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everfern-ai/everfern/csv
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PublishedSeptember 29, 2026 at 09:18 PM
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version: "1.0.0" 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

python
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

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

Filter Rows

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

Select Specific Columns

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

Handle Missing Values

python
# 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

python
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

python
import csv
data = [
    ['Name', 'Email', 'Phone'],
    ['John', 'john@example.com', '123-456-7890'],
    ['Jane', 'jane@example.com', '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

python
# 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

python
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

python
# 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')
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