So far, we have worked with numerical marks. But a Series can also contain text data.
For example:

names = pd.Series(["Amit", "Rahul", "Priya", "Neha"])

For string data, Pandas provides the .str accessor.

• str.lower(): The str.lower() method converts all text values into lowercase.
  For example:

names.str.lower()

output:

0     amit

1    rahul

2    priya

3     neha

dtype: object

• str.upper(): The str.upper() method converts all text values into uppercase.
  For example:

names.str.upper()

output:

0     AMIT

1    RAHUL

2    PRIYA

3     NEHA

dtype: object

• str.title(): The str.title() method converts the first character of each word into uppercase.
  For example:

names = pd.Series(["amit kumar", "rahul singh"])

names.str.title()

output:

0    Amit Kumar

1    Rahul Singh

dtype: object

• str.strip(): The str.strip() method removes unnecessary spaces from the beginning and end of each string.
  For example:

names = pd.Series([" Amit ", " Rahul", "Priya "])

names.str.strip()

output:

0     Amit

1    Rahul

2    Priya

dtype: object

This is particularly useful when cleaning text data.

• str.replace(): The str.replace() method replaces a particular text or pattern with another value.
  For example:

names = pd.Series(["Amit", "Rahul", "Priya"])

names.str.replace("Amit", "Aman")

output:

0     Aman

1    Rahul

2    Priya

dtype: object

• str.contains(): The str.contains() method checks whether a particular text or pattern exists inside each value.
  For example:

names = pd.Series(["Amit", "Rahul", "Anita", "Neha"])

names.str.contains("A")

output:

0     True

1    False

2     True

3    False

dtype: bool

It returns True for values containing "A" and False for the others.

• str.startswith(): The str.startswith() method checks whether each string starts with the given value.
  For example:

names = pd.Series(["Amit", "Rahul", "Anita", "Neha"])

names.str.startswith("A")

output:

0     True

1    False

2     True

3    False

dtype: bool

• str.endswith(): The str.endswith() method checks whether each string ends with the given value.
  For example:

names = pd.Series(["Amit", "Rahul", "Priya", "Neha"])

names.str.endswith("a")

output:

0    False

1    False

2     True

3     True

dtype: bool

• str.len(): The str.len() method returns the number of characters in each string.
  For example:

names = pd.Series(["Amit", "Rahul", "Priya"])

names.str.len()

output:

0    4

1    5

2    5

dtype: int64

• str.split(): The str.split() method splits each string based on the separator we provide.
  For example:

names = pd.Series(["Amit Kumar", "Rahul Singh"])

names.str.split(" ")

output:

0      [Amit, Kumar]

1    [Rahul, Singh]

dtype: object

For example, if the Series contains full names, we can use str.split(" ") to separate the first name and last name.