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DATA SCIENCE PYTHON PLAYGROUND

Data Foundations · A little practice goes a long way.

← Choose a deckTINY TABLES · REAL PYTHON · YOUR PACE
Deck 01

Inspect

Meet a table, find your way around it, and ask your first useful questions.

01 / Getting oriented

I01Core
Concept sketch: named lists → rows and columnsdataname: [A,B]price: [2,4]namepriceA2B4named lists → rows and columns

Meet a DataFrame

Build a small table from named columns.

8 min · 3 practices
I02Core
Concept sketch: head(2): first two complete rowsnamepricescoreA28B46C39head(2): first two complete rows

Take the first look

Look at a few rows before making assumptions.

8 min · 3 practices
I01CSVCore
Concept sketch: CSV text → DataFrametiny.csvname,priceA,2B,4namepriceA2B4CSV text → DataFrame

Load a CSV

Load a file and inspect what arrived.

8 min · 3 practices
I03Core
Concept sketch: shape → (3 rows, 3 columns)namepricescoreA28B46C39shape → (3 rows, 3 columns)

How big is it?

Read the row and column counts.

8 min · 3 practices
I04Core
Concept sketch: columns → name, price, scorenamepricescoreA28B46C39columns → name, price, score

What columns arrived?

Find the exact column and row labels.

8 min · 3 practices
I05Core
Concept sketch: inspect each column’s data typenamepriceA2B4columndtypenameobjectpriceint64inspect each column’s data type

What types are these?

Distinguish numeric columns from text.

8 min · 3 practices
IR1Review
Concept sketch: Take the first look; How big is it?; What columns arrived?; What types are these?namepricescoreA28B46C39I02namepricescoreA28B46C39I03I02 + I03 + I04 + I05

Review · First contact

Retrieve earlier skills on a fresh table.

12 min · 4 retrieval tasks

02 / Selecting data

I06Core
Concept sketch: one selected column → SeriesnamepricescoreA28B46C39one selected column → Series

Pick one column

Select one named column as a Series.

8 min · 3 practices
I07Core
Concept sketch: select two columns in a chosen ordernamepricescoreA28B46C39scoreprice826493select two columns in a chosen order

Pick several columns

Keep a two-dimensional table of selected columns.

8 min · 3 practices
I08Core
Concept sketch: iloc[:2, :2]: positions 0 and 1posnameprice0A21B42C3iloc[:2, :2]: positions 0 and 1

Rows by position

Select rows using their zero-based positions.

8 min · 3 practices
I09Core
Concept sketch: loc: labels B–D, only pricelabelnamepriceAMilo2BLuna4CRex3DPip5loc: labels B–D, only price

Rows and columns by label

Select named rows and columns with loc.

8 min · 3 practices
I10Core
Concept sketch: price > 2: keep matching rowsrowpriceA2B4C3rowpriceB4C3price > 2: keep matching rows

Filter rows

Keep rows where a condition is true.

8 min · 3 practices
I11Core
Concept sketch: keep only rows passing BOTH testsprice>2fruity?keepnoyesnoyesnonoyesyesyeskeep only rows passing BOTH tests

Combine conditions

Combine row tests without losing their meaning.

8 min · 3 practices
IR2Review
Concept sketch: Pick several columns; Rows and columns by label; Combine conditionsnamepricescoreA28B46C39scoreprice826493I07labelnamepriceAMilo2BLuna4CRex3DPip5I09I07 + I09 + I11

Review · Select with intent

Retrieve earlier skills on a fresh table.

12 min · 3 retrieval tasks

03 / Understanding values

I12Core
Concept sketch: sort values: highest firstrowratingA8B6C9rowratingC9A8B6sort values: highest first

Sort the table

Order filtered rows to answer a question.

8 min · 3 practices
I13Go Further
Concept sketch: largest 2: order AND limit rowsrowpriceA2B4C3rowpriceB4C3largest 2: order AND limit rows

Find extremes

Ask for the largest or smallest observations.

8 min · 3 practices
I14Core
Concept sketch: 3 observations, 2 distinct categoriesvalueAABuniquenuniqueA2B3 observations, 2 distinct categories

What values exist?

Find distinct category values.

8 min · 3 practices
I15Core
Concept sketch: category counts → proportionsvalueAABvalueshareA2/3B1/3category counts → proportions

Count categories

Compare category frequencies and proportions.

8 min · 3 practices
I16Core
Concept sketch: count missing cells per columnpricetip2NaNNaN14NaNcolumnmissingprice1tip2count missing cells per column

Find missing values

Locate gaps before summarising.

8 min · 3 practices
I17Core
Concept sketch: one EXTRA duplicate; keep all rowsnamepriceextra?A2noB4noA2yesone EXTRA duplicate; keep all rows

Find duplicate rows

Count repeated records without removing anything.

8 min · 3 practices
IR3Review
Concept sketch: Sort the table; Count categories; Find missing values; Find duplicate rowsrowratingA8B6C9rowratingC9A8B6I12valueAABvalueshareA2/3B1/3I15I12 + I15 + I16 + I17

Review · Find the surprises

Retrieve earlier skills on a fresh table.

12 min · 4 retrieval tasks

04 / Reading the whole table

I18Core
Concept sketch: describe: summaries across columnsstatpriceqtycount33mean42min21max63describe: summaries across columns

Describe numeric columns

Read a compact numerical profile.

8 min · 3 practices
I18SCore
Concept sketch: one column → specific numerical answersprice246methodresultmean4sum12q755one column → specific numerical answers

Answer one numerical question

Choose a direct summary for a specific question.

8 min · 3 practices
I19Core
Concept sketch: collapse each group to its meangroupvalueA2A4B8groupmeanA3B8collapse each group to its mean

Summarise groups

Compare a simple average across categories.

8 min · 3 practices
I20Go Further
Concept sketch: crosstab: counts for category pairsXYA21B03crosstab: counts for category pairs

Compare categories

Count combinations of two categories.

8 min · 3 practices
I21Go Further
Concept sketch: correlation: symmetric, from −1 to 1xyzx1.00.5-0.5y0.51.00.0z-0.50.01.0correlation: symmetric, from −1 to 1

Inspect numeric relationships

Read correlation as association, not causation.

8 min · 3 practices
I22Review
Concept sketch: column types and missing counts; original unchangednamepriceA2BNaNfielddtypemissnameobject0pricefloat641column types and missing counts; original unchanged

Inspect checkpoint

Profile an unfamiliar eight-row table from scratch.

15 min · 3 retrieval tasks