BP101T • Unit 2Topic 3 of 4⏱ 5 Min Read

3. Pandas Series & DataFrames for Clinical & QC Laboratory Records

💡 Core Concept & Clinical Context:

Pandas is the leading Python data manipulation library. It introduces two fundamental structures: the `Series` (a 1-dimensional labeled array) and the `DataFrame` (a 2-dimensional labeled spreadsheet with heterogenous columns). Unlike NumPy arrays, Pandas DataFrames can hold mixed data types (text batch numbers, dates, float assay percentages, boolean release flags), making them ideal for pharmaceutical QC reports and clinical trials.

Pharmaceutical Code & Calculation Examples:

1Automated QC Laboratory Batch Screening Script

Building a DataFrame from production batch assays, calculating statistical limits, and flagging Out-of-Specification (OOS) batches.

example_1.py

import pandas as pd

qc_data = {
    "Batch_ID": ["B2601", "B2602", "B2603", "B2604", "B2605", "B2606"],
    "Drug_Name": ["Ciprofloxacin"] * 6,
    "Assay_Percent": [98.4, 101.2, 88.5, 99.8, 102.1, 94.0],  # IP Standard: 90.0% - 110.0%
    "Hardness_kg": [6.2, 5.8, 4.1, 6.0, 6.5, 3.8],            # Target: >= 4.5 kg
    "Disintegration_min": [8.2, 9.1, 14.5, 8.0, 7.9, 18.2]    # Target: <= 15 min
}

df = pd.DataFrame(qc_data)

# Boolean filtering for compliance
df["Assay_Pass"] = df["Assay_Percent"].between(90.0, 110.0)
df["Hardness_Pass"] = df["Hardness_kg"] >= 4.5
df["DT_Pass"] = df["Disintegration_min"] <= 15.0
df["Batch_Status"] = df["Assay_Pass"] & df["Hardness_Pass"] & df["DT_Pass"]

print("--- QC Batch Release Summary ---")
print(df[["Batch_ID", "Assay_Percent", "Hardness_kg", "Disintegration_min", "Batch_Status"]])

Comparison & Reference Table:

Method / Attribute	NumPy ndarray	Pandas DataFrame	Pharma Use Case
Data Homogeneity	Strictly homogeneous	Heterogeneous (mixed types)	Storing batch codes (str) + assays (float)
Labeling	Integer indices only	Custom column names & row index	Column headers for Assay, Hardness, DT
File Import	`np.loadtxt()` (numeric only)	`pd.read_csv()`, `pd.read_excel()`	Importing HPLC and dissolution data sheets
Missing Data	Limited NaN support	Built-in `.dropna()`, `.fillna()`	Handling missing lab readings in stability studies

📋Key Takeaways & Exam Highlights

Pandas Series: 1D labeled array capable of holding any data type.
Pandas DataFrame: 2D table structure with labeled axes (`index` for rows, `columns` for column names).
Boolean Indexing: `df[df['Assay_Percent'] < 90.0]` isolates failing batches instantly.
`df.describe()` outputs summary statistics: count, mean, std, min, 25%, 50%, 75%, max.

🇮🇳 हिन्दी व्याख्या (3. Pandas Series & DataFrames for Clinical & QC Laboratory Records)

Pandas DataFrame एक्सेल शीट की तरह काम करता है। इसमें बैच नंबर, दवा का नाम, प्रतिशत शुद्धता और हार्डनेस एक साथ स्टोर करके फेल बैचों को आसानी से छांटा जा सकता है।