Split (explode) pandas dataframe string entry to separate rows

Question

I have a pandas dataframe in which one column of text strings contains comma-separated values. I want to split each CSV field and create a new row per entry (as

Answer 1

TL;DR

import pandas as pd
import numpy as np

def explode_str(df, col, sep):
    s = df[col]
    i = np.arange(len(s)).repeat(s.str.count(sep) + 1)
    return df.iloc[i].assign(**{col: sep.join(s).split(sep)})

def explode_list(df, col):
    s = df[col]
    i = np.arange(len(s)).repeat(s.str.len())
    return df.iloc[i].assign(**{col: np.concatenate(s)})

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Demonstration

explode_str(a, 'var1', ',')

  var1  var2
0    a     1
0    b     1
0    c     1
1    d     2
1    e     2
1    f     2

Let's create a new dataframe d that has lists

d = a.assign(var1=lambda d: d.var1.str.split(','))

explode_list(d, 'var1')

  var1  var2
0    a     1
0    b     1
0    c     1
1    d     2
1    e     2
1    f     2

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General Comments

I'll use np.arange with repeat to produce dataframe index positions that I can use with iloc.

FAQ

Why don't I use loc?

Because the index may not be unique and using loc will return every row that matches a queried index.

Why don't you use the values attribute and slice that?

When calling values, if the entirety of the the dataframe is in one cohesive "block", Pandas will return a view of the array that is the "block". Otherwise Pandas will have to cobble together a new array. When cobbling, that array must be of a uniform dtype. Often that means returning an array with dtype that is object. By using iloc instead of slicing the values attribute, I alleviate myself from having to deal with that.

Why do you use assign?

When I use assign using the same column name that I'm exploding, I overwrite the existing column and maintain its position in the dataframe.

Why are the index values repeat?

By virtue of using iloc on repeated positions, the resulting index shows the same repeated pattern. One repeat for each element the list or string.
This can be reset with reset_index(drop=True)

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For Strings

I don't want to have to split the strings prematurely. So instead I count the occurrences of the sep argument assuming that if I were to split, the length of the resulting list would be one more than the number of separators.

I then use that sep to join the strings then split.

def explode_str(df, col, sep):
    s = df[col]
    i = np.arange(len(s)).repeat(s.str.count(sep) + 1)
    return df.iloc[i].assign(**{col: sep.join(s).split(sep)})

For Lists

Similar as for strings except I don't need to count occurrences of sep because its already split.

I use Numpy's concatenate to jam the lists together.

import pandas as pd
import numpy as np

def explode_list(df, col):
    s = df[col]
    i = np.arange(len(s)).repeat(s.str.len())
    return df.iloc[i].assign(**{col: np.concatenate(s)})

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