Data Analysis Plus

Enhanced data analysis with Python/R code templates, visualization gallery, statistical tests, and automated report generation. Covers hypothesis testing, regression, clustering, time series, and more.

Enhanced data analysis with code templates, visualization gallery, and statistical methods.

Features

• Code Templates: Python/R ready-to-use templates
• Visualization Gallery: Charts for every analysis type
• Statistical Methods: Hypothesis testing, regression, clustering
• Automated Reports: Decision-ready output formats
• Data Validation: Quality checks before analysis

Quick Reference

Analysis Type	Python Template	R Template
Descriptive	df.describe()	summary(df)
Hypothesis	scipy.stats.ttest_ind()	t.test()
Regression	sklearn.linear_model	lm()
Clustering	sklearn.cluster.KMeans	kmeans()
Time Series	statsmodels.tsa	forecast::auto.arima()

Python Templates

Data Loading

python

import pandas as pd
import numpy as np

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

# Excel
df = pd.read_excel("data.xlsx")

# JSON
df = pd.read_json("data.json")

# Database
import sqlalchemy
engine = sqlalchemy.create_engine("sqlite:///data.db")
df = pd.read_sql("SELECT * FROM table", engine)

Descriptive Statistics

python

# Basic stats
df.describe()

# By group
df.groupby("category").agg({
    "value": ["mean", "median", "std", "count"]
})

# Correlation
df.corr()

Data Cleaning

python

# Missing values
df.isnull().sum()
df.fillna(df.mean())
df.dropna()

# Duplicates
df.duplicated().sum()
df.drop_duplicates()

# Outliers
Q1 = df["value"].quantile(0.25)
Q3 = df["value"].quantile(0.75)
IQR = Q3 - Q1
df = df[(df["value"] >= Q1 - 1.5*IQR) & (df["value"] <= Q3 + 1.5*IQR)]

Hypothesis Testing

python

from scipy import stats

# T-test
group1 = df[df["group"] == "A"]["value"]
group2 = df[df["group"] == "B"]["value"]
stat, p_value = stats.ttest_ind(group1, group2)

# Chi-square
contingency = pd.crosstab(df["cat1"], df["cat2"])
chi2, p_value, dof, expected = stats.chi2_contingency(contingency)

# ANOVA
f_stat, p_value = stats.f_oneway(group1, group2, group3)

Regression

python

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

X = df[["feature1", "feature2"]]
y = df["target"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model = LinearRegression()
model.fit(X_train, y_train)

print(f"R²: {model.score(X_test, y_test)}")
print(f"Coefficients: {model.coef_}")

Clustering

python

from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_scaled = scaler.fit_transform(df[["feature1", "feature2"]])

kmeans = KMeans(n_clusters=3, random_state=42)
df["cluster"] = kmeans.fit_predict(X_scaled)

Time Series

python

import pandas as pd
from statsmodels.tsa.seasonal import seasonal_decompose

# Parse dates
df["date"] = pd.to_datetime(df["date"])
df = df.set_index("date")

# Decompose
decomposition = seasonal_decompose(df["value"], model="additive", period=12)
decomposition.plot()

R Templates

Data Loading

r

library(readr)
library(readxl)

# CSV
df <- read_csv("data.csv")

# Excel
df <- read_excel("data.xlsx")

# JSON
library(jsonlite)
df <- fromJSON("data.json")

Descriptive Statistics

r

# Basic stats
summary(df)

# By group
library(dplyr)
df %>%
  group_by(category) %>%
  summarise(
    mean = mean(value, na.rm = TRUE),
    median = median(value, na.rm = TRUE),
    sd = sd(value, na.rm = TRUE),
    n = n()
  )

# Correlation
cor(df[, sapply(df, is.numeric)], use = "complete.obs")

Hypothesis Testing

r

# T-test
t.test(value ~ group, data = df)

# Chi-square
chisq.test(table(df$cat1, df$cat2))

# ANOVA
aov_result <- aov(value ~ group, data = df)
summary(aov_result)

Regression

r

# Linear regression
model <- lm(target ~ feature1 + feature2, data = df)
summary(model)

# Logistic regression
model <- glm(binary_target ~ feature1 + feature2, data = df, family = "binomial")
summary(model)

Clustering

r

# K-means
library(cluster)
df_scaled <- scale(df[, c("feature1", "feature2")])
kmeans_result <- kmeans(df_scaled, centers = 3)
df$cluster <- kmeans_result$cluster

