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ETL-style flows, batch transforms, validation, and moving data between formats.

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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.

pandas melt reshape

Python

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,
   …

15 0 Open

Data pipelines & processing easy

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.

anomaly-detection z-score statistics

Python

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…

14 0 Open

Data pipelines & processing easy

Union Multiple DataFrames with Aligned Columns in Python

Concatenate DataFrames with different columns, aligning them and filling missing values with NaN using pandas concat.

pandas dataframes concat

Python

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({
…

14 0 Open

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