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Ignore Nulls in pandas map dictionary


Ignore Nulls in pandas map dictionary

By : user3099912
Date : January 11 2021, 05:14 PM
this one helps. Try using the below code, I first use the apply function, than I drop the NaNs, then I convert it into a list then I use the list.index method for each value in the new list, and list.index gives the index of the first occurence of the value, after that convert it into the Series, and make the index the index of the series without NaNs, I am doing that since after I drop the NaNs it will turn from index 0, 1, 2, 3 to 0, 2, 3 or something like that, whereas the missing index will be NaN again, after that I add a underscore to each column, and I join it with the original dataframe:
code :
print(df.join(df.apply(lambda x: pd.Series(map(x.dropna().tolist().index, x.dropna()), index=x.dropna().index)).add_suffix('_')))
  COL1 COL2 COL3  COL1_  COL2_  COL3_
0    A    M    X    0.0    0.0      0
1    B    F    Y    1.0    1.0      1
2  NaN    M    Y    NaN    0.0      1
3    A  NaN    Y    0.0    NaN      1


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Ignore nulls in SQL

Ignore nulls in SQL


By : Alireza_Armn
Date : March 29 2020, 07:55 AM
I wish this helpful for you Your test data suggests that the TIME and TYPE are linked, and that the values always rise with time. In which case this solution will work:
code :
select Time
       , Type
       , max(Value_A) as Value_A
       , max(Value_B) as Value_B
from your_table
group by Time
         , Type
select distinct Time
       , Type
       , last_value(Value_A ignore nulls) 
           over (partition by time, type order by Loaded_time_sequence
                ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) Value_A
       , last_value(Value_B ignore nulls) 
           over (partition by time, type order by Loaded_time_sequence
               ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) Value_B
from t23
;
Format datetime field as MON-YYYY in python pandas & ignore nulls

Format datetime field as MON-YYYY in python pandas & ignore nulls


By : user3377413
Date : March 29 2020, 07:55 AM
To fix the issue you can do is this the format you wanted?
Basically we can drop the NaN rows first and then call apply and use datetime.strftime to apply a new format:
code :
In [24]:

df = df.dropna()
df['fomatted_date'] = df['vendor_created_date'].apply(lambda x: dt.datetime.strftime(x,'%b-%Y'))
df
Out[24]:
           NAME SETID  VENDOR_ID vendor_created_date fomatted_date
Index                                                             
0       Vendor1    SD         93 2002-11-22 11:04:33      Nov-2002
1       Vendor2    SD         94 2003-08-09 11:40:59      Aug-2003
2      Service1    SD         95 2003-10-31 10:29:21      Oct-2003
Order by min of two dates with nulls first for one column, ignore nulls for another

Order by min of two dates with nulls first for one column, ignore nulls for another


By : Puspahas Das
Date : March 29 2020, 07:55 AM
hop of those help? I want to order my data by the minimum date of two columns, with equal precedence. If Date1 has nulls, I want them to appear first. If Date2 has nulls, I just want to go by Date1. If both dates are null, it should appear at the top. , LEAST and COALESCE should give you what you're after:
code :
with sample_data as (select to_date('01/01/2012', 'dd/mm/yyyy') date1, to_date('01/01/2000', 'dd/mm/yyyy') date2 from dual union all
                     select null date1, null date2 from dual union all
                     select to_date('01/01/2009', 'dd/mm/yyyy') date1, to_date('01/01/2015', 'dd/mm/yyyy') date2 from dual union all
                     select to_date('01/01/2013', 'dd/mm/yyyy') date1, null date2 from dual union all
                     select to_date('01/01/2003', 'dd/mm/yyyy') date1, to_date('01/01/2003', 'dd/mm/yyyy') date2 from dual)
select date1,
       date2
from   sample_data
order by least(date1, coalesce(date2, date1)) nulls first;

DATE1      DATE2     
---------- ----------

01/01/2012 01/01/2000
01/01/2003 01/01/2003
01/01/2009 01/01/2015
01/01/2013           
GROUP BY but ignore nulls unless no not-nulls

GROUP BY but ignore nulls unless no not-nulls


By : Wiinterfell
Date : March 29 2020, 07:55 AM
it helps some times Imagine you have two lists; a list of drivers and a list of vehicles.
You want the complete list of drivers, plus any vehicle records if they exist.
code :
SELECT    drivers.driver_uuid,
          vehicles.vehicle_uuid,
          vehicles.document_uuid
FROM      driver_vehicle_documents drivers
LEFT JOIN driver_vehicle_documents vehicles ON  vehicles.driver_uuid = drivers.driver_uuid 
                                            AND vehicles.vehicle_uuid IS NOT NULL
Replacing nulls by fillna still returns nulls in pandas

Replacing nulls by fillna still returns nulls in pandas


By : Dave Magno
Date : March 29 2020, 07:55 AM
it fixes the issue Inplace fillna does not work on pd.Series columns because they return a copy, and the copy is modified, leaving the original untouched.
Why not just do -
code :
df.loc[:, 'A':'D'] = df.loc[:, 'A':'D'].fillna('Global') 
df.loc[:, ['A', 'B', 'C', 'D']] = df.loc[:, ['A', 'B', 'C', 'D']].fillna('Global')
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