Python Code
Samples
Easy snippets you can copy, study, and run in the browser editor.
How to Explode an Array Field into Multiple Rows in Python
This code flattens a list of dictionaries by exploding each array field value into its own row, duplicating the other fields as needed.
from collections import defaultdict
data = [
{"id": 1, "name": "Alice", "tags": ["python", "data", "ai"]},
{"id": 2, "name": "Bob", "tags": ["web", "devops"]},
{"id": 3, "name": "Carol", "tags": []},
]
def explode_array_field(records, array_field):
result = []
for record in records:
for v…How to Unpivot Wide to Long with pandas melt in Python
This code demonstrates how to use pandas.melt to unpivot a wide DataFrame into a tidy long format, converting subject columns into rows.
import pandas as pd
# Sample wide-format data
df_wide = pd.DataFrame({
'id': [1, 2, 3],
'name': ['Alice', 'Bob', 'Charlie'],
'math': [90, 85, 95],
'science': [80, 92, 88]
})
print("Original wide DataFrame:")
print(df_wide)
# Melt: unpivot subject columns into rows
df_long = pd.melt(
df_wide,
…Union Multiple DataFrames with Aligned Columns in Python
Concatenate DataFrames with different columns, aligning them and filling missing values with NaN using pandas concat.
import pandas as pd
from io import StringIO
# Sample dataframes with different columns
df1 = pd.DataFrame({
'id': [1, 2, 3],
'name': ['Alice', 'Bob', 'Charlie'],
'age': [25, 30, 35]
})
df2 = pd.DataFrame({
'id': [4, 5],
'name': ['Diana', 'Eve'],
'city': ['NYC', 'LA']
})
df3 = pd.DataFrame({
…How to Mock DataFrame Schema Columns in Python
Create an empty pandas DataFrame with only the specified column names to mock a schema before any data is loaded.
import pandas as pd
def mock_schema(columns):
return pd.DataFrame(columns=columns)
if __name__ == "__main__":
cols = ["name", "age", "city"]
df = mock_schema(cols)
print(df)
print(f"Columns: {list(df.columns)}, Shape: {df.shape}")How to Load CSV Training Data in Python Without Pandas
Load CSV training data using Python's standard library and mock it with io.StringIO for testing, returning headers and rows as dictionaries.
import csv
from pathlib import Path
def load_csv_training_data(file_path: str | Path) -> tuple[list[str], list[dict[str, str]]]:
"""Load CSV training data and return headers plus rows as dictionaries."""
with open(file_path, mode="r", newline="", encoding="utf-8") as csv_file:
reader = csv.DictReader…Load CSV Training Data Without Pandas in Python
This code loads a CSV file into a list of dictionaries using only the standard library, ideal for small ML training data without heavy dependencies.
import csv
from pathlib import Path
def load_csv(path):
"""Load CSV file into list of dicts without pandas."""
rows = []
with open(path, newline='', encoding='utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
rows.append(dict(row))
return rows
if __name__ == "__m…Difference in Differences Mock in Python
Generate mock panel data with a known treatment effect and compute a difference-in-differences estimate using group and period means.
import numpy as np
import pandas as pd
# Generate mock panel data: 2 groups (control=0, treatment=1) × 2 periods (pre=0, post=1)
rng = np.random.default_rng(42)
n_per_cell = 50
data = []
for group in [0, 1]:
for period in [0, 1]:
# True effect: treatment increases outcome by 5 in the post period
…How to Speed Up Column Lookups with DataFrame Index in Python
Use pandas set_index to make repeated column value lookups O(1)-style fast instead of scanning the whole DataFrame each time.
import pandas as pd
# Mock dataset with duplicate customer IDs
data = {"customer_id": [101, 102, 103, 101, 104, 102],
"order_amount": [250.0, 85.5, 300.0, 175.25, 420.0, 95.75]}
df = pd.DataFrame(data)
df = df.set_index("customer_id")
# Simulated lookup request
search_id = 102
# Fast index-based lookup (no…Browse by section
Each section groups closely related Python snippets.
Guide: free Python code samples library
Copy-ready Python snippets for learners and developers
PythonSkillset code samples are short, focused examples organised by topic and difficulty. Every snippet is server-rendered HTML — readable by search engines and easy to copy. Open any sample, read the notes, copy the code, then press Try in editor to run it in the browser with Pyodide.
How to use this library
- Pick a topic section — strings, lists, files, functions, and more
- Open a sample, read How it works, and copy the code block
- Run it in the IDE, tweak values, then take a related quiz or tutorial lesson
Samples vs tutorials and challenges
Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.