In this case I am working with 2 columns that are substracted from 2 Dataframes. The columns are ["# Externo","Nro Envio ML"]]
My target is to recieve the numbers that exist in "# Externo" but no exist in"Nro Envio ML" , only that number/ or numbers that fill to that condition.
To take a look what I am talking about:
dfn.info()
dfn
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1143 entries, 0 to 2151
Data columns (total 2 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 # Externo 404 non-null object
1 Nro Envio ML 894 non-null object
dtypes: object(2)
memory usage: 26.8 KB
# Externo Nro Envio ML
0 41764660663 NaN
1 41765189264 NaN
2 41765105927 NaN
3 41765931626 NaN
4 41766474810 NaN
... ... ...
2143 NaN 41768876815
2146 NaN 41784067107
2147 NaN 41784051958
2149 NaN 41785977098
2151 NaN 41796142562
1143 rows × 2 columns
# Externo True
Nro Envio ML True
dtype: bool
I am expecting ro recieve 41764660663 if exist in column "# Externo" and not in "Nro Envio ML"
This is What I tried:
df1 = df1[df1['Unnamed: 26'] == 'Flex']
df2= pd.concat([df, df1], axis=1)
df2
import numpy as np
df2['Nro Envio ML']=df2['Unnamed: 13']
dfn=df2[["# Externo","Nro Envio ML"]]
print(dfn.notnull().any(axis=0))
dfn= dfn.loc[:,dfn.notnull().any(axis=0)]
print(dfn)
print(dfn.dropna(axis=1,how='all'))
dfn.loc[~df['# Externo'].isin(dfn['Nro Envio ML'].tolist())]
The error I recieve:
IndexingError Traceback (most recent call last)
<ipython-input-144-54d975e5ad81> in <module>
3 print(dfn)
4 print(dfn.dropna(axis=1,how='all'))
----> 5 dfn.loc[~df['# Externo'].isin(dfn['Nro Envio ML'].tolist())]
3 frames
/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py in check_bool_indexer(index, key)
2387 if mask.any():
2388 raise IndexingError(
-> 2389 "Unalignable boolean Series provided as "
2390 "indexer (index of the boolean Series and of "
2391 "the indexed object do not match)."
IndexingError: Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match).
Here is not filtered:
dfn3= dfn.loc[~ dfn['# Externo'].isin(dfn['Nro Envio ML']),'# Externo'].values
dfn3
array(['41764660663', '41765189264', '41765105927', '41765931626',
'41766474810', '41766693570', '41767023186', '41766664967',
'41765527475', '41766933520', '41758431387', '41767065141',
'41766834461', '41763758747', '41767007000', '41764139836',
'41767128958', '41767958453', '41768439109', '41767519460',
'41768746394', '41767537504', '41768245931', '41768435988',
'41768710593', '41767850751', '41769343996', '41768163019',
'41767792365', '41769430226', '41769362435', '41767613260',
'41767399871', '41769237788', '41769335922', '41768743591',
'41768970216', '41768972816', '41767801455', '41767351959',
'41768856005', '41769069211', '41768960289', '41768876815',
'41768796242', '41768606054', '41769594117', '41768301217',
'41769316065', '41769275644', '41768747851', '41768992109',
'41767973684', '41768588967', '41769021462', '41768655275',
'41769195649', '41771323517', '41770997916', '41770624787',
'41771124135', '41767953692', '41771990757', '41771503073',
'41771518432', '41770587159', '41771770302', '41771264986',
'41770622684', '41771712719', '41770043750', '41769920549',
'41771890393', '41771093881', '41770335018', '41769851289',
'41769691702', '41770178002', '41770083356', '41771478219',
'41771689312', '41770310781', '41770503120', '41771320102',
'41770872304', '41772333923', '41773077420', '41774107375',
'41774470025', '41772354195', '41774278154', '41774516055',
'41764063012', '41773238895', '41770358839', '41773410325',
'41772497677', '41772207643', '41774095335', '41774540961',
'41773924133', '41772759005', '41772493934', '41773676496',
'41772632879', '41772582155', '41772586341', '41772592180',
'41774973883', '41775140655', '41775320466', '41775999294',
'41775447715', '41776040324', '41774633931', '41775257392',
'41775471162', '41771934549', '41775499496', '41774856789',
'41775136607', '41775410928', '41776142924', '41776094067',
'41775191189', '41775749633', '41775907614', '41774792841',
'41776033160', '41775490223', '41778623933', '41777644508',
'41780014741', '41778994962', '41777323701', '41776219972',
'41780552222', '41777798847', '41779796901', '41780799923',
'41780472850', '41772305897', '41780180889', '41780555214',
'41778280294', '41780767290', '41779889603', '41780667613',
'41778248797', '41778766814', '41780236744', '41779887066',
