CODEWITHSUNDEEP
python-3.xpandaspandas-datareader
user5813071
user5813071

Reputation:

How to split Pandas string column into different rows?

Here is my issue. I have data like this:

data = {
    'name': ["Jack ;; Josh ;; John", "Apple ;; Fruit ;; Pear"], 
    'grade': [11, 12],
    'color':['black', 'blue']
}

df = pd.DataFrame(data)

It looks like:

                     name  grade  color
0    Jack ;; Josh ;; John     11  black
1  Apple ;; Fruit ;; Pear     12   blue

I want it to look like:

     name   age  color      
0    Jack   11   black
1    Josh   11   black
2    John   11   black
3    Apple  12   blue
4    Fruit  12   blue
5    Pear   12   blue

So first I'd need to split name by using ";;" and then explode that list into different rows

Upvotes: 1

Views: 53

Answers (2)

adhg
adhg

Reputation: 10883

You have 2 challenges:

  1. split the name with ;; into a list AND have each item in the list as a column such that:

    df['name']=df.name.str.split(';;') 
df_temp = df.name.apply(pd.Series)  
df = pd.concat([df[:], df_temp[:]], axis=1) 
df.drop('name', inplace=True, axis=1)

result:

    grade   color   0       1       2
0   11      black   Jack    Josh    John
1   12      blue    Apple   Fruit   Pear

  1. Melt the list to get desired result:

    df.melt(id_vars=["grade", "color"], 
    value_name="Name").sort_values('grade').drop('variable', axis=1)

desired result:

    grade   color   Name
0   11      black   Jack
2   11      black   Josh
4   11      black   John
1   12      blue    Apple
3   12      blue    Fruit
5   12      blue    Pear

Upvotes: 0

jezrael
jezrael

Reputation: 863166

Use Series.str.split with reshape by DataFrame.stack and add orriginal another columns by DataFrame.join:

c = df.columns
s = (df.pop('name')
       .str.split(' ;; ', expand=True)
       .stack()
       .reset_index(level=1, drop=True)
       .rename('name'))
df = df.join(s).reset_index(drop=True).reindex(columns=c)
print (df)
    name  grade  color
0   Jack     11  black
1   Josh     11  black
2   John     11  black
3  Apple     12   blue
4  Fruit     12   blue
5   Pear     12   blue

Upvotes: 3

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