Reading CSV Files with Pandas – B.Pharm Notes (Ch 18)

These free B.Pharm notes on reading csv files with pandas read_csv cover chapter 18 of Basics of Python Programming for Pharmaceutical Sciences (BP101T), a first-semester subject. Read the key points below, then study the complete chapter PDF. You can browse every chapter in the Basics of Python Programming for Pharmaceutical Sciences category.

Read the following document for full information

Overview of reading csv files with pandas read_csv

These notes explain how pd.read_csv() loads pharmaceutical data into a DataFrame, using a PK study dataset and an adverse drug reaction (ADR) dataset as examples. They cover practising in the W3Schools online environment, basic reading with file paths, and reading with explicit options such as na_values, parse_dates, sep and dtype.

Key points on reading csv files with pandas read_csv

The pd.read_csv() function reads a comma-separated values file from disk and loads its contents into a Pandas DataFrame.
Calling pd.read_csv() with only the filename works when the file is in the same folder as the script; otherwise the full path is needed.
The na_values parameter converts special codes such as BLQ, LLOQ, NA or a period into proper NaN values during import.
The parse_dates parameter converts named columns, such as sample collection times, into datetime objects when the file is loaded.
Setting sep=’\t’ reads tab-separated files, and dtype keeps a subject ID such as 001 as text instead of the integer 1.

Relevance to pharmacy practice

Pharmaceutical data usually comes from clinical data management systems, LIMS and pharmacovigilance platforms as CSV or Excel files. Reading PK concentration profiles and ADR reports correctly, including BLQ values, helps a student prepare clinical data accurately for analysis.

Every chapter in this subject builds on the one before it, so do not skip topics that look simple. Definitions, principles, procedures and examples are the parts that examiners return to most often, and a clear understanding here will also make later subjects easier to follow in the coming semesters.

Frequently asked questions on reading csv files with pandas read_csv

What does BLQ mean in a PK study dataset?

BLQ means below the limit of quantification. It is recorded when the plasma concentration drops below the assay’s detection threshold, as at the 12-hour sample in the notes.

How do you treat BLQ values as missing in pd.read_csv()?

Pass a list of codes to the na_values parameter, such as na_values=[“NA”, “.”, “BLQ”, “LLOQ”], and Pandas converts them into NaN values during import.

How do you read a tab-separated file in Pandas?

Set sep=’\t’ in pd.read_csv() so that Pandas splits on tabs instead of commas, or use pd.read_table() for the same purpose.

Quick revision checklist

Use this self-test after reading the chapter on reading csv files with pandas read_csv. If you can do all of the following without looking at your notes, you are ready for the exam.

I can explain: how pd.read_csv() loads a DataFrame
I can explain: the PK study and ADR sample datasets
I can explain: using file paths when reading CSV files
I can explain: the role of na_values and parse_dates
I can explain: why sep and dtype options matter

Study and exam tips

Start by reading the chapter once for the overall idea, then revise reading csv files with pandas read_csv using headings, definitions, principles, reactions and examples. Short-answer questions in the university exam usually ask for definitions, principles, procedures, reactions and uses, so write these in your own words and practise the equations, tables and flow charts that the chapter has.

For wider reading, standard books and open resources such as the NCBI Bookshelf help you confirm terms and see more examples. Revise reading csv files with pandas read_csv again a day later and again a week later, because spaced revision improves long-term memory.

This page is part of the Basics of Python Programming for Pharmaceutical Sciences series for first-semester B.Pharm students on B.Pharm Guru, where every chapter has a short summary and a full PDF. Bookmark this page and share it with classmates who are preparing for the same exam.

Next: 19. INSPECTING DATASETS USING FUNCTIONS SUCH AS HEAD(), TAIL(), INFO(), AND DESCRIBE(). Back to the Basics of Python Programming for Pharmaceutical Sciences category.