DataFrames को जोड़ना

Polars के साथ Data Transformation

Liam Brannigan

Data Scientist & Polars Contributor

Reviews डेटा

shape: (5, 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     |
| Costa Coffee     | Waterloo    | 4.1    | 8     |
| The Queens Head  | Denman St.  | 4.7    | 25    |

Inspections डेटासेट

inspections = pl.read_csv("restaurant_inspections.csv")

shape: (5, 4)
| business        | location    | type       | hygiene_rating |
| ---             | ---         | ---        | ---            |
| str             | str         | str        | i64            |
|-----------------|-------------|------------|----------------|
| 7burgers        | Wakey Wakey | restaurant | 4              |
| Costa Coffee    | City Point  | café       | 5              |
| Costa Coffee    | Waterloo    | café       | 3              |
| The Queens Head | Denman St.  | bar        | 5              |
| Wagamama        | Soho        | restaurant | 4              |

Join क्या है?

एकल DataFrame दिखाता डायग्राम.

Join क्या है?

दो DataFrames दिखाता डायग्राम.

Join क्या है?

मेल खाते key को हाइलाइट किए दो DataFrames का डायग्राम.

Join क्या है?

साझा कॉलम पर मिलते rows के आधार पर दो DataFrames को जोड़ते हुए डायग्राम.

Join क्या है?

साझा कॉलम पर मिलते rows से जुड़े दो DataFrames, मेल खाते key को हाइलाइट के साथ.

एक कॉलम पर join

reviews.join(                                       )

एक कॉलम पर join

reviews.join(inspections,                           )

एक कॉलम पर join

reviews.join(inspections, on="business"             )

एक कॉलम पर join

reviews.join(inspections, on="business", how="inner")

shape: (6, 7)
| business      | location    | ... | location_right | hygiene_rating |
| ---           | ---         | ... | ---            | ---            |
| str           | str         | ... | str            | i64            |
|---------------|-------------|-----|----------------|----------------|
| 7burgers      | Wakey Wakey | ... | Wakey Wakey    | 4              |
| Costa Coffee  | City Point  | ... | City Point     | 5              |
| Costa Coffee  | Waterloo    | ... | City Point     | 5              |
| Costa Coffee  | City Point  | ... | Waterloo       | 3              |
| Costa Coffee  | Waterloo    | ... | Waterloo       | 3              |
| The Queens Hd | Denman St.  | ... | Denman St.     | 5              |

एकाधिक कॉलम पर join

reviews.join(inspections, on=["business", "location"])

एकाधिक कॉलम पर join

reviews.join(inspections, on=["business", "location"])

shape: (4, 6)
| business        | location    | review | price | type       | hygiene_rating |
| ---             | ---         | ---    | ---   | ---        | ---            |
| str             | str         | f64    | i64   | str        | i64            |
|-----------------|-------------|--------|-------|------------|----------------|
| 7burgers        | Wakey Wakey | 4.2    | 15    | restaurant | 4              |
| Costa Coffee    | City Point  | 4.5    | 8     | café       | 5              |
| Costa Coffee    | Waterloo    | 4.1    | 8     | café       | 3              |
| The Queens Head | Denman St.  | 4.7    | 25    | bar        | 5              |

Left join

reviews.join(inspections, on=["business", "location"],           )

Left join

reviews.join(inspections, on=["business", "location"], how="left")

shape: (5, 6)
| business         | location    | review | price | type       | hygiene_rating |
| ---              | ---         | ---    | ---   | ---        | ---            |
| str              | str         | f64    | i64   | str        | i64            |
|------------------|-------------|--------|-------|------------|----------------|
| 7burgers         | Wakey Wakey | 4.2    | 15    | restaurant | 4              |
| Bang Bang Burger | Forest Rd.  | 3.8    | 12    | null       | null           |
| Costa Coffee     | City Point  | 4.5    | 8     | café       | 5              |
| Costa Coffee     | Waterloo    | 4.1    | 8     | café       | 3              |
| The Queens Head  | Denman St.  | 4.7    | 25    | bar        | 5              |

Full join

reviews.join(inspections, on=["business", "location"],                          )

Full join

reviews.join(inspections, on=["business", "location"], how="full"               )

Full join

reviews.join(inspections, on=["business", "location"], how="full", coalesce=True)

shape: (6, 6)
| business         | location    | review | price | type       | hygiene_rating |
| ---              | ---         | ---    | ---   | ---        | ---            |
| str              | str         | f64    | i64   | str        | i64            |
|------------------|-------------|--------|-------|------------|----------------|
| 7burgers         | Wakey Wakey | 4.2    | 15    | restaurant | 4              |
| Costa Coffee     | City Point  | 4.5    | 8     | café       | 5              |
| Costa Coffee     | Waterloo    | 4.1    | 8     | café       | 3              |
| The Queens Head  | Denman St.  | 4.7    | 25    | bar        | 5              |
| Wagamama         | Soho        | null   | null  | restaurant | 4              |
| Bang Bang Burger | Forest Rd.  | 3.8    | 12    | null       | null           |

Join रणनीति कैसे चुनें

2 टेबल

Inner join

Inner join का निरूपण

Left join

Left join का निरूपण

Full join

Full join का निरूपण

अभ्यास करते हैं!

Polars के साथ Data Transformation