CODEWITHSUNDEEP
pythonpandasdataframefiltertuples
R_abcdefg
R_abcdefg

Reputation: 165

How to filter a pandas DataFrame according to a list of tuples?

How to filter dataframe from a set of tuples, so that the pairing is the same? I need a more elegant way of writing. Im trying not to use merge because it will make it less efficient.

So I have a list of tuple called tup_list: [('118', '35'), ('35', '35'), ('118', '202') Assuming the first element in each tuple is A, and the second is B, I am trying to filter my dataframe according to this tup_list, where the pairing needs to be the same.

Original dataframe:

A   B
118 35
118 40
35  202
118 1
35  35

After filtering according to the tup_list, the new dataframe should be:

A   B
118 35
35  35

Only exact pairings should be returned.

Currently Im using df= df.merge(tup_list, on=['A','B'], how='inner'). But is not very efficient as my actual data is larger.

Please advise on more efficient way of writing.

Upvotes: 7

Views: 4640

Answers (3)

Neo
Neo

Reputation: 627

With your tup_list and dataframe named df here is a one liner for the requested output:

df[[x in tup_list for x in list(zip(df.A,df.B))]]

Upvotes: 4

It_is_Chris
It_is_Chris

Reputation: 14103

use boolean indexing:

tup_list = [(118, 35), (35, 35), (118, 202)]
df[pd.Series(list(zip(df['A'], df['B']))).isin(tup_list)]

    A   B
0   118 35
4   35  35

list(zip(df['A'], df['B'])) turns your two columns into a list of tuples:

[(118, 35), (118, 40), (35, 202), (118, 1), (35, 35)]

which you are turning into a series and using isin to return a boolean:

0     True
1    False
2    False
3    False
4     True
dtype: bool

Which can be used in boolean indexing

Upvotes: 8

b-fg
b-fg

Reputation: 4137

With pandas.DataFrame.query you can also filter your dataframe according to your list of tuples

import numpy as np
import pandas as pd

f = [('118', '35'), ('35', '35'), ('118', '202')]
idxs = [df.query('A=='+ t[0] + ' and B==' + t[1]).index.values for t in f]
idxs = np.concatenate(idxs).ravel().tolist()
df2 = df.iloc[idxs,:]
print(df2)
#      A   B
# 0  118  35
# 4   35  35

Upvotes: 0

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