'41776670687', '41777525040', '41780960139', 'nan', '41777644374',
'41779923800', '41777002840', '41777753678', '41778182378',
'41776301694', '41779886597', '41779667714', '41781000946',
'41777189468', '41780087137', '41780155654', '41780775906',
'41778329111', '41783067184', '41782721889', '41781632703',
'41783780618', '41783873395', '41783998100', '41783931503',
'41782490708', '41778620781', '41776593233', '41783231988',
'41782256463', '41783528314', '41782914027', '41784027619',
'41781822829', '41784004699', '41783211341', '41784033505',
'41782545928', '41784051958', '41781766311', '41783040125',
'41783951875', '41784068580', '41783813820', '41783067755',
'41783016716', '41784060487', '41783803363', '41782531020',
'41781388743', '41785977098', '41786030848', '41786287968',
'41784805290', '41786552267', '41786879966', '41786460175',
'41786610058', '41785551493', '41786710599', '41786958316',
'41781724264', '41785445012', '41786594197', '41785477465',
'41786482621', '41784728916', '41786163574', '41785240433',
'41784798439', '41786406137', '41786330557', '41787005790',
'41786634121', '41786210955', '41784198119', '41786024295',
'41785069315', '41782349052', '41786708909', '41788240277',
'41788955033', '41789046308', '41784596066', '41788063455',
'41787694599', '41789136771', '41787403317', '41787409226',
'41789241747', '41787555666', '41787430932', '41787309404',
'41788910204', '41787568748', '41789414846', '41788177940',
'41789528530', '41789382342', '41789803654', '41788514458',
'41784831727', '41787377624', '41787828042', '41789205824',
'41789308552', '41789288899', '41789701434', '41787553674',
'41787681573', '41789442389', '41789190629', '41780044631',
'41789895907', '41788809900', '41789122350', '41788438919',
'41787977304', '41788642761', '41789281426', '41791796789',
'41791686344', '41790988049', '41787229497', '41790708372',
'41791150645', '41790453941', '41791020142', '41790384927',
'41790434960', '41791900221', '41791780863', '41792045890',
'41789979877', '41790213389', '41792328962', '41791367184',
'41791135752', '41792275060', '41791890035', '41792546856',
'41791884595', '41790134693', '41792095927', '41790458720',
'41791526022', '41792143565', '41791680878', '41790832413',
'41792463288', '41791972322', '41791084950', '41791591750',
'41792018279', '41791891437', '41790340322', '41792490749',
'41791949185', '41792273084', '41792942400', '41793195303',
'41793116161', '41793560497', '41793420765', '41793390721',
'41792995107', '41792853373', '41794017254', '41792829460',
'41794146341', '41794097400', '41793917806', '41793085795',
'41793153713', '41793479285', '41793672321', '41794163188',
'41792913806', '41795638686', '41796745322', '41796518007',
'41796793000', '41795214845', '41796220240', '41796073319',
'41796781702', '41795312941', '41797871757', '41797732193',
'41796831262', '41798441839', '41792712332', '41794174553',
'41798690031', '41798308119', '41798875026', '41798261237',
'41796142562', '41794298123', '41798116617', '41798838185',
'41798387675', '41794457006', '41797766954', '41798516007',
'41797807112', '41797868790', '41797073652', '41798109141',
'41797925241', '41798587922', '41798206365', '41795797834',
'41798921136', '41798844409', '41797860445', '41798137866',
'41798816124', '41794976940', '41795115092', '41794826346',
'41798335167', '41797220545', '41797338131', '41798519643',
'41798503487', '41796975986', '41796122923', '41797229414',
'41799541385', '41800565067', '41801544241', '41800619941',
'41800606765', '41801602923', '41800814367', '41799433986',
'41800528875', '41798885157', '41799587807', '41800708489',
'41799422642', '41801323370', '41799993602', '41800526158',
'41801058190', '41799946619', '41800698887', '41801171856',
'41801569361', '41800715567', '41800154420'], dtype=object)
Also I tried without luck:
newdf = dfn.drop_duplicates(
subset = ['Nro Envio ML', '# Externo'],
keep = 'last').reset_index(drop = True)
newdf
CodePudding user response：
I think you were quite close. Does this achieve what you're trying to do?
dfn['Externo'][~dfn['Externo'].isin(dfn['Nro Envio ML'])].dropna().tolist()
It returns all non-NaN values in the 'Externo' column that are not in the 'Nro Envio ML' column as a list.
I think the IndexingError you received may have been due to using df instead of dfn in the last line of your code, but I'm not 100% sure because df is not defined in your snippet.