CODEWITHSUNDEEP
pythonpandas
Chris
Chris

Reputation: 13660

Test if value in a Pandas series is in an array

This seems like the answer will be something obvious but...

I have a series like this:

Dataset['Variable'] = ['a','b','b','a','e','c','d']

and a list like this:

List_vals1 = ['a','b']
List_vals2 = ['e','c','d']

I want to create 2 new variables in the dataset to see if the value of Dataset['Variable'] is in each list.

Dataset['Var_for_List_vals1'] = ['1','1','1','1','0','0','0']
Dataset['Var_for_List_vals2'] = ['0','0','0','0','1','1','1']

I tried to do this:

Dataset['Var_for_List_vals1'] = (Dataset[Dataset['Variable' in List_vals1]])*1 (times 1 to convert to numeric)

and python did not like that solution.

This seems like an obvious one but nothing seems to work for me. Thanks in advance for the help!

Upvotes: 1

Views: 537

Answers (2)

CT Zhu
CT Zhu

Reputation: 54330

Note that using map will become much slower than the numpy.in1d method, when data dimension gets large:

In [1]:

import pandas as pd
import numpy as np
In [7]:

df = pd.DataFrame({'Variable': ['a','b','b','a','e','c','d']*100}) #700 lines of data
List_vals1 = ['a','b']
List_vals2 = ['e','c','d']
In [8]:

df['var_for_List_vals1'] = np.in1d(df.Variable, List_vals1)
#return Boolean values
In [9]:

%timeit np.in1d(df.Variable, List_vals1)
10000 loops, best of 3: 112 µs per loop
In [10]:

%timeit map(lambda x: 1 if x in List_vals1 else 0, df['Variable'])
1000 loops, best of 3: 287 µs per loop

See doc here

Upvotes: 1

Anzel
Anzel

Reputation: 20553

Use map and lambda can achieve what you need:

# I believe you want 1, 0 not '1', '0'.
Dataset['var_for_List_vals1'] = map(lambda x: 1 if x in List_vals1 else 0, Dataset['Variable'])
Dataset['var_for_List_vals1']
[1, 1, 1, 1, 0, 0, 0]

Dataset['var_for_List_vals2'] = map(lambda x: 1 if x in List_vals2 else 0, Dataset['Variable'])   
Dataset['var_for_List_vals2']
[0, 0, 0, 0, 1, 1, 1]

If you do want '1' and '0' feel free to change ...(lambda x: '1' ... else '0')

Upvotes: 1

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