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How to use notin method in pandera

Best Python code snippet using pandera_python

filters.py

1from rest_framework.filters import OrderingFilter2from django_filters.rest_framework import DjangoFilterBackend, FilterSet3from django_filters import rest_framework as filters4from distutils.util import strtobool5from .models import Platform, Institution, Parameter, Ferrybox, Cdf_Institution, getModel6from django import forms7#imports for custom lookups8from .lookups import NotEqual, NotIn9from django.db.models.fields import Field10BOOLEAN_CHOICES = (('false', 'False'), ('true', 'True'),)11class PlatformFilter(FilterSet):12    Field.register_lookup(NotEqual)13    #Field.register_lookup(NotIn)14    status = filters.TypedChoiceFilter(choices=BOOLEAN_CHOICES, coerce=strtobool)15    class Meta:16        model = Platform17        fields = {18            #filters:'exact','ne', 'lt', 'gt', 'lte', 'gte', 'in', icontains19            'id': ['exact', 'ne', 'in'], #notin20            'pid': ['exact', 'ne','in'], #notin21            'tspr': ['exact', 'ne'],22            'type': ['exact', 'ne', 'in'], #notin23            'inst': ['exact'], #einai kai kleidi gia institutions opote ftiaxnei drop down me ta institutions24            'inst__id': ['exact', 'in'],25            'dts': [ 'lt', 'gt', 'lte', 'gte', 'icontains'],26            'dte': [ 'lt', 'gt', 'lte', 'gte', 'icontains'],27            'lat':  ['lt', 'gt', 'lte', 'gte'],28            'lon':  ['lt', 'gt', 'lte', 'gte'],29            'status': [],30            'params' : ['icontains'], 31            'platform_code': [],32            'wmo': ['exact', 'ne', 'icontains'],33            'pi_name' : ['icontains'], 34            'author' : [],35            'contact' : [],36            'island': [],37            'pl_name' : [],38            'inst_ref' : [],39            'assembly_center' : ['exact', 'ne', 'in'],40            'site_code' : [],41            'source' : [],42            'cdf_inst': ['exact']43        }44class InstitutionFilter(FilterSet):45    Field.register_lookup(NotEqual)46    #Field.register_lookup(NotIn)47    class Meta:48        model = Institution49        fields = {50            #filters:'exact','ne', 'lt', 'gt', 'lte', 'gte', 'in', icontains51            'id': ['exact', 'ne', 'in'], #notin52            'name_native' : ['exact', 'ne', 'icontains'],53            'abrv' : ['exact', 'ne', 'in', 'icontains'], #notin54            'country' : ['exact', 'ne', 'in', 'icontains'], #notin55            'cdf_name' : [] 56        }57class Cdf_InstitutionFilter(FilterSet):58    Field.register_lookup(NotEqual)59    #Field.register_lookup(NotIn)60    class Meta:61        model = Cdf_Institution62        fields = {63            #filters:'exact','ne', 'lt', 'gt', 'lte', 'gte', 'in', icontains64            'id': ['exact', 'ne', 'in'], #notin65            'name' : ['exact', 'ne', 'icontains'],66            'inst_id' : ['exact', 'in']67        }68class ParameterFilter(FilterSet):69    Field.register_lookup(NotEqual)70    #Field.register_lookup(NotIn)71    class Meta:72        model = Parameter73        fields = {74            #filters:'exact','ne', 'lt', 'gt', 'lte', 'gte', 'in', icontains75            'id': ['exact', 'ne', 'in'], #notin76            'pname': ['exact', 'ne', 'in', 'icontains'], #notin77            'unit': ['exact', 'ne', 'in', 'icontains'], #notin78            'long_name': ['icontains'], 79            'stand_name': ['exact', 'ne', 'in', 'icontains'], #notin 80            'fval_qc': [], 81            'fval': [], 82            'category_long': ['exact', 'ne', 'in', 'icontains'], #notin83            'category_short': ['exact', 'ne', 'in', 'icontains'], #notin84        }85class FerryboxFilter(FilterSet):86    Field.register_lookup(NotEqual)87    #Field.register_lookup(NotIn)88    class Meta:89        model = Ferrybox90        fields = {91            #filters:'exact','ne', 'lt', 'gt', 'lte', 'gte', 'in', icontains92            'id': ['exact', 'ne', 'in'], #notin93            'dt': ['lt', 'gt', 'lte', 'gte', 'icontains'],94            'lat': ['lt', 'gt', 'lte', 'gte'],95            'lon': ['lt', 'gt', 'lte', 'gte'],96            'posqc': ['exact', 'ne', 'in','lt', 'gt', 'lte', 'gte'], #notin97            'pres': ['lt', 'gt', 'lte', 'gte'],98            'presqc': ['exact', 'ne', 'in', 'lt', 'gt', 'lte', 'gte'], #notin99            'param': ['exact'],100            'param__id' : ['exact','ne', 'in'], #notin101            'val': ['lt', 'gt', 'lte', 'gte'],102            'valqc': ['exact', 'ne', 'in', 'lt', 'gt', 'lte', 'gte'], #notin103            'route_id': ['exact'],...

