csv-processing
Use this skill when reading sensor data from CSV files, writing simulation results to CSV, processing time-series data with pandas, or handling missing values in datasets.
Quellinformationen
- Repository
- benchflow-ai/skillsbench
- Letzte Quellaktivität
- 23. Januar 2026 um 21:26
- Erkannte Sprache von SKILL.md
- Englisch
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- 1.837
- Forks
- 371
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SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau- name
- csv-processing
- description
- Use this skill when reading sensor data from CSV files, writing simulation results to CSV, processing time-series data with pandas, or handling missing values in datasets.
# CSV Processing with Pandas
## Reading CSV
```python
import pandas as pd
df = pd.read_csv('data.csv')
# View structure
print(df.head())
print(df.columns.tolist())
print(len(df))
```
## Handling Missing Values
```python
# Read with explicit NA handling
df = pd.read_csv('data.csv', na_values=['', 'NA', 'null'])
# Check for missing values
print(df.isnull().sum())
# Check if specific value is NaN
if pd.isna(row['column']):
# Handle missing value
```
## Accessing Data
```python
# Single column
values = df['column_name']
# Multiple columns
subset = df[['col1', 'col2']]
# Filter rows
filtered = df[df['column'] > 10]
filtered = df[(df['time'] >= 30) & (df['time'] < 60)]
# Rows where column is not null
valid = df[df['column'].notna()]
```
## Writing CSV
```python
import pandas as pd
# From dictionary
data = {
'time': [0.0, 0.1, 0.2],
'value': [1.0, 2.0, 3.0],
'label': ['a', 'b', 'c']
}
df = pd.DataFrame(data)
df.to_csv('output.csv', index=False)
```
## Building Results Incrementally
```python
results = []
for item in items:
row = {
'time': item.time,
'value': item.value,
'status': item.status if item.valid else None
}
results.append(row)
df = pd.DataFrame(results)
df.to_csv('results.csv', index=False)
```
## Common Operations
```python
# Statistics
mean_val = df['column'].mean()
max_val = df['column'].max()
min_val = df['column'].min()
std_val = df['column'].std()
# Add computed column
df['diff'] = df['col1'] - df['col2']
# Iterate rows
for index, row in df.iterrows():
process(row['col1'], row['col2'])
```
Auf GitHub ansehen