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How to Deal With Missing Data: Pandas Fillna() and Dropna()

- November 25, 2023

Here are the best examples of Pandas fillna(), dropna() and sum() methods. We have explained the process in two steps - Counting and Replacing the Null values. Count Nulls ## count null values column-wise null_counts = df.isnull(). sum() print(null_counts) ``` Output: ``` Column1 1 Column2 1 Column3 5 dtype: int64 ``` In the above code, we first create a sample Pandas DataFrame `df` with some null values. Then, we use the `isnull()` function to create a DataFrame of the same shape as `df`, where each element is a boolean value indicating whether that element is null or not. Finally, we use the `sum()` function to count the number of null values in each column of the resulting DataFrame. The output shows the count of null values column-wise. to count null values column-wise: ``` df.isnull().sum() ``` ##Code snippet to count null values row-wise: ``` df.isnull().sum(axis=1) ``` In the above code, `df` is the Pandas DataFrame for which you want to count the null values. The `isnu

Python: How to Work With Various File Formats

- November 11, 2023

Here is Python logic that shows Parse and Read Different Files in Python. The formats are XML, JSON, CSV, Excel, Text, PDF, Zip files, Images, SQLlite, and Yaml. Python Reading Files import pandas as pd import json import xml.etree.ElementTree as ET from PIL import Image import pytesseract import PyPDF2 from zipfile import ZipFile import sqlite3 import yaml Reading Text Files # Read text file (.txt) def read_text_file(file_path): with open(file_path, 'r') as file: text = file.read() return text Reading CSV Files # Read CSV file (.csv) def read_csv_file(file_path): df = pd.read_csv(file_path) return df Reading JSON Files # Read JSON file (.json) def read_json_file(file_path): with open(file_path, 'r') as file: json_data = json.load(file) return json_data Reading Excel Files # Read Excel file (.xlsx, .xls) def read_excel_file(file_path): df = pd.read_excel(file_path) return df Reading PDF files # Read PDF file (.pdf) def rea

A Beginner's Guide to Pandas Project for Immediate Practice

- October 20, 2023

Pandas is a powerful data manipulation and analysis library in Python that provides a wide range of functions and tools to work with structured data. Whether you are a data scientist, analyst, or just a curious learner, Pandas can help you efficiently handle and analyze data. In this blog post, we will walk through a step-by-step guide on how to start a Pandas project from scratch. By following these steps, you will be able to import data, explore and manipulate it, perform calculations and transformations, and save the results for further analysis. So let's dive into the world of Pandas and get started with your own project! Simple Pandas project Import the necessary libraries: import pandas as pd import numpy as np Read data from a file into a Pandas DataFrame: df = pd.read_csv('/path/to/file.csv') Explore and manipulate the data: View the first few rows of the DataFrame: print(df.head()) Access specific columns or rows in the DataFrame: print(df['column_name'])

How to Write Complex Python Script: Explained Each Step

- October 08, 2023

Creating a complex Python script is challenging, but I can provide you with a simplified example of a script that simulates a basic bank account system. In a real-world application, this would be much more elaborate, but here's a concise version. Python Complex Script Here is an example of a Python script that explains each step: class BankAccount: def __init__(self, account_holder, initial_balance=0): self.account_holder = account_holder self.balance = initial_balance def deposit(self, amount): if amount > 0: self.balance += amount print(f"Deposited ${amount}. New balance: ${self.balance}") else: print("Invalid deposit amount.") def withdraw(self, amount): if 0 < amount <= self.balance: self.balance -= amount print(f"Withdrew ${amount}. New balance: ${self.balance}") else: print("Invalid withdrawal amount o

Python Regex: The 5 Exclusive Examples

- September 23, 2023

Regular expressions (regex) are powerful tools for pattern matching and text manipulation in Python. Here are five Python regex examples with explanations: 01 Matching a Simple Pattern import re text = "Hello, World!" pattern = r"Hello" result = re.search(pattern, text) if result: print("Pattern found:", result.group()) Output: Output: Pattern found: Hello This example searches for the pattern "Hello" in the text and prints it when found. 02 Matching Multiple Patterns import re text = "The quick brown fox jumps over the lazy dog." patterns = [r"fox", r"dog"] for pattern in patterns: if re.search(pattern, text): print(f"Pattern '{pattern}' found.") Output: Pattern 'fox' found. Pattern 'dog' found. It searches for both "fox" and "dog" patterns in the text and prints when they are found. 03 Matching Any Digit import re text = "The price of the

Best Practices for Handling Duplicate Elements in Python Lists

- August 27, 2023

Here are three awesome ways that you can use to remove duplicates in a list. These are helpful in resolving your data analytics solutions. 01. Using a Set Convert the list into a set , which automatically removes duplicates due to its unique element nature, and then convert the set back to a list. Solution: original_list = [2, 4, 6, 2, 8, 6, 10] unique_list = list(set(original_list)) 02. Using a Loop Iterate through the original list and append elements to a new list only if they haven't been added before. Solution: original_list = [2, 4, 6, 2, 8, 6, 10] unique_list = [] for item in original_list: if item not in unique_list: unique_list.append(item) 03. Using List Comprehension Create a new list using a list comprehension that includes only the elements not already present in the new list. Solution: original_list = [2, 4, 6, 2, 8, 6, 10] unique_list = [] [unique_list.append(item) for item in original_list if item not in unique_list] All three methods will result in uni

10 Exclusive Python Projects for Interviews

- August 18, 2023

Here are ten Python projects along with code and possible solutions for your practice. 01. Palindrome Checker: Description: Write a function that checks if a given string is a palindrome (reads the same backward as forward). def is_palindrome(s): s = s.lower().replace(" ", "") return s == s[::-1] # Test the function print(is_palindrome("radar")) # Output: True print(is_palindrome("hello")) # Output: False 02. Word Frequency Counter: Description: Create a program that takes a text file as input and counts the frequency of each word in the file. def word_frequency(file_path): with open(file_path, 'r') as file: text = file.read().lower() words = text.split() word_count = {} for word in words: word_count[word] = word_count.get(word, 0) + 1 return word_count # Test the function file_path = 'sample.txt' word_count = word_frequency(file_path) print(word_count) 03. Guess the Nu

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