Python for Data Science: Beginner-to-Advanced Tutorial

Build the mental model behind Python-based analysis Tutorial

A typical beginner task is loading a CSV of customer orders, converting the order date to a datetime, grouping revenue by month and checking whether any categories contain missing values.

• When learning Python-based analysis, connect the theory to a measurable question.
• Python is popular in data science because its syntax is approachable and its ecosystem connects notebooks, data frames, numerical computing, machine learning and visualization.
• If the result cannot be explained in plain language, inspect intermediate values before adding more complexity.
• The goal is not to memorize every Python feature.

Run small examples and inspect every intermediate object

For Python-based analysis, predict one visible result before each snippet runs—a row count, shape, statistic, ranking or error pattern—then compare it with the printed checkpoint.

# Python-based analysis · example 1
import pandas as pd

df = pd.read_csv('orders.csv', parse_dates=['order_date'])
print(df.isna().sum())
monthly = df.groupby(df['order_date'].dt.to_period('M'))['revenue'].sum()
print(monthly.tail())

Python-based analysis checkpoint 1: Run the cell and inspect the printed values before changing anything.

# Python-based analysis · example 2
import pandas as pd

df = pd.read_csv('practice.csv')
print(df.shape)
print(df.dtypes)
print(df.head(3).to_string(index=False))

Python-based analysis checkpoint 2: (30, 5)
Column types are printed, followed by the first three rows.

# Python-based analysis · example 3
missing = df.isna().sum().sort_values(ascending=False)
print(missing[missing.gt(0)])

Python-based analysis checkpoint 3: Only columns with at least one missing value are listed.

# Python-based analysis · example 4
summary = df.select_dtypes('number').agg(['count','mean','median','min','max']).T
print(summary.round(2))

Python-based analysis checkpoint 4: A compact numeric summary appears with one row per numeric column.

# Python-based analysis · example 5
test = df.copy()
if len(test):
    test.iloc[0, 0] = test.iloc[0, 0]
print('rows:', len(test), 'duplicates:', test.duplicated().sum())

Python-based analysis checkpoint 5: The row count and duplicate count make the check visible instead of implicit.

Debug shape, dtype and missing-value problems

Self-check with answers

Turn the lesson into a reusable notebook