Cogxen

Appearance

Splitting and Combining Columns ​

Working with text data in pandas often requires splitting and combining columns to suit our analytical needs. Understanding how to effectively manage these operations can greatly enhance your data analysis capabilities.

Splitting Columns ​

Imagine you have a dataset containing information about National Parks. One of the columns, "Location," includes both the city and the state, separated by a comma. If you want to count how many parks are in each state, you'll need to split this column into two separate columns.

How to Split a Column? ​

To split a column in pandas, you use the str.split() method. This method divides a string based on a specified delimiter and can expand the split strings into separate columns.

Here’s a step-by-step guide:

  1. Identify the Delimiter: In our example, the delimiter is a comma ,, which separates the city from the state.

  2. Apply the str.split() Method: Use the str.split() method to split the column into two separate columns. You can specify the delimiter within the method.

python
loc_split = parks['Location'].str.split(pat=',', expand=True)
  • .str tells pandas to treat the entries in "Location" as strings.
  • .split() is the method that splits the strings based on the specified delimiter.
  • pat=',' specifies that the comma is the delimiter.
  • expand=True ensures that the split parts are expanded into separate columns.

Here's how the "Location" column looks before splitting:

plaintext
Location
Gatlinburg, TN
Springdale, UT
Jackson, WY

After splitting, loc_split will look like this:

plaintext
        0        1
0   Gatlinburg   TN
1  Springdale    UT
2     Jackson    WY

Column 0 contains the city names, and column 1 contains the state abbreviations.

  1. Assign the Split Columns to the DataFrame: To add these new columns to the original DataFrame, you can use the following code:
python
parks['City'] = loc_split[0]
parks['State'] = loc_split[1]

This will add two new columns, "City" and "State," to your original DataFrame.

Combining Columns ​

There are times when you need to combine two or more columns into one. For example, if you have separate "City" and "State" columns and want to create a combined "Location" column, you use the str.cat() method.

How to Combine Columns? ​

To combine columns in pandas, you use the str.cat() method. This method concatenates strings from multiple columns along a specified axis.

  1. Concatenate the Columns: Use the str.cat() method to combine the "City" and "State" columns into a new "Location" column.
python
parks['Location'] = parks['City'].str.cat(parks['State'], sep=',')
  • .str.cat() concatenates the text from two columns.
  • sep=',' specifies the separator to be placed between the concatenated strings.

Here's the combined result:

plaintext
    City        State    Location
0   Gatlinburg  TN       Gatlinburg,TN
1  Springdale   UT       Springdale,UT
2     Jackson   WY       Jackson,WY

Examples ​

Splitting Columns ​

Imagine the "Location" column as follows:

plaintext
Location
Gatlinburg, TN
Springdale, UT
Jackson, WY

Using the steps outlined above, you can split this column into two separate columns, "City" and "State."

python
loc_split = parks['Location'].str.split(pat=',', expand=True)
parks['City'] = loc_split[0]
parks['State'] = loc_split[1]

Now, your DataFrame has two new columns:

plaintext
    City        State
0   Gatlinburg  TN
1  Springdale   UT
2     Jackson   WY

Combining Columns ​

If you want to combine the "City" and "State" columns back into a single "Location" column, you can use the following code:

plaintext
    City        State
0   Gatlinburg  TN
1  Springdale   UT
2     Jackson   WY

Combine them back into a single "Location" column:

python
parks['Location'] = parks['City'].str.cat(parks['State'], sep=',')

The resulting DataFrame will look like this:

plaintext
    City        State    Location
0   Gatlinburg  TN       Gatlinburg,TN
1  Springdale   UT       Springdale,UT
2     Jackson   WY       Jackson,WY

Conclusion ​

Mastering the techniques of splitting and combining columns in pandas allows you to manipulate your text data efficiently, making it easier to analyze and draw meaningful insights. Whether you need to break down complex data into manageable parts or combine pieces to form a coherent whole, these methods provide powerful tools to enhance your data handling capabilities. By keeping these techniques in your toolkit, you ensure your datasets are always in the most useful format for your analysis.