Visualization Gallery

Chart Selection Guide

Question Type	Chart	Python	R
Trend over time	Line	matplotlib	ggplot2
Comparison	Bar	seaborn.barplot	ggplot2::geom_bar
Distribution	Histogram	seaborn.histplot	ggplot2::geom_histogram
Relationship	Scatter	seaborn.scatterplot	ggplot2::geom_point
Composition	Pie/Stacked Bar	matplotlib.pyplot.pie	ggplot2::geom_bar(position="fill")
Correlation	Heatmap	seaborn.heatmap	pheatmap

Python Visualization

python

import matplotlib.pyplot as plt
import seaborn as sns

# Line plot
plt.figure(figsize=(10, 6))
sns.lineplot(data=df, x="date", y="value", hue="category")
plt.title("Trend Over Time")
plt.savefig("trend.png", dpi=300, bbox_inches="tight")

# Scatter plot
plt.figure(figsize=(10, 6))
sns.scatterplot(data=df, x="feature1", y="feature2", hue="target")
plt.title("Feature Relationship")
plt.savefig("scatter.png", dpi=300, bbox_inches="tight")

# Heatmap
plt.figure(figsize=(10, 8))
sns.heatmap(df.corr(), annot=True, cmap="coolwarm", center=0)
plt.title("Correlation Matrix")
plt.savefig("heatmap.png", dpi=300, bbox_inches="tight")

R Visualization

r

library(ggplot2)

# Line plot
ggplot(df, aes(x = date, y = value, color = category)) +
  geom_line() +
  labs(title = "Trend Over Time") +
  theme_minimal()

# Scatter plot
ggplot(df, aes(x = feature1, y = feature2, color = target)) +
  geom_point() +
  labs(title = "Feature Relationship") +
  theme_minimal()

# Bar plot
ggplot(df, aes(x = category, fill = category)) +
  geom_bar() +
  labs(title = "Category Distribution") +
  theme_minimal()

Statistical Methods

Hypothesis Testing Decision Tree

text

Is the data categorical?
├── Yes → Chi-square test
└── No → Is the data normally distributed?
    ├── Yes → T-test (2 groups) / ANOVA (3+ groups)
    └── No → Mann-Whitney U (2 groups) / Kruskal-Wallis (3+ groups)

Sample Size Calculator

python

def sample_size计算器(effect_size, alpha=0.05, power=0.8):
    from statsmodels.stats.power import TTestIndPower
    analysis = TTestIndPower()
    n = analysis.solve_power(effect_size=effect_size, alpha=alpha, power=power)
    return int(np.ceil(n * 2))  # Total sample size

Report Templates

Executive Summary

markdown

# [Analysis Title]

## Key Findings
1. [Finding 1 with evidence]
2. [Finding 2 with evidence]
3. [Finding 3 with evidence]

## Methodology
- Data: [source, timeframe, sample size]
- Methods: [statistical tests used]
- Limitations: [caveats]

## Recommendations
1. [Action 1]
2. [Action 2]
3. [Action 3]

## Appendix
- Charts: [list of visualizations]
- Statistical tables: [p-values, confidence intervals]

Technical Report

markdown

# [Analysis Title] - Technical Report

## Data
- Source: [database/file]
- Records: [count]
- Variables: [list with types]
- Missing values: [summary]

## Methods
- [Method 1]: [justification]
- [Method 2]: [justification]

## Results
### [Test 1]
- Statistic: [value]
- p-value: [value]
- Effect size: [value]
- Confidence interval: [range]

## Code
[Reproducible code]

Best Practices

1. Start with EDA - Understand data before analysis
2. Validate data - Check quality, missing values, outliers
3. Document assumptions - State methodology choices
4. Report uncertainty - Confidence intervals, p-values
5. Visualize results - Charts communicate better
6. Make reproducible - Save code, set seeds
7. Peer review - Have someone check your work

Common Pitfalls

Pitfall	Solution
P-hacking	Pre-register hypotheses
Correlation ≠ causation	Use experiments when possible
Selection bias	Random sampling
Survivorship bias	Include failures
Simpson's paradox	Stratify analysis