[Python Libraries] pandas, numpy, matploilib, seaborn, scikit-learn, statsmodels, scipy, tensorflow, pytorch

pandas Library

import pandas as pd
df = pd.read_excel(file_address)
print(df)

 

numpy

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(arr.mean())

 

matploilib

import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()

 

seaborn

import seaborn as sns
import pandas as pd
data_sample = pd.DataFrame({'x':[1, 2, 3, 4], 'y':[1, 4, 9, 16]})
sns.barplot(data=data_sample, x='x', y='y')

 

scikit-learn

from sklearn.datasets import load_iris
from sklearn.linear_model import LinearRegression

# Iris 데이터셋 불러오기
iris = load_iris()

# Iris 데이터셋에서 특정 범위의 데이터 슬라이싱하기
X_train = iris.data[:,:-1]  # 데이터 값들 추출
print("학습 데이터:", X_train)
y_train = iris.data[:,-1:]  # 정답값 추출
print("학습 데이터:", y_train)

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

 

statsmodels

import statsmodels.api as sm
model = sm.OLS(y_train, X_train)
result = model.fit()
print(result.summary())

 

scipy

import numpy as np
from scipy.integrate import quad

# 적분할 함수 정의
def integrand(x):
    return np.exp(-x ** 2)

# 정적분 구간
a = 0
b = np.inf

# 적분 계산
result, error = quad(integrand, a, b)

print("결과:", result)
print("오차:", error)

 

tensorflow

import tensorflow as tf

input_size = 3

model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, activation='relu', input_shape=(input_size,)),
    tf.keras.layers.Dense(1)
])
model.compile(optimizer='adam', loss='mse')

 

pytorch

import torch
import torch.nn as nn
class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, output_size)
    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x