Manipulating Text

Oftentimes, there will be something a bit off with the string data in your dataset. You may want to replace some characters, change the case, or strip the whitespace. You know, anything you normally need to do with strings.

Now this might lead you to want to loop through each row and manipulate the data, but before you do that, step back and lean into vectorization.

A Series provides a way to use vectorized string methods in a property named str and the vectorized methods are then available.

Let's take a look at some examples that require us to use these methods.

In [1]:
# Setup
import os

import pandas as pd

from utils import make_chaos

pd.options.display.max_rows = 10
transactions = pd.read_csv(os.path.join('data', 'transactions.csv'), index_col=0)
# Pay no attention to the person behind the curtain
make_chaos(transactions, 42, ['sender'], lambda val: '$' + val)
make_chaos(transactions, 88, ['receiver'], lambda val: val.upper())

Replacing Text

When CashBox first got started, usernames were allowed to start with a dollar sign. As time progressed, they changed their mind. They made a mass update to the system. However, someone on the Customer Support team reported that there are some records in the transactions DataFrame still showing some senders whose user name still had the $ prefix.

In order to get ahold of those rows where the sender starts with a $, we can use the Series.str.startswith method. This will return a boolean Series which we can use as an index.

In [2]:
sender receiver amount sent_date
59 $porter gail7896 75.16 2018-05-14
70 $emily.lewis kevin 5.49 2018-05-21
158 $robinson rodriguez 8.91 2018-06-25
168 $nancy margaret265 84.15 2018-06-26
198 $acook adam.saunders 9.31 2018-07-04
... ... ... ... ...
877 $april9082 jacob.davis 50.37 2018-09-21
889 $victor anthony1788 39.06 2018-09-21
900 $andersen corey.ingram 4.81 2018-09-22
927 $janet.williams bsmith 50.15 2018-09-23
934 $robert8280 roger 98.35 2018-09-24

42 rows × 4 columns

We can now just go ahead and replace all $ with an empty string, essentially removing all $ from every sender by using the Series.str.replace method.

In [3]:
# Replace all "$" in the sender field with an empty string
transactions.sender = transactions.sender.str.replace('$', '') 
# Verify we got them all

Changing Case

When you want to select or merge by specific values, the case, you know upper case or lower case, matters.

Our CashBox customer support representative also raised another issue for us to take a look at. All usernames should be lowercased, but they have reported that they noticed the receiver column has some uppercased values.

We can get a handle on those by using the Series.str.isupper method which will return a boolean Series that we can use for an index.

In [4]:
sender receiver amount sent_date
2 rose.eaton EMILY.LEWIS 62.67 2018-02-15
5 francis.hernandez LMOORE 91.46 2018-03-14
14 palmer CHAD.CHEN 36.27 2018-04-07
28 elang DONNA1922 26.07 2018-04-23
34 payne GRIFFIN4992 85.21 2018-04-26
... ... ... ... ...
963 stanley7729 JOSEPH.LOPEZ 50.84 2018-09-25
977 martha6969 PATRICIA 87.33 2018-09-25
987 alvarado PAMELA 48.74 2018-09-25
990 robert HEATHER.WADE 86.44 2018-09-25
992 pamela CALEB 25.01 2018-09-25

88 rows × 4 columns

So let's select the rows we want from transactions and then update the receiver column to the matching lowercased value. We can use the Series.str.lower vectorized method.

In [5]:
# Update the receiver column of the specific rows that are uppercased.
transactions.loc[transactions.receiver.str.isupper(), 'receiver'] = transactions.receiver.str.lower()
# Verify that we got them

Learn More

As you work on cleaning up datasets, you'll end up in this space quite a bit. Make sure to check out the documentation on String handling so you know what super powers you have on your side.