Check if certain value is contained in a dataframe column in pandas [duplicate]

Answer 1

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  

Answer 2

You can use any:

print any(df.column == 07311954)
True       #true if it contains the number, false otherwise

If you rather want to see how many times '07311954' occurs in a column you can use:

df.column[df.column == 07311954].count()

Answer 3

You can simply use this:

'07311954' in df.date.values which returns True or False

---

Here is the further explanation:

In pandas, using in check directly with DataFrame and Series (e.g. val in df or val in series ) will check whether the val is contained in the Index.

BUT you can still use in check for their values too (instead of Index)! Just using val in df.col_name.values or val in series.values. In this way, you are actually checking the val with a Numpy array.

And .isin(vals) is the other way around, it checks whether the DataFrame/Series values are in the vals. Here vals must be set or list-like. So this is not the natural way to go for the question.