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How to use dataframe_check method in pandera

Best Python code snippet using pandera_python

taskflowdataschema.py

Source:taskflowdataschema.py

1"""2Schema repository for task flow data and task flow data subsets3"""4from pandera.typing import Series, String5import pandas as pd6import pandera as pa7from cdadagbuilder.genflow.utils.check import (8    check_empty_df,9    invalid_seq_number,10)11class TaskFlowDataSchema(pa.SchemaModel):12    """13    Pandera schema model including the validations for fields14    in the configuration files for task flow data dataframe15    """16    variabletablegroupname: Series[String] = pa.Field(17        nullable=False, coerce=True18    )19    variableworkflowstepname: Series[String] = pa.Field(20        nullable=False, coerce=True, allow_duplicates=False21    )22    variableworkflowstepquerytype: Series[String] = pa.Field(23        nullable=False, coerce=True24    )25    variableworkflowstepexecutionorder: Series[int] = pa.Field(26        nullable=False, coerce=True27    )28    variableworkflowstepschema: Series[String] = pa.Field(29        nullable=False, coerce=True30    )31    variableworkflowstepquery: Series[String] = pa.Field(32        nullable=False, coerce=True33    )34    workflowstepqueryparameters: Series[String] = pa.Field(35        nullable=False, coerce=True36    )37    # pylint: disable=R020138    @pa.dataframe_check39    def validate_taskflow_data_dataframe(40        self, task_flow_data: pd.DataFrame41    ) -> bool:42        """43        Raise a error if TaskDataSchema dataframe is empty44        :param task_flow_data:45        :type task_flow_data: DataFrame46        """47        _indicator = check_empty_df(task_flow_data)48        return _indicator == 049class TaskFlowDataSubsetSchema(pa.SchemaModel):50    """51    Pandera schema model including the validations for fields52    in the configuration files for task flow data subset dataframe53    """54    variableworkflowstepname: Series[String] = pa.Field(55        nullable=False, coerce=True, allow_duplicates=False56    )57    variableworkflowstepquerytype: Series[String] = pa.Field(58        nullable=False, coerce=True59    )60    variableworkflowstepexecutionorder: Series[int] = pa.Field(61        nullable=False, coerce=True, ge=162    )63    variableworkflowstepschema: Series[String] = pa.Field(64        nullable=False, coerce=True65    )66    variableworkflowstepquery: Series[String] = pa.Field(67        nullable=False, coerce=True68    )69    workflowstepqueryparameters: Series[String] = pa.Field(70        nullable=False, coerce=True71    )72    # pylint: disable=R020173    @pa.dataframe_check74    def validate_taskflow_data_subset_dataframe(75        self, task_flow_data_subset: pd.DataFrame76    ) -> bool:77        """78        Raise a error if TaskDataFlowSubsetSchema dataframe is empty79        :param task_flow_data_subset:80        :type task_flow_data_subset: DataFrame81        """82        _indicator = check_empty_df(task_flow_data_subset)83        return _indicator == 084    # pylint: disable=R020185    @pa.dataframe_check86    def validate_task_step_buildorder(87        self, task_flow_data_subset: pd.DataFrame88    ) -> bool:89        """90        Identify the missing task step build sequence numbers91        configured in the tasks data config file92        :param task_flow_data_subset:93        :type task_flow_data_subset: DataFrame94        """95        _taskflow_step_buildorder = task_flow_data_subset[96            "variableworkflowstepexecutionorder"97        ].tolist()98        _invalid_num = invalid_seq_number(_taskflow_step_buildorder)99        return _invalid_num == 0100    # pylint: disable=R0201101    @pa.dataframe_check102    def validate_min_task_step_buildorder(103        self, task_flow_data_subset: pd.DataFrame104    ) -> bool:105        """106        Minimum build order has to be 1107        :param task_flow_data_subset:108        :type task_flow_data_subset: DataFrame109        """110        _taskflow_step_buildorder = task_flow_data_subset[111            "variableworkflowstepexecutionorder"112        ].tolist()113        _min_build_order = min(_taskflow_step_buildorder)...

match_kingdoms.py

Source:match_kingdoms.py

1import os2import random3import numpy as np4import pandas as pd56tsk1 = []7import pandas as pd8import csv9from datetime import datetime,timedelta1011path='C:\\Users\\Administrator\\Desktop\\sumup1.csv'  # è·åæä»¶å¤¹çè·¯å¾12data = pd.read_csv(path,error_bad_lines=False)1314path='C:\\Users\\Administrator\\Desktop\\check.csv'  # è·åæä»¶å¤¹çè·¯å¾15data_check = pd.read_csv(path,error_bad_lines=False)1617dataframe_1=pd.DataFrame(data)1819print(len(data),len(data_check))20new=[]21dataframe_check=pd.DataFrame(data_check)22for i in range(len(data)):23    ind=024    for j in range(len(data_check)):25       if dataframe_1['city'][i]==dataframe_check['LAD20NM'][j]:26           ind=j27           break28    new.append(dataframe_check['LAD20CD'][ind])29dataframe_1['region']=new
...

correlation_matrix.py

Source:correlation_matrix.py

1# /usr/bin/env python2import pandas as pd3def dataframe_check(mat):4    if type(mat) != pd.core.frame.DataFrame:5        raise Exception("argument mat must be a pandas DataFrame")6        7def normalize(mat):8    dataframe_check(mat)9    mat = mat.sub(mat.mean(axis = 1), axis = 0)10    mat = mat.div((mat * mat).sum(axis = 1)**(1 / 2), axis = 0)11    return mat12def pearson_corr_mat(mat):13    dataframe_check(mat)14    mat_norm = normalize(mat)15    return mat_norm.dot(mat_norm.transpose())16def spearman_corr_mat(mat):17    dataframe_check(mat)18    mat_rank = mat.rank(axis = 1)...

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