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

4. Importing CSV/Excel Stability Data & Handling Missing Values

💡 Core Concept & Clinical Context:

Analytical instruments (HPLC, UV Spectrophotometers, Dissolution testers) export run logs in CSV or Excel format. Real-world laboratory datasets often contain missing entries due to instrument baselines, sensor disconnects, or sample loss. Pandas provides methods like `pd.read_csv()`, `.isna()`, `.dropna()`, and `.fillna()` to clean data before statistical analysis.

Pharmaceutical Code & Calculation Examples:

1Handling Missing Readings in Accelerated Stability Data

Cleaning a 6-month stability study dataset containing missing assay values.

example_1.py

import pandas as pd
import numpy as np

# Simulated stability dataset
stability_data = {
    "Month": [0, 1, 2, 3, 6],
    "Assay_Temp25C": [100.0, 99.4, 98.9, 98.2, 97.5],
    "Assay_Temp40C": [100.0, 97.8, np.nan, 93.1, 89.4]  # Missing Month 2 at 40°C
}

df_stab = pd.DataFrame(stability_data)

# Check for missing values
missing_count = df_stab.isna().sum()

# Impute missing value using linear interpolation
df_stab["Assay_Temp40C_Clean"] = df_stab["Assay_Temp40C"].interpolate(method="linear")

print("Cleaned Stability Data:")
print(df_stab)

📋Key Takeaways & Exam Highlights

`pd.read_csv('filename.csv')` and `pd.read_excel('filename.xlsx')` load external datasets.
`.isna()` or `.isnull()` returns boolean mask of missing cells.
`.dropna()` drops rows or columns containing missing values.
`.interpolate(method='linear')` estimates intermediate missing values along a continuous time series.

🇮🇳 हिन्दी व्याख्या (4. Importing CSV/Excel Stability Data & Handling Missing Values)

लैब मशीनों से निकलने वाले CSV और Excel फाइल्स को pd.read_csv() से लोड किया जाता है। छूटे हुए डेटा (Missing Values) को interpolate() से ठीक किया जाता है।