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Команда дня: pipe

Сегодня делимся полезной фишкой из библиотеки pandas — метод .pipe() для создания чистых и читаемых цепочек обработки данных.

import pandas as pd

# Пример: очистка и преобразование данных в одну цепочку
def clean_data(df):
    return df.dropna().reset_index(drop=True)

def add_age_group(df):
    df['age_group'] = pd.cut(df['age'], bins=[0, 18, 35, 60, 100], labels=['Kid', 'Young', 'Adult', 'Senior'])
    return df

# Используем pipe для последовательной обработки
df = (pd.read_csv('data.csv')
      .pipe(clean_data)
      .pipe(add_age_group))

Зачем это нужно:

🎌

.pipe() позволяет организовать преобразования данных в логическую цепочку, улучшая читаемость кода
🎌 Удобно для сложных ETL-процессов (Extract, Transform, Load)
🎌 Легко добавлять новые шаги обработки

Пример в деле:

def normalize_column(df, col):
    df[col] = (df[col] - df[col].mean()) / df[col].std()
    return df

df = (pd.DataFrame({'value': [10, 20, 30, 40]})
      .pipe(normalize_column, col='value'))

Библиотека дата-сайентиста #буст

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2.1K viewsMay 7 at 07:58

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Create: 2025-05-07
Last Update: 2025-06-01 19:06:54

✅ Команда дня: pipe

Сегодня делимся полезной фишкой из библиотеки pandas — метод .pipe() для создания чистых и читаемых цепочек обработки данных.

import pandas as pd

# Пример: очистка и преобразование данных в одну цепочку
def clean_data(df):
    return df.dropna().reset_index(drop=True)

def add_age_group(df):
    df['age_group'] = pd.cut(df['age'], bins=[0, 18, 35, 60, 100], labels=['Kid', 'Young', 'Adult', 'Senior'])
    return df

# Используем pipe для последовательной обработки
df = (pd.read_csv('data.csv')
      .pipe(clean_data)
      .pipe(add_age_group))

Зачем это нужно:

🎌

def normalize_column(df, col):
    df[col] = (df[col] - df[col].mean()) / df[col].std()
    return df

df = (pd.DataFrame({'value': [10, 20, 30, 40]})
      .pipe(normalize_column, col='value'))

Библиотека дата-сайентиста #буст

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