csv

name	csv
description	Use this skill any time a CSV (Comma-Separated Values) file is the primary input or output. This includes tasks to: read, parse, analyze, clean, transform, or export CSV files; create new CSV files from data; merge or split CSV files; perform data analysis on tabular data in CSV format. If the user mentions a CSV file by name, extension, or path, use this skill.

CSV File Handling in EverFern

Overview

Users may ask you to read, analyze, create, or modify CSV files. Use Python with pandas for robust CSV operations on Windows with absolute paths (e.g., C:\Users\Username\Downloads\data.csv).

Reading and Analyzing CSV Files

Basic CSV Operations with pandas

import pandas as pd

# Read CSV
df = pd.read_csv(r'C:\path\to\file.csv')

# Preview data
print(df.head())           # First 5 rows
print(df.info())           # Column info and types
print(df.describe())       # Statistical summary
print(df.shape)            # Rows and columns count
print(df.columns.tolist()) # Column names

# Analyze data
print(df['ColumnName'].value_counts())  # Count unique values
print(df.isnull().sum())                # Missing values

Data Cleaning and Transformation

Remove Duplicates

df_clean = df.drop_duplicates()
df_clean.to_csv(r'C:\path\to\output.csv', index=False)

Filter Rows

filtered = df[df['ColumnName'] > 100]
filtered.to_csv(r'C:\path\to\filtered.csv', index=False)

Select Specific Columns

selected = df[['Column1', 'Column2', 'Column3']]
selected.to_csv(r'C:\path\to\selected.csv', index=False)

Handle Missing Values

# Remove rows with missing values
df_clean = df.dropna()

# Fill missing values
df_filled = df.fillna(0)  # Fill with 0
df_filled = df.fillna(df.mean())  # Fill with column mean

df_filled.to_csv(r'C:\path\to\output.csv', index=False)

Creating CSV Files

From Python Data

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['NYC', 'LA', 'Chicago']
}

df = pd.DataFrame(data)
df.to_csv(r'C:\path\to\new_file.csv', index=False)

From Lists

import csv

data = [
    ['Name', 'Email', 'Phone'],
    ['John', '[e-mail]', '123-456-7890'],
    ['Jane', '[e-mail]', '098-765-4321']
]

with open(r'C:\path\to\file.csv', 'w', newline='') as f:
    writer = csv.writer(f)
    writer.writerows(data)

Data Analysis

Group By and Aggregation

# Group by a column and calculate statistics
summary = df.groupby('Category')['Amount'].sum()
summary.to_csv(r'C:\path\to\summary.csv')

# Multiple aggregations
summary = df.groupby('Category').agg({
    'Amount': 'sum',
    'Count': 'count',
    'Average': 'mean'
})
summary.to_csv(r'C:\path\to\summary.csv')

Merge CSV Files

df1 = pd.read_csv(r'C:\path\to\file1.csv')
df2 = pd.read_csv(r'C:\path\to\file2.csv')

# Concatenate rows
merged = pd.concat([df1, df2], ignore_index=True)

# Merge by common column
merged = pd.merge(df1, df2, on='CommonColumn')

merged.to_csv(r'C:\path\to\merged.csv', index=False)

Export Options

# Tab-separated
df.to_csv(r'C:\path\to\file.tsv', sep='\t', index=False)

# With encoding
df.to_csv(r'C:\path\to\file.csv', index=False, encoding='utf-8')

# Custom delimiter
df.to_csv(r'C:\path\to\file.csv', sep=';', index=False)

Tips and Best Practices

• Always check data types: Use df.dtypes to verify columns were parsed correctly
• Specify encoding: Use encoding='utf-8' or encoding='latin-1' if needed
• Index handling: Use index=False when writing to avoid extra row numbers
• Large files: Use chunksize parameter for memory efficiency: pd.read_csv(file, chunksize=1000)
• Dialects: Specify dialect for special CSV formats: pd.read_csv(file, dialect='excel-tab')