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Courses/Intermediate Python/Lesson 2

Lesson 2 of 5

File Handling & Data Processing

Read the overview in
English overview

Read and write files safely, choose CSV for flat tables and JSON for structured data, and use context managers and libraries to avoid data loss.

14:25 lectureIntermediate11 video chapters30 flashcards + 30 questions
Official Binary Tree uploadIntermediate Python Lesson 2Published 2026-08-05 · embedded with chapters, checkpoints, deep notes, and a project

Four clear stages

Learn → Project → Check → Finish

Watch and work through the lecture →
  1. 1LearnWatch and work through the lectureUse the chapter notebook and answer each video checkpoint.Do this now
  2. 2ProjectCode a CSV-to-JSON category summaryPlan it, create it, then prove it meets the definition of done.Next
  3. 3CheckAnswer all 30 questionsCorrect weak spots using the explanation after each answer.Next
  4. 4FinishMark the lesson completeThen move to APIs & External Libraries.Next
Course outlineIntermediate Python

Interactive lecture

Watch, pause, think, apply.

Read tabular data safely, compare CSV with JSON, import modules deliberately, and filter a pandas DataFrame for a documented question.

0:003 thinking points marked14:25

Numbered markers show where the video will pause. Seeking past one opens the first unanswered check.

Connecting to the lecture…Open on YouTube ↗

Chapter-by-chapter lecture notebook

Everything in the video, organized for learning

The lecture joins file formats, imports, and pandas. The core skill is turning an external file into a checked, explainable table.

Source reviewed14:25 lectureReviewed against the public lecture with timestamped slide sampling across the full runtime and cross-checked against the source lesson context.
11

video chapters mapped into notes, examples, and a concrete action.

This is a detailed learning companion reconstructed from the reviewed lecture—not a verbatim transcript.
01
0:00 in the lectureType review
What the video is teaching

Machine learning begins with a question and data process, not a model call. Files need known provenance, schema, units, missing-value rules, and permission.

Write what one row represents and what each column means before analysis. Preserve an untouched source copy and record transformations.

What to noticeWorked example

In an attendance file, one row might be one learner-week; attendance_rate needs a stated range and assessment_score needs a known scale.

Do this before continuing

Create a six-field data dictionary for a small CSV and mark one privacy risk.

Replay this chapter on YouTube ↗
02
1:15 in the lectureLooking ahead to machine learning
What the video is teaching

Machine learning begins with a question and data process, not a model call. Files need known provenance, schema, units, missing-value rules, and permission.

Write what one row represents and what each column means before analysis. Preserve an untouched source copy and record transformations.

What to noticeWorked example

In an attendance file, one row might be one learner-week; attendance_rate needs a stated range and assessment_score needs a known scale.

Do this before continuing

Create a six-field data dictionary for a small CSV and mark one privacy risk.

Replay this chapter on YouTube ↗
03
2:30 in the lectureCSV reader, writer, and dictionary variants
What the video is teaching

CSV is compact and natural for flat rows and columns; JSON supports nested records and preserves basic value types. CSV fields arrive as text until parsed.

Use csv.DictReader for labeled rows, newline-safe writers for output, and json load/dump for structured records. Validate headers and required fields.

What to noticeWorked example

A contact export fits CSV; a course with nested lessons, quizzes, and settings fits JSON.

Do this before continuing

Represent the same three records in CSV and JSON, then explain which loses or gains structure.

Replay this chapter on YouTube ↗
04
3:40 in the lectureCSV versus JSON
What the video is teaching

CSV is compact and natural for flat rows and columns; JSON supports nested records and preserves basic value types. CSV fields arrive as text until parsed.

Use csv.DictReader for labeled rows, newline-safe writers for output, and json load/dump for structured records. Validate headers and required fields.

What to noticeWorked example

A contact export fits CSV; a course with nested lessons, quizzes, and settings fits JSON.

Do this before continuing

Represent the same three records in CSV and JSON, then explain which loses or gains structure.

Replay this chapter on YouTube ↗
05
4:45 in the lectureImport modules, libraries, and functions
What the video is teaching

The standard library ships with Python; third-party packages require installation. import module, import package as alias, and from module import name expose different namespaces.

Record required versions, avoid name collisions, and confirm that a package source is trustworthy before installation.

What to noticeWorked example

import csv uses the standard library; import pandas as pd requires an installed pandas package documented in the project dependencies.

Do this before continuing

Create a dependency list for a script using csv, json, and pandas, noting which need installation.

Replay this chapter on YouTube ↗
06
5:50 in the lecturePandas and DataFrames
What the video is teaching

A DataFrame is a labeled two-dimensional table. Read the file, inspect shape, columns, types, head, missing values, and duplicate behavior before selecting rows.

Use loc with an explicit Boolean condition, retain enough columns to interpret the result, and count how many records were excluded.

What to noticeWorked example

df.loc[df["Age"] > 25] answers a defined filter, but only after Age is confirmed numeric and missing ages have a documented rule.

Do this before continuing

Write a five-step pandas audit and three filters tied to real questions.

