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")
a['Names'], use the na parameter of the contains() function. pandas.pydata.org/pandas-docs/stable/reference/api/…
str.contains('Mel') matches on every substring of every row in dataframe column. So ABCMelABC == Mel.
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
'Mel' in df['names'] to work?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.
For case-insensitive search.
a['Names'].str.lower().str.contains('mel').any()
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")
import re
s = 'string'
df['Name'] = df['Name'].str.findall(s, flags = re.IGNORECASE)
#or
df['Name'] = df[df['Name'].isin(['string1', 'string2'])]
import pandas as pd
(data_frame.col_name=='str_name_to_check').sum()
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']]))
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")
