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- Question: Data Extraction using Python dataframe
- Time: Asked 26 hours ago
- Modified: Asked 27 days ago
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Data Extraction using Python dataframe
Python dataframe is a powerful tool that can be used to extract data from various sources. Here are some common methods to extract data using a dataframe:
- Reading data from CSV file: Use pandas library to read data from CSV file and store it in a dataframe.
import pandas as pd
df = pd.read_csv("filename.csv")
- Reading data from Excel file: Use pandas library to read data from Excel file and store it in a dataframe.
import pandas as pd
df = pd.read_excel("filename.xlsx")
- Extracting data from a database: Use SQLAlchemy library to connect to the database and retrieve data into a dataframe.
import pandas as pd
from sqlalchemy import create_engine
# create database connection
engine = create_engine('postgresql://username:password@host:port/database')
# execute a query and store results in a dataframe
df = pd.read_sql_query('SELECT * FROM table_name', engine)
- Extracting data from APIs: Use requests library to fetch data from APIs and store it in a dataframe.
import pandas as pd
import requests
# make API request
response = requests.get('https://api.example.com/data')
# store response data in a dataframe
df = pd.DataFrame(response.json())
- Extracting data from HTML pages: Use BeautifulSoup library to parse HTML pages and extract data into a dataframe.
import pandas as pd
import requests
from bs4 import BeautifulSoup
# make HTTP request and parse HTML response
response = requests.get('https://www.example.com')
soup = BeautifulSoup(response.content, 'html.parser')
# extract data from HTML and store in a dataframe
data = []
for item in soup.find_all('div', {'class': 'item'}):
name = item.find('h2').text
price = item.find('span', {'class': 'price'}).text
data.append([name, price])
df = pd.DataFrame(data, columns=['Name', 'Price'])
These are just a few examples of how to extract data using a Python dataframe. Depending on the data source, different libraries and techniques may be required.
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You can see some more information related to Data Extraction using Python dataframe here
- Python | Pandas Extracting rows using .loc[] – GeeksforGeeks
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- Data Extraction and Manipulation using Python – Medium
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