Replay this chapter on YouTube ↗
07
7:10 in the lectureRows, columns, and loc
What the video is teaching

A DataFrame is a labeled two-dimensional table. Read the file, inspect shape, columns, types, head, missing values, and duplicate behavior before selecting rows.

Use loc with an explicit Boolean condition, retain enough columns to interpret the result, and count how many records were excluded.

What to noticeWorked example

df.loc[df["Age"] > 25] answers a defined filter, but only after Age is confirmed numeric and missing ages have a documented rule.

Do this before continuing

Write a five-step pandas audit and three filters tied to real questions.

Replay this chapter on YouTube ↗
08
8:55 in the lectureImport questions
What the video is teaching

A DataFrame is a labeled two-dimensional table. Read the file, inspect shape, columns, types, head, missing values, and duplicate behavior before selecting rows.

Use loc with an explicit Boolean condition, retain enough columns to interpret the result, and count how many records were excluded.

What to noticeWorked example

df.loc[df["Age"] > 25] answers a defined filter, but only after Age is confirmed numeric and missing ages have a documented rule.

Do this before continuing

Write a five-step pandas audit and three filters tied to real questions.

Replay this chapter on YouTube ↗
09
10:10 in the lectureRead CSV with pandas
What the video is teaching

A DataFrame is a labeled two-dimensional table. Read the file, inspect shape, columns, types, head, missing values, and duplicate behavior before selecting rows.

Use loc with an explicit Boolean condition, retain enough columns to interpret the result, and count how many records were excluded.

What to noticeWorked example

df.loc[df["Age"] > 25] answers a defined filter, but only after Age is confirmed numeric and missing ages have a documented rule.

Do this before continuing

Write a five-step pandas audit and three filters tied to real questions.

Replay this chapter on YouTube ↗
10
11:40 in the lectureConditional filtering
What the video is teaching

A DataFrame is a labeled two-dimensional table. Read the file, inspect shape, columns, types, head, missing values, and duplicate behavior before selecting rows.

Use loc with an explicit Boolean condition, retain enough columns to interpret the result, and count how many records were excluded.

What to noticeWorked example

df.loc[df["Age"] > 25] answers a defined filter, but only after Age is confirmed numeric and missing ages have a documented rule.

Do this before continuing

Write a five-step pandas audit and three filters tied to real questions.

Replay this chapter on YouTube ↗
11
13:10 in the lectureConceptual review
What the video is teaching

A DataFrame is a labeled two-dimensional table. Read the file, inspect shape, columns, types, head, missing values, and duplicate behavior before selecting rows.

Use loc with an explicit Boolean condition, retain enough columns to interpret the result, and count how many records were excluded.

What to noticeWorked example

df.loc[df["Age"] > 25] answers a defined filter, but only after Age is confirmed numeric and missing ages have a documented rule.

Do this before continuing

Write a five-step pandas audit and three filters tied to real questions.

Replay this chapter on YouTube ↗

Deep explanations

The ideas behind each chapter

Use these sections when the video moves quickly or you need another example.

011:15

Connect file handling to data questions

Machine learning begins with a question and data process, not a model call. Files need known provenance, schema, units, missing-value rules, and permission.

Write what one row represents and what each column means before analysis. Preserve an untouched source copy and record transformations.

Worked example

In an attendance file, one row might be one learner-week; attendance_rate needs a stated range and assessment_score needs a known scale.

Try it now

Create a six-field data dictionary for a small CSV and mark one privacy risk.

022:30

Choose CSV or JSON from structure

CSV is compact and natural for flat rows and columns; JSON supports nested records and preserves basic value types. CSV fields arrive as text until parsed.

Use csv.DictReader for labeled rows, newline-safe writers for output, and json load/dump for structured records. Validate headers and required fields.

Worked example

A contact export fits CSV; a course with nested lessons, quizzes, and settings fits JSON.

Try it now

Represent the same three records in CSV and JSON, then explain which loses or gains structure.

034:45

Import with dependency clarity

The standard library ships with Python; third-party packages require installation. import module, import package as alias, and from module import name expose different namespaces.

Record required versions, avoid name collisions, and confirm that a package source is trustworthy before installation.

Worked example

import csv uses the standard library; import pandas as pd requires an installed pandas package documented in the project dependencies.

Try it now

Create a dependency list for a script using csv, json, and pandas, noting which need installation.

045:50

Inspect before filtering a DataFrame

A DataFrame is a labeled two-dimensional table. Read the file, inspect shape, columns, types, head, missing values, and duplicate behavior before selecting rows.

Use loc with an explicit Boolean condition, retain enough columns to interpret the result, and count how many records were excluded.

Worked example

df.loc[df["Age"] > 25] answers a defined filter, but only after Age is confirmed numeric and missing ages have a documented rule.

Try it now

Write a five-step pandas audit and three filters tied to real questions.

Language of the lesson

Know these ideas

CSV
A flat text format representing rows with separated fields.
JSON
A structured text format supporting objects, arrays, and typed primitives.
Schema
The expected fields, types, and relationships in data.
Module
A file or package exposing reusable Python code.
DataFrame
A labeled two-dimensional pandas data structure.
Boolean mask
True/False values used to select matching rows.

