5 Effective Ways to Filter and Export Cities and States Starting with ‘K’ from a DataFrame to CSV in Python

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πŸ’‘ Problem Formulation: Python developers often need to manipulate and extract data based on specific criteria. In this case, you may have a DataFrame containing city and state names, and you want to export those names that start with the letter ‘K’ into a new CSV file. For instance, from an input DataFrame with various city names, you want to create a new CSV file that only lists the cities and states that begin with ‘K’, such as ‘Kansas City, Kansas’.

Method 1: Using Pandas with String Matching

One reliable method involves using the Pandas library to handle data manipulation within the DataFrame. Specifically, the str.startswith() method is used to filter the data. Pandas proves to be efficient for such string matching tasks, especially when working with large data.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'City': ['Kansas City', 'New York', 'Kent', 'Boston'],
                   'State': ['Kansas', 'New York', 'Washington', 'Massachusetts']})

# Filtering cities and states that start with 'K'
filtered_df = df[df['City'].str.startswith('K') & df['State'].str.startswith('K')]

# Saving to CSV
filtered_df.to_csv('cities_starting_with_k.csv', index=False)

The output would be a CSV file containing the cities and states starting with ‘K’.

This snippet demonstrates how to use Pandas to filter out rows in a DataFrame where both the city and state columns have values that start with ‘K’. After the filtering, it saves the result to a CSV file. The use of the logical AND operator allows for compound conditions to be checked, thus providing accurate filtering based on both columns concurrently.

Method 2: Apply Function with Lambda

Another method is to use the DataFrame’s apply method along with a lambda function to perform row-wise filtration. This method provides more customizability for complex conditions and can be a more familiar syntax for some Python developers.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'City': ['Kansas City', 'New York', 'Kent', 'Boston'],
                   'State': ['Kansas', 'New York', 'Washington', 'Massachusetts']})

# Define a custom filter function
def starts_with_k(row):
    return row['City'].startswith('K') and row['State'].startswith('K')

# Apply the function and filter the DataFrame
filtered_df = df[df.apply(lambda row: starts_with_k(row), axis=1)]

# Saving to CSV
filtered_df.to_csv('cities_starting_with_k.csv', index=False)

The output would be the same CSV file as before, containing cities and states that start with ‘K’.

This code utilizes a custom function that checks if both ‘City’ and ‘State’ start with ‘K’. The apply method combined with a lambda function allows this check to be applied to each row. The result is then saved as a CSV, showcasing a versatile approach to data manipulation.

Method 3: Using Query Method

The query method in Pandas allows for filtering using a string that represents the condition. It’s an intuitive and SQL-like way to express your filtering conditions, which can be more readable for those familiar with SQL queries.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'City': ['Kansas City', 'New York', 'Kent', 'Boston'],
                   'State': ['Kansas', 'New York', 'Washington', 'Massachusetts']})

# Using query to filter data
filtered_df = df.query("City.str.startswith('K') and State.str.startswith('K')", engine='python')

# Saving to CSV
filtered_df.to_csv('cities_starting_with_k.csv', index=False)

The output is a CSV file with the selected cities and states.

This approach utilizes the DataFrame’s query method to select rows based on a query string. It is a concise and easily readable way to perform complex filtering. After filtering, the DataFrame is saved to a CSV without the index.

Method 4: Combining Conditionals with loc

The loc accessor in Pandas is another way to select data based on labeling information. Combined with conditional boolean indexing, it allows for fine-grained row selection.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'City': ['Kansas City', 'New York', 'Kent', 'Boston'],
                   'State': ['Kansas', 'New York', 'Washington', 'Massachusetts']})

# Use loc with conditionals
filtered_df = df.loc[df['City'].str.startswith('K') & df['State'].str.startswith('K')]

# Saving to CSV
filtered_df.to_csv('cities_starting_with_k.csv', index=False)

The output remains consistent, with the correct data saved to a CSV file.

By using loc, this snippet filters the DataFrame for rows where both ‘City’ and ‘State’ start with the letter ‘K’. The use of Boolean indexing with the AND operator β€˜&’ proves efficient in filtering based on multiple conditions.

Bonus One-Liner Method 5: Comprehensive List Comprehension

For more Pythonic code, one-liners like list comprehension can be used for filtering. This method might be preferable for simplicity and avoiding the use of external libraries for small datasets.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'City': ['Kansas City', 'New York', 'Kent', 'Boston'],
                   'State': ['Kansas', 'New York', 'Washington', 'Massachusetts']})

# One-liner using list comprehension
filtered_df = pd.DataFrame([row for index, row in df.iterrows() 
                            if row['City'].startswith('K') and row['State'].startswith('K')])

# Saving to CSV
filtered_df.to_csv('cities_starting_with_k.csv', index=False)

Output includes the filtered data saved to a CSV file, just as before.

This one-liner uses list comprehension to iterate over the DataFrame’s rows and filters out those that don’t start with ‘K’ for both city and state. It then saves the result into a CSV file. This is a clean and concise way to write filtering operations in Python.

Summary/Discussion

  • Method 1: Pandas String Matching. Strengths: Direct, efficient for large datasets. Weaknesses: Requires familiarity with Pandas.
  • Method 2: Apply Function with Lambda. Strengths: Customizable, good for complex conditions. Weaknesses: May be slower for large datasets.
  • Method 3: Using Query Method. Strengths: Readable, SQL-like syntax. Weaknesses: Requires Python engine for string functions, less efficient than vectorized methods.
  • Method 4: Combining Conditionals with loc. Strengths: Explicit, uses powerful indexing features of Pandas. Weaknesses: Slightly more verbose syntax.
  • Bonus Method 5: List Comprehension. Strengths: Pythonic, no need for external libraries for small data. Weaknesses: Not as efficient for large data, harder to read for more complex operations.