Python Cheat Sheet (NumPy, pandas, scikit-learn)

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split

NumPy Quick Reference

arr = np.array([1, 2, 3, 4])
mean = np.mean(arr) # Mean of array

pandas Quick Reference

data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)
df.head() # View first few rows

scikit-learn Quick Reference

X = df[['Age']]
y = df['Name']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

R Programming Cheat Sheet

R provides similar functionality for data manipulation and visualization

Example:

df <- data.frame(Name=c("Alice", "Bob"), Age=c(25, 30))

summary(df)

SQL Cheat Sheet (Basic Query Examples)

Basic SQL query structure

SELECT Name, Age FROM users WHERE Age > 25;

Machine Learning Cheat Sheet (Model Training Example)

from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit(X_train, y_train)

Deep Learning Cheat Sheet (Using TensorFlow/Keras)

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential([
Dense(64, activation='relu', input_dim=8),
Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

Statistics Cheat Sheet (Common Formulas)

import scipy.stats as stats

mean = np.mean(arr) # Mean calculation
std_dev = np.std(arr) # Standard deviation
p_value = stats.ttest_1samp(arr, 0) # One-sample t-test

Data Visualization Cheat Sheet (Matplotlib & Seaborn)

import matplotlib.pyplot as plt
import seaborn as sns

Simple plot using Matplotlib

plt.plot([1, 2, 3], [4, 5, 6])
plt.show()

Simple plot using Seaborn

sns.boxplot(x=df['Age'])
plt.show()