smartanalytics
A Python library for data cleaning, statistical analysis, and data insights.
Installation
pip install smartanalytics
Modules
1. cleaning — Data Cleaning
Clean and preprocess your DataFrames before analysis.
| Function | Description |
|---|---|
remove_nulls(df) |
Remove rows with missing values |
remove_duplicates(df) |
Remove duplicate rows |
fill_missing(df, value) |
Fill NaN values with a given value |
normalize_data(df) |
Min-Max normalize all numeric columns |
2. stats — Statistics
Compute core statistical measures from lists.
| Function | Description |
|---|---|
mean(data) |
Arithmetic mean |
median(data) |
Middle value |
mode(data) |
Most frequent value(s) |
standard_deviation(data) |
Spread of the data |
3. insights — Data Insights
Advanced analysis functions for real-world data understanding.
| Function | Description |
|---|---|
detect_outliers(data) |
IQR-based outlier detection |
correlation_matrix(df) |
Pearson correlation between columns |
dataset_summary(df) |
Shape, dtypes, nulls, and stats overview |
missing_value_report(df) |
Column-wise missing value report |
Quick Example
import pandas as pd
from smartanalytics import cleaning, stats, insights
# Sample dataset
df = pd.DataFrame({
'Age': [25, 30, None, 22, 30],
'Score': [88, 92, 95, 88, 92],
'Salary': [30000, 45000, 50000, 28000, 45000]
})
# Clean the data
df = cleaning.remove_nulls(df)
df = cleaning.remove_duplicates(df)
# Statistical analysis
print(stats.mean(df['Score'].tolist()))
print(stats.standard_deviation(df['Salary'].tolist()))
# Generate insights
print(insights.missing_value_report(df))
print(insights.correlation_matrix(df))
License
MIT
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