TL;DR version:

For the simple case of:

• I have a text column with a delimiter and I want two columns

The simplest solution is:

df[['A', 'B']] = df['AB'].str.split(' ', n=1, expand=True) 

You must use expand=True if your strings have a non-uniform number of splits and you want None to replace the missing values.

Notice how, in either case, the .tolist() method is not necessary. Neither is zip().

In detail:

Andy Hayden's solution is most excellent in demonstrating the power of the str.extract() method.

But for a simple split over a known separator (like, splitting by dashes, or splitting by whitespace), the .str.split() method is enough¹. It operates on a column (Series) of strings, and returns a column (Series) of lists:

>>> import pandas as pd >>> df = pd.DataFrame({'AB': ['A1-B1', 'A2-B2']}) >>> df AB 0 A1-B1 1 A2-B2 >>> df['AB_split'] = df['AB'].str.split('-') >>> df AB AB_split 0 A1-B1 [A1, B1] 1 A2-B2 [A2, B2] 

_{1: If you're unsure what the first two parameters of .str.split() do, I recommend the docs for the plain Python version of the method.}

But how do you go from:

• a column containing two-element lists

to:

• two columns, each containing the respective element of the lists?

Well, we need to take a closer look at the .str attribute of a column.

It's a magical object that is used to collect methods that treat each element in a column as a string, and then apply the respective method in each element as efficient as possible:

>>> upper_lower_df = pd.DataFrame({"U": ["A", "B", "C"]}) >>> upper_lower_df U 0 A 1 B 2 C >>> upper_lower_df["L"] = upper_lower_df["U"].str.lower() >>> upper_lower_df U L 0 A a 1 B b 2 C c 

But it also has an "indexing" interface for getting each element of a string by its index:

>>> df['AB'].str[0] 0 A 1 A Name: AB, dtype: object >>> df['AB'].str[1] 0 1 1 2 Name: AB, dtype: object 

Of course, this indexing interface of .str doesn't really care if each element it's indexing is actually a string, as long as it can be indexed, so:

>>> df['AB'].str.split('-', 1).str[0] 0 A1 1 A2 Name: AB, dtype: object >>> df['AB'].str.split('-', 1).str[1] 0 B1 1 B2 Name: AB, dtype: object 

Then, it's a simple matter of taking advantage of the Python tuple unpacking of iterables to do

>>> df['A'], df['B'] = df['AB'].str.split('-', n=1).str >>> df AB AB_split A B 0 A1-B1 [A1, B1] A1 B1 1 A2-B2 [A2, B2] A2 B2 

Of course, getting a DataFrame out of splitting a column of strings is so useful that the .str.split() method can do it for you with the expand=True parameter:

>>> df['AB'].str.split('-', n=1, expand=True) 0 1 0 A1 B1 1 A2 B2 

So, another way of accomplishing what we wanted is to do:

>>> df = df[['AB']] >>> df AB 0 A1-B1 1 A2-B2 >>> df.join(df['AB'].str.split('-', n=1, expand=True).rename(columns={0:'A', 1:'B'})) AB A B 0 A1-B1 A1 B1 1 A2-B2 A2 B2 

The expand=True version, although longer, has a distinct advantage over the tuple unpacking method. Tuple unpacking doesn't deal well with splits of different lengths:

>>> df = pd.DataFrame({'AB': ['A1-B1', 'A2-B2', 'A3-B3-C3']}) >>> df AB 0 A1-B1 1 A2-B2 2 A3-B3-C3 >>> df['A'], df['B'], df['C'] = df['AB'].str.split('-') Traceback (most recent call last): [...] ValueError: Length of values does not match length of index >>> 

But expand=True handles it nicely by placing None in the columns for which there aren't enough "splits":

>>> df.join( ... df['AB'].str.split('-', expand=True).rename( ... columns={0:'A', 1:'B', 2:'C'} ... ) ... ) AB A B C 0 A1-B1 A1 B1 None 1 A2-B2 A2 B2 None 2 A3-B3-C3 A3 B3 C3 

You can extract the different parts out quite neatly using a regex pattern:

In [11]: df.row.str.extract('(?P<fips>\d{5})((?P<state>[A-Z ]*$)|(?P<county>.*?), (?P<state_code>[A-Z]{2}$))') Out[11]: fips 1 state county state_code 0 00000 UNITED STATES UNITED STATES NaN NaN 1 01000 ALABAMA ALABAMA NaN NaN 2 01001 Autauga County, AL NaN Autauga County AL 3 01003 Baldwin County, AL NaN Baldwin County AL 4 01005 Barbour County, AL NaN Barbour County AL [5 rows x 5 columns] 

To explain the somewhat long regex:

(?P<fips>\d{5}) 

• Matches the five digits (\d) and names them "fips".

