csvlisted
Install: claude install-skill Everfern-AI/Everfern
# 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