String Methods and Operations

Split Text into Parts

code.py

import pandas as pd

df = pd.DataFrame({
    'Full_Name': ['John Doe', 'Sarah Smith', 'Mike Johnson']
})

# Split by space
df[['First', 'Last']] = df['Full_Name'].str.split(' ', expand=True)
print(df)

Output:

      Full_Name First      Last
0      John Doe  John       Doe
1   Sarah Smith Sarah     Smith
2  Mike Johnson  Mike   Johnson

Get Part of Split

code.py

# Get only first name
df['First'] = df['Full_Name'].str.split(' ').str[0]

# Get only last name
df['Last'] = df['Full_Name'].str.split(' ').str[1]

Extract Part of Text

Get specific characters:

code.py

df = pd.DataFrame({
    'Code': ['ABC-123', 'DEF-456', 'GHI-789']
})

# Get first 3 characters
df['Letters'] = df['Code'].str[:3]

# Get last 3 characters
df['Numbers'] = df['Code'].str[-3:]

print(df)

Output:

      Code Letters Numbers
0  ABC-123     ABC     123
1  DEF-456     DEF     456
2  GHI-789     GHI     789

Check Start and End

code.py

df = pd.DataFrame({
    'Email': ['john@gmail.com', 'sarah@yahoo.com', 'test@gmail.com']
})

# Check if ends with gmail.com
df['Is_Gmail'] = df['Email'].str.endswith('gmail.com')

# Check if starts with 'john'
df['Is_John'] = df['Email'].str.startswith('john')

print(df)

Output:

            Email  Is_Gmail  Is_John
0  john@gmail.com      True     True
1 sarah@yahoo.com     False    False
2  test@gmail.com      True    False

Find Position of Text

code.py

df = pd.DataFrame({
    'Text': ['Hello World', 'Python is great', 'Data Science']
})

# Find where 'o' first appears (starts from 0)
df['Position'] = df['Text'].str.find('o')
print(df)

Output:

              Text  Position
0      Hello World         4
1  Python is great         4
2     Data Science        -1  <- not found

-1 means not found.

Count How Many Times Text Appears

code.py

df = pd.DataFrame({
    'Text': ['banana', 'apple', 'pineapple']
})

# Count letter 'a'
df['A_Count'] = df['Text'].str.count('a')
print(df)

Output:

        Text  A_Count
0     banana        3
1      apple        1
2  pineapple        1

Pad Text with Zeros

Good for codes that need fixed length:

code.py

df = pd.DataFrame({
    'ID': ['1', '23', '456']
})

# Make all IDs 5 digits with leading zeros
df['ID_Padded'] = df['ID'].str.zfill(5)
print(df)

Output:

    ID ID_Padded
0    1     00001
1   23     00023
2  456     00456

Quick Reference

Method	What It Does
.str.split('x')	Split text by 'x'
.str[:3]	First 3 characters
.str[-3:]	Last 3 characters
.str.startswith('x')	Starts with 'x'?
.str.endswith('x')	Ends with 'x'?
.str.find('x')	Position of 'x'
.str.count('x')	How many 'x'?
.str.zfill(5)	Pad with zeros

Key Points

split() breaks text into parts
str[start:end] extracts part of text
startswith() and endswith() check text
find() returns position (-1 if not found)
count() counts occurrences

What's Next?

For complex text patterns, learn Regular Expressions - a powerful search language.