CODEWITHSUNDEEP
pythonlistdataframe
user20777609
user20777609

Reputation: 33

find the rows with more than 4 values in a list in a column

The dataframe I have, df:

      name        list

0    kfjh       [[a,b,c],[d,f,h],[g,k,l]]
1    jhkg       [[a,b,c],[d,f,h],[g,k,l],[f,k,j]]
2    khfg       [[a,b,c],[g,k,l]]
3    khkjgr     [[a,b,c],[d,f,h]]
4    kjrgjg     [[d,f,h]]
5    jkdgr      [[a,b,c],[d,f,h],[g,k,l, [g,j,l],[f,l,p]]
6    hgyr       [[a,b,c],[d,kf,h],[g,k,l, [g,j,l],[f,l,p]]
7    jkgtjd     [[f,l,p]]
8    nkjgrd     [t,t,i]

if the list has more than 4 list, then I would like to get df1. The desired output, df1 :

    name              list


5    jkdgr      [[a,b,c],[d,f,h],[g,k,l, [g,j,l],[f,l,p]]
6    hgyr       [[a,b,c],[d,kf,h],[g,k,l, [g,j,l],[f,l,p]]

and, df2:

     name        list

0    kfjh       [[a,b,c],[d,f,h],[g,k,l]]
1    jhkg       [[a,b,c],[d,f,h],[g,k,l],[f,k,j]]
2    khfg       [[a,b,c],[g,k,l]]
3    khkjgr     [[a,b,c],[d,f,h]]
4    kjrgjg     [[d,f,h]]
7    jkgtjd     [[f,l,p]]
8    nkjgrd     [t,t,i]

Upvotes: 0

Views: 120

Answers (3)

user19077881
user19077881

Reputation: 5420

You can use map(len) to give the number of elements in a List in a column. So you could use:

df1 = df[df['list'].map(len) > 4]
df2 = df[df['list'].map(len) <= 4]

which gives the two sets of results you present

Upvotes: 0

Jonatas Delatorre
Jonatas Delatorre

Reputation: 198

You can do something like this if column list is a string. if the list is list of lists with every element as a string, you can change the split for only len of the array and compare to 4 to do it.

import pandas as pd

data = {
    'name': ['kfjh', 'jhkg', 'khfg', 'khkjgr', 'kjrgjg', 'jkdgr', 'hgyr', 'jkgtjd', 'nkjgrd'],
    'list': ['[[a,b,c],[d,f,h],[g,k,l]]', '[[a,b,c],[d,f,h],[g,k,l],[f,k,j]]', '[[a,b,c],[g,k,l]]', '[[a,b,c],[d,f,h]]', '[[d,f,h]]', '[[a,b,c],[d,f,h],[g,k,l],[g,j,l],[f,l,p]]', '[[a,b,c],[d,f,h],[g,kf,l],[g,j,l],[f,l,p]]', '[[f,l,p]]', '[t,t,i]']
}

df = pd.DataFrame(data)

df['drop'] = df.apply(lambda row : 'no' if len(row['list'].split('[')) > 6 else 'yes', axis = 1)

df1 = df.loc[df['drop'] == 'yes']
df2 = df.loc[df['drop'] == 'no']

df1 = df1.drop(columns=['drop'])
df2 = df2.drop(columns=['drop'])

print(df1)
print(df2)

enter image description here

Upvotes: 1

jjislam
jjislam

Reputation: 563

Try this:

from ast import literal_eval

df.list.apply(literal_eval)

Upvotes: 0

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