Featured Post

15 Python Tips : How to Write Code Effectively

-
Image
 Here are some Python tips to keep in mind that will help you write clean, efficient, and bug-free code.     Python Tips for Effective Coding 1. Code Readability and PEP 8  Always aim for clean and readable code by following PEP 8 guidelines.  Use meaningful variable names, avoid excessively long lines (stick to 79 characters), and organize imports properly. 2. Use List Comprehensions List comprehensions are concise and often faster than regular for-loops. Example: squares = [x**2 for x in range(10)] instead of creating an empty list and appending each square value. 3. Take Advantage of Python’s Built-in Libraries  Libraries like itertools, collections, math, and datetime provide powerful functions and data structures that can simplify your code.   For example, collections.Counter can quickly count elements in a list, and itertools.chain can flatten nested lists. 4. Use enumerate Instead of Range     When you need both the index and the value in a loop, enumerate is a more Pyth
Post a Comment

How to Convert Dictionary to Dataframe: Pandas from_dict

-

 Pandas is a data analysis Python library.  The example shows you to convert a dictionary to a data frame. The point to note here is DataFrame will take only 2D data. So you need to supply 2D data. 


Dictionary to Data frame example


Pandas Dictionary to Dataframe


import pandas as pd

import numpy as np

data_dict = {'item1' : np.random.randn(4), 'item2' : np.random.randn(4)}

df3=pd.DataFrame.from_dict(data_dict, orient='index')

print(df3)


Output


0 1 2 3 item1 -0.109300 -0.483624 0.375838 1.248651 item2 -0.274944 -0.857318 -1.203718 -0.061941


Explanation

Using the NumPy package, created a dictionary with random values. There are two items - item 1 and item 2. The data_dict is input to the data frame. The from_dict method needs two parameters. These are data_dict and index. Here's the syntax you can refer to quickly.


Related

Comments

Post a Comment

Thanks for your message. We will get back you.

Popular posts from this blog

How to Fix datetime Import Error in Python Quickly

-
Image
Here's a quick resolution for import datetime Python error . The reason is your .py python script name and datetime  are the same. I'll show you how this error happens and its resolution.   Here's the Resolution for ImportError I've created a script called 'datetime.py.' to check whether the minute value is 'odd' or not. During the import of my python script, I got the import error cannot import name datetime. My Script: datetime.py My script's intention is to find whether the minute value is odd or not. Python Logic from datetime import datetime odds = [ 1 , 3 , 5 , 7 , 9 , 11 , 13 , 15 , 17 , 19 , 21 , 23 , 25 , 27 , 29 , 31 , 33 , 35 , 37 , 39 , 41 , 43 , 45 , 47 , 49 , 51 , 53 , 55 , 57 , 59 ] right_this_minute = datetime.today().minute if right_this_minute in odds: print ( "This minute seems a little odd." ) else : print ( "Not an odd minute." ) ImportError I have imported my datetime.py from Linux. It gives an error
Read more

SQL Query: 3 Methods for Calculating Cumulative SUM

-
Image
SQL provides various constructs for calculating cumulative sums, offering flexibility and efficiency in data analysis. In this article, we explore three distinct SQL queries that facilitate the computation of cumulative sums. Each query leverages different SQL constructs to achieve the desired outcome, catering to diverse analytical needs and preferences. Using Window Functions (e.g., PostgreSQL, SQL Server, Oracle) SELECT id, value, SUM(value) OVER (ORDER BY id) AS cumulative_sum  FROM your_table; This query uses the SUM() window function with the OVER clause to calculate the cumulative sum of the value column ordered by the id column. Using Subqueries (e.g., MySQL, SQLite): SELECT t1.id, t1.value, SUM(t2.value) AS cumulative_sum FROM your_table t1 JOIN your_table t2 ON t1.id >= t2.id GROUP BY t1.id, t1.value ORDER BY t1.id; This query uses a self-join to calculate the cumulative sum. It joins the table with itself, matching rows where the id in the first table is greater than or
Read more

Python placeholder '_' Perfect Way to Use it

-
Image
What is placeholder in Python? The purpose of it is to mask the variable that you don't want to use in a function. In python, y ou can call the underscore ( _ ) operator placeholder. Below, you'll find how to use single and double placeholders in a function. What is placeholder in python The purpose of placeholder in Python is to mask variables that you don't want to use in a function. So that your code will be readable. Moreover, in future, if you want to use those variables you can replace the placeholders with the names you want. In This Page You'll know in three steps how to use placeholder correctly. Creating a function Logic to use single placeholder Logic to use two placeholders 1. Creating a function. def function_that_returns_multiple_values(x):        return x*2, x*3, x+1        for i in range(0,5):             square, cube, added_one = function_that_returns_multiple_values(i)             print(square, cube) Here, in print, it returns two variables. I will s
Read more