how to extract rows of dataframe from user input

Question

data = {'Sample':['S1', 'S1', 'S1' ,'S1' ,'S2' ,'S2' ,'S3' ,'S3', 'S4', 'Negative', 'Positive', 'Negative',
                 'S1', 'S1', 'S1' ,'S2' ,'S2' ,'S2' ,'S3' ,'S4', 'S4', 'Positive', 'Positive', 'Negative'], 
       'Location':['A1', 'A2', 'A3' ,'A4' ,'A5' ,'A6' ,'A7' ,'A8', 'A9', 'A10', 'A11', 'A12',
                   'B1', 'B2', 'B3' ,'B4' ,'B5' ,'B6' ,'B7' ,'B8', 'B9', 'B10', 'B11', 'B12'],
    'Repeat Number':['1', '2', '3' ,'4' ,'1' ,'2' ,'1' ,'2', '1', '1', '1', '2',
                  '1', '2', '3' ,'1' ,'2' ,'3' ,'1' ,'1', '2', '1', '2', '1',],
   'Identifier' :['asd01', 'asd02', 'asd03', 'asd04', 'asd05', 'asd06', 'asd07', 'asd08', 'asd09'
                 ,'asd10' ,'asd11' ,'asd12' ,'asd13' ,'asd14' ,'asd15', 'asd16', 'asd17', 'asd18',
                 'asd19', 'asd20', 'asd21', 'asd22', 'asd23', 'asd24']}

df1 = pd.DataFrame(data)

In the frame above, there are 4 S1 in location group A and they are repeats because they are in same location group A. For location B, there are 3 S1 and they are repeats because they are in same location group B. So they are given the repeat number(1,2,3,...).

For the example code above, I want to extract row for itself and its repeats when I give user input for 'Sample', 'Location'.

For instance, If I input Negative for 'Sample' and A for 'Location', Ideal result would look like this:

data = {'Sample':[ 'Negative', 'Negative'], 
       'Location':[ 'A10',  'A12'],
    'Repeat Number':[ '1', '2'],
   'Identifier' : ['asd10' ,'asd12']}

Also, I want to know how to only extract Identifier after the row selection.

I tried using df.loc[] but I dont know how to make user input on this since the inputs contain strings

Dhiraj Bansal · Accepted Answer · 2020-09-01 04:01:46Z

3

Using below code, you would be able to extract data from dataframe:

sample = input('Enter Sample: ')
location = input('Enter Location: ')
df.loc[(df['Sample'] == sample) & (df['Location'].str.contains(location))]

This is output of above code:

Enter Sample: S2
Enter Location: B

    Sample  Location    Repeat Number   Identifier
15  S2  B4  1   asd16
16  S2  B5  2   asd17
17  S2  B6  3   asd18

answered Sep 1, 2020 at 4:01

Dhiraj Bansal

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Comments

Henry Yik · Accepted Answer · 2020-09-01 03:59:40Z

3

Just chain your conditions and use to_dict("list"):

print (df.loc[df["Sample"].eq("Negative")&df["Location"].str.contains("A")].to_dict("list"))

#{'Sample': ['Negative', 'Negative'], 'Location': ['A10', 'A12'], 'Repeat Number': ['1', '2'], 'Identifier': ['asd10', 'asd12']}

answered Sep 1, 2020 at 3:59

Henry Yik

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Comments

Sayan Dey · Accepted Answer · 2020-09-01 04:04:25Z

1

try this:

df[(df.Sample=='Negative') & (df.Location.str.startswith('A'))]

answered Sep 1, 2020 at 4:04

Sayan Dey

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Comments

Bruno Vermeulen · Accepted Answer · 2020-09-01 04:38:00Z

The following would work. I believe in this case str.startswith is better suited than str.contains:

import pandas as pd

data = {
    'Sample': [
        'S1', 'S1', 'S1', 'S1', 'S2', 'S2', 'S3', 'S3', 'S4', 'Negative', 'Positive', 'Negative',
        'S1', 'S1', 'S1', 'S2', 'S2', 'S2', 'S3', 'S4', 'S4', 'Positive', 'Positive', 'Negative'
    ],
    'Location': [
        'A1', 'A2', 'A3', 'A4', 'A5', 'A6', 'A7', 'A8', 'A9', 'A10', 'A11', 'A12',
        'B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B9', 'B10', 'B11', 'B12'
    ],
    'Repeat Number': [
        '1', '2', '3', '4', '1', '2', '1', '2', '1', '1', '1', '2',
        '1', '2', '3', '1', '2', '3', '1', '1', '2', '1', '2', '1'
    ],
    'Identifier': [
        'asd01', 'asd02', 'asd03', 'asd04', 'asd05', 'asd06', 'asd07', 'asd08', 'asd09',
        'asd10', 'asd11', 'asd12', 'asd13', 'asd14', 'asd15', 'asd16', 'asd17', 'asd18',
        'asd19', 'asd20', 'asd21', 'asd22', 'asd23', 'asd24'
    ]
}


location_start = 'A'
sample_result = 'Negative'

df1 = pd.DataFrame(data)

# filter on the two criteria
df2 = df1[df1['Location'].str.startswith(location_start, na=False) & (df1['Sample'] == sample_result)]

print(df2)

      Sample Location Repeat Number Identifier
9   Negative      A10             1      asd10
11  Negative      A12             2      asd12

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