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I have a dataframe with columns as below -

u'wellthie_issuer_identifier', u'issuer_name', u'service_area_identifier', u'hios_plan_identifier', u'plan_year', u'type'

I need to validate values in each column and finally have a dataframe which is valid.

For example, I need to check if plan_year column satisfies below validation

presence: true, numericality: true, length: { is: 4 }

hios_plan_identifier column satisfies below regex.

          format: /\A(\d{5}[A-Z]{2}[a-zA-Z0-9]{3,7}-TMP|\d{5}[A-Z]{2}\d{3,7}(\-?\d{2})*)\z/,
          presence: true, length: { minimum: 10 },

type column contains,

in: ['MetalPlan', 'MedicarePlan', 'BasicHealthPlan', 'DualPlan', 'MedicaidPlan', 'ChipPlan']

There are lot of columns which I need to validate. I have tried to give an example data.

I am able to check regex with str.contains('\A(\d{5}[A-Z]{2}[a-zA-Z0-9]{3,7}-TMP|\d{5}[A-Z]{2}\d{3,7}(\-?\d{2})*)\Z', regex=True)

Similary I can check other validation as well individually. I am confused as to how to put all the validation together. Should I put all in a if loop with and conditions. Is there a easy way to validate the dataframe columns ? Need help here

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2 Answers 2

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6

There are multiple pandas functions you could use of. Basically the syntax you could use to filter your dataframe by content is:

df = df[(condition1) & (condition2) & ...] # filter the df and assign to the same df

Specifically for your case, you could replace condition with following functions(expressions):

df[some_column] == some_value 
df[some_column].isin(some_list_of_values) # This check whether the value of the column is one of the values in the list
df[some_column].str.contains() # You can use it the same as str.contains()
df[some_column].str.isdigit() # Same usage as str.isdigit(), check whether string is all digits, need to make sure column type is string in advance
df[some_column].str.len() == 4 # Filter string with length of 4

Finally, if you want to reset the index, you could use df = df.reset_index(drop=True) to reset your output df index to 0,1,2,...

Edit: To check for NaN, NaT, None values you could use

df[some_column].isnull()

For multiple columns, you could use 

df[[col1, col2]].isin(valuelist).all(axis=1)
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7 Comments

How to check if particualar column is not empty? using is not null?
df[some_column].isnull() can check for NaN, NaT, and None, but cannot check for inf and empty string, for empty string you can check the length instead
Hi Kevin, ((df['plan_year'].notnull().any()) & (df['plan_year'].str.isdigit()) & (df['plan_year'].str.len() == 4)) . is this the correct way to do it or can I group it somehow . Also this is throwing me Can only use .str accessor with string values, error.
@user1896796 You don't need the .any(), and you have to make sure your column type is string before using .str.isdigit(), if you're not sure you can do .astype(str).str.isdigit()
Thanks Kevin . One more help pls. I have same set of validations on multiple columns . Can I somehow group all columns together or I will have to validate again and again for all?
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follow the python script:

import pandas as pd
from typing import Optional, List, Tuple, Dict, Union

def validate_dataframe(df: pd.DataFrame, 
                        n_cols: Optional[int] = None, 
                        n_rows: Optional[Tuple[int, int]] = None, 
                        columns: Optional[List[str]] = None, 
                        column_types: Optional[Dict[str, type]] = None,
                        check_duplicates: Optional[bool] = False,
                        check_null_values: Optional[bool] = False,
                        unique_columns: Optional[List[str]] = None, 
                        column_ranges: Optional[Dict[str, Tuple[Union[int, float], Union[int, float]]]] = None,
                        date_columns: Optional[List[str]] = None,
                        categorical_columns: Optional[Dict[str, List[Union[str, int, float]]]] = None
                        ) -> Tuple[bool, str]:
    """
    Validates a Pandas DataFrame based on specified criteria.

    Parameters:
    - df (pd.DataFrame): The DataFrame to be validated.
    - n_cols (int, optional): Number of expected columns in the DataFrame.
    - n_rows (tuple, optional): Tuple (min_rows, max_rows) specifying the expected range of rows.
    - columns (list, optional): List of column names that should be present in the DataFrame.
    - column_types (dict, optional): Dictionary mapping column names to the expected data types.
    - check_duplicates (bool, optional): Check for the presence of duplicate rows in the DataFrame.
    - check_null_values (bool, optional): Check for the presence of null values in the DataFrame.
    - unique_columns (list, optional): List of columns that should have only unique values.
    - column_ranges (dict, optional): Dictionary mapping numeric columns to the allowed ranges.
    - date_columns (list, optional): List of columns containing date values to validate the format.
    - categorical_columns (dict, optional): Dictionary mapping categorical columns to allowed values.

    Returns:
    - tuple: (bool, str) indicating success or failure, and an optional description of the problem.
    """
    
    # Validate number of columns
    if n_cols is not None and len(df.columns) != n_cols:
        return (False, f"Error: Expected {n_cols} columns, but found {len(df.columns)} columns.")

    # Validate row range
    if n_rows is not None:
        min_rows, max_rows = n_rows
        if not (min_rows <= len(df) <= max_rows):
            return (False, f"Error: Number of rows should be between {min_rows} and {max_rows}.")

    # Validate columns
    if columns is not None:
        if not set(columns).issubset(df.columns):
            missing_columns = set(columns) - set(df.columns)
            return (False, f"Error: Missing columns: {missing_columns}.")

    # Validate column types
    if column_types is not None:
        if set(column_types.keys()).issubset(df.columns):
            for col, expected_type in column_types.items():
                if not df[col].dtype == expected_type:
                    return (False, f"Error: Column '{col}' should have type {expected_type}.")
        else:
            return (False, "Error: Some columns in column_types are not present in the DataFrame.")

    # Validate duplicates ££ ONLY SPECIFIC COLUMNS
    if check_duplicates and df.duplicated().any():
        return (False, "Duplicates found in the DataFrame.")

    # Validate null values ££ ONLY SPECIFIC COLUMNS
    if check_null_values and df.isnull().any().any():
        return (False, "DataFrame contains null values.")

    # Validate unique values in specific columns
    if unique_columns is not None:
        if set(unique_columns).issubset(df.columns):
            if df[unique_columns].nunique().ne(df[unique_columns].count()).any():
                return (False, "Some columns should have only unique values.")

    # Validate values in a specific range
    if column_ranges is not None:
        for col, value_range in column_ranges.items():
            if col in df.columns and not df[col].between(*value_range).all():
                return (False, f"Values in '{col}' should be between {value_range[0]} and {value_range[1]}.")

    # Validate date format (assuming 'date_columns' are date columns)
    if date_columns is not None:
        for col in date_columns:
            if col in df.columns:
                try:
                    pd.to_datetime(df[col], errors='raise')
                except ValueError:
                    return (False, f"'{col}' should be in a valid date format.")

    # Validate categorical values
    if categorical_columns is not None:
        for col, allowed_values in categorical_columns.items():
            if col in df.columns and not df[col].isin(allowed_values).all():
                return (False, f"Values in '{col}' should be {allowed_values}.")

    # If all validations pass, return True
    return (True, "DataFrame has passed all validations.")
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