CODEWITHSUNDEEP
pythonpandasdataframe
mattdonders
mattdonders

Reputation: 1356

Filter multiple dataframe columns on a list

I have a data frame that contains 5 Player columns which contain random player names. I want to be able to pass in a list of players and only return rows where both players are present in that row (across those 5 columns).

This is the code that generates the dataframe and successfully filters out rows that contain either person in the row. How would I go about making sure that the row contains both people?

random_events = ('SHOT', 'MISSED_SHOT', 'GOAL')
random_team = ('Preferred', 'Other')
events = list()

for i in range(6):
    event = dict()
    event['event_type'] = random.choice(random_events)
    event['team'] = random.choice(random_team)
    event['coords_x'] = round(random.uniform(-100, 100), 2)
    event['coords_y'] = round(random.uniform(-42.5, 42.5), 2)
    event['person_1'] = f'Person {random.randint(1, 2)}'
    event['person_2'] = f'Person {random.randint(3, 4)}'
    event['person_3'] = f'Person {random.randint(5, 6)}'
    event['person_4'] = f'Person {random.randint(7, 8)}'
    event['person_5'] = f'Person {random.randint(9, 10)}'
    events.append(event)

df = pd.DataFrame(events)
print(df)


filter_list = ['Person 1', 'Person 3']
filtered_df = df.loc[
    (df['person_1'].isin(filter_list)) |
    (df['person_2'].isin(filter_list)) |
    (df['person_3'].isin(filter_list)) |
    (df['person_4'].isin(filter_list)) |
    (df['person_5'].isin(filter_list))]

print(filtered_df)

This is the result I get -- showing rows with only Person 1 or Person 3, as well as Person 1 and Person 3 are returned. In the example below I would only want Row with Index 2 returned to me

   coords_x  coords_y   event_type  person_1  person_2  person_3  person_4   person_5       team
0     38.82    -39.18  MISSED_SHOT  Person 1  Person 4  Person 6  Person 7   Person 9   Preferred
2     94.43     30.13         GOAL  Person 1  Person 3  Person 5  Person 8   Person 9       Other
3    -68.38    -24.42  MISSED_SHOT  Person 2  Person 3  Person 5  Person 7  Person 10   Preferred
4     99.48     22.79         SHOT  Person 1  Person 4  Person 5  Person 7   Person 9   Preferred

Thank you in advance.

Upvotes: 1

Views: 627

Answers (1)

grge
grge

Reputation: 145

Here's a general approach. You'll probably want to play around with it depending on your exact situation.

# Define a list of all of the person columns in the dataframe
person_cols = [f'person_{i}' for i in [1, 2, 3, 4, 5]]

# Which rows contain Person 2 in any column? (creates a series of True or False)
(df[person_cols] == "Person 2").any(axis='columns')

# Which rows contain both Person 2 and Person 3? 
# This time I'm saving the series to use as a selection mask
mask = (
        (df[person_cols] == "Person 2").any(axis='columns') 
      & (df[person_cols] == "Person 3").any(axis='columns') 
)

# show just the rows where the mask above is True
print(df[mask])

Edit:

Setting up a mask for an arbitrary list of players who must all be present.

from operator import and_
from functools import reduce

players = ['Player 1', 'Player 3', 'Player 4']
filters = [(df[person_cols] == p).any(axis='columns') for p in players]
mask = reduce(and_, filters, True)

print(df[mask])

Upvotes: 2

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