👍6❤3
Python Most Important Interview Questions
Question 1: Calculate the average stock price for Company X over the last 6 months.
Question 2: Identify the month with the highest total sales for Company Y using their monthly sales data.
Question 3: Find the maximum and minimum stock price for Company Z on any given day in the last year.
Question 4: Create a column in the DataFrame showing the percentage change in stock price from the previous day for Company X.
Question 5: Determine the number of days when the stock price of Company Y was above its 30-day moving average. Question
6: Compare the average stock price of Companies X and Z in the first quarter of the year.
#Data#
----------------------------------------------
import pandas as pd
data = { 'Date': pd.date_range(start='2023-01-01', periods=180, freq='D'), 'CompanyX_StockPrice': pd.np.random.randint(50, 150, 180), 'CompanyY_Sales': pd.np.random.randint(20000, 50000, 180), 'CompanyZ_StockPrice': pd.np.random.randint(70, 200, 180) }
df = pd.DataFrame(data)
Question 1: Calculate the average stock price for Company X over the last 6 months.
Question 2: Identify the month with the highest total sales for Company Y using their monthly sales data.
Question 3: Find the maximum and minimum stock price for Company Z on any given day in the last year.
Question 4: Create a column in the DataFrame showing the percentage change in stock price from the previous day for Company X.
Question 5: Determine the number of days when the stock price of Company Y was above its 30-day moving average. Question
6: Compare the average stock price of Companies X and Z in the first quarter of the year.
#Data#
----------------------------------------------
import pandas as pd
data = { 'Date': pd.date_range(start='2023-01-01', periods=180, freq='D'), 'CompanyX_StockPrice': pd.np.random.randint(50, 150, 180), 'CompanyY_Sales': pd.np.random.randint(20000, 50000, 180), 'CompanyZ_StockPrice': pd.np.random.randint(70, 200, 180) }
df = pd.DataFrame(data)
👍19❤6👏4
Complete Python Course with Building 10 Real-world Applications
👇👇
https://www.linkedin.com/posts/sql-analysts_python-viral-pythonprogramming-activity-7141826568485605376-tf7V
👇👇
https://www.linkedin.com/posts/sql-analysts_python-viