How to Check If a Value Is in the List in Selection from Pandas Data Frame

How to check if a value is in the list in selection from pandas data frame?

Use isin

df_new[df_new['l_ext'].isin([31, 22, 30, 25, 64])]

How to check if Pandas column has value from list of string?

Use apply and lambda like:

df['Names'].apply(lambda x: any([k in x for k in kw]))

0     True
1     True
2     True
3     True
4    False
Name: Names, dtype: bool

Pandas Dataframe Check if column value is in column list

Use apply:

df['flag'] = df.apply(lambda x: int(x['id'] in x['idlist']), axis=1)
print (df)
    id                idlist  flag
0   12    [1, 5, 7, 12, 112]     1
1  112  [5, 7, 12, 111, 113]     0

Similar:

df['flag'] = df.apply(lambda x: x['id'] in x['idlist'], axis=1).astype(int)
print (df)
    id                idlist  flag
0   12    [1, 5, 7, 12, 112]     1
1  112  [5, 7, 12, 111, 113]     0

With list comprehension:

df['flag'] = [int(x[0] in x[1]) for x in df[['id', 'idlist']].values.tolist()]
print (df)
    id                idlist  flag
0   12    [1, 5, 7, 12, 112]     1
1  112  [5, 7, 12, 111, 113]     0

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Solutions for filtering:

df = df[df.apply(lambda x: x['id'] in x['idlist'], axis=1)]
print (df)
   id              idlist
0  12  [1, 5, 7, 12, 112]

df = df[[x[0] in x[1] for x in df[['id', 'idlist']].values.tolist()]]
print (df)

   id              idlist
0  12  [1, 5, 7, 12, 112]

Filter dataframe rows if value in column is in a set list of values

Use the isin method:

rpt[rpt['STK_ID'].isin(stk_list)]

Check if certain value is contained in a dataframe column in pandas

I think you need str.contains, if you need rows where values of column date contains string 07311954:

print df[df['date'].astype(str).str.contains('07311954')]

Or if type of date column is string:

print df[df['date'].str.contains('07311954')]

If you want check last 4 digits for string 1954 in column date:

print df[df['date'].astype(str).str[-4:].str.contains('1954')]

Sample:

print df['date']
0    8152007
1    9262007
2    7311954
3    2252011
4    2012011
5    2012011
6    2222011
7    2282011
Name: date, dtype: int64

print df['date'].astype(str).str[-4:].str.contains('1954')
0    False
1    False
2     True
3    False
4    False
5    False
6    False
7    False
Name: date, dtype: bool

print df[df['date'].astype(str).str[-4:].str.contains('1954')]
     cmte_id trans_typ entity_typ state  employer  occupation     date  \
2  C00119040       24K        CCM    MD       NaN         NaN  7311954   

   amount     fec_id    cand_id  
2    1000  C00140715  H2MD05155  

Pandas dataframe select rows where a list-column contains any of a list of strings

IIUC Re-create your df then using isin with any should be faster than apply

df[pd.DataFrame(df.species.tolist()).isin(selection).any(1).values]
Out[64]: 
  molecule            species
0        a              [dog]
2        c         [cat, dog]
3        d  [cat, horse, pig]

Check if a string in a Pandas DataFrame column is in a list of strings

frame = pd.DataFrame({'a' : ['the cat is blue', 'the sky is green', 'the dog is black']})

frame
                  a
0   the cat is blue
1  the sky is green
2  the dog is black

The str.contains method accepts a regular expression pattern:

mylist = ['dog', 'cat', 'fish']
pattern = '|'.join(mylist)

pattern
'dog|cat|fish'

frame.a.str.contains(pattern)
0     True
1    False
2     True
Name: a, dtype: bool

Because regex patterns are supported, you can also embed flags:

frame = pd.DataFrame({'a' : ['Cat Mr. Nibbles is blue', 'the sky is green', 'the dog is black']})

frame
                     a
0  Cat Mr. Nibbles is blue
1         the sky is green
2         the dog is black

pattern = '|'.join([f'(?i){animal}' for animal in mylist])  # python 3.6+

pattern
'(?i)dog|(?i)cat|(?i)fish'
 
frame.a.str.contains(pattern)
0     True  # Because of the (?i) flag, 'Cat' is also matched to 'cat'
1    False
2     True

Use a list of values to select rows from a Pandas dataframe

You can use the isin method:

In [1]: df = pd.DataFrame({'A': [5,6,3,4], 'B': [1,2,3,5]})

In [2]: df
Out[2]:
   A  B
0  5  1
1  6  2
2  3  3
3  4  5

In [3]: df[df['A'].isin([3, 6])]
Out[3]:
   A  B
1  6  2
2  3  3

And to get the opposite use ~:

In [4]: df[~df['A'].isin([3, 6])]
Out[4]:
   A  B
0  5  1
3  4  5

How to determine whether a Pandas Column contains a particular value

in of a Series checks whether the value is in the index:

In [11]: s = pd.Series(list('abc'))

In [12]: s
Out[12]: 
0    a
1    b
2    c
dtype: object

In [13]: 1 in s
Out[13]: True

In [14]: 'a' in s
Out[14]: False

One option is to see if it's in unique values:

In [21]: s.unique()
Out[21]: array(['a', 'b', 'c'], dtype=object)

In [22]: 'a' in s.unique()
Out[22]: True

or a python set:

In [23]: set(s)
Out[23]: {'a', 'b', 'c'}

In [24]: 'a' in set(s)
Out[24]: True

As pointed out by @DSM, it may be more efficient (especially if you're just doing this for one value) to just use in directly on the values:

In [31]: s.values
Out[31]: array(['a', 'b', 'c'], dtype=object)

In [32]: 'a' in s.values
Out[32]: True

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