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How to Align Text in Two Columns with ljust in Python
Format pairs of strings into two aligned columns using ljust padding.
items = [
("apple", "red"),
("banana", "yellow"),
("cherry", "dark red"),
("date", "brown")
]
col1_width = max(len(name) for name, _ in items) + 2
for name, color in items:
print(name.ljust(col1_width) + color)How to Transpose a Matrix in Python (List of Lists)
Swap rows and columns of a 2D list using nested loops to produce a transposed matrix.
def transpose(matrix):
# Number of rows and columns in the original matrix
rows = len(matrix)
cols = len(matrix[0]) if rows > 0 else 0
# Create a new matrix with dimensions swapped
result = []
for j in range(cols):
new_row = []
for i in range(rows):
new_row.appe…Detect Outliers in CSV Data Using Z-Score in Python
Read a CSV file and detect outliers in a numeric column by computing z-scores, flagging those exceeding a given threshold — no machine learning required.
import csv
import statistics
from math import sqrt
def detect_outliers(csv_path, column_name, threshold=2.0):
"""Detect outliers in a numeric column using z-score method."""
values = []
with open(csv_path, 'r', newline='') as f:
reader = csv.DictReader(f)
if column_name not in reader.field…Export SQLite Query Results to CSV in Python
Connects to a SQLite database, runs a query, and writes the result rows and column headers to a CSV file using the standard library.
import sqlite3
import csv
def export_query_to_csv(db_path, query, csv_path):
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
cursor.execute(query)
rows = cursor.fetchall()
column_names = [description[0] for description in cursor.description]
with open(csv_path, 'w', newline='', encodi…How to Convert CSV Column Types While Reading in Python
Read a CSV file and automatically convert column values to int, float, str, or bool based on type suffixes in the header names.
import csv
from pathlib import Path
from typing import Any
def read_csv_with_types(filepath: str) -> list[dict[str, Any]]:
"""Read CSV and convert column types based on header suffixes."""
converters = {
"int": int,
"float": float,
"str": str,
"bool": lambda v: v.strip().lower(…How to Filter CSV Rows by Column Value in Python
Filter CSV rows based on a column value condition using the standard csv module and a lambda function.
import csv
def filter_csv(input_file, output_file, column, condition):
with open(input_file, newline='', encoding='utf-8') as infile, \
open(output_file, 'w', newline='', encoding='utf-8') as outfile:
reader = csv.DictReader(infile)
fieldnames = reader.fieldnames
writer = csv.Dict…How to Handle Missing Values in a CSV Numeric Column in Python
Clean missing entries in a CSV numeric column by filling them with the mean, median, a custom value, or dropping rows.
import csv
from pathlib import Path
import statistics
def clean_csv_numeric(input_path: str, output_path: str, column: str, strategy: str = "mean") -> None:
"""
Handles missing values in a numeric column of a CSV file.
Strategies: 'mean', 'median', 'drop', or 'fill' with a specified value.
"""
row…How to Sum a CSV Column by Group in Python
This code reads a CSV string and sums a specified column for each unique value of a group key using the csv module and defaultdict.
import csv
from collections import defaultdict
from io import StringIO
def aggregate_csv(csv_data, group_key, sum_column):
totals = defaultdict(float)
reader = csv.DictReader(StringIO(csv_data))
for row in reader:
key = row[group_key]
totals[key] += float(row[sum_column])
return dict(t…Normalize CSV Column Names to snake_case in Python
Convert CSV header names to snake_case using a regular expression and write the updated file in place.
import csv
import re
import sys
def to_snake_case(header):
header = re.sub(r"(?<=[a-z0-9])(?=[A-Z])", "_", header)
header = re.sub(r"[^a-zA-Z0-9]+", "_", header).strip("_").lower()
return header
def normalize_csv_headers(input_path, output_path=None):
with open(input_path, newline="", encoding="utf…Parse Fixed Width Data File by Column Slices in Python
Extract fields from fixed-width text by slicing each line at defined column offsets, with a dictionary describing the boundaries.
from pathlib import Path
def parse_fixed_width(data: str, slices: dict[str, tuple[int, int]]) -> list[dict[str, str]]:
lines = data.strip().splitlines()
records = []
for line in lines:
record = {}
for name, (start, end) in slices.items():
record[name] = line[start:end].strip()…Read a CSV File with csv.DictReader in Python
Read a CSV file as a list of dictionaries, using csv.DictReader to map each row to column names.
