vamseeachanta
vamseeachanta/workspace-hub
相关职业SOC
基于 SOC 职业分类
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基于 SOC 职业分类
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| name | ydata-profiling-6-comparison-reports |
| description | Sub-skill of ydata-profiling: 6. Comparison Reports. |
| version | 1.0.0 |
| category | data-analysis |
| type | reference |
| scripts_exempt | true |
Comparing Two Datasets:
from ydata_profiling import ProfileReport, compare
import pandas as pd
import numpy as np
# Create two related datasets
np.random.seed(42)
# Training data
df_train = pd.DataFrame({
"feature_1": np.random.randn(5000),
"feature_2": np.random.exponential(100, 5000),
"feature_3": np.random.choice(["A", "B", "C"], 5000),
"target": np.random.randint(0, 2, 5000)
})
# Test data (slightly different distribution)
df_test = pd.DataFrame({
"feature_1": np.random.randn(2000) * 1.2, # Different variance
"feature_2": np.random.exponential(120, 2000), # Different mean
"feature_3": np.random.choice(["A", "B", "C", "D"], 2000), # New category
"target": np.random.randint(0, 2, 2000)
})
# Generate individual profiles
profile_train = ProfileReport(df_train, title="Training Data")
profile_test = ProfileReport(df_test, title="Test Data")
# Compare profiles
comparison = compare([profile_train, profile_test])
# Save comparison report
comparison.to_file("train_test_comparison.html")
Before/After Comparison:
from ydata_profiling import ProfileReport, compare
import pandas as pd
import numpy as np
# Original data
df_before = pd.DataFrame({
"value": np.concatenate([
np.random.randn(900),
np.array([100, -50, 200, 150, -100]) # Outliers
]),
"category": np.random.choice(["A", "B", "C"], 905),
"score": np.random.uniform(0, 100, 905)
})
# Add missing values
df_before.loc[np.random.choice(905, 50), "value"] = np.nan
# Cleaned data (after preprocessing)
df_after = df_before.copy()
# Remove outliers
Q1 = df_after["value"].quantile(0.25)
Q3 = df_after["value"].quantile(0.75)
IQR = Q3 - Q1
df_after = df_after[
(df_after["value"] >= Q1 - 1.5 * IQR) &
(df_after["value"] <= Q3 + 1.5 * IQR)
]
# Fill missing values
df_after["value"] = df_after["value"].fillna(df_after["value"].median())
# Compare before and after
profile_before = ProfileReport(df_before, title="Before Cleaning")
profile_after = ProfileReport(df_after, title="After Cleaning")
comparison = compare([profile_before, profile_after])
comparison.to_file()