Python & Pandas: How to query if a list-type column contains something?

Question

I have a dataframe, which contains info about movies. It has a column called genre, which contains a list of genres it belongs to. For example:

Answer 1

According to the source code, you can use .str.contains(..., regex=False).

Answer 2

One liner using boolean indexing and list comprehension:

searchTerm = 'something'
df[[searchTerm in x for x in df['arrayColumn']]]

Answer 3

A complete example:

import pandas as pd

data = pd.DataFrame([[['foo', 'bar']],
                    [['bar', 'baz']]], columns=['list_column'])
print(data)
  list_column
0  [foo, bar]
1  [bar, baz]

filtered_data = data.loc[
    lambda df: df.list_column.apply(
        lambda l: 'foo' in l
    )
]
print(filtered_data)
  list_column
0  [foo, bar]

Answer 4

You can use apply for create mask and then boolean indexing:

mask = df.genre.apply(lambda x: 'comedy' in x)
df1 = df[mask]
print (df1)
                       genre
0           [comedy, sci-fi]
1  [action, romance, comedy]

Answer 5

using sets

df.genre.map(set(['comedy']).issubset)

0     True
1     True
2    False
3    False
dtype: bool

---

df.genre[df.genre.map(set(['comedy']).issubset)]

0             [comedy, sci-fi]
1    [action, romance, comedy]
dtype: object

---

presented in a way I like better

comedy = set(['comedy'])
iscomedy = comedy.issubset
df[df.genre.map(iscomedy)]

more efficient

comedy = set(['comedy'])
iscomedy = comedy.issubset
df[[iscomedy(l) for l in df.genre.values.tolist()]]

using str in two passes
slow! and not perfectly accurate!

df[df.genre.str.join(' ').str.contains('comedy')]