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How to Confirm That a Python String Contains Another String

If you need to check whether a string contains a substring, use Python’s membership operator in. In Python, this is the recommended way to confirm the existence of a substring in a string:

>>> raw_file_content = """Hi there and welcome.
... This is a special hidden file with a SECRET secret.
... I don't want to tell you The Secret,
... but I do want to secretly tell you that I have one."""

>>> "secret" in raw_file_content
True

The in membership operator gives you a quick and readable way to check whether a substring is present in a string. You may notice that the line of code almost reads like English.

When you use in, the expression returns a Boolean value:

• True if Python found the substring
• False if Python didn’t find the substring

You can use this intuitive syntax in conditional statements to make decisions in your code:

>>> if "secret" in raw_file_content:
...    print("Found!")
...
Found!

In this code snippet, you use the membership operator to check whether "secret" is a substring of raw_file_content. If it is, then you’ll print a message to the terminal. Any indented code will only execute if the Python string that you’re checking contains the substring that you provide.

The membership operator in is your best friend if you just need to check whether a Python string contains a substring.

However, what if you want to know more about the substring? If you read through the text stored in raw_file_content, then you’ll notice that the substring occurs more than once, and even in different variations!

Which of these occurrences did Python find? Does capitalization make a difference? How often does the substring show up in the text? And what’s the location of these substrings? If you need the answer to any of these questions, then keep on reading.

Generalize Your Check by Removing Case Sensitivity

Python strings are case sensitive. If the substring that you provide uses different capitalization than the same word in your text, then Python won’t find it. For example, if you check for the lowercase word "secret" on a title-case version of the original text, the membership operator check returns False:

>>> title_cased_file_content = """Hi There And Welcome.
... This Is A Special Hidden File With A Secret Secret.
... I Don't Want To Tell You The Secret,
... But I Do Want To Secretly Tell You That I Have One."""

>>> "secret" in title_cased_file_content
False

Despite the fact that the word secret appears multiple times in the title-case text title_cased_file_content, it never shows up in all lowercase. That’s why the check that you perform with the membership operator returns False. Python can’t find the all-lowercase string "secret" in the provided text.

Humans have a different approach to language than computers do. This is why you’ll often want to disregard capitalization when you check whether a string contains a substring in Python.

You can generalize your substring check by converting the whole input text to lowercase:

>>> file_content = title_cased_file_content.lower()

>>> print(file_content)
hi there and welcome.
this is a special hidden file with a secret secret.
i don't want to tell you the secret,
but i do want to secretly tell you that i have one.

>>> "secret" in file_content
True

Converting your input text to lowercase is a common way to account for the fact that humans think of words that only differ in capitalization as the same word, while computers don’t.

Now that you’ve converted the string to lowercase to avoid unintended issues stemming from case sensitivity, it’s time to dig further and learn more about the substring.

Learn More About the Substring

The membership operator in is a great way to descriptively check whether there’s a substring in a string, but it doesn’t give you any more information than that. It’s perfect for conditional checks—but what if you need to know more about the substrings?

Python provides many additonal string methods that allow you to check how many target substrings the string contains, to search for substrings according to elaborate conditions, or to locate the index of the substring in your text.

In this section, you’ll cover some additional string methods that can help you learn more about the substring.

By using in, you confirmed that the string contains the substring. But you didn’t get any information on where the substring is located.

If you need to know where in your string the substring occurs, then you can use .index() on the string object:

>>> file_content = """hi there and welcome.
... this is a special hidden file with a secret secret.
... i don't want to tell you the secret,
... but i do want to secretly tell you that i have one."""

>>> file_content.index("secret")
59

When you call .index() on the string and pass it the substring as an argument, you get the index position of the first character of the first occurrence of the substring.

But what if you want to find other occurrences of the substring? The .index() method also takes a second argument that can define at which index position to start looking. By passing specific index positions, you can therefore skip over occurrences of the substring that you’ve already identified:

>>> file_content.index("secret", 60)
66

When you pass a starting index that’s past the first occurrence of the substring, then Python searches starting from there. In this case, you get another match and not a ValueError.

That means that the text contains the substring more than once. But how often is it in there?

You can use .count() to get your answer quickly using descriptive and idiomatic Python code:

>>> file_content.count("secret")
4

You used .count() on the lowercase string and passed the substring "secret" as an argument. Python counted how often the substring appears in the string and returned the answer. The text contains the substring four times. But what do these substrings look like?

You can inspect all the substrings by splitting your text at default word borders and printing the words to your terminal using a for loop:

>>> for word in file_content.split():
...    if "secret" in word:
...        print(word)
...
secret
secret.
secret,
secretly

In this example, you use .split() to separate the text at whitespaces into strings, which Python packs into a list. Then you iterate over this list and use in on each of these strings to see whether it contains the substring "secret".

Now that you can inspect all the substrings that Python identifies, you may notice that Python doesn’t care whether there are any characters after the substring "secret" or not. It finds the word whether it’s followed by whitespace or punctuation. It even finds words such as "secretly".

That’s good to know, but what can you do if you want to place stricter conditions on your substring check?

Find a Substring With Conditions Using Regex

You may only want to match occurrences of your substring followed by punctuation, or identify words that contain the substring plus other letters, such as "secretly".

For such cases that require more involved string matching, you can use regular expressions, or regex, with Python’s re module.

For example, if you want to find all the words that start with "secret" but are then followed by at least one additional letter, then you can use the regex word character (w) followed by the plus quantifier (+):

>>> import re

>>> file_content = """hi there and welcome.
... this is a special hidden file with a secret secret.
... i don't want to tell you the secret,
... but i do want to secretly tell you that i have one."""

