilbert@icloud.com  +33 (0)5 24 81 50 39   68417.180    EUR   93  63797.860051
    Munoz      Eugène  None  2001-10-16  1.0  FR1773393443400319003480793  ...         eugene.munoz@yahoo.com         +34 849856047   94105.300    EUR   96           NaN
  Tessier    Philippe     F  2008-02-29  1.0  FR5869598054756805717971833  ...            aaron50@example.net            0474360958         NaN    EUR   98  63797.860051
    Petit  Alexandria     F  2003-11-18  0.0                         None  ...     alexandria.petit@yahoo.com                  None   82053.900    EUR  100  63797.860051