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https://github.com/dimagutierrez/datascience

Python Scripts
https://github.com/dimagutierrez/datascience

csv data-science json kotlin matplotlib pandas pandas-python python3 requests scikit-learn sklearn sklearn-library smtplib

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Python Scripts

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# DataScience

🐍 Python Scripts for query methods.



## 1. A script that automatically downloads data from an API: `API_kotlin.py`

```python

import requests

import json



url = "https://api.example.com/data"

response = requests.get(url)

data = json.loads(response.text)

print(data)

```

This script uses the `requests` library to send an HTTP request to an API and the json library to parse the JSON response.



## 2. A script that reads data from a CSV file and converts it to a Pandas dataframe: `csv_kotlin.py`

```python

import pandas as pd



data = pd.read_csv('data.csv')

df = pd.DataFrame(data)

print(df.head())

```

This script uses the `pandas` library to read data from a CSV file and convert it into a Pandas dataframe, which is a useful data structure for data analysis.



## 3. A script that uses the Scikit-learn library to create a linear regression model: `scss.py`

```python

from sklearn.linear_model import LinearRegression

import pandas as pd



data = pd.read_csv('data.csv')

X = data[['feature_1', 'feature_2']]

y = data['target']

model = LinearRegression()

model.fit(X, y)

print(model.coef_)

```

This script uses the `Scikit-learn` library to create a linear regression model and fit it to the data.



## 4. A script that uses the Matplotlib library to create a bar chart: `matplotlib.py`

```python

import matplotlib.pyplot as plt



x = [1, 2, 3, 4, 5]

y = [10, 8, 6, 4, 2]



plt.bar(x, y)

plt.show()

```

This script uses the `Matplotlib` library to create a simple bar chart.



## 5. A script that automates the task of sending an email with an attachment: `send_mail.py`

```python

import smtplib

import os



FROM_EMAIL = '[email protected]'

FROM_PWD = 'password'

TO_EMAIL = '[email protected]'

SUBJECT = 'Datos adjuntos'

FILE_PATH = 'data.csv'



server = smtplib.SMTP('smtp.gmail.com', 587)

server.starttls()

server.login(FROM_EMAIL, FROM_PWD)



with open(FILE_PATH, 'rb') as f:

    file_data = f.read()

    file_name = os.path.basename(FILE_PATH)

    msg = 'Subject: {}\n\n'.format(SUBJECT)

    msg += 'Adjunto encontrará los datos solicitados.'

    server.sendmail(FROM_EMAIL, TO_EMAIL, msg, file_data)

    

server.quit()

```

This script uses the `smtplib` library to send an email with an attached file. This type of script is useful for automating the task of sending reports or data to colleagues or clients.



# Examples



## 1. Datos meteorológicos

```python

import requests

import json



url = 'https://api.openweathermap.org/data/2.5/weather?q=London&appid=API_KEY'

response = requests.get(url)



if response.status_code == 200:

    data = json.loads(response.text)

    temperatura = data['main']['temp']

    presion = data['main']['pressure']

    humedad = data['main']['humidity']

    print('La temperatura en Londres es', temperatura, 'K')

    print('La presión atmosférica en Londres es', presion, 'hPa')

    print('La humedad en Londres es', humedad, '%')

else:

    print('No se pudo obtener los datos meteorológicos')

```

En este ejemplo, se utiliza la API de OpenWeatherMap para obtener los datos meteorológicos de Londres. Se utiliza la biblioteca requests para enviar una solicitud HTTP a la API y la biblioteca json para analizar la respuesta JSON.



## 2. Datos de criptomonedas

```python

import requests

import json



url = 'https://api.coinmarketcap.com/v1/ticker/?limit=10'

response = requests.get(url)



if response.status_code == 200:

    data = json.loads(response.text)

    for moneda in data:

        nombre = moneda['name']

        precio = moneda['price_usd']

        capitalizacion = moneda['market_cap_usd']

        print(nombre, 'tiene un precio de', precio, 'USD y una capitalización de mercado de', capitalizacion, 'USD')

else:

    print('No se pudo obtener los datos de criptomonedas')

```

En este ejemplo, se utiliza la API de CoinMarketCap para obtener los datos de las 10 criptomonedas más populares. Se utiliza la biblioteca requests para enviar una solicitud HTTP a la API y la biblioteca json para analizar la respuesta JSON.



## 3. Datos de noticias

```python

import requests

import json



url = 'https://newsapi.org/v2/top-headlines?country=us&apiKey=API_KEY'

response = requests.get(url)



if response.status_code == 200:

    data = json.loads(response.text)

    for noticia in data['articles']:

        titulo = noticia['title']

        descripcion = noticia['description']

        url = noticia['url']

        print(titulo)

        print(descripcion)

        print(url)

else:

    print('No se pudo obtener los datos de noticias')

```

En este ejemplo, se utiliza la API de NewsAPI para obtener las principales noticias de EE. UU. Se utiliza la biblioteca requests para enviar una solicitud HTTP a la API y la biblioteca json para analizar la respuesta JSON.



```python



```

## From scratch 

[Scratschpads.pdf](https://github.com/DimaGutierrez/DataScience/blob/main/Scratchpads.pdf)

### Files

`databases.py` `logistic_regression.py` `neural_networks.py` `statistics.py` `network_analysis.py`