ydata-profiling-1-basic-profile-report-generation

name	ydata-profiling-1-basic-profile-report-generation
description	Sub-skill of ydata-profiling: 1. Basic Profile Report Generation (+1).
version	1.0.0
category	data-analysis
type	reference
scripts_exempt	true

1. Basic Profile Report Generation (+1)

1. Basic Profile Report Generation

Simplest Usage:

from ydata_profiling import ProfileReport
import pandas as pd

# Load data
df = pd.read_csv("data.csv")

# Generate profile report
profile = ProfileReport(df, title="Data Quality Report")

# Save to HTML file
profile.to_file("report.html")

# Display in Jupyter notebook
profile.to_notebook_iframe()

With Configuration:

from ydata_profiling import ProfileReport
import pandas as pd

df = pd.read_csv("sales_data.csv")

# Customized profile report
profile = ProfileReport(
    df,
    title="Sales Data Quality Report",
    explorative=True,  # Enable all analyses
    dark_mode=False,
    orange_mode=False,
    config_file=None,  # Or path to custom config
    lazy=True  # Defer computation
)

# Access specific sections
print(profile.description_set)  # Variable descriptions
print(profile.get_description())  # Full description

# Save report
profile.to_file("sales_report.html")

From DataFrame with Sample Data:

from ydata_profiling import ProfileReport
import pandas as pd
import numpy as np
from datetime import datetime, timedelta

# Create sample dataset
np.random.seed(42)
n = 5000

df = pd.DataFrame({
    "customer_id": range(1, n + 1),
    "name": [f"Customer_{i}" for i in range(n)],
    "age": np.random.normal(40, 15, n).astype(int),
    "income": np.random.exponential(50000, n),
    "category": np.random.choice(["A", "B", "C", "D"], n, p=[0.4, 0.3, 0.2, 0.1]),
    "registration_date": [
        datetime(2020, 1, 1) + timedelta(days=int(d))
        for d in np.random.uniform(0, 1825, n)
    ],
    "is_active": np.random.choice([True, False], n, p=[0.8, 0.2]),
    "score": np.random.uniform(0, , n),
    : [  i  (n)]
})


df.loc[np.random.choice(n, ), ] = np.nan
df.loc[np.random.choice(n, ), ] = np.nan


profile = ProfileReport(df, title=)
profile.to_file()

2. Variable Analysis

Understanding Variable Types:

from ydata_profiling import ProfileReport
import pandas as pd
import numpy as np

# Dataset with various variable types
df = pd.DataFrame({
    # Numeric variables
    "integer_col": np.random.randint(1, 100, 1000),
    "float_col": np.random.randn(1000) * 100,

    # Categorical variables
    "category_high_card": [f"cat_{i}" for i in np.random.randint(1, 100, 1000)],
    "category_low_card": np.random.choice(["A", "B", "C"], 1000),

    # Boolean
    "boolean_col": np.random.choice([True, False], 1000),

    # Date/Time
    "date_col": pd.date_range("2020-01-01", periods=1000, freq="H"),

    # Text
    "text_col": ["Sample text " * np.random.randint(1, 10) for _ in range(1000)],

    # URL
    "url_col": [f"https://example.com/page/"  i  ()],

    
    : [] * ,

    
    : ()
})

profile = ProfileReport(
    df,
    title=,
    explorative=
)











profile.to_file()

Detailed Variable Statistics:

from ydata_profiling import ProfileReport
import pandas as pd
import numpy as np

df = pd.DataFrame({
    "revenue": np.random.exponential(1000, 5000),
    "quantity": np.random.randint(1, 100, 5000),
    "discount": np.random.uniform(0, 0.5, 5000),
    "category": np.random.choice(["Electronics", "Clothing", "Food"], 5000)
})

profile = ProfileReport(df, title="Sales Variables Analysis")

# Access variable-level statistics programmatically
description = profile.get_description()

# Numeric variable statistics
for var_name, var_data in description.variables.items():
    print(f"\n{var_name}:")
    print(f"  Type: {var_data['type']}")
    if "mean" in var_data:
        print(f"  Mean: {var_data['mean']:.2f}")
        print(f"  Std: {var_data['std']:.2f}")
        print(f"  Min: {var_data['min']:f}")
        ()