Uploading в цикле Pandas DataFrames в таблицу встроенной базы данных SQLite3 on Python

 Пример кода и выполнения

(.env) [boris@fedora34server PDSQLITE]$ cat YQDatFrms.py

import numpy as np                                                                   

import pandas as pd                                                                  

from datetime import datetime                                                        

import sqlite3                                                                       

conn = sqlite3.connect('flight.db')                                                  

curs = conn.cursor()                                                                 

curs.execute("create table daily_flights (id integer,  \                           departure date, arrival date, number text, route_id integer)") 

conn.commit()                                                                        

for j in range(1,12):                                                                

   df = pd.DataFrame([[j, datetime(2021, j, j+1, 3+j,10) , \               

      datetime(2021, j, j+1, 5+j,15), chr(64+j)+'1', 3*j+2]], \             

           columns=["id", "departure", "arrival", "number",   \

        "route_id"])                   

   df.to_sql("daily_flights", conn, if_exists="append",index = False)                

data = pd.read_sql_query("select * from daily_flights", conn)

print(data)

Python depicting a helix via matplotlib and numpy

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

n = 1000

fig = plt.figure()

ax = fig.add_subplot(111, projection='3d')

# Plot a helix along the x-axis

theta_max = 6*np.pi

# theta = np.linspace(0, theta_max, n)

theta = np.linspace(-theta_max,theta_max, n)

x = theta

z =  np.sin(theta)

y =  np.cos(theta)

ax.plot(x, y, z, 'b', lw=2)

# An line through the centre of the helix

ax.plot((-theta_max*1.2, theta_max * 1.2), (0,0), (0,0), color='g', lw=2)

# sin/cos components of the helix (e.g. electric and magnetic field

# components of a circularly-polarized electromagnetic wave

ax.plot(x, y, 0, color='r', lw=1, alpha=0.5)

ax.plot(x, [0]*n, z, color='m', lw=1, alpha=0.5)

# Remove axis planes, ticks and labels

# ax.set_axis_off()

plt.show()

Решение задачи 17 371 Вариант Ларина с помощью Python (numpy,matplotlib)

 Текст задачи

==============

Python code

==============

import matplotlib.pyplot as plt

import numpy as np

delta = 0.025

x = np.arange(-10, 10, delta)

y = np.arange(-10, 10, delta)

p, q = np.meshgrid(x, y)

plt.axhline(y = 5, color = 'red', linestyle = '-')

plt.axhline(y = 4, color = 'green', linestyle = '-')

plt.axhline(y = 1, color = 'green', linestyle = '-')

plt.axhline(y = 0, color = 'red', linestyle = '-')

plt.axvline(0, -10, 10, label='y axis')

# define some function f(x,y)

f = lambda x, y: x**4 - 2*x**3 - 4*x**2 + 10*x \

        - 5 - 2*x*y + 6*y - y**2

z=f(p,q)

# plot contour line of f(x,y)==0

plt.contour(p, q, z , [0], colors=["b"])

# make legend

proxy, = plt.plot([], color="b")

plt.legend(handles=[proxy], labels=["f(x,y) = 0"])

plt.show()

Из плота нетрудно видеть при каких а горизонтальные линии имеют не более 3-ех точек пересения с графиком.

Ответ : а ∈ (-infinity ; 0] ∪ {1;4}∪[5 ; +infinity)

Метрики для оценки алгоритмов машинного обучения в Python

 (.env) [boris@fedora34server YANDEXQ]$ cat predict.py

import pandas

from sklearn import model_selection

from sklearn.linear_model import LogisticRegression

from sklearn.metrics import classification_report

url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv"

names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']

dataframe = pandas.read_csv(url, names=names)

array = dataframe.values

X = array[:,0:8]

Y = array[:,8]

test_size = 0.33

seed = 7

X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, test_size=test_size, random_state=seed)

model = LogisticRegression(solver='lbfgs', max_iter=120)

model.fit(X_train, Y_train)

predicted = model.predict(X_test)

report = classification_report(Y_test, predicted)

print(report)

(.env) [boris@fedora34server YANDEXQ]$ python predict.py

              precision    recall  f1-score   support

         0.0       0.81      0.88      0.84       162

         1.0       0.74      0.63      0.68        92

    accuracy                           0.79       254

   macro avg       0.78      0.75      0.76       254

weighted avg       0.78      0.79      0.78       254

(.env) [boris@fedora34server YANDEXQ]$ cat regres01.py

import pandas

from sklearn import model_selection

from sklearn.linear_model import LinearRegression

url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.data"

names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']

dataframe = pandas.read_csv(url, delim_whitespace=True, names=names)

array = dataframe.values

X = array[:,0:13]

Y = array[:,13]

seed = 7

kfold = \ model_selection.KFold(n_splits=10,shuffle=True,random_state=seed)

model = LinearRegression()

scoring = 'neg_mean_absolute_error'

results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)

print(("MAE: %.3f (%.3f)") % (results.mean(), results.std()))

(.env) [boris@fedora34server YANDEXQ]$ python regres01.py

MAE: -3.387 (0.667)

(.env) [boris@fedora34server YANDEXQ]$ cat regres02.py

# Cross Validation Regression MSE

import pandas

from sklearn import model_selection

from sklearn.linear_model import LinearRegression

url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.data"

names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']

dataframe = pandas.read_csv(url, delim_whitespace=True, names=names)

array = dataframe.values

X = array[:,0:13]

Y = array[:,13]

seed = 7

kfold = \ model_selection.KFold(n_splits=10,shuffle=True,random_state=seed)

model = LinearRegression()

scoring = 'neg_mean_squared_error'

results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)

print(("MSE: %.3f (%.3f)") % (results.mean(), results.std()))

(.env) [boris@fedora34server YANDEXQ]$ python regres02.py

MSE: -23.747 (11.143)

(.env) [boris@fedora34server YANDEXQ]$ cat regres03.py

import pandas

from sklearn import model_selection

from sklearn.linear_model import LinearRegression

url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.data"

names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']

dataframe = pandas.read_csv(url, delim_whitespace=True, names=names)

array = dataframe.values

X = array[:,0:13]

Y = array[:,13]

seed = 7

kfold    \ =  model_selection.KFold(n_splits=10,shuffle=True,random_state=seed)

model = LinearRegression()

scoring = 'r2'

results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)

print(("R^2: %.3f (%.3f)") % (results.mean(), results.std()))

(.env) [boris@fedora34server YANDEXQ]$ python regres03.py

R^2: 0.718 (0.099)

References

https://machinelearningmastery.com/metrics-evaluate-machine-learning-algorithms-python/