5 Best Ways to Filter City Column Elements by Removing Unique Prefix in a Python DataFrame

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πŸ’‘ Problem Formulation: When dealing with DataFrame in Python, a common task is to clean up the data. For example, you may have a DataFrame with a city column where each city name starts with a unique prefix that needs to be removed for data normalization or analysis purposes. If the city column contains values like “TX-Houston”, “CA-Los Angeles”, and “NY-New York”, your goal would be to have a new column with the values “Houston”, “Los Angeles”, and “New York”.

Method 1: Using str.split() and lambda function

This method employs pandas library’s str.split() method in conjunction with a lambda function to split the strings on the separator and then select the part of the string you want to keep. The str.split() separates the string into two parts based on a delimiter, and the lambda function specifies to keep the second part [1], which is the city name without the prefix.

Here’s an example:

import pandas as pd

# Sample dataframe
df = pd.DataFrame({'City': ['TX-Houston', 'CA-Los Angeles', 'NY-New York']})

# Removing unique prefix from the City column
df['City_Clean'] = df['City'].apply(lambda x: x.split('-')[1])

print(df)

Output:

            City   City_Clean
0      TX-Houston      Houston
1  CA-Los Angeles  Los Angeles
2      NY-New York     New York

This code snippet creates a new column called ‘City_Clean’ that contains the city names without the prefixes. The split('-') divides the string at the hyphen and a lambda function is used to select the second element of the resulting list, which contains the city name.

Method 2: Using DataFrame.apply() with a custom function

Method 2 involves defining a custom function to process each city name and then applying it to the DataFrame using DataFrame.apply(). This provides the flexibility to perform more complex operations on each element while still using the convenient DataFrame interface.

Here’s an example:

import pandas as pd

# Sample dataframe
df = pd.DataFrame({'City': ['TX-Houston', 'CA-Los Angeles', 'NY-New York']})

# Custom function to remove prefix
def remove_prefix(city):
    return city.split('-')[1]

# Applying custom function to the City column
df['City_Clean'] = df['City'].apply(remove_prefix)

print(df)

Output:

            City   City_Clean
0      TX-Houston      Houston
1  CA-Los Angeles  Los Angeles
2      NY-New York     New York

This code snippet works similarly to Method 1, but instead of a lambda function, it uses a named function remove_prefix() that performs the string splitting operation.

Method 3: Using str.replace() with Regular Expressions

Method 3 takes advantage of Python regular expressions with pandas str.replace() method to remove the prefix pattern from the city names. Regular expressions are powerful for pattern matching and work well when prefixes have a consistent structure.

Here’s an example:

import pandas as pd

# Sample dataframe
df = pd.DataFrame({'City': ['TX-Houston', 'CA-Los Angeles', 'NY-New York']})

# Removing prefix using regex
df['City_Clean'] = df['City'].str.replace(r'^.*-', '', regex=True)

print(df)

Output:

            City   City_Clean
0      TX-Houston      Houston
1  CA-Los Angeles  Los Angeles
2      NY-New York     New York

The code snippet uses the str.replace() method in combination with a regular expression that matches any characters up to and including the hyphen, indicating the prefix to be removed.

Method 4: Using DataFrame.replace() with a mapping dictionary

In Method 4, a mapping dictionary is used with the DataFrame.replace() function to replace the complete city designation, including its prefix, with only the city name. This is more precise and controlled, especially when prefixes are non-standard or if only specific entries need their prefixes removed.

Here’s an example:

import pandas as pd

# Sample dataframe
df = pd.DataFrame({'City': ['TX-Houston', 'CA-Los Angeles', 'NY-New York']})

# Mapping dictionary to replace full city strings
city_mapping = {'TX-Houston': 'Houston', 'CA-Los Angeles': 'Los Angeles', 'NY-New York': 'New York'}

# Replacing the city names using the mapping dictionary
df['City_Clean'] = df['City'].replace(city_mapping)

print(df)

Output:

            City   City_Clean
0      TX-Houston      Houston
1  CA-Los Angeles  Los Angeles
2      NY-New York     New York

This code snippet creates a dictionary mapping each city with a prefix to just the city name and then uses DataFrame’s replace() method to apply it to the city column.

Bonus One-Liner Method 5: Using List Comprehension

Bonus Method 5 is a concise one-liner that uses list comprehension to process each city name. It splits each string on the delimiter and directly constructs a new list with the desired parts, which can then be assigned to a new or existing DataFrame column.

Here’s an example:

import pandas as pd

# Sample dataframe
df = pd.DataFrame({'City': ['TX-Houston', 'CA-Los Angeles', 'NY-New York']})

# One-liner to remove prefixes using list comprehension
df['City_Clean'] = [city.split('-')[1] for city in df['City']]

print(df)

Output:

            City   City_Clean
0      TX-Houston      Houston
1  CA-Los Angeles  Los Angeles
2      NY-New York     New York

This code snippet uses list comprehension to split each element in the ‘City’ column and takes the city name from each resulting list to create the ‘City_Clean’ column.

Summary/Discussion

  • Method 1: Using str.split() and lambda function. Straightforward and concise. However, it might become less readable with more complex data manipulation.
  • Method 2: Using DataFrame.apply() with a custom function. It is clear and flexible for more complex operations but could be slower than vectorized operations.
  • Method 3: Using str.replace() with Regular Expressions. Very powerful for pattern matching, supports complex patterns, but can be overkill for simple cases and harder to read.
  • Method 4: Using DataFrame.replace() with a mapping dictionary. Precise mapping, great for specific replacements. Requires constructing a dictionary in advance, which may not be feasible for large data sets or dynamic prefixes.
  • Bonus Method 5: Using List Comprehension. Pythonic and efficient for simple operations. However, it lacks the pandas method chaining readability and might lead to less maintainable code for longer expressions.