What is the difference between Series.replace and Series.str.replace?

If you are comparing str.replace with replace, I would assume that you are thinking of replacing strings only.

The two thumb rules that help (especially when using .apply() and lambda) are:

1. If you want to replace many things at once use df.replace({dict}). Remember the defaults as mentioned by cs95 or in the docs.
2. If you want to use regex AND case sensitivity options use str.replace(): lambda x: x.str.replace('^default$', '', regex = True, case = False).

One final thing to note is that the inplace parameter is only available in the replace function and not in str.replace which may be a deciding factor in your code especially if you are chaining.

Skip to the TLDR; at the bottom of this answer for a brief summary of the differences.

It is easy to understand the difference if you think of these two methods in terms of their utility.

.str.replace is a method with a very specific purpose—to perform string or regex substitution on string data.

OTOH, .replace is more of an all-purpose Swiss Army knife which can replace anything with anything else (and yes, this includes string and regex).

Consider the simple DataFrame below, this will form the basis of our forthcoming discussion.

# Setup
df = pd.DataFrame({
    'A': ['foo', 'another foo bar', 'baz'],
    'B': [0, 1, 0]
})
df

                 A  B
0              foo  0
1  another foo bar  1
2              baz  0

The main differences between the two functions can be summarised in terms of

1. Purpose
2. Usage
3. Default behavior

---

Use str.replace for substring replacements on a single string column, and replace for any general replacement on one or more columns.

The docs market str.replace as a method for "simple string replacement", so this should be your first choice when performing string/regex substitution on a pandas Series or column—think of it as a "vectorised" equivalent to python's string replace() function (or re.sub() to be more accurate).

# simple substring replacement
df['A'].str.replace('foo', 'bar', regex=False)

0                bar
1    another bar bar
2                baz
Name: A, dtype: object

# simple regex replacement
df['A'].str.replace('ba.', 'xyz')

0                foo
1    another foo xyz
2                xyz
Name: A, dtype: object

replace works for string as well as non-string replacement. What's more, it is also meant to **work for multiple columns at a time (you can access replace as a DataFrame method df.replace() as well, if you need to replace values across the entire DataFrame.

# DataFrame-wide replacement
df.replace({'foo': 'bar', 1: -1})

                 A  B
0              bar  0
1  another foo bar -1
2              baz  0

---

str.replace can replace one thing at a time. replace lets you perform multiple independent replacements, i.e., replace many things at once.

You can only specify a single substring or regex pattern to str.replace. repl can be a callable (see the docs), so there's room to get creative with regex to somewhat simulate multiple substring replacements, but these solutions are hacky at best).

A common pandaic (pandorable, pandonic) pattern is to use str.replace to remove multiple unwanted substrings by pipe-separating substrings using the regex OR pipe |, and the replacement string is '' (the empty string).

replace should be preferred when you have multiple independent replacements of the form {'pat1': 'repl1', 'pat2':repl2, ...}. There are various ways of specifying independent replacements (lists, Series, dicts, etc). See the documentation.

To illustrate the difference,

df['A'].str.replace('foo', 'text1').str.replace('bar', 'text2')

0                  text1
1    another text1 text2
2                    baz
Name: A, dtype: object

Would be better expressed as

df['A'].replace({'foo': 'text1', 'bar': 'text2'}, regex=True)

0                  text1
1    another text1 text2
2                    baz
Name: A, dtype: object

---

In the context of string operations, str.replace enables regex replacement by default. replace only performs a full match unless the regex=True switch is used.

Everything you do with str.replace, you can do with replace as well. However, it is important to note the following differences in the default behaviour of both methods.

1. substring replacements - str.replace will replace every occurrence of the substring, replace will only perform whole word matches by default
2. regex replacement - str.replace interprets the first argument as a regular expression unless you specify regex=False. replace is the exact opposite.

Contrast the difference between

df['A'].replace('foo', 'bar')

0                bar
1    another foo bar
2                baz
Name: A, dtype: object

And

df['A'].replace('foo', 'bar', regex=True)

0                bar
1    another bar bar
2                baz
Name: A, dtype: object

It is also worth mentioning that you can only perform string replacement when regex=True. So, for example, df.replace({'foo': 'bar', 1: -1}, regex=True) would be invalid.

---

TLDR;

To summarise, the main differences are,

1. Purpose. Use str.replace for substring replacements on a single string column, and replace for any general replacement on one or more columns.

2. Usage. str.replace can replace one thing at a time. replace lets you perform multiple independent replacements, i.e., replace many things at once.

3. Default behavior. str.replace enables regex replacement by default. replace only performs a full match unless the regex=True switch is used.