Python Data Science
From Data to Insights

import numpy as np

arr1 = np.array([1, 2, 3, 4, 5])
arr2 = np.zeros(5)
arr3 = np.ones(3)
arr4 = np.arange(0, 10, 2)

print("Array from list:", arr1)
print("Zeros:", arr2)
print("Ones:", arr3)
print("Arange:", arr4)

print("First element:", arr1[0])
print("Last element:", arr1[-1])

print("arr1 + 10:", arr1 + 10)
print("arr1 * 2:", arr1 * 2)
print("arr1 ** 2:", arr1 ** 2)

print("Shape:", arr1.shape)
print("Size:", arr1.size)
print("Dtype:", arr1.dtype)

import numpy as np

matrix = np.array([
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
])
print("Matrix:\n", matrix)

print("Row 0:", matrix[0])
print("Column 1:", matrix[:, 1])
print("Element at (1, 2):", matrix[1, 2])

print("Mean:", np.mean(matrix))
print("Median:", np.median(matrix))
print("Standard deviation:", np.std(matrix))
print("Sum:", np.sum(matrix))

print("Mean of each column:", np.mean(matrix, axis=0))
print("Mean of each row:", np.mean(matrix, axis=1))

flat = np.arange(12)
reshaped = flat.reshape(3, 4)
print("Reshaped:\n", reshaped)

import pandas as pd

s1 = pd.Series([10, 20, 30, 40, 50])
print("Series from list:\n", s1)

s2 = pd.Series([85, 92, 78, 90], index=["Alice", "Bob", "Charlie", "Diana"])
print("\nSeries with custom index:\n", s2)

print("\nAlice's score:", s2["Alice"])
print("First value:", s2.iloc[0])

s3 = pd.Series({"Math": 90, "Science": 85, "English": 88})
print("\nSeries from dict:\n", s3)

print("\nScores + 5:\n", s2 + 5)
print("Mean:", s2.mean())
print("Summary:\n", s2.describe())

import pandas as pd

data = {
    "Name": ["Alice", "Bob", "Charlie", "Diana", "Eve"],
    "Age": [25, 30, 35, 28, 32],
    "City": ["NYC", "LA", "Chicago", "SF", "Boston"],
    "Salary": [70000, 85000, 60000, 90000, 75000]
}
df = pd.DataFrame(data)
print("DataFrame:\n", df)

print("\nFirst 3 rows:\n", df.head(3))
print("\nInfo:\n")
df.info()
print("\nDescribe:\n", df.describe())

print("\nNames column:\n", df["Name"])
print("\nMultiple columns:\n", df[["Name", "Salary"]])

print("\nRow 0:\n", df.iloc[0])
print("\nRows 0-2:\n", df.iloc[0:3])

print("\nSalary > 70000:\n", df[df["Salary"] > 70000])

print("\nAge >= 30 and City = LA:\n", df[(df["Age"] >= 30) & (df["City"] == "LA")])

import pandas as pd
import numpy as np

df = pd.DataFrame({
    "A": [1, 2, np.nan, 4, 5],
    "B": [10, np.nan, 30, 40, 50],
    "C": [100, 200, 300, np.nan, 500]
})
print("Original:\n", df)

print("\nIs null?\n", df.isnull())
print("\nMissing count:\n", df.isnull().sum())

print("\nDrop rows with missing values:\n", df.dropna())

print("\nDrop rows where all missing:\n", df.dropna(how="all"))

print("\nFill with 0:\n", df.fillna(0))
print("\nFill with mean of each column:\n", df.fillna(df.mean()))

df_dup = pd.DataFrame({"X": [1, 2, 2, 3, 3, 3], "Y": ["a", "b", "b", "c", "c", "c"]})
print("\nWith duplicates:\n", df_dup)
print("\nWithout duplicates:\n", df_dup.drop_duplicates())

import pandas as pd
import numpy as np

data = {
    "Department": ["Sales", "Sales", "IT", "IT", "Sales", "IT", "HR", "HR"],
    "Employee": ["Alice", "Bob", "Charlie", "Diana", "Eve", "Frank", "Grace", "Henry"],
    "Salary": [70000, 65000, 85000, 90000, 72000, 88000, 60000, 62000],
    "Experience": [5, 3, 8, 10, 4, 9, 2, 3]
}
df = pd.DataFrame(data)

print("Summary (numerical):\n", df.describe())
print("\nSummary (categorical):\n", df.describe(include=["object"]))

print("\nDepartment counts:\n", df["Department"].value_counts())

print("\nAverage salary by department:\n", df.groupby("Department")["Salary"].mean())
print("\nAggregate functions:\n", df.groupby("Department")["Salary"].agg(["mean", "min", "max"]))

print("\nPivot table:\n", df.pivot_table(values="Salary", index="Department", columns="Experience", aggfunc="mean"))

