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Python best way to process specific Pandas DataDrame column if value not null


By : Letty Rico
Date : September 15 2020, 09:00 AM
Does that help What's the best way to process values in a specific set of Dataframe columns only if it's not null? , IIUC, try pandas.DataFrame.where:
code :
# Sample df
df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two',
                            'two'],
                   'bar': ['A', 'B', np.nan, 'A', 'B', 'C'],
                   'baz': [1, 2, np.nan, 4, 5, 6],
                   'zoo': ['x', 'y', 'z', 'q', 'w', 't']})

columns_to_process = ['bar', 'baz']
df[columns_to_process] = df[columns_to_process].where(df[columns_to_process].isna(), lambda x: x.astype(str)+'!')
df
   bar    baz  foo zoo
0  A!!  1.0!!  one   x
1  B!!  2.0!!  one   y
2  NaN    NaN  one   z
3  A!!  4.0!!  two   q
4  B!!  5.0!!  two   w
5  C!!  6.0!!  two   t


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Difference between === null and isNull in Spark DataDrame


By : kevin
Date : March 29 2020, 07:55 AM
should help you out First and foremost don't use null in your Scala code unless you really have to for compatibility reasons.
Regarding your question it is plain SQL. col("c1") === null is interpreted as c1 = NULL and, because NULL marks undefined values, result is undefined for any value including NULL itself.
code :
spark.sql("SELECT NULL = NULL").show
+-------------+
|(NULL = NULL)|
+-------------+
|         null|
+-------------+
spark.sql("SELECT NULL != NULL").show
+-------------------+
|(NOT (NULL = NULL))|
+-------------------+
|               null|
+-------------------+
spark.sql("SELECT TRUE != NULL").show
+------------------------------------+
|(NOT (true = CAST(NULL AS BOOLEAN)))|
+------------------------------------+
|                                null|
+------------------------------------+
spark.sql("SELECT TRUE = NULL").show
+------------------------------+
|(true = CAST(NULL AS BOOLEAN))|
+------------------------------+
|                          null|
+------------------------------+
spark.sql("SELECT NULL IS NULL").show
+--------------+
|(NULL IS NULL)|
+--------------+
|          true|
+--------------+
spark.sql("SELECT TRUE IS NULL").show
+--------------+
|(true IS NULL)|
+--------------+
|         false|
+--------------+
spark.sql("SELECT NULL IS NOT NULL").show
+------------------+
|(NULL IS NOT NULL)|
+------------------+
|             false|
+------------------+
spark.sql("SELECT TRUE IS NOT NULL").show
+------------------+
|(true IS NOT NULL)|
+------------------+
|              true|
+------------------+
spark.sql("SELECT NULL IS NOT DISTINCT FROM NULL").show
+---------------+
|(NULL <=> NULL)|
+---------------+
|           true|
+---------------+
spark.sql("SELECT NULL IS NOT DISTINCT FROM TRUE").show
+--------------------------------+
|(CAST(NULL AS BOOLEAN) <=> true)|
+--------------------------------+
|                           false|
+--------------------------------+
spark.sql("SELECT NULL AS col1, NULL AS col2").select($"col1" <=> $"col2").show
+---------------+
|(col1 <=> col2)|
+---------------+
|           true|
+---------------+
spark.sql("SELECT NULL AS col1, TRUE AS col2").select($"col1" <=> $"col2").show
+---------------+
|(col1 <=> col2)|
+---------------+
|          false|
+---------------+

Change column name by mapping with another dataDrame


By : Leo
Date : March 29 2020, 07:55 AM
This might help you I have imported csv file with 14 columns & I want to rename all the column name which is present in one dataframe "columnNameDF"
code :
df

   SourceColumns                  Rename
0        Column1           Snapshot Date
1        Column2             Quarter End
2        Column3                Year End
3        Column4      RIA Ownership Type
4        Column5             Age Bracket
5        Column6                  Gender
6        Column7            Channel Type
7        Column8   Exclude Non Producers
8        Column9                    Firm
9       Column10               Firm Type
10      Column11                 License
11      Column12          Retail BD Type
12      Column13  Retail BD Primary Type
13      Column14             Years A Rep

df2

   Column1  Column2  Column3  Column4  Column5  Column6  Column7  Column8  \
0        1        2        3        4        5        6        7        8   

   Column9  Column10  Column11  Column12  Column13  Column14  
0        9        10        11        12        13        14  
df2 = df2.rename(columns=df.set_index('SourceColumns').Rename.to_dict())
df2


   Snapshot Date  Quarter End  Year End  RIA Ownership Type  Age Bracket  \
0              1            2         3                   4            5   

   Gender  Channel Type  Exclude Non Producers  Firm  Firm Type  License  \
0       6             7                      8     9         10       11   

   Retail BD Type  Retail BD Primary Type  Years A Rep  
0              12                      13           14  

How to replace specific character in pandas column with null?


By : Pasha Khosravi
Date : March 29 2020, 07:55 AM
may help you . I Have a column within a dataset, regarding categorical company sizes, which currently looks like this, where the '-' hyphens are currently representing missing data: , You need not to use regex=True.
code :
df['Company Size'].replace({'-': None},inplace =True)

Replace null values in a column corresponding to specific value in another column pandas


By : Ken Green
Date : March 29 2020, 07:55 AM
like below fixes the issue I have a dataframe as below : , You can try below code if ffill is not an option,
code :
df['Region'] = np.select((df.Country.isin(['USA', 'MEX']), df.Country == 'UK'),
                         ('Americas', 'Europe'), df.Region) 

Subset a pandas datadrame based on a variable of pd.series type


By : user3152176
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further Convert values to strings, because columns names are strings by Index.astype and then to list and select by subset:
code :
df = df[['location', 'store'] + var.astype(str).tolist()]
print (df)
  location store  12345  65432  34254
0    north     a      2      4      0
1    south     b      4      6      0
2    south     c      3      5      9
3    north     d      6      7      1
var = pd.Series([12345, 65432, 34254, 1000])

df = df[['location', 'store'] + df.columns.intersection(var.astype(str), sort=False).tolist()]
print (df)
  location store  12345  65432  34254
0    north     a      2      4      0
1    south     b      4      6      0
2    south     c      3      5      9
3    north     d      6      7      1
df = df.set_index(['location','store'])
df = df[var.astype(str).tolist()]
#if possible some values not match
#df = df[df.columns.intersection(var.astype(str), sort=False).tolist()]
print (df)
                12345  65432  34254
location store                     
north    a          2      4      0
south    b          4      6      0
         c          3      5      9
north    d          6      7      1
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