Introduction to Python

This article follows on from Why learn to code? and the introduction to R. Python and R largely do the same things for data analysis; their concepts transfer from one language to the other.

Why Python

Python was not designed for statistics, but to be a general-purpose, readable language. That gives it strengths complementary to R’s:

• Versatility: beyond data analysis, Python is used to automate tasks, handle files, query websites, build applications.
• Machine learning: the major machine learning and artificial intelligence libraries (scikit-learn, PyTorch…) are in Python. It is unavoidable once you move towards predictive modelling.
• Readability: its clean syntax makes it a good first language, including for those who have never programmed.

Jupyter notebooks

In data science, you often write Python in Jupyter notebooks: a document that alternates code cells, their results (tables, charts) and explanatory text. It is ideal for exploring data step by step and sharing a commented analysis. Free environments such as Google Colab let you use notebooks straight in the browser, with nothing to install.

pandas: working with tables

pandas is the central library for handling tabular data in Python. It organises data in an object called a DataFrame — a table with named rows and columns, like a spreadsheet.

import pandas as pd

# A table of patients (sex, age, creatinine)
patients = pd.DataFrame({
  "sex":        ["F", "M", "F", "F", "M", "F"],
  "age":        [72, 45, 34, 78, 60, 69],
  "creatinine": [142, 88, 71, 156, 97, 168],
})

# Filter women over 65
older_women = patients[(patients["sex"] == "F") & (patients["age"] > 65)]

# Mean creatinine by sex
patients.groupby("sex")["creatinine"].mean()

sex
F    134.25
M     92.50
Name: creatinine, dtype: float64

If you read the SQL and R articles, you recognise the same logic: filter rows, group, summarise. Only the syntax changes. In practice, you often load the data from a CSV file (a text table where columns are separated by commas) with pd.read_csv("patients.csv"), a very common exchange format.

matplotlib: visualising

matplotlib is the base library for plotting charts. Most plots fit in a few lines.

Python

import matplotlib.pyplot as plt

plt.scatter(patients["age"], patients["creatinine"])
plt.xlabel("Age (years)")
plt.ylabel("Creatinine (µmol/L)")
plt.title("Creatinine by age")
plt.show()

Result

patients.shape       # (number of rows, number of columns)
patients.info()      # type and count of non-missing values per column
patients.describe()  # min, max, mean, quartiles of numeric columns

Resources to learn

Python and Linkr

Once your data is extracted from a warehouse — in OMOP format, for example — Python and pandas are an excellent choice for extending the analysis beyond what the Study Designer generates automatically. The two approaches are complementary: the tool to design and frame, the code to explore without limits.