Reason like a practitioner

Misconceptions to correct

  • CSV values keep their original numeric types.CSV stores text; the reader or analysis tool must parse types.
  • pandas fixes bad data automatically.The analyst must inspect and define cleaning rules.
  • A filter result is meaningful without documenting exclusions.Missing, invalid, and excluded records affect interpretation.
Transfer challenge

Create a documented CSV-to-DataFrame workflow with schema, provenance, import list, type checks, missing-data rule, three filters, and a saved result that preserves the source file.

Lesson project · Python data tool

Code a CSV-to-JSON category summary

A checked data-processing function with category counts, a predictable summary, and missing-data handling.

0%0 of 4 checks
Your brief

Load a small CSV of community resources, calculate a count by category, and save a JSON summary. Handle a missing file without crashing the program.

  1. 1
    Plan the work

    State the goal, audience or user, and the evidence a strong result needs. Explain how File mode changes your plan.

  2. 2
    Build and test

    Load a small CSV of community resources, calculate a count by category, and save a JSON summary. Handle a missing file without crashing the program.

  3. 3
    Prove and improve

    Use CSV and JSON to check the result. Record one piece of evidence, one correction, and one improvement you would make next.

Offline referenceRead the independent walkthrough and practice notes

Why this lesson matters

Read and write files safely, choose CSV for flat tables and JSON for structured data, and use context managers and libraries to avoid data loss.

The goal is not to memorize vocabulary. By the end of the lesson, you should be able to use the ideas in a realistic situation, explain the reason for your choices, and check whether the result actually works for the intended person or task.

Learning objectives

  • Explain File mode in your own words.
  • Apply CSV to a realistic classroom or community example.
  • Connect File mode with JSON when making a decision.
  • Complete the practice task and reflect on one improvement.

Core ideas

1. File mode

The instruction passed to open—such as read, write, or append—that determines what operations are allowed and whether existing content is preserved.

In practice: Look for this idea while you complete the lesson task. Pause before each major step and explain how File mode changes what you choose, create, or check.

2. CSV

A plain-text format for flat rows and columns that is easy to exchange with spreadsheets but stores values as text.

In practice: Look for this idea while you complete the lesson task. Pause before each major step and explain how CSV changes what you choose, create, or check.

3. JSON

A text format that preserves nested objects, arrays, numbers, booleans, and text for structured data exchange.

In practice: Look for this idea while you complete the lesson task. Pause before each major step and explain how JSON changes what you choose, create, or check.

How the ideas connect

Start with File mode to understand the foundation of the lesson. Use CSV to turn that understanding into an action. Then apply JSON to check the quality, safety, or usefulness of the result. The three ideas are strongest when you can explain their relationship rather than treating them as separate definitions.

Guided walkthrough

  1. Name the goal. In one sentence, write what you are trying to understand, create, or improve.
  2. Make a prediction. Before touching a device, use File mode and CSV to predict what a strong result should look like.
  3. Complete the task. Load a small CSV of community resources, calculate a count by category, and save a JSON summary. Handle a missing file without crashing the program.
  4. Check the outcome. Use JSON to inspect the result. Ask what worked, what did not, and what evidence supports your judgment.
  5. Explain and revise. Tell a partner what you changed and why. Make one small improvement, then compare the new result with the first one.

Worked classroom scenario

Imagine two learners sharing one device. The first learner is the driver and performs the steps; the second is the navigator and reads the goal, predicts the next step, and checks the result. Halfway through the task, switch roles. Both learners should be able to explain how File mode, CSV, and JSON appeared in the work.

If no device is available, complete the same reasoning on paper: sketch the screen or result, label each decision, and describe what you would test when a device becomes available.

Common mistakes and fixes

  • Rushing into the tool: Write the goal and prediction first so every click or step has a reason.
  • Copying without understanding: After each major step, explain it in your own words to a partner.
  • Accepting the first result: Compare the outcome with the goal and make at least one deliberate improvement.
  • Letting one person control a shared device: Rotate driver and navigator roles so both learners think and practice.

Independent practice

Load a small CSV of community resources, calculate a count by category, and save a JSON summary. Handle a missing file without crashing the program.

For an extra challenge, adapt the task for a different audience or community need. Write two sentences explaining what changed and which lesson idea guided your decision.

Check your understanding

  1. How would you explain File mode to someone new to the topic?
  2. What is one realistic example of CSV outside this classroom?
  3. When might JSON prevent a weak, unsafe, or confusing result?
  4. How are File mode and CSV connected?
  5. What evidence would convince you that your practice result works?
  6. If you repeated the activity tomorrow, what would you improve first and why?

Key takeaway

Read and write files safely, choose CSV for flat tables and JSON for structured data, and use context managers and libraries to avoid data loss.

You are ready to move on when you can explain the three core ideas, complete the practice without copying, and describe one improvement using evidence from your result.

Open lecture on YouTube ↗Open lecture deck ↗Study in focused mode