To split (explode) a Pandas dataframe string entry to separate rows, you can use either of the following approaches:

Solution 1: Using the explode method

If you have Pandas version 0.25 or newer, you can use the explode method to explode a column of lists (or in this case, a column of comma-separated strings) into separate rows. First, you can split the comma-separated string values into lists using the str.split method. Next, you can use the assign method to replace the original column with the new lists, and then use the explode method to explode the column into separate rows.

import pandas as pd

df = pd.DataFrame({'var1': ['a,b,c', 'd,e,f'], 'var2': [1, 2]})
print(df)

# Output:
#     var1  var2
# 0  a,b,c     1
# 1  d,e,f     2

b = df.assign(var1=df['var1'].str.split(',')).explode('var1')
print(b)

# Output:
#   var1  var2
# 0    a     1
# 0    b     1
# 0    c     1
# 1    d     2
# 1    e     2
# 1    f     2

Note that the explode method only works on a single column for now. If you want to explode multiple columns, you can use a lambda function with the apply method, like this:

(df.set_index(['var3'])
 .apply(lambda col: col.str.split(',').explode())
 .reset_index()
 .reindex(df.columns, axis=1))

Solution 2: Using the str.split and stack methods

In this approach, you can first split the comma-separated string values into a list using the str.split method. Next, you can convert the list of lists into a stacked Pandas series using the stack method. Finally, you can reset the index and rename the columns to get the desired output.

import pandas as pd

df = pd.DataFrame({'var1': ['a,b,c', 'd,e,f'], 'var2': [1, 2]})
print(df)

# Output:
#     var1  var2
# 0  a,b,c     1
# 1  d,e,f     2

b = pd.DataFrame(df.var1.str.split(',').tolist(), index=df.var2).stack()
b = b.reset_index()[[0, 'var2']]
b.columns = ['var1', 'var2']
print(b)

# Output:
#   var1  var2
# 0    a     1
# 1    b     1
# 2    c     1
# 3    d     2
# 4    e     2
# 5    f     2

Conclusion

In conclusion, when dealing with large datasets or messy data, it is essential to know how to manipulate and transform the data effectively. There are several approaches that can be used to achieve this goal, including splitting a row into multiple rows, splitting a row in two, expanding a column based on a delimiter, exploding a string, and separating rows. By using these techniques, analysts and data scientists can better understand the structure of their data and uncover insights that may not have been apparent before. It's important to choose the approach that best suits the specific data and analytical goals, as each method has its advantages and limitations. Whether it's splitting rows to better analyze individual elements or expanding columns to get a clearer picture of the data, these techniques can be powerful tools in the data scientist's arsenal.