Featured Post

Python: Built-in Functions vs. For & If Loops – 5 Programs Explained

-
Image
Python’s built-in functions make coding fast and efficient. But understanding how they work under the hood is crucial to mastering Python. This post shows five Python tasks, each implemented in two ways: Using built-in functions Using for loops and if statements ✅ 1. Sum of a List ✅ Using Built-in Function: numbers = [ 10 , 20 , 30 , 40 ] total = sum (numbers) print ( "Sum:" , total) 🔁 Using For Loop: numbers = [ 10 , 20 , 30 , 40 ] total = 0 for num in numbers: total += num print ( "Sum:" , total) ✅ 2. Find Maximum Value ✅ Using Built-in Function: values = [ 3 , 18 , 7 , 24 , 11 ] maximum = max (values) print ( "Max:" , maximum) 🔁 Using For and If: values = [ 3 , 18 , 7 , 24 , 11 ] maximum = values[ 0 ] for val in values: if val > maximum: maximum = val print ( "Max:" , maximum) ✅ 3. Count Vowels in a String ✅ Using Built-ins: text = "hello world" vowel_count = sum ( 1 for ch in text if ch i...
Post a Comment

5 Python Pandas Tricky Examples for Data Analysis

-
Here are five tricky Python Pandas examples. These provide detailed insights to work with Pandas in Python,


Pandas examples

#1 Dealing with datetime data (parse_dates pandas example)


import pandas as pd

# Convert a column to datetime format

data['date_column'] = pd.to_datetime(data['date_column'])


# Extract components from datetime (e.g., year, month, day)

data['year'] = data['date_column'].dt.year

data['month'] = data['date_column'].dt.month


# Calculate the time difference between two datetime columns

data['time_diff'] = data['end_time'] - data['start_time']


#2 Working with text data

 

# Convert text to lowercase

data['text_column'] = data['text_column'].str.lower()


# Count the occurrences of specific words in a text column

data['word_count'] = data['text_column'].str.count('word')


# Extract information using regular expressions

data['extracted_info'] = data['text_column'].str.extract(r'(\d+)')


#3 Handling large datasets efficiently


# Read a large dataset in chunks

chunk_size = 100000

data_chunks = pd.read_csv('large_data.csv', chunksize=chunk_size)

# Process data in chunks

for chunk in data_chunks:

    # Perform calculations or manipulations on each chunk


# Append data from multiple files

file_list = ['file1.csv', 'file2.csv', 'file3.csv']

combined_data = pd.concat([pd.read_csv(file) for file in file_list])


#4 Pivot tables and reshaping data


# Create a pivot table

pivot_table = data.pivot_table(values='column2', index='column1', columns='column3', aggfunc='mean')


# Unstack a multi-index DataFrame

unstacked_data = pivot_table.unstack().reset_index()


# Melt a DataFrame from wide to long format

melted_data = pd.melt(data, id_vars=['id'], value_vars=['var1', 'var2'], var_name='variable', value_name='value')


#5 Efficient memory usage


# Optimize memory usage of DataFrame columns

data['numeric_column'] = pd.to_numeric(data['numeric_column'], downcast='integer')

data['category_column'] = data['category_column'].astype('category')


# Load a subset of columns from a large dataset

selected_columns = ['column1', 'column2', 'column3']

data_subset = pd.read_csv('large_data.csv', usecols=selected_columns)


These examples demonstrate more advanced techniques for handling datetime data, text data, large datasets, reshaping data, and optimizing memory usage. They highlight some of the powerful features that pandas provide for complex data analysis tasks.


Related

Comments

Post a Comment

Thanks for your message. We will get back you.

Popular posts from this blog

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...
Read more

5 SQL Queries That Popularly Used in Data Analysis

-
Image
 Here are five popular SQL queries frequently used in data analysis. 1. SELECT with Aggregations Summarize data by calculating aggregates like counts, sums, averages, etc. SELECT department, COUNT(*) as employee_count, AVG(salary) as average_salary FROM employees GROUP BY department; 2. JOIN Operations  Combine data from multiple tables based on a related column. SELECT e.employee_id, e.name, d.department_name FROM employees e JOIN departments d ON e.department_id = d.department_id; 3. WHERE Clause for Filtering Filter records based on specified conditions. SELECT * FROM sales WHERE sale_date BETWEEN '2024-01-01' AND '2024-12-31'   AND amount > 1000; 4. ORDER BY Clause for Sorting Sort results in ascending or descending order based on one or more columns. SELECT product_name, price FROM products ORDER BY price DESC; 5. GROUP BY with HAVING Clause Group records and apply conditions to the aggregated results. SELECT department, SUM(salary) as total_salaries FROM employ...
Read more

Big Data: Top Cloud Computing Interview Questions (1 of 4)

-
Image
The below are frequently asked interview questions on Cloud computing: 1) What is the difference between Cloud and Grid? Grid: -Information service -Security Service -Data management -Execution Manageement Cloud: - Maintains up-to-date information of resources -Create VMs according to user requirement -Application deploment -User management 2) What are the different cloud standards? -Interoperability standards -Security standards -Portability Standards -Governance and Risk standards 3) What are the two different sub-systems in Cloud computing ? -Management sub system -Resource sub system 4)What is Cloud compouting? The promise of cloud computing is ubiquitous access to a broad set of applications and services, which are delivered over the network to multiple customer. 5) Why we need specialized network for Cloud services? The public Internet is the simplest choice for delivering cloud-based services. In this model, the cloud provider simply purchases Inter...
Read more