converting list like column values into multiple rows using Pandas DataFrame

Question

CSV file: (sample1.csv)

Location_City, Location_State, Name, hobbies
Los Angeles,   CA,             John, "['Music', 'Running']"
Texas,         TX,             Jack, "['Swimming', 'Trekking']"

I want to convert hobbies column of CSV into following output

Location_City, Location_State, Name, hobbies
Los Angeles,   CA,             John, Music
Los Angeles,   CA,             John, Running
Texas,         TX,             Jack, Swimming
Texas,         TX,             Jack, Trekking

I have read csv into dataframe but I don't know how to convert it?

 data = pd.read_csv("sample1.csv") 
 df=pd.DataFrame(data)
 df

Answer 1

We can solve this using pandas.DataFrame.explode function which was introduced in version 0.25.0 if you have same or higher version, you can use below code.
explode function reference: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.explode.html

import pandas as pd
import ast

data = {
    'Location_City': ['Los Angeles','Texas'],
    'Location_State': ['CA','TX'],
    'Name': ['John','Jack'],
    'hobbies': ["['Music', 'Running']", "['Swimming', 'Trekking']"]
}
df = pd.DataFrame(data)

# Converting a string representation of a list into an actual list object

list_eval = lambda x: ast.literal_eval(x)
df['hobbies'] = df['hobbies'].apply(list_eval)

# Exploding the list
df = df.explode('hobbies')

print(df)

  Location_City Location_State  Name   hobbies
0   Los Angeles             CA  John     Music
0   Los Angeles             CA  John   Running
1         Texas             TX  Jack  Swimming
1         Texas             TX  Jack  Trekking