import csv
from pathlib import Path
def read_csv_with_dictreader(file_path):
data = []
with open(file_path, mode='r', newline='', encoding='utf-8') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
data.append(row)
return data
if __name__ == "__main__":
# Cre…Parse CSV Data with a Python Class
Encapsulate CSV file loading and column/row access methods in a reusable DataParser class for beginners.
class DataParser:
def __init__(self, file_path):
self.file_path = file_path
self.data = []
def load_data(self):
with open(self.file_path, 'r') as file:
for line in file:
row = line.strip().split(',')
self.data.append(row)
return self.…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,
…How to detect anomalies in a column using z-score in Python
Detect outliers in a list of numbers using z-score statistics, flagging values that deviate significantly from the mean.
import random
def z_score_anomaly_detection(data, threshold=2.0):
"""
Detect anomalies in a list of numbers using z-score.
"""
mean = sum(data) / len(data)
variance = sum((x - mean) ** 2 for x in data) / len(data)
std_dev = variance ** 0.5
if std_dev == 0:
return []
a…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 Explode an Array Column in Python
This code demonstrates a mock explode operation that converts an array column into multiple rows, similar to Spark's explode function.
import json
def explode_array_column(data, column):
"""Mock explode: split array column into multiple rows."""
exploded = []
for row in data:
values = row.get(column, [])
for value in values:
new_row = dict(row)
new_row[column] = value
exploded.append(n…How to Filter and Project Spark DataFrames with PySpark SQL
Simulate a SQL SELECT with WHERE using PySpark DataFrame select and filter to project columns and apply conditions.
from pyspark.sql import SparkSession
from pyspark.sql.functions import col
spark = SparkSession.builder.appName("QueryFilterMock").master("local[2]").getOrCreate()
data = [
("Alice", 28, "Engineering"),
("Bob", 35, "Sales"),
("Carol", 32, "Engineering"),
("David", 25, "Marketing"),
("Eve", 29, "E…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 select specific columns in Python with SQLite
A reusable function that connects to a SQLite database and returns only the requested columns from a given table.
import sqlite3
def select_pruned_columns(db_path, table, columns):
with sqlite3.connect(db_path) as conn:
cursor = conn.cursor()
col_list = ", ".join(columns)
query = f"SELECT {col_list} FROM {table}"
return cursor.execute(query).fetchall()
if __name__ == "__main__":
conn = sq…Modeling a Hive Metastore Table Schema in Python
A dataclass that mimics a Hive metastore table schema—columns, partition keys, storage format, and location—with helper methods for description and mutation.
from dataclasses import dataclass, field
from typing import Dict, List, Optional
@dataclass
class HiveTable:
"""Simple mock of a Hive metastore table schema."""
name: str
database: str = "default"
columns: List[Dict[str, str]] = field(default_factory=list)
partition_keys: List[Dict[str, str]] = f…Create a Minimal Great Expectations Suite Mock in Python
Build a small Python class that mimics a Great Expectations suite, storing and serializing column expectations as JSON.
import json
class GreatExpectationsSuite:
"""A minimal mock of a Great Expectations suite."""
def __init__(self, suite_name, expectations=None):
self.suite_name = suite_name
self.expectations = expectations or []
def add_expectation(self, expectation_type, column=None, kwargs=None):
…How to Avoid SELECT * and Mock SQL Column Queries in Python
Mock a SQLite cursor to verify that queries specify explicit columns instead of using SELECT *.
import sqlite3
from unittest.mock import Mock, patch
def get_user_emails(connection):
"""Fetch only the required columns instead of SELECT *."""
cursor = connection.cursor()
cursor.execute("SELECT email FROM users")
return [row[0] for row in cursor.fetchall()]
def test_get_user_emails_specific_colu…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…How to enforce a unique index constraint in Python
Mock a database unique index in Python that rejects duplicate rows based on one or more columns.
class MockIndex:
def __init__(self, columns):
self.columns = columns
self._values = set()
def insert(self, row):
key = tuple(row[col] for col in self.columns)
if key in self._values:
raise ValueError(f"Duplicate key {key} for columns {self.columns}")
self._v…Browse by section
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