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Pandas has a method to split a string based on a separator/delimiter. We will use the pandas str.split()function.

str.split()Split a string into two lists/columns using the function in Python Pandas

The string can be saved as a list of Series, or a single delimited string, multi-column DataFrame.

The functions used are similar to Python's default split()methods, but they can only be applied to a single string.

grammar:

Syntax:Series.str.split(pat=None, n=-1, expand=False)
Let's define each of the parameters of syntax
Parameters:
pat:String value, separator, or delimiter used to separate strings
n=The maximum number of separations to make in a single string; the default is -1, which signifies all.
expand: If True, this Boolean value returns a data frame with different values in separate columns. Otherwise, it returns a series containing a collection of strings.
return: Depending on the expand parameter, a series of lists or a data frame will be generated.

First, we explain with a simple example, and then a CSV file.

Split a string column in a Pandas DataFrame into multiple columns using basic syntax

data[["A", "B"]] = data["A"].str.split(",", 1, expand=True)

See the examples below, which demonstrate the use of this syntax in practice.

Split the column by comma:

import pandas as pd

df = pd.DataFrame(
    {"Name": ["Anu,Ais ", "Bag, Box", "fox, fix"], "points": [112, 104, 127]}
)
df

Output:

# split team column into two columns
df[["Name", "lastname"]] = df["Name"].str.split(",", 2, expand=True)
df

Output:

For a download of the CSV file used in the code, click here.

The student grades data is contained in the DataFrame used in the following examples. Append the image of the DataFrame before any operations.

We explain string splitting in two ways.

• Convert a string to a list
• Creating separate columns from strings

Convert String to List in Python Pandas

The split function is used in this data dto split the lunch column at each . The option is set to 1, and the maximum number of separations in a single string is 1.

The expand parameter is set to False. Instead of a Series of DataFrames, a list of strings is returned.

import pandas as pd

df = pd.read_csv("/content/drive/MyDrive/StudentsPerformance.csv")
# dropping null value columns to avoid errors
df.dropna(inplace=True)
# new data frame with split value columns
df["lunch"] = df["lunch"].str.split("d", n=1, expand=True)
# df display
df.head(9)

Output:

The output image shows that the lunch column now has one list because the n option is set to 1.

dThe string is delimited at the first occurrence , not at subsequent occurrences (maximum 1 delimiter in the string).

Creating separate columns from strings in Python Pandas

In this example, the parent education columns are " "separated by spaces and the expand option is set to True.

This means it will return a DataFrame with all the separated strings in different columns. The new column is then constructed using the Dataframe.

Use drop()the method to remove the old parent education level column.

import pandas as pd

df = pd.read_csv("/content/drive/MyDrive/StudentsPerformance.csv")
# dropping null value columns to avoid errors
df.dropna(inplace=True)
new = df["parental level of education"].str.split(" ", n=1, expand=True)
df["educational level"] = new[0]
df["insititute"] = new[1]
# Dropping old Name columns
df.drop(columns=["parental level of education"], inplace=True)
# df display
df.head(9)

Output:

split()The function provides a new DataFrame which creates two new columns (Education Level and College) in the DataFrame.

The above image shows the new columns. We can also view the new columns using the new keyword which shows the newly created columns.

new.head(9)

Output:

in conclusion

Therefore, there is usually a section in your Pandas infobox that needs to be split into two parts in the info outline.

For example, if one part of your information outline is your full name, you may want to separate it into first name and last name.