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DataRecipe

Table of Contents

  1. Overview
  2. Functions
  3. Contact Information

Overview

This toolkit provides a variety of Python functions to facilitate common data manipulation, data import/export, and database operations.

Functions

General Features

send_email

Sends an email using SMTP with SSL/TLS options, supporting attachments if provided.

  • Parameters:
    • subject: Email subject as a string.
    • body: Main content of the email.
    • send_email_address: Sender's email address.
    • send_email_password: Sender's email password for SMTP authentication.
    • receive_email_address: Recipient's email address.
    • attachment_path: Directory path where attachments are stored (optional).
    • attachment_list: List of filenames to be attached (optional).
    • smtp_address: SMTP server address (default: 'smtp.feishu.cn').
    • smtp_port: SMTP server port (default: 465).

Example with Attachments:

send_email(
    "Meeting Documents", 
    "Please see attached documents for the upcoming meeting.", 
    "sender@example.com", 
    "password123", 
    "receiver@example.com", 
    attachment_path="/path/to/documents", 
    attachment_list=["agenda.pdf", "minutes.docx"]
)

Data Validation and Cleaning

check_empty

Checks for empty entries in specified DataFrame columns.

  • Parameters:
    • df: DataFrame to check.
    • columns: Columns to check for missing values.
    • output_cols: Columns to include in the output.

Example:

empty_data = check_empty(df, columns=["name", "email"])

clean_dataframe

Cleans DataFrame by replacing infinite values with NaN.

  • Parameters:
    • df: DataFrame to clean.

Example:

clean_dataframe(df)

Data Import/Export

local_to_df

Converts files from a local directory to a pandas DataFrame.

  • Parameters:
    • path: Directory path to search for files.
    • partial_file_name: File name pattern to match.
    • skip_rows: Number of rows to skip at the start of each file.
    • keep_file_name: If True, adds a column with the file name.
    • sheet_num: For Excel files, specifies the sheet number to read.
    • encoding: Character encoding of the files.

Example with CSV files:

df = local_to_df("./data", "sample", keep_file_name=True)

Example with Excel files:

df = local_to_df("./data", "report", sheet_num=2, encoding='utf-8')

df_to_xlsx

Saves a DataFrame to an Excel file.

  • Parameters:
    • df: DataFrame to save.
    • directory_path: Path to directory where the file will be saved.
    • file_name: Name of the output file.

Example:

df_to_xlsx(df, "./output", "output_data")

df_to_csv

Saves a DataFrame to a CSV file.

  • Parameters:
    • df: DataFrame to save.
    • directory_path: Path to directory where the file will be saved.
    • file_name: Name of the output file.

Example:

df_to_csv(df, "./output", "output_data")

Database Operations

update

Updates records in a database table based on conditions.

  • Parameters:
    • raw_df: DataFrame containing new data to update.
    • database: Database name.
    • table: Table name.
    • yaml_file_name: YAML file name with DB configuration.
    • clause: SQL clause for record deletion.
    • date_col: Column name containing date data.
    • custom_path: Path to directory containing the YAML file.

Example:

update(df, "test_db", "user_data", clause="user_id > 10")

sql_query

Executes a SELECT SQL query and returns a DataFrame.

  • Parameters:
    • database: Database name.
    • sql: SQL SELECT statement.
    • yaml_file_name: YAML file name with DB configuration.
    • custom_path: Optional path to directory containing the YAML file.

Example:

result_df = sql_query("test_db", "SELECT * FROM users")

Contact Information

For any questions or suggestions regarding the toolkit, please contact us at:

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