How to create a list from Pandas Series?-CodePudding

My dataframe looks like this. I am trying to create a list of Names. For ex: ["Mike", "Jean"]:

0. Mike, Jean
1. May, Weather
2. Jack, 100

What I've tried:

df["NAME"] = df["NAME"].str.split(",")
for i in range(len(df["NAME"])):
    df["NAME"][i] = df["NAME"][i] .split(",")

OUTPUT

0. [Mike, Jean]
1. [May, Weather]
2. [Jack, 100]

OUTPUT I WANT

0. ["Mike", "Jean"]
1. ["May", "Weather"]
2. ["Jack", "100"]

I am new to Python and Pandas.

CodePudding user response：

Assuming this input:

df = pd.DataFrame({'Name': ['Mike, Jean', 'May, Weather', 'Jack, 100']})

When you run:

df['Name'].str.split(', ')

and get:

0      [Mike, Jean]
1    [May, Weather]
2       [Jack, 100]
Name: Name, dtype: object

The [Mike, Jean] format is just a representation.

The real data is indeed a Series of lists, as show by an explicit conversion of the Series to list:

df['Name'].str.split(', ').to_list()

output:

[['Mike', 'Jean'],
 ['May', 'Weather'],
 ['Jack', '100']]

CodePudding user response：

You don't really need to use a for-loop, you can do the split with:

df['Name'] = df['Name'].str.split()

This will return a pandas series containing a list per row, such as:

0 ["Mike", "Jean"]
1 ["May", "Weather"]
2 ["Jack", "100"]

If you wish to extract the Series' values as list itself then you can use:

name_lists = df['Name'].str.split().values.tolist()

Returning:

[["Mike","Jean"],["May","Weather"],["Jack","100"]]

CodePudding user response：

You can iterate over the values of your dataframe and easily convert the rows of the resulting Numpy array into lists (cf example code below)

import pandas as pd

df = pd.DataFrame()

df['Name'] = ['Name1', 'Name2']
df['FirstName'] = ['FirstName1', 'FirstName2']

L = []
for row in df.values:
    L.append(list(row))
    
print(L)

Cheers

CodePudding user response：

Code snippet below should solve your purpose :)

import pandas as pd

df = pd.DataFrame(["[Mike, Jean]" , "[May, Weather]", "[Jack, 100]"], columns=['name'])

df.head()

             name
0    [Mike, Jean]
1  [May, Weather]
2     [Jack, 100]

df['type_name']  = df.apply(lambda y: type(y['name']), axis=1)

df['name1'] = df.apply(lambda y: y['name'].replace('[', '').replace(']', '').split(", "), axis=1)

df['type_name1']  = df.apply(lambda y: type(y['name1']), axis=1)

df.head()

             name      type_name           name1      type_name1
0    [Mike, Jean]  <class 'str'>    [Mike, Jean]  <class 'list'>
1  [May, Weather]  <class 'str'>  [May, Weather]  <class 'list'>
2     [Jack, 100]  <class 'str'>     [Jack, 100]  <class 'list'>

final_list = df['name1'].values.tolist()

print(final_list)

[['Mike', 'Jean'], ['May', 'Weather'], ['Jack', '100']]