CODEWITHSUNDEEP
pythonpandasdataframe
Edamame
Edamame

Reputation: 25366

python pandas: filter out records with null or empty string for a given field

I am trying to filter out records whose field_A is null or empty string in the data frame like below:

my_df[my_df.editions is not None]
my_df.shape

This gives me error:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-40-e1969e0af259> in <module>()
      1 my_df['editions'] = my['editions'].astype(str)
----> 2 my_df = my_df[my_df.editions is not None]
      3 my_df.shape

/home/edamame/anaconda2/lib/python2.7/site-packages/pandas/core/frame.pyc in __getitem__(self, key)
   1995             return self._getitem_multilevel(key)
   1996         else:
-> 1997             return self._getitem_column(key)
   1998 
   1999     def _getitem_column(self, key):

/home/edamame/anaconda2/lib/python2.7/site-packages/pandas/core/frame.pyc in _getitem_column(self, key)
   2002         # get column
   2003         if self.columns.is_unique:
-> 2004             return self._get_item_cache(key)
   2005 
   2006         # duplicate columns & possible reduce dimensionality

/home/edamame/anaconda2/lib/python2.7/site-packages/pandas/core/generic.pyc in _get_item_cache(self, item)
   1348         res = cache.get(item)
   1349         if res is None:
-> 1350             values = self._data.get(item)
   1351             res = self._box_item_values(item, values)
   1352             cache[item] = res

/home/edamame/anaconda2/lib/python2.7/site-packages/pandas/core/internals.pyc in get(self, item, fastpath)
   3288 
   3289             if not isnull(item):
-> 3290                 loc = self.items.get_loc(item)
   3291             else:
   3292                 indexer = np.arange(len(self.items))[isnull(self.items)]

/home/edamame/anaconda2/lib/python2.7/site-packages/pandas/indexes/base.pyc in get_loc(self, key, method, tolerance)
   1945                 return self._engine.get_loc(key)
   1946             except KeyError:
-> 1947                 return self._engine.get_loc(self._maybe_cast_indexer(key))
   1948 
   1949         indexer = self.get_indexer([key], method=method, tolerance=tolerance)

pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4154)()

pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4018)()

pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12368)()

pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12322)()

KeyError: True

or

my_df[my_df.editions != None]
my_df.shape

This one gave no error but didn't filter out any None values.

I also tried:

my_df = my_df[my_df.editions.notnull()]

This one doesn't give error but doesn't filter out any None values either.

Could anyone please advise how to solve this problem? Thanks!

Upvotes: 35

Views: 101777

Answers (6)

Fontanka16
Fontanka16

Reputation: 1321

Null or Whitespace version:

import pandas    
df= df[~df['editions'].str.contains('^(?:\s+)?$', na=True, regex=True)]

na=True means that NaN:s will return True.

Upvotes: 0

Thomas Zimmer
Thomas Zimmer

Reputation: 51

Seems to work with query as well:

import pandas
df = pandas.DataFrame([{"role": ""}, {"role": "a"}, {"role": "b"}])
df.query('role != ""')

gives:
  role
1    a
2    b

Upvotes: 0

NRK Rao
NRK Rao

Reputation: 62

In case we want to filter out based on both Null and Empty string we can use

df = df[ (df['str_field'].isnull()) | (df['str_field'].str.len() == 0) ]

Use logical operator ('|' , '&', '~') for mixing two conditions

Upvotes: -2

StackG
StackG

Reputation: 2858

You can filter out empty strings in your dataframe like this:

df = df[df['str_field'].str.len() > 0]

Upvotes: 36

Gonzalo Ferreiro Volpi
Gonzalo Ferreiro Volpi

Reputation: 389

You can negativize a condition while filtering using ~.

So in your case you should do:

my_df = my_df[~my_df.editions.isnull()]

Upvotes: 29

MattR
MattR

Reputation: 5126

Can you create a new dataframe from the filtering?

Dataframe before:

a     b
1     9
2    10
3    11
4    12
5    13
6    14
7    15
8  null

Example: 

import pandas

my_df = pandas.DataFrame({"a":[1,2,3,4,5,6,7,8],"b":[9,10,11,12,13,14,15,"null"]})

my_df2= my_df[(my_df['b']!="null")]
print(my_df2)

dataframe after:

a   b
1   9
2  10
3  11
4  12
5  13
6  14
7  15

What it is doing is looking for "null" and excluding it. You could do the same thing with empty strings.

Upvotes: 6

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