We can use Pandas’ string manipulation functions to do that easily. Let us first create a simple Pandas data frame using Pandas’ DataFrame function.
# import Pandas as pd
import pandas as pd
# create a new data frame
df = pd.DataFrame({'Name': ['Steve Smith', 'Joe Nadal',
'Roger Federer'],
'Age':[32, 34, 36]})
df
Splitting the Original DataFrame’s Single Column into Multiple Columns
We can use Pandas’ str.split function to split the column of interest. Here we want to split the column “Name” and we can select the column using chain operation and split the column with expand=True option.
str.split() with expand=True option results in a data frame and without that we will get Pandas Series object as output.
df.Name.str.split(expand=True,)
0 1
0 Steve Smith
1 Joe Nadal
2 Roger Federer
If we want to have the results in the original dataframe with specific names, we can add as new columns like shown below.
df[['First','Last']] = df.Name.str.split(" ",expand=True,)
df
And we will get two new columns in addition to the original data frame.
Age Name First Last 0 32 Steve Smith Steve Smith 1 34 Joe Nadal Joe Nadal 2 36 Roger Federer Roger Federer
Note that we applied str.split method without specifying any specific delimiter. By default, str.split uses a single space as delimiter and we can specify a delimiter as follows. For example, if the text in our column were separated by under score,
df = pd.DataFrame({'Name': ['Steve_Smith', 'Joe_Nadal',
'Roger_Federer'],
'Age':[32,34,36]})
df
Age Name
0 32 Steve_Smith
1 34 Joe_Nadal
2 36 Roger_Federer
we can use under score as our delimiter to split the column into two columns.
df[['First','Last']] = df.Name.str.split("_",expand=True,)
df
Age Name First Last
0 32 Steve_Smith Steve Smith
1 34 Joe_Nadal Joe Nadal
2 36 Roger_Federer Roger Federer
