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import numpy as np
# Creating an array
array = np.array([1, 2, 3, 4])
# Perform arithmetic operations
array = array * 2
# Matrix multiplication
matrix1 = np.array([[1, 2], [3, 4]])
matrix2 = np.array([[5, 6], [7, 8]])
result = np.dot(matrix1, matrix2)
result
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array([[19, 22],
[43, 50]])
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import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35]}
df = pd.DataFrame(data)
ages = df['Age']
filtered_df = df[df['Age'] > 25]
filtered_df
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| Name | Age | |
|---|---|---|
| 1 | Bob | 30 |
| 2 | Charlie | 35 |
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import matplotlib.pyplot as plt
x = [1, 2, 3, 4]
y = [10, 20, 25, 30]
plt.plot(x, y)
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.title('Sample Line Plot')
plt.show()
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from scipy import optimize
def f(x):
return x**2 + 5*np.sin(x)
result = optimize.minimize(f, x0=0)
print(result)
message: Optimization terminated successfully.
success: True
status: 0
fun: -3.2463942726915387
x: [-1.111e+00]
nit: 5
jac: [-2.980e-08]
hess_inv: [[ 1.544e-01]]
nfev: 12
njev: 6
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from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
iris = datasets.load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))
Accuracy: 0.9333333333333333
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import torch
a = torch.tensor([1.0, 2.0, 3.0])
b = torch.tensor([4.0, 5.0, 6.0])
result = a + b
print(result)
tensor([5., 7., 9.])
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import numpy as np
# Creating an array from a list
arr = np.array([1, 2, 3, 4])
print(arr)
[1 2 3 4]
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zeros_arr = np.zeros((3, 3))
print(zeros_arr)
[[0. 0. 0.] [0. 0. 0.] [0. 0. 0.]]
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ones_arr = np.ones((2, 2))
print(ones_arr)
[[1. 1.] [1. 1.]]
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arr = np.arange(0, 10, 2)
print(arr)
[0 2 4 6 8]
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arr = np.linspace(0, 1, 5)
print(arr)
[0. 0.25 0.5 0.75 1. ]
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arr = np.array([1, 2, 3, 4, 5, 6])
reshaped_arr = arr.reshape((2, 3))
print(reshaped_arr)
[[1 2 3] [4 5 6]]
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arr = np.array([[1, 2], [3, 4]])
transposed_arr = np.transpose(arr)
print(transposed_arr)
[[1 3] [2 4]]
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arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
concatenated_arr = np.concatenate((arr1, arr2))
print(concatenated_arr)
[1 2 3 4 5 6]
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arr = np.array([[1, 2, 3], [4, 5, 6]])
sum_arr = np.sum(arr)
print(sum_arr)
21
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arr = np.array([1, 2, 3, 4, 5])
mean_val = np.mean(arr)
print(mean_val)
3.0
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arr = np.array([1, 2, 3, 4, 5])
max_val = np.max(arr)
min_val = np.min(arr)
print(f"Max: {max_val}, Min: {min_val}")
Max: 5, Min: 1
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arr = np.array([1, 4, 9, 16])
sqrt_arr = np.sqrt(arr)
print(sqrt_arr)
[1. 2. 3. 4.]
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arr1 = np.array([1, 2])
arr2 = np.array([3, 4])
dot_product = np.dot(arr1, arr2)
print(dot_product)
11
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arr = np.array([1, 2, 3, 4, 5])
std_val = np.std(arr)
print(std_val)
1.4142135623730951
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var_val = np.var(arr)
print(var_val)
2.0
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arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
correlation = np.corrcoef(arr1, arr2)
print(correlation)
[[1. 1.] [1. 1.]]
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import pandas as pd
series = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
print(series)
a 1 b 2 c 3 d 4 dtype: int64
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data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35]}
df = pd.DataFrame(data)
print(df)
Name Age 0 Alice 25 1 Bob 30 2 Charlie 35
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print(df.loc[0])
Name Alice Age 25 Name: 0, dtype: object
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print(df.iloc[1])
Name Bob Age 30 Name: 1, dtype: object
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print(df.at[0, 'Name'])
Alice
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print(df.head(2))
Name Age 0 Alice 25 1 Bob 30
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print(df.tail(1))
Name Age 2 Charlie 35
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df = df.drop(columns=['Age'])
print(df)
Name 0 Alice 1 Bob 2 Charlie
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df = df.rename(columns={'Name': 'Full Name'})
print(df)
Full Name 0 Alice 1 Bob 2 Charlie
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df = pd.DataFrame({'Name': ['Alice', 'Bob', None], 'Age': [25, None, 35]})
print(df.isnull())
Name Age 0 False False 1 False True 2 True False
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df_filled = df.fillna({'Name': 'Unknown', 'Age': 0})
print(df_filled)
Name Age 0 Alice 25.0 1 Bob 0.0 2 Unknown 35.0
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df_clean = df.dropna()
print(df_clean)
Name Age 0 Alice 25.0
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data = {'Name': ['Alice', 'Bob', 'Charlie', 'Alice'], 'Age': [25, 30, 35, 28]}
df = pd.DataFrame(data)
grouped = df.groupby('Name').mean()
print(grouped)
Age Name Alice 26.5 Bob 30.0 Charlie 35.0
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df = pd.DataFrame({'Age': [25, 30, 35], 'Score': [85, 90, 95]})
mean_vals = df.mean()
print(mean_vals)
Age 30.0 Score 90.0 dtype: float64
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import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
import seaborn as sns
import matplotlib.pyplot as plt
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from sklearn.datasets import load_iris
# Load the iris dataset
iris = load_iris()
X = iris.data # Features
y = iris.target # Target (species)
# Convert it into a DataFrame for better visualization
iris_df = pd.DataFrame(X, columns=iris.feature_names)
iris_df['species'] = y
print(iris_df.head())
sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \ 0 5.1 3.5 1.4 0.2 1 4.9 3.0 1.4 0.2 2 4.7 3.2 1.3 0.2 3 4.6 3.1 1.5 0.2 4 5.0 3.6 1.4 0.2 species 0 0 1 0 2 0 3 0 4 0
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# Split the data into 80% training and 20% testing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# Create a KNN classifier with K=3
knn = KNeighborsClassifier(n_neighbors=3)
# Train the model using the training data
knn.fit(X_train, y_train)
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KNeighborsClassifier(n_neighbors=3)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
KNeighborsClassifier(n_neighbors=3)
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# Predict the target values (species) for the test set
y_pred = knn.predict(X_test)
# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy * 100:.2f}%")
# Confusion Matrix
conf_matrix = confusion_matrix(y_test, y_pred)
print("Confusion Matrix:")
print(conf_matrix)
# Classification Report
print("Classification Report:")
print(classification_report(y_test, y_pred))
Accuracy: 100.00%
Confusion Matrix:
[[10 0 0]
[ 0 9 0]
[ 0 0 11]]
Classification Report:
precision recall f1-score support
0 1.00 1.00 1.00 10
1 1.00 1.00 1.00 9
2 1.00 1.00 1.00 11
accuracy 1.00 30
macro avg 1.00 1.00 1.00 30
weighted avg 1.00 1.00 1.00 30
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# Plot the confusion matrix
sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=iris.target_names, yticklabels=iris.target_names)
plt.xlabel('Predicted')
plt.ylabel('True')
plt.title('Confusion Matrix of KNN Model')
plt.show()
