Data Analysis

Data Analysis

Scientific data analysis with Python. All scripts use the venv at /Users/zhangmingda/clawd/.venv.

Setup

source /Users/zhangmingda/clawd/.venv/bin/activate

Workflow

1. Data Loading

import pandas as pd
import numpy as np

# CSV
df = pd.read_csv('data.csv')
# Excel
df = pd.read_excel('data.xlsx', sheet_name='Sheet1')
# JSON
df = pd.read_json('data.json')
# Clipboard (from user paste)
# Save user's data to a temp file first, then read

# Quick inspection
print(f"Shape: {df.shape}")
print(f"Columns: {list(df.columns)}")
print(df.dtypes)
print(df.describe())
print(f"Missing values:\n{df.isnull().sum()}")

2. Data Cleaning

# Missing values
df.dropna(subset=['critical_column'])
df['col'].fillna(df['col'].median(), inplace=True)

# Duplicates
df.drop_duplicates(inplace=True)

# Outliers (IQR method)
Q1, Q3 = df['col'].quantile([0.25, 0.75])
IQR = Q3 - Q1
mask = (df['col'] >= Q1 - 1.5*IQR) & (df['col'] <= Q3 + 1.5*IQR)
df_clean = df[mask]

# Type conversion
df['date'] = pd.to_datetime(df['date'])
df['category'] = df['category'].astype('category')

3. Exploratory Data Analysis

import matplotlib.pyplot as plt
import seaborn as sns

# Distribution
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
for i, col in enumerate(numeric_cols[:4]):
    ax = axes[i//2, i%2]
    sns.histplot(df[col], kde=True, ax=ax)
    ax.set_title(col)
plt.tight_layout()
plt.savefig('distributions.png', dpi=150)

# Correlation matrix
corr = df[numeric_cols].corr()
sns.heatmap(corr, annot=True, cmap='RdBu_r', center=0, fmt='.2f')
plt.savefig('correlation.png', dpi=150)

# Pairplot for key variables
sns.pairplot(df[key_cols], hue='group')
plt.savefig('pairplot.png', dpi=150)

4. Statistical Tests

Choose test based on:

• Data type: continuous vs categorical
• Distribution: normal vs non-normal (Shapiro-Wilk test)
• Groups: 2 vs 3+ groups
• Pairing: independent vs paired/repeated

from scipy import stats

# Normality test
stat, p = stats.shapiro(df['col'])
print(f"Shapiro-Wilk: W={stat:.4f}, p={p:.4f}")

# t-test
t, p = stats.ttest_ind(group1, group2)
# Effect size (Cohen's d)
d = (group1.mean() - group2.mean()) / np.sqrt((group1.std()**2 + group2.std()**2) / 2)

# ANOVA
f, p = stats.f_oneway(g1, g2, g3)

# Chi-square
chi2, p, dof, expected = stats.chi2_contingency(pd.crosstab(df['a'], df['b']))

# Correlation
r, p = stats.pearsonr(df['x'], df['y'])

5. Regression

import statsmodels.api as sm
import statsmodels.formula.api as smf

# OLS
model = smf.ols('y ~ x1 + x2 + C(group)', data=df).fit()
print(model.summary())

# Logistic
model = smf.logit('outcome ~ x1 + x2', data=df).fit()
print(model.summary())

# Mixed effects
model = smf.mixedlm('y ~ x1 + x2', data=df, groups=df['subject']).fit()

6. Reporting

Always report:

• Sample size (N) and any exclusions
• Descriptive statistics (M, SD or Median, IQR)
• Test statistic, degrees of freedom, p-value
• Effect size with confidence interval
• Assumptions checked (normality, homogeneity of variance)

Format: "A significant difference was found between groups, t(48) = 2.31, p = .025, Cohen's d = 0.65, 95% CI [0.08, 1.22]."

Tips

• Always check assumptions before parametric tests
• Report effect sizes, not just p-values
• Use Bonferroni or FDR correction for multiple comparisons
• Visualize data before and after analysis
• Save all outputs as files the user can download