Конкатенация DataFrame
Преобразование данных с помощью Polars
Liam Brannigan
Data Scientist & Polars Contributor
Возвращаемся к приложению для ресторанов
shape: (4, 4)
| business | location | review | price |
| --- | --- | --- | --- |
| str | str | f64 | i64 |
|------------------|-------------|--------|-------|
| 7burgers | Wakey Wakey | 4.2 | 15 |
| Bang Bang Burger | Forest Rd. | 3.8 | 12 |
| Costa Coffee | City Point | 4.5 | 8 |
| The Queens Head | Denman St. | 4.7 | 25 |
Конкатенация
Конкатенация
Данные по центральному Лондону
import polars as pl
central = pl.read_csv("restaurants_central.csv")
shape: (4, 4)
| business | location | review | price |
| --- | --- | --- | --- |
| str | str | f64 | i64 |
|------------------|-------------|--------|-------|
| 7burgers | Wakey Wakey | 4.2 | 15 |
| Bang Bang Burger | Forest Rd. | 3.8 | 12 |
| Costa Coffee | City Point | 4.5 | 8 |
| The Queens Head | Denman St. | 4.7 | 25 |
Данные по южному Лондону
south = pl.read_csv("restaurants_south.csv")
shape: (3, 4)
| business | location | review | price |
| --- | --- | --- | --- |
| str | str | f64 | i64 |
|----------------|----------------|--------|-------|
| Pizzeria Bella | Bermondsey St. | 3.5 | 18 |
| Costa Coffee | Waterloo | 4.1 | 8 |
| Nando's | Brixton Rd. | 4.0 | 20 |
$$
- Вертикальная конкатенация — размещение DataFrame один над другим
Вертикальная конкатенация
reviews = pl.concat( )
Вертикальная конкатенация
reviews = pl.concat([central, south] )
Вертикальная конкатенация
reviews = pl.concat([central, south], how="vertical")
shape: (7, 4)
| business | location | review | price |
| --- | --- | --- | --- |
| str | str | f64 | i64 |
|------------------|----------------|--------|-------|
| 7burgers | Wakey Wakey | 4.2 | 15 |
| Bang Bang Burger | Forest Rd. | 3.8 | 12 |
| Costa Coffee | City Point | 4.5 | 8 |
| The Queens Head | Denman St. | 4.7 | 25 |
| Pizzeria Bella | Bermondsey St. | 3.5 | 18 |
| Costa Coffee | Waterloo | 4.1 | 8 |
| Nando's | Brixton Rd. | 4.0 | 20 |
Вертикальная конкатенация
reviews = pl.read_csv("restaurants*.csv")
shape: (7, 4)
| business | location | review | price |
| --- | --- | --- | --- |
| str | str | f64 | i64 |
|------------------|----------------|--------|-------|
| 7burgers | Wakey Wakey | 4.2 | 15 |
| Bang Bang Burger | Forest Rd. | 3.8 | 12 |
| Costa Coffee | City Point | 4.5 | 8 |
| The Queens Head | Denman St. | 4.7 | 25 |
| Pizzeria Bella | Bermondsey St. | 3.5 | 18 |
| Costa Coffee | Waterloo | 4.1 | 8 |
| Nando's | Brixton Rd. | 4.0 | 20 |
Третий набор с пропущенными данными
north = pl.read_csv("north.csv")
shape: (2, 3)
| business | location | review |
| --- | --- | --- |
| str | str | f64 |
|----------- |---------------|--------|
| Pig & Butcher | Liverpool Rd. | 3.2 |
| The Castle | Angle | 4.4 |
Вертикальная конкатенация завершается ошибкой
pl.concat([central, south, north], how="vertical")
ShapeError: unable to append to a DataFrame of width 4 with a DataFrame of width 3
Диагональная конкатенация
pl.concat([central, south, north], how="diagonal")
Диагональная конкатенация
pl.concat([central, south, north], how="diagonal")
shape: (9, 4)
| business | location | review | price |
| --- | --- | --- | --- |
| str | str | f64 | i64 |
|------------------|---------------------|--------|-------|
| 7burgers | Wakey Wakey | 4.2 | 15 |
| ... | ... | ... | ... |
| Nando's | Brixton Rd. | 4.0 | 20 |
| Pig & Butcher | Liverpool Rd. | 3.2 | null |
| The Castle | Angle | 4.4 | null |
Горизонтальная конкатенация
cuisine = pl.read_csv("cuisine.csv")
shape: (7, 1)
| cuisine |
| --- |
| str |
|----------|
| burgers |
| burgers |
| coffee |
| ... |
| chicken |
- Горизонтальная конкатенация — размещение DataFrame рядом
Горизонтальная конкатенация
pl.concat([reviews, cuisine], how="horizontal")
shape: (7, 5)
| business | location | review | price | cuisine |
| --- | --- | --- | --- | --- |
| str | str | f64 | i64 | str |
|------------------|----------------|--------|-------|----------|
| 7burgers | Wakey Wakey | 4.2 | 15 | burgers |
| Bang Bang Burger | Forest Rd. | 3.8 | 12 | burgers |
| Costa Coffee | City Point | 4.5 | 8 | coffee |
| The Queens Head | Denman St. | 4.7 | 25 | pub_food |
| Pizzeria Bella | Bermondsey St. | 3.5 | 18 | pizza |
| Costa Coffee | Waterloo | 4.1 | 8 | coffee |
| Nando's | Brixton Rd. | 4.0 | 20 | chicken |
Добавление строк с помощью extend
new_listing = pl.DataFrame({
"business": ["Wagamama"],
"location": ["Soho"],
"review": [4.3],
"price": [16]
})
shape: (1, 4)
| business | location | review | price |
| --- | --- | --- | --- |
| str | str | f64 | i64 |
|------------------|--------------|--------|-------|
| Wagamama | Soho | 4.3 | 16 |
Добавление строк с помощью extend
reviews.extend(new_listing)
shape: (8, 4)
| business | location | review | price |
| --- | --- | --- | --- |
| str | str | f64 | i64 |
|------------------|----------------|--------|-------|
| 7burgers | Wakey Wakey | 4.2 | 15 |
| ... | ... | ... | ... |
| Nando's | Brixton Rd. | 4.0 | 20 |
| Wagamama | Soho | 4.3 | 16 |
Давайте потренируемся!
Преобразование данных с помощью Polars