The next part:

((?P<state>[A-Z ]*$)|(?P<county>.*?), (?P<state_code>[A-Z]{2}$)) 

Does either (|) one of two things:

(?P<state>[A-Z ]*$) 

• Matches any number (*) of capital letters or spaces ([A-Z ]) and names this "state" before the end of the string ($),

or

(?P<county>.*?), (?P<state_code>[A-Z]{2}$)) 

• matches anything else (.*) then
• a comma and a space then
• matches the two digit state_code before the end of the string ($).

In the example:
Note that the first two rows hit the "state" (leaving NaN in the county and state_code columns), whilst the last three hit the county, state_code (leaving NaN in the state column).

df[['fips', 'row']] = df['row'].str.split(' ', n=1, expand=True) 

You can use str.split by whitespace (default separator) and parameter expand=True for DataFrame with assign to new columns:

df = pd.DataFrame({'row': ['00000 UNITED STATES', '01000 ALABAMA', '01001 Autauga County, AL', '01003 Baldwin County, AL', '01005 Barbour County, AL']}) print (df) row 0 00000 UNITED STATES 1 01000 ALABAMA 2 01001 Autauga County, AL 3 01003 Baldwin County, AL 4 01005 Barbour County, AL df[['a','b']] = df['row'].str.split(n=1, expand=True) print (df) row a b 0 00000 UNITED STATES 00000 UNITED STATES 1 01000 ALABAMA 01000 ALABAMA 2 01001 Autauga County, AL 01001 Autauga County, AL 3 01003 Baldwin County, AL 01003 Baldwin County, AL 4 01005 Barbour County, AL 01005 Barbour County, AL 

Modification if need remove original column with DataFrame.pop

df[['a','b']] = df.pop('row').str.split(n=1, expand=True) print (df) a b 0 00000 UNITED STATES 1 01000 ALABAMA 2 01001 Autauga County, AL 3 01003 Baldwin County, AL 4 01005 Barbour County, AL 

What is same like:

df[['a','b']] = df['row'].str.split(n=1, expand=True) df = df.drop('row', axis=1) print (df) a b 0 00000 UNITED STATES 1 01000 ALABAMA 2 01001 Autauga County, AL 3 01003 Baldwin County, AL 4 01005 Barbour County, AL 

If get error:

#remove n=1 for split by all whitespaces df[['a','b']] = df['row'].str.split(expand=True) 

ValueError: Columns must be same length as key

You can check and it return 4 column DataFrame, not only 2:

print (df['row'].str.split(expand=True)) 0 1 2 3 0 00000 UNITED STATES None 1 01000 ALABAMA None None 2 01001 Autauga County, AL 3 01003 Baldwin County, AL 4 01005 Barbour County, AL 

Then solution is append new DataFrame by join:

df = pd.DataFrame({'row': ['00000 UNITED STATES', '01000 ALABAMA', '01001 Autauga County, AL', '01003 Baldwin County, AL', '01005 Barbour County, AL'], 'a':range(5)}) print (df) a row 0 0 00000 UNITED STATES 1 1 01000 ALABAMA 2 2 01001 Autauga County, AL 3 3 01003 Baldwin County, AL 4 4 01005 Barbour County, AL df = df.join(df['row'].str.split(expand=True)) print (df) a row 0 1 2 3 0 0 00000 UNITED STATES 00000 UNITED STATES None 1 1 01000 ALABAMA 01000 ALABAMA None None 2 2 01001 Autauga County, AL 01001 Autauga County, AL 3 3 01003 Baldwin County, AL 01003 Baldwin County, AL 4 4 01005 Barbour County, AL 01005 Barbour County, AL 

