adding new column assigning other column first value from list-CodePudding

hello i have got a situation here. got dataset and in several 'price' columns there are two values instead of one. looks like "$1.99 64.78" etc.. data look like this

my code looks like

no_na['Kaina'] = no_na['price'].str.split(' ').astype('str')
no_na

this creates new column and adds list of prices. the question is how to add only one element from list to a new column? tried to use pop but got errors

well it worked with

no_na['Kaina'] = no_na['price'].apply(lambda x: x[0:5]).astype(float)
no_na

but still i believe there should be a better way, because if price is 111.11 the result would be 111.1 i guess. now looks like this

CodePudding user response：

I give an example on my data. A dataframe is created and the first element is taken through the slice. The apply function is used to retrieve a value from a list.

import pandas as pd

df = pd.DataFrame({'aaa':[1, 5, 7, 10, 15],
                   'bbb':['16.99 64.78', '16.99 18.99',
                          '111', '5', '15.74 555']})


df['ddd'] = df['bbb'].str.split(' ').str[:1].apply(lambda a: a[0])
print(df)

Output

   aaa          bbb    ddd
0    1  16.99 64.78  16.99
1    5  16.99 18.99  16.99
2    7          111    111
3   10            5      5
4   15    15.74 555  15.74

CodePudding user response：

You can take your first item on your column with this formula:

no_na['price'] = no_na['kaina'].apply(lambda x: x[0])
no_na

your dataframe will be like this

if you want float, you only need to remove "$" and change the type

no_na['price'] = no_na['kaina'].apply(lambda x: x[0]).str.replace("$","").astype(float)
no_na