Analizar CSVs

Escala y optimiza canalizaciones de datos con Polars

Liam Brannigan

Data Scientist & Polars Contributor

Extracto CSV de Finanzas

Chicago Finance Department
Generated: 2026-02-01
WARD;TYPE;STATUS;REQUEST_COST
11;Pothole in Street Complaint;Completed;125
42;Street Light Out;Open;88
27;Alley Light Out;Completed;61

🛑 El encabezado no está en la primera fila
🛑 Columnas separadas por punto y coma

Omitir filas

vendor_requests = pl.read_csv(
    "ward_service_requests.csv",
    skip_rows=2,

)

Omitir filas extra de encabezado

vendor_requests = pl.read_csv(
    "ward_service_requests.csv",
    skip_rows=2,
    separator=";",
)

Comprobar el archivo parseado

vendor_requests.head(3)

shape: (3, 4)
| WARD | TYPE                        | STATUS    | REQUEST_COST |
| ---  | ---                         | ---       | ---          |
| i64  | str                         | str       | i64          |
|------|-----------------------------|-----------|--------------|
| 11   | Pothole in Street Complaint | Completed | 125          |
| 42   | Street Light Out            | Open      |
| 27   | Alley Light Out             | Completed | 61           |

Un problema de inferencia de esquema

WARD;TYPE;STATUS;REQUEST_COST
11;Pothole in Street Complaint;Completed;125
42;Street Light Out;Open;88
27;Alley Light Out;Completed;61

Primeras 100 filas → esquema inferido
Solo ints → columna como Int64

Inferencia de esquema

WARD;TYPE;STATUS;REQUEST_COST
11;Pothole in Street Complaint;Completed;125
42;Street Light Out;Open;88
27;Alley Light Out;Completed;61
...
3;Rodent Baiting Service Request;Completed;61.5

Primeras 100 filas → esquema inferido
Solo ints → columna como Int64

Inferencia de esquema

vendor_requests = pl.read_csv(
    "ward_service_requests.csv",
    separator=";",
    skip_rows=2,
    infer_schema_length=200,
)

Inferencia de esquema

vendor_requests.schema

Schema({'WARD': Int64, 'TYPE': String, 'STATUS': String, 'REQUEST_COST': Float64})

Proporcionar el esquema

vendor_requests = pl.read_csv(
    "ward_service_requests.csv",
    separator=";",
    skip_rows=2,
    schema={
        "WARD": pl.Int64,
        "TYPE": pl.String,
        "STATUS": pl.String,
        "REQUEST_COST": pl.Float64,
    },
)

Sobrescribir el esquema inferido

vendor_requests = pl.read_csv(
    "ward_service_requests.csv",
    separator=";",
    skip_rows=2,
    schema_overrides={"REQUEST_COST": pl.Float64},
)

Comprobar la sobrescritura

vendor_requests.schema

Schema({'WARD': Int64, 'TYPE': String, 'STATUS': String, 'REQUEST_COST': Float64})

Un problema de datos erróneos

WARD;TYPE;STATUS;REQUEST_COST
11;Pothole in Street Complaint;Completed;125.0
unknown;Street Light Out;Open;88.5
27;Alley Light Out;Completed;61.0

Ignorar errores de parseo

pl.read_csv(
    "ward_service_requests.csv",
    separator=";",
    skip_rows=2,
    infer_schema_length=200,
    ignore_errors=True,
)

Valores erróneos → null
⚠ Oculta otros errores

Marcar valores nulos

pl.read_csv(
    "ward_service_requests.csv",
    separator=";",
    skip_rows=2,
    infer_schema_length=200,
    null_values={"WARD": "unknown"},
)

Conserva el dtype previsto
Más seguro que ignore_errors

¡Vamos a practicar!

Escala y optimiza canalizaciones de datos con Polars