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

plt.figure(figsize=(10, 6))

plt.plot(x, y1, label="sin(x)", color="blue", linewidth=2)
plt.plot(x, y2, label="cos(x)", color="orange", linewidth=2, linestyle="--")

plt.xlabel("X axis")
plt.ylabel("Y axis")
plt.title("Sine and Cosine Functions")
plt.legend()
plt.grid(True, alpha=0.3)

print("Plot created successfully!")
print("(In a real environment, plt.show() would display the plot)")

categories = ["A", "B", "C", "D"]
values = [25, 40, 30, 55]
plt.figure(figsize=(8, 5))
plt.bar(categories, values, color="teal")
plt.title("Bar Chart Example")
plt.xlabel("Categories")
plt.ylabel("Values")
print("\nBar chart created!")

data = np.random.normal(100, 15, 1000)
plt.figure(figsize=(8, 5))
plt.hist(data, bins=30, color="purple", alpha=0.7)
plt.title("Histogram of Normally Distributed Data")
plt.xlabel("Value")
plt.ylabel("Frequency")
print("\nHistogram created!")

import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

np.random.seed(42)
data = pd.DataFrame({
    "Feature_A": np.random.randn(100) * 10 + 50,
    "Feature_B": np.random.randn(100) * 8 + 60,
    "Feature_C": np.random.randn(100) * 12 + 40,
    "Target": np.random.randn(100) * 5 + 70
})

sns.set_style("darkgrid")
print("Seaborn style set")

plt.figure(figsize=(8, 6))
sns.scatterplot(data=data, x="Feature_A", y="Target", color="teal")
plt.title("Scatter Plot: Feature A vs Target")
print("\nScatter plot created")

print("\nPair plot would show all pairwise relationships")
print("(In a real environment, sns.pairplot(data) would display)")

plt.figure(figsize=(8, 6))
corr = data.corr()
sns.heatmap(corr, annot=True, cmap="coolwarm", center=0)
plt.title("Correlation Heatmap")
print("\nHeatmap created")

plt.figure(figsize=(8, 5))
sns.histplot(data["Target"], bins=30, kde=True, color="purple")
plt.title("Distribution of Target Variable")
print("\nDistribution plot created")

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score, mean_squared_error

np.random.seed(42)
X = np.random.randn(100, 1) * 10 + 20
y = 3 * X.flatten() + 7 + np.random.randn(100) * 3

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

model = LinearRegression()
model.fit(X_train, y_train)
print("Model trained")

y_pred = model.predict(X_test)

print(f"Coefficient (slope): {model.coef_[0]:.4f}")
print(f"Intercept: {model.intercept_:.4f}")
print(f"R² Score: {r2_score(y_test, y_pred):.4f}")
print(f"RMSE: {np.sqrt(mean_squared_error(y_test, y_pred)):.4f}")

new_x = np.array([[30]])
prediction = model.predict(new_x)
print(f"\nPrediction for X=30: {prediction[0]:.2f}")

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, classification_report

np.random.seed(42)
X = np.random.randn(200, 2)
y = (X[:, 0] + X[:, 1] > 0).astype(int)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

model = LogisticRegression()
model.fit(X_train, y_train)
print("Model trained")

y_pred = model.predict(X_test)

print(f"Accuracy: {accuracy_score(y_test, y_pred):.4f}")
print(f"Precision: {precision_score(y_test, y_pred):.4f}")
print(f"Recall: {recall_score(y_test, y_pred):.4f}")
print("\nClassification Report:")
print(classification_report(y_test, y_pred))

new_sample = np.array([[1.0, -0.5]])
pred = model.predict(new_sample)
prob = model.predict_proba(new_sample)
print(f"\nNew sample {new_sample.flatten()} → Class: {pred[0]}, Probabilities: {prob[0]}")

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score

np.random.seed(42)
n = 150
data = pd.DataFrame({
    "Square_Feet": np.random.randn(n) * 500 + 2000,
    "Bedrooms": np.random.randint(1, 6, n),
    "Age": np.random.randint(0, 30, n),
    "Price": np.random.randn(n) * 50000 + 350000
})
data.loc[10:15, "Bedrooms"] = np.nan

print("=== 1. Data Overview ===")
print(data.head())
print(f"\nShape: {data.shape}")
print(f"Missing values:\n{data.isnull().sum()}")

data["Bedrooms"] = data["Bedrooms"].fillna(data["Bedrooms"].median())
print(f"\nMissing values after cleaning:\n{data.isnull().sum()}")

print("\n=== 2. EDA ===")
print(data.describe())

corr = data.corr()
print("\nCorrelations with Price:")
print(corr["Price"].sort_values(ascending=False))

X = data[["Square_Feet", "Bedrooms", "Age"]]
y = data["Price"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

print("\n=== 3. Model Results ===")
print(f"R² Score: {r2_score(y_test, y_pred):.4f}")
print(f"Coefficients:\n{model.coef_}")
print(f"Intercept: {model.intercept_:.2f}")

print("\nFull data science workflow completed!")
print("(In a real project, you would also tune hyperparameters and perform cross‑validation)")