Check if string is in a pandas dataframe

I would like to see if a particular string exists in a particular column within my dataframe.

I'm getting the error

> ValueError: The truth value of a Series is ambiguous. Use a.empty, > a.bool(), a.item(), a.any() or a.all().

import pandas as pd

BabyDataSet = [('Bob', 968), ('Jessica', 155), ('Mary', 77), ('John', 578), ('Mel', 973)]

a = pd.DataFrame(data=BabyDataSet, columns=['Names', 'Births'])

if a['Names'].str.contains('Mel'):
    print ("Mel is there")

a['Names'].str.contains('Mel') will return an indicator vector of boolean values of size len(BabyDataSet)

Therefore, you can use

mel_count=a['Names'].str.contains('Mel').sum()
if mel_count>0:
    print ("There are {m} Mels".format(m=mel_count))

Or any(), if you don't care how many records match your query

if a['Names'].str.contains('Mel').any():
    print ("Mel is there")

You should use any()

In [98]: a['Names'].str.contains('Mel').any()
Out[98]: True

In [99]: if a['Names'].str.contains('Mel').any():
   ....:     print "Mel is there"
   ....:
Mel is there

a['Names'].str.contains('Mel') gives you a series of bool values

In [100]: a['Names'].str.contains('Mel')
Out[100]:
0    False
1    False
2    False
3    False
4     True
Name: Names, dtype: bool

OP meant to find out whether the string 'Mel' exists in a particular column, not contained in any string in the column. Therefore the use of contains is not needed, and is not efficient.

A simple equals-to is enough:

df = pd.DataFrame({"names": ["Melvin", "Mel", "Me", "Mel", "A.Mel"]})

mel_count = (df['names'] == 'Mel').sum() 
print("There are {num} instances of 'Mel'. ".format(num=mel_count)) 
 
mel_exists = (df['names'] == 'Mel').any() 
print("'Mel' exists in the dataframe.".format(num=mel_exists)) 

mel_exists2 = 'Mel' in df['names'].values 
print("'Mel' is in the dataframe: " + str(mel_exists2)) 

Prints:

There are 2 instances of 'Mel'. 
'Mel' exists in the dataframe.
'Mel' is in the dataframe: True

I bumped into the same problem, I used:

if "Mel" in a["Names"].values:
    print("Yep")

But this solution may be slower since internally pandas create a list from a Series.

If there is any chance that you will need to search for empty strings,

    a['Names'].str.contains('') 

will NOT work, as it will always return True.

Instead, use

    if '' in a["Names"].values

to accurately reflect whether or not a string is in a Series, including the edge case of searching for an empty string.

Pandas seem to be recommending df.to_numpy since the other methods still raise a FutureWarning: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_numpy.html#pandas.DataFrame.to_numpy

So, an alternative that would work int this case is:

b=a['Names']
c = b.to_numpy().tolist()
if 'Mel' in c:
     print("Mel is in the dataframe column Names")

For case-insensitive search.

a['Names'].str.lower().str.contains('mel').any()

If you want to save the results then you can use this:

a['result'] = a['Names'].apply(lambda x : ','.join([item for item in str(x).split() if item.lower() in ['mel', 'etc']]))

import pandas as pd

(data_frame.col_name=='str_name_to_check').sum()

import re
s = 'string'

df['Name'] = df['Name'].str.findall(s, flags = re.IGNORECASE)

#or
df['Name'] = df[df['Name'].isin(['string1', 'string2'])]

You should check the value of your line of code like adding checking length of it.

if(len(a['Names'].str.contains('Mel'))>0):
    print("Name Present")