With remove original column (if there are also another columns):

df = df.join(df.pop('row').str.split(expand=True)) print (df) a 0 1 2 3 0 0 00000 UNITED STATES None 1 1 01000 ALABAMA None None 2 2 01001 Autauga County, AL 3 3 01003 Baldwin County, AL 4 4 01005 Barbour County, AL 

If you don't want to create a new dataframe, or if your dataframe has more columns than just the ones you want to split, you could:

df["flips"], df["row_name"] = zip(*df["row"].str.split().tolist()) del df["row"] 

Use df.assign to create a new df. See https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.assign.html

split = df_selected['name'].str.split(',', 1, expand=True) df_split = df_selected.assign(first_name=split[0], last_name=split[1]) df_split.drop('name', 1, inplace=True) 

Or in method chain form:

df_split = (df_selected .assign(list_col=lambda df: df['name'].str.split(',', 1, expand=False), first_name=lambda df: df.list_col.str[0], last_name=lambda df: df.list_col.str[1]) .drop(columns=['list_col'])) 

Surprised I haven't seen this one yet. If you only need two splits, I highly recommend. . .

Series.str.partition

partition performs one split on the separator, and is generally quite performant.

df['row'].str.partition(' ')[[0, 2]] 0 2 0 00000 UNITED STATES 1 01000 ALABAMA 2 01001 Autauga County, AL 3 01003 Baldwin County, AL 4 01005 Barbour County, AL 

If you need to rename the rows,

df['row'].str.partition(' ')[[0, 2]].rename({0: 'fips', 2: 'row'}, axis=1) fips row 0 00000 UNITED STATES 1 01000 ALABAMA 2 01001 Autauga County, AL 3 01003 Baldwin County, AL 4 01005 Barbour County, AL 

If you need to join this back to the original, use join or concat:

df.join(df['row'].str.partition(' ')[[0, 2]]) 

pd.concat([df, df['row'].str.partition(' ')[[0, 2]]], axis=1) row 0 2 0 00000 UNITED STATES 00000 UNITED STATES 1 01000 ALABAMA 01000 ALABAMA 2 01001 Autauga County, AL 01001 Autauga County, AL 3 01003 Baldwin County, AL 01003 Baldwin County, AL 4 01005 Barbour County, AL 01005 Barbour County, AL 

If you want to split a string into more than two columns based on a delimiter you can omit the 'maximum splits' parameter.
You can use:

df['column_name'].str.split('/', expand=True) 

This will automatically create as many columns as the maximum number of fields included in any of your initial strings.

I saw that no one had used the slice method, so here I put my 2 cents here.

df["<col_name>"].str.slice(stop=5) df["<col_name>"].str.slice(start=6) 

This method will create two new columns.

I prefer exporting the corresponding pandas series (i.e. the columns I need), using the apply function to split the column content into multiple series and then join the generated columns to the existing DataFrame. Of course, the source column should be removed.

e.g.

 col1 = df["<col_name>"].apply(<function>) col2 = ... df = df.join(col1.to_frame(name="<name1>")) df = df.join(col2.toframe(name="<name2>")) df = df.drop(["<col_name>"], axis=1) 

To split two words strings function should be something like that:

lambda x: x.split(" ")[0] # for the first element lambda x: x.split(" ")[-1] # for the last element 

FutureWarning: In a future version of pandas all arguments of StringMethods.split except for the argument 'pat' will be keyword-only.

Just a small update to the first answer.
If you encounter the warning message above, (I am currently using the pd.__version__ = 1.5.3) that in future versions will become an error, you can avoid it by adding the name of the arguments to your arguments. So, the "simplest solution" that LeoRochael posted:

# Will raise a warning or error df[['A', 'B']] = df['AB'].str.split(' ', 1, expand=True) 

should be:

# For newer pandas versions df[['A', 'B']] = df['AB'].str.split(' ', n=1, expand=True) 

Other examples also posted here will need the name of the argument as well.

# For instance, if you want to split and remove the splitted columns df[['A','B']] = df.pop('AB').str.split(n=1, expand=True) 

I hope this can help.