Category: Python Answers

Sometimes, we want to install tkinter for Python.

In this article, we’ll look at how to install tkinter for Python.

How to install tkinter for Python?

To install tkinter for Python, we can install the python3-tk package with Ubuntu.

We run

sudo apt-get install python3-tk

to install the python3-tk package.

If we’re using Fedora, we install the python3-tkinter package by running

sudo dnf install python3-tkinter

Conclusion

To install tkinter for Python, we can install the python3-tk package with Ubuntu.

Sometimes, we want to get rid of \n when using .readlines() in Python.

In this article, we’ll look at how to get rid of \n when using .readlines() in Python.

How to get rid of \n when using .readlines() in Python?

To get rid of \n when using .readlines() in Python, we call read with splitlines.

For instance, we write

with open(filename) as f:
    mylist = f.read().splitlines() 

to open the filename file with open.

Then we call read to read the file into an iterator with the lines in the filename file.

And then we call splitlines to split the lines into a list of line strings.

Conclusion

To get rid of \n when using .readlines() in Python, we call read with splitlines.

Sometimes, we want to validate a URL with a regular expression in Python.

In this article, we’ll look at how to validate a URL with a regular expression in Python.

How to validate a URL with a regular expression in Python?

To validate a URL with a regular expression in Python, w ecan use the PreparedRequest class from the requests library.

For instance, we write

from requests.models import PreparedRequest
import requests.exceptions


def check_url(url):
    prepared_request = PreparedRequest()
    try:
        prepared_request.prepare_url(url, None)
        return prepared_request.url
    except requests.exceptions.MissingSchema, e:
        raise SomeException

to create the check_url function.

In it, we create a PreparedRequest object.

Then we call prepare_url with the url parameter to try to parse the url string as a URL.

If it succeeds, we return prepared_request.url.

Otherwise, the requests.exceptions.MissingSchema will be raised and we catch that.

Conclusion

To validate a URL with a regular expression in Python, w ecan use the PreparedRequest class from the requests library.

Sometimes, we want to parse table with Python BeautifulSoup.

In this article, we’ll look at how to parse table with Python BeautifulSoup.

How to parse table with Python BeautifulSoup?

To parse table with Python BeautifulSoup, we can use the find_all method.

For instance, we write

data = []
table = soup.find('table', attrs={'class':'lineItemsTable'})
table_body = table.find('tbody')

rows = table_body.find_all('tr')
for row in rows:
    cols = row.find_all('td')
    cols = [ele.text.strip() for ele in cols]
    data.append([ele for ele in cols if ele]) 

to call soup.find to find the table element with class lineItemsTable.

Then we call find with 'tbody' to find the tbody element from the table.

Next, we call table_body.find_all to find all tr elements.

Then we loop through the returned rows with a for loop.

In it, we call find_all again to find all the td elements in the tr element.

And then we put all the text content of each td element into a list.

And then we call data.append to append the values into the data list.

Conclusion

To parse table with Python BeautifulSoup, we can use the find_all method.

Sometimes, we want to convert a pandas Series or index to a Numpy array.

In this article, we’ll look at how to convert a pandas Series or index to a Numpy array.

How to convert a pandas Series or index to a Numpy array?

To convert a pandas Series or index to a Numpy array, we can use the to_numpy method.

For instance, we write

a = df.index.to_numpy()
b = df['A'].to_numpy()

to call df.index.to_numpy to convert the df data frame’s index into a NumPy array.

Likewise, we call df['A'].to_numpy to convert the values in column A in the df data frame into a NumPy array.

Conclusion

To convert a pandas Series or index to a Numpy array, we can use the to_numpy method.