filter_dict.py

Source: filter_dict.py

1filters = {2    'assets': {3        'id': {'operators': ['equals', 'like', 'in', 'notIn']},4        'name': {'operators': ['equals', 'like', 'in', 'notIn']},5        'first_seen': {'name': 'firstSeen', 'operators': ['equals', 'like', 'in', 'less', 'greater', 'notIn']},6        'last_seen': {'name': 'lastSeen', 'operators': ['equals', 'like', 'in', 'less', 'greater', 'notIn']},7        'addresses': {'operators': ['contains', 'like', 'notIn']},8        'direct_addresses': {'name': 'directAddresses', 'operators': ['contains', 'like', 'notIn']},9        'type': {'operators': ['equals', 'like', 'in', 'notIn']},10        'purdue_level': {'name': 'purdueLevel', 'operators': ['equals', 'like', 'in', 'notIn']},11        'vendor': {'operators': ['equals', 'like', 'in', 'notIn']},12        'run_status': {'name': 'runStatus', 'operators': ['equals', 'like', 'in', 'notIn']},13        'runStatusTime': {'operators': ['equals', 'like', 'in', 'less', 'greater', 'notIn']},14        'location': {'operators': ['equals', 'like', 'in', 'notIn']},15        'description': {'operators': ['equals', 'like', 'in', 'notIn']},16        'os': {'operators': ['equals', 'like', 'in', 'notIn']},17        'family': {'operators': ['equals', 'like', 'in', 'notIn']},18        'modelName': {'operators': ['equals', 'like', 'in', 'notIn']},19        'firmware': {'operators': ['equals', 'like', 'in', 'notIn']},20        'slot': {'operators': ['equals', 'like', 'in', 'less', 'greater', 'notIn']},21        'backplane': {'operators': ['equals', 'like', 'in', 'notIn']},22        'risk': {'operators': ['equals', 'like', 'in', 'less', 'greater', 'notIn']},23        'criticality': {'operators': ['equals', 'like', 'in', 'less', 'greater', 'notIn']},24        'site': {'operators': ['equals', 'like', 'in', 'notIn']},25        'hidden': {'operators': ['equals', 'like', 'in', 'notIn']},26        'customField1': {'operators': ['equals', 'like', 'in', 'notIn']},27        'customField2': {'operators': ['equals', 'like', 'in', 'notIn']},28        'customField3': {'operators': ['equals', 'like', 'in', 'notIn']},29        'customField4': {'operators': ['equals', 'like', 'in', 'notIn']},30        'customField5': {'operators': ['equals', 'like', 'in', 'notIn']},31        'customField6': {'operators': ['equals', 'like', 'in', 'notIn']},32        'customField7': {'operators': ['equals', 'like', 'in', 'notIn']},33        'customField8': {'operators': ['equals', 'like', 'in', 'notIn']},34        'customField9': {'operators': ['equals', 'like', 'in', 'notIn']},35        'customField10': {'operators': ['equals', 'like', 'in', 'notIn']},36    }...

temp.py

Source:temp.py

1# -*- coding: utf-8 -*-2"""34"""56'''71. åæä¸¤ä¸ªè¡¨(old1,old2)åå¹¶, 82. ç¶åååºéå¤çæ°æ®, ä¿çä¸æ¡93. ä¿åå°æ°è¡¨new1104. å§old1è¡¨ånew1è¡¨åå¹¶115. ç¶åååºéå¤çæ°æ®å¹¶å é¤ï¼å¾å°å·®éï¼å°±æ¯å¨old2ä¸ä½ä¸å¨old1ä¸çæ°æ®126. å§old2è¡¨ånew1è¡¨åå¹¶137. ç¶åååºéå¤çæ°æ®å¹¶å é¤ï¼å¾å°å·®éï¼å°±æ¯å¨old1ä¸ä½ä¸å¨old2ä¸çæ°æ®14'''151617import pandas as pd181920# openexcel21data1 = pd.read_excel('C:\\Users\\JZ\\.spyder-py3\\1.xlsx')22df1 = data1['Server Name'] # è¯»åååä¸ºServer Nameçåå®¹23#print(df1)2425data2 = pd.read_excel('C:\\Users\\JZ\\.spyder-py3\\2.xlsx')26df2 = data2['Host'] # è¯»åååä¸ºHostçåå®¹27#print(df2)282930# 1.merge_excel31res = pd.concat([df1, df2], axis=0, ignore_index=True)32#print(res)333435# 2,3.get_compilation36#compilation_del = res.drop_duplicates(keep=False)37compilation = res.drop_duplicates()38compilation.to_excel('C:\\Users\\JZ\\.spyder-py3\\res.xlsx')39#print(compilation_del)40#print(compilation)4142# 4.merge ITAM and compilation43notin_df1_file = pd.concat([df1, compilation], axis=0, ignore_index=True)44#print(notin_df1_file)4546# 5.get subtraction47inventory_notin_itam = notin_df1_file.drop_duplicates(keep=False)48#print(inventory_notin_itam)49inventory_notin_itam.to_excel('C:\\Users\\JZ\\.spyder-py3\\inventory_notin_itam.xlsx')5051# 6.merge inventory and compilation52notin_df2_file = pd.concat([df2, compilation],axis=0, ignore_index=True)53#print(notin_df2_file)5455# 7.get subtraction56itam_notin_inventory = notin_df2_file.drop_duplicates(keep=False)57#print(itam_notin_inventory)58itam_notin_inventory.to_excel('C:\\Users\\JZ\\.spyder-py3\\itam_notin_inventory.xlsx')596061626364
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