>>> re.search(r"secretw+", file_content)
<re.Match object; span=(128, 136), match='secretly'>

The re.search() function returns both the substring that matched the condition as well as its start and end index positions—rather than just True!

You can then access these attributes through methods on the Match object, which is denoted by m:

>>> m = re.search(r"secretw+", file_content)

>>> m.group()
'secretly'

>>> m.span()
(128, 136)

These results give you a lot of flexibility to continue working with the matched substring.

For example, you could search for only the substrings that are followed by a comma (,) or a period (.):

>>> re.search(r"secret[.,]", file_content)
<re.Match object; span=(66, 73), match='secret.'>

There are two potential matches in your text, but you only matched the first result fitting your query. When you use re.search(), Python again finds only the first match. What if you wanted all the mentions of "secret" that fit a certain condition?

To find all the matches using re, you can work with re.findall():

>>> re.findall(r"secret[.,]", file_content)
['secret.', 'secret,']

By using re.findall(), you can find all the matches of the pattern in your text. Python saves all the matches as strings in a list for you.

When you use a capturing group, you can specify which part of the match you want to keep in your list by wrapping that part in parentheses:

>>> re.findall(r"(secret)[.,]", file_content)
['secret', 'secret']

By wrapping secret in parentheses, you defined a single capturing group. The findall() function returns a list of strings matching that capturing group, as long as there’s exactly one capturing group in the pattern. By adding the parentheses around secret, you managed to get rid of the punctuation!

Using re.findall() with match groups is a powerful way to extract substrings from your text. But you only get a list of strings, which means that you’ve lost the index positions that you had access to when you were using re.search().

If you want to keep that information around, then re can give you all the matches in an iterator:

>>> for match in re.finditer(r"(secret)[.,]", file_content):
...    print(match)
...
<re.Match object; span=(66, 73), match='secret.'>
<re.Match object; span=(103, 110), match='secret,'>

When you use re.finditer() and pass it a search pattern and your text content as arguments, you can access each Match object that contains the substring, as well as its start and end index positions.

You may notice that the punctuation shows up in these results even though you’re still using the capturing group. That’s because the string representation of a Match object displays the whole match rather than just the first capturing group.

But the Match object is a powerful container of information and, like you’ve seen earlier, you can pick out just the information that you need:

>>> for match in re.finditer(r"(secret)[.,]", file_content):
...    print(match.group(1))
...
secret
secret

By calling .group() and specifying that you want the first capturing group, you picked the word secret without the punctuation from each matched substring.

You can go into much more detail with your substring matching when you use regular expressions. Instead of just checking whether a string contains another string, you can search for substrings according to elaborate conditions.

Using regular expressions with re is a good approach if you need information about the substrings, or if you need to continue working with them after you’ve found them in the text. But what if you’re working with tabular data? For that, you’ll turn to pandas.

Find a Substring in a pandas DataFrame Column

If you work with data that doesn’t come from a plain text file or from user input, but from a CSV file or an Excel sheet, then you could use the same approach as discussed above.

However, there’s a better way to identify which cells in a column contain a substring: you’ll use pandas! In this example, you’ll work with a CSV file that contains fake company names and slogans. You can download the file below if you want to work along:

When you’re working with tabular data in Python, it’s usually best to load it into a pandas DataFrame first:

>>> import pandas as pd

>>> companies = pd.read_csv("companies.csv")

>>> companies.shape
(1000, 2)

>>> companies.head()
             company                                     slogan
0      Kuvalis-Nolan      revolutionize next-generation metrics
1  Dietrich-Champlin  envisioneer bleeding-edge functionalities
2           West Inc            mesh user-centric infomediaries
3         Wehner LLC               utilize sticky infomediaries
4      Langworth Inc                 reinvent magnetic networks

In this code block, you loaded a CSV file that contains one thousand rows of fake company data into a pandas DataFrame and inspected the first five rows using .head().

After you’ve loaded the data into the DataFrame, you can quickly query the whole pandas column to filter for entries that contain a substring:

>>> companies[companies.slogan.str.contains("secret")]
              company                                  slogan
7          Maggio LLC                    target secret niches
117      Kub and Sons              brand secret methodologies
654       Koss-Zulauf              syndicate secret paradigms
656      Bernier-Kihn  secretly synthesize back-end bandwidth
921      Ward-Shields               embrace secret e-commerce
945  Williamson Group             unleash secret action-items

You can use .str.contains() on a pandas column and pass it the substring as an argument to filter for rows that contain the substring.

When you’re working with .str.contains() and you need more complex match scenarios, you can also use regular expressions! You just need to pass a regex-compliant search pattern as the substring argument:

>>> companies[companies.slogan.str.contains(r"secretw+")]
          company                                  slogan
656  Bernier-Kihn  secretly synthesize back-end bandwidth

In this code snippet, you’ve used the same pattern that you used earlier to match only words that contain secret but then continue with one or more word character (w+). Only one of the companies in this fake dataset seems to operate secretly!

You can write any complex regex pattern and pass it to .str.contains() to carve from your pandas column just the rows that you need for your analysis.

Conclusion

Like a persistent treasure hunter, you found each "secret", no matter how well it was hidden! In the process, you learned that the best way to check whether a string contains a substring in Python is to use the in membership operator.

You also learned how to descriptively use two other string methods, which are often misused to check for substrings:

• .count() to count the occurrences of a substring in a string
• .index() to get the index position of the beginning of the substring

After that, you explored how to find substrings according to more advanced conditions with regular expressions and a few functions in Python’s re module.

Finally, you also learned how you can use the DataFrame method .str.contains() to check which entries in a pandas DataFrame contain a substring .

You now know how to pick the most idiomatic approach when you’re working with substrings in Python. Keep using the most descriptive method for the job, and you’ll write code that’s delightful to read and quick for others to understand.