Author: John Au-Yeung

We can get the Linux console window width in Python by using the os module to execute a shell command and parse its output.

On Linux, we can use the stty command to retrieve terminal settings, including the width of the console window.

To do this we write

import os

def get_terminal_width():
    try:
        # Execute the stty command to get terminal settings
        result = os.popen('stty size', 'r').read().split()
        # Extract the width (second element in the result)
        width = int(result[1])
        return width
    except Exception as e:
        print("Error:", e)
        return None

# Test the function
width = get_terminal_width()
if width:
    print("Terminal width:", width)
else:
    print("Unable to get terminal width.")

This code will execute the stty size command in the shell and parse its output to extract the terminal width.

Note that this method relies on parsing the output of a shell command, so it may not be platform-independent or work in all environments.

Additionally, it requires the stty command to be available in the system.

In Python’s pip, there isn’t a direct equivalent to npm install package --save-dev, because Python’s dependency management system doesn’t make a distinction between dependencies needed for development and those needed for production.

However, you can achieve a similar effect by using pip to install the package and then manually adding it to a requirements.txt file with an appropriate comment indicating that it’s a development dependency.

Here’s how you can do it:

1. Install the package using pip:

pip install package_name

2. Manually add the package to your requirements.txt file with a comment indicating it’s for development:

package_name  # for development

This way, you have a record of the package in your requirements.txt file, and it’s clear that it’s a development dependency.

When someone else wants to install the dependencies, they can run pip install -r requirements.txt, and all dependencies, including those for development, will be installed.

We can select rows from a MultiIndex DataFrame in Pandas using the .loc[] accessor.

We need to provide the index labels for each level of the MultiIndex.

For instance, we write

import pandas as pd

# Create a MultiIndex DataFrame
arrays = [['A', 'A', 'B', 'B'], [1, 2, 1, 2]]
index = pd.MultiIndex.from_arrays(arrays, names=('first', 'second'))
df = pd.DataFrame({'data': [1, 2, 3, 4]}, index=index)

# Select rows for first level 'A'
rows_A = df.loc['A']
print("Rows for first level 'A':")
print(rows_A)

# Select rows for first level 'B' and second level 1
rows_B_1 = df.loc[('B', 1)]
print("\nRows for first level 'B' and second level 1:")
print(rows_B_1)

This code will output the selected rows based on the specified index labels.

Keep in mind that you can use different indexers based on your requirements. .loc[] is primarily label-based, while .iloc[] is integer-based.

In the case of MultiIndex DataFrames, .loc[] is generally more convenient.

We can check if a string is binary in Python by iterating through each character in the string and verifying if it’s either ‘0’ or ‘1’. Here’s a simple function to do that:

def is_binary(s):
    for char in s:
        if char != '0' and char != '1':
            return False
    return True

# Example usage:
string1 = "101010101"  # binary string
string2 = "10101012"   # non-binary string

print(is_binary(string1))  # Output: True
print(is_binary(string2))  # Output: False

This function iterates through each character in the input string s.

If any character is not ‘0’ or ‘1’, it returns False, indicating that the string is not binary. If all characters are ‘0’ or ‘1’, it returns True, indicating that the string is binary.