Python Printing Readable Results

There are different ways to print readable results in Python depending on what kind of data you have. For example:

If you have a complex data structure such as a nested dictionary or list, you can use pprint module to format it nicely with indentation and line breaks. For example:

import pprint
data = {"name": "Alice", "age": 25, "hobbies": ["reading", "coding", "gaming"]}
pprint.pprint(data)

# Output:
{'age': 25,
 'hobbies': ['reading', 'coding', 'gaming'],
 'name': 'Alice'}

If you have a numerical data such as a matrix or an array, you can use numpy module to print it with precision and alignment. For example:

import numpy as np
matrix = np.array([[1.2345, 2.3456], [3.4567, 4.5678]])
np.set_printoptions(precision=2)
print(matrix)

# Output:
[[1.23 2.35]
[3.46 4.57]]

If you have a text data such as a string or a paragraph, you can use f-strings to format it with placeholders and expressions. For example:

name = "Bob"
age = 30
message = f"Hello, {name}. You are {age} years old."
print(message)

# Output:
Hello, Bob. You are 30 years old.

The normal print statement works fine in most situations but when the output is in the form of tables, JSON, or contains multiple results then you have to add some functionality to it or use some external package.

# pprint (pretty print)
from pprint import pprint
import json
f = open('data.json',)
data = json.load(f)
pprint(data)
f.close()

# Output
{'glossary':
 {'GlossDiv':
  {'GlossDef':
   {'GlossSeeAlso': ['GML', 'XML'],'para': 'A meta-markup language, used '
                      'to create markup languages ''such as DocBook.'},
                           'GlossSee': 'markup'},
              'title': 'example glossary'}}

# Normal Print
import json
f = open('data.json',)
data = json.load(f)
print(data)
f.close()

# Output
{'glossary': {'title': 'example glossary', 'GlossDiv': {'GlossDef': {'para': 'A meta-markup language, used to create markup languages such as DocBook.', 'GlossSeeAlso': ['GML', 'XML']}, 'GlossSee': 'markup'}}}

If you have any questions about this code, you can drop a line in comment.

Comments

Post a Comment

Popular posts from this blog

Python chr() Built in Function

The Python chr() function is used to get a string representing a character that corresponds to a Unicode code integer. For example, chr(97) returns the string 'a'. This function takes an integer argument and throws an error if it exceeds the range of 0 to 1,114,111. Here are some examples of using the chr() function in Python: # Get the character for ASCII code 65 print(chr(65)) # Output: A # Get the character for Unicode code point 1200 print(chr(1200)) # Output: Ұ # Get the characters for a list of integers codes = [71, 101, 101, 107, 115] chars = [chr(code) for code in codes] print(''.join(chars)) # Output: Geeks To print ‘Hello World’ using chr() , you can write: print(chr(72) + chr(101) + chr(108) + chr(108) + chr(111) + chr(32) + chr(87) + chr(111) + chr(114) + chr(108) + chr(100)) To print a list of all uppercase letters using chr() , you can write: letters = [] for i in range(65, 91):     letters.append(chr(i)) print(letters) To print a smiley face emoji...
Read more

Stock Market Predictions with LSTM in Python

Stock Market Predictions with LSTM in Python is a tutorial that shows how to create a three-layer LSTM model using TensorFlow and Keras. Here is a snippet of how to define the model: # Define the LSTM model model = Sequential() model.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1], 1))) model.add(LSTM(units=50)) model.add(Dense(1))   # Compile and fit the model model.compile(loss='mean_squared_error', optimizer='adam') model.fit(x_train, y_train, epochs=25, batch_size=32) Stock Market Analysis + Prediction using LSTM is a notebook that shows how to load stock market data from Yahoo finance using yfinance module, how to explore and visualize time-series data using pandas, matplotlib, and seaborn, how to measure the correlation between stocks, how to measure the risk of investing in a particular stock, and how to use LSTM (Long Short-Term Memory) model for predicting future stock prices. Here is a snippet of how to create and train an LSTM model f...
Read more

Collections In Python

Python collections are data structures that store multiple items of the same or different types. There are four built-in collection types in Python: list, tuple, set and dictionary. Additionally, there is a module called collections that provides more specialized collection types such as Counter, OrderedDict, defaultdict and namedtuple. Here are some examples of using Python collections: List: A list is a collection that is ordered and changeable. The Python lists are defined with square brackets. # Create a list of fruits fruits = ["apple", "banana", "cherry"] # Print the list print(fruits) # Output: ['apple', 'banana', 'cherry'] Tuple: A tuple is a collection that is ordered and unchangeable. The Python tuples are written with round brackets. # Create a tuple of colors colors = ("red", "green", "blue") # Print the tuple print(colors) # Output: ('red', 'green', 'blue') Set: A...
Read more

Python Count Occurrence Of Elements

There are several ways to count the occurrence of elements in a Python list. One way is to use the list.count() method which takes an element as an argument and returns the number of times it appears in the list. For example: a = [1, 2, 3, 4, 1, 2, 1] b = a.count(1) # b is 3 c = a.count(5) # c is 0 Another way is to use a loop and a counter variable to iterate over the list and increment the counter whenever the element is found. For example: a = [1, 2, 3, 4, 1, 2, 1] x = 1 count = 0 for ele in a:     if ele == x:         count += 1 # count is 3  A third way is to use a dictionary or a defaultdict to store the elements as keys and their counts as values. For example: from collections import defaultdict a = [1, 2, 3, 4, 1 ,2 ,1] occurrence = defaultdict(lambda:0) for ele in a:     occurrence[ele] += 1 # occurrence is {1:3 ,2:2 ,3:1 ,4:1} It is good to have an idea of the number of times elements occurred in your data...
Read more

Python One Liner Functions

Python one-liner functions are functions that can be written in a single line of code. They are often used to perform simple tasks or expressions without defining a named function. One way to create a one-liner function is to use the lambda keyword which creates an anonymous function that takes some arguments and returns a value. For example: # A lambda function that returns the square of a number square = lambda x: x**2 print(square(5)) # 25 # A lambda function that checks if a number is even is_even = lambda x: x%2 == 0 print(is_even(4)) # True Another way to create a one-liner function is to use the def keyword with a colon and an expression after it. This creates a named function that returns the value of the expression. For example: # A one-liner function that returns the sum of two numbers def add(x, y): return x + y print(add(3, 4)) # 7 # A one-liner function that reverses a string def reverse(s): return s[::-1] print(reverse("hello")) # olleh Python offers a one-l...
Read more