How to determine whether a Pandas Column contains a particular value
Question:
I am trying to determine whether there is an entry in a Pandas column that has a particular value. I tried to do this with if x in df['id']. I thought this was working, except when I fed it a value that I knew was not in the column 43 in df['id'] it still returned True. When I subset to a data frame only containing entries matching the missing id df[df['id'] == 43] there are, obviously, no entries in it. How to I determine if a column in a Pandas data frame contains a particular value and why doesn’t my current method work? (FYI, I have the same problem when I use the implementation in this answer to a similar question).
Answers:
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
You can also use pandas.Series.isin although it’s a little bit longer than 'a' in s.values:
In [2]: s = pd.Series(list('abc'))
In [3]: s
Out[3]:
0 a
1 b
2 c
dtype: object
In [3]: s.isin(['a'])
Out[3]:
0 True
1 False
2 False
dtype: bool
In [4]: s[s.isin(['a'])].empty
Out[4]: False
In [5]: s[s.isin(['z'])].empty
Out[5]: True
But this approach can be more flexible if you need to match multiple values at once for a DataFrame (see DataFrame.isin)
>>> df = DataFrame({'A': [1, 2, 3], 'B': [1, 4, 7]})
>>> df.isin({'A': [1, 3], 'B': [4, 7, 12]})
A B
0 True False # Note that B didn't match 1 here.
1 False True
2 True True
Simple condition:
if any(str(elem) in ['a','b'] for elem in df['column'].tolist()):
Or use Series.tolist or Series.any:
>>> s = pd.Series(list('abc'))
>>> s
0 a
1 b
2 c
dtype: object
>>> 'a' in s.tolist()
True
>>> (s=='a').any()
True
Series.tolist makes a list about of a Series, and the other one i am just getting a boolean Series from a regular Series, then checking if there are any Trues in the boolean Series.
found = df[df['Column'].str.contains('Text_to_search')]
print(found.count())
the found.count() will contains number of matches
And if it is 0 then means string was not found in the Column.
I did a few simple tests:
In [10]: x = pd.Series(range(1000000))
In [13]: timeit 999999 in x.values
567 µs ± 25.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [24]: timeit 9 in x.values
666 µs ± 15.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [16]: timeit (x == 999999).any()
6.86 ms ± 107 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [21]: timeit x.eq(999999).any()
7.03 ms ± 33.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [22]: timeit x.eq(9).any()
7.04 ms ± 60 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [15]: timeit x.isin([999999]).any()
9.54 ms ± 291 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [17]: timeit 999999 in set(x)
79.8 ms ± 1.98 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
Interestingly it doesn’t matter if you look up 9 or 999999, it seems like it takes about the same amount of time using the in syntax (must be using some vectorized computation)
In [24]: timeit 9 in x.values
666 µs ± 15.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [25]: timeit 9999 in x.values
647 µs ± 5.21 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [26]: timeit 999999 in x.values
642 µs ± 2.11 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [27]: timeit 99199 in x.values
644 µs ± 5.31 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [28]: timeit 1 in x.values
667 µs ± 20.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
Seems like using x.values is the fastest, but maybe there is a more elegant way in pandas?
Use
df[df['id']==x].index.tolist()
If x is present in id then it’ll return the list of indices where it is present, else it gives an empty list.
Suppose you dataframe looks like :
Now you want to check if filename “80900026941984” is present in the dataframe or not.
You can simply write :
if sum(df["filename"].astype("str").str.contains("80900026941984")) > 0:
print("found")
You can try this to check a particular value ‘x’ in a particular column named ‘id’
if x in df['id'].values
I had a CSV file to read:
df = pd.read_csv('50_states.csv')
And after trying:
if value in df.column:
print(True)
which never printed true, even though the value was in the column;
I tried:
for values in df.column:
if value == values:
print(True)
#Or do something
else:
print(False)
Which worked. I hope this can help!
I am trying to determine whether there is an entry in a Pandas column that has a particular value. I tried to do this with if x in df['id']. I thought this was working, except when I fed it a value that I knew was not in the column 43 in df['id'] it still returned True. When I subset to a data frame only containing entries matching the missing id df[df['id'] == 43] there are, obviously, no entries in it. How to I determine if a column in a Pandas data frame contains a particular value and why doesn’t my current method work? (FYI, I have the same problem when I use the implementation in this answer to a similar question).
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
You can also use pandas.Series.isin although it’s a little bit longer than 'a' in s.values:
In [2]: s = pd.Series(list('abc'))
In [3]: s
Out[3]:
0 a
1 b
2 c
dtype: object
In [3]: s.isin(['a'])
Out[3]:
0 True
1 False
2 False
dtype: bool
In [4]: s[s.isin(['a'])].empty
Out[4]: False
In [5]: s[s.isin(['z'])].empty
Out[5]: True
But this approach can be more flexible if you need to match multiple values at once for a DataFrame (see DataFrame.isin)
>>> df = DataFrame({'A': [1, 2, 3], 'B': [1, 4, 7]})
>>> df.isin({'A': [1, 3], 'B': [4, 7, 12]})
A B
0 True False # Note that B didn't match 1 here.
1 False True
2 True True
Simple condition:
if any(str(elem) in ['a','b'] for elem in df['column'].tolist()):
Or use Series.tolist or Series.any:
>>> s = pd.Series(list('abc'))
>>> s
0 a
1 b
2 c
dtype: object
>>> 'a' in s.tolist()
True
>>> (s=='a').any()
True
Series.tolist makes a list about of a Series, and the other one i am just getting a boolean Series from a regular Series, then checking if there are any Trues in the boolean Series.
found = df[df['Column'].str.contains('Text_to_search')]
print(found.count())
the found.count() will contains number of matches
And if it is 0 then means string was not found in the Column.
I did a few simple tests:
In [10]: x = pd.Series(range(1000000))
In [13]: timeit 999999 in x.values
567 µs ± 25.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [24]: timeit 9 in x.values
666 µs ± 15.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [16]: timeit (x == 999999).any()
6.86 ms ± 107 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [21]: timeit x.eq(999999).any()
7.03 ms ± 33.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [22]: timeit x.eq(9).any()
7.04 ms ± 60 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [15]: timeit x.isin([999999]).any()
9.54 ms ± 291 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [17]: timeit 999999 in set(x)
79.8 ms ± 1.98 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
Interestingly it doesn’t matter if you look up 9 or 999999, it seems like it takes about the same amount of time using the in syntax (must be using some vectorized computation)
In [24]: timeit 9 in x.values
666 µs ± 15.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [25]: timeit 9999 in x.values
647 µs ± 5.21 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [26]: timeit 999999 in x.values
642 µs ± 2.11 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [27]: timeit 99199 in x.values
644 µs ± 5.31 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [28]: timeit 1 in x.values
667 µs ± 20.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
Seems like using x.values is the fastest, but maybe there is a more elegant way in pandas?
Use
df[df['id']==x].index.tolist()
If x is present in id then it’ll return the list of indices where it is present, else it gives an empty list.
Suppose you dataframe looks like :
Now you want to check if filename “80900026941984” is present in the dataframe or not.
You can simply write :
if sum(df["filename"].astype("str").str.contains("80900026941984")) > 0:
print("found")
You can try this to check a particular value ‘x’ in a particular column named ‘id’
if x in df['id'].values
I had a CSV file to read:
df = pd.read_csv('50_states.csv')
And after trying:
if value in df.column:
print(True)
which never printed true, even though the value was in the column;
I tried:
for values in df.column:
if value == values:
print(True)
#Or do something
else:
print(False)
Which worked. I hope this can help!