I'm a bit confused regarding the best way to check a pandas dataframe column for items.
I am writing a program whereby if the dataframe has elements in a certain column which are not allowed, an error is raised.
Here's an example:
import pandas as pd
raw_data = {'first_name': ['Jay', 'Jason', 'Tina', 'Jake', 'Amy'],
'last_name': ['Jones', 'Miller', 'Ali', 'Milner', 'Cooze'],
'age': [47, 42, 36, 24, 73],
'preTestScore': [4, 4, 31, 2, 3],
'postTestScore': [27, 25, 57, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'preTestScore', 'postTestScore'])
print(df)
which outputs
first_name last_name age preTestScore postTestScore
0 Jay Jones 47 4 27
1 Jason Miller 42 4 25
2 Tina Ali 36 31 57
3 Jake Milner 24 2 62
4 Amy Cooze 73 3 70
If column last_name contains anything besides Jones, Miller, Ali, Milner, or Cooze, raise a warning.
One could possibly use pandas.DataFrame.isin, but it's not clear to me this is the most efficient approach.
Something like:
if df.isin('last_name':{'Jones', 'Miller', 'Ali', 'Milner', 'Cooze'}).any() == False:
raise:
ValueError("Column `last_name` includes ill-formed elements.")
I think you can use all for check if match all values:
if not df['last_name'].isin(['Jones', 'Miller', 'Ali', 'Milner', 'Cooze']).all():
raise ValueError("Column `last_name` includes ill-formed elements.")
Another solution with issubset:
if not set(['Jones', 'Miller', 'Ali', 'Milner', 'Cooze']).issubset(df['last_name']):
raise ValueError("Column `last_name` includes ill-formed elements.")
Timings:
np.random.seed(123)
N = 10000
L = list('abcdefghijklmno')
df = pd.DataFrame({'last_name': np.random.choice(L, N)})
print (df)
In [245]: %timeit df['last_name'].isin(L).all()
The slowest run took 4.73 times longer than the fastest. This could mean that an intermediate result is being cached.
1000 loops, best of 3: 421 µs per loop
In [247]: %timeit set(L).issubset(df['last_name'])
The slowest run took 4.50 times longer than the fastest. This could mean that an intermediate result is being cached.
1000 loops, best of 3: 273 µs per loop
In [248]: %timeit df.loc[~df['last_name'].isin(L), 'last_name'].any()
1000 loops, best of 3: 562 µs per loop
Caveat:
Performance really depend on the data - number of rows and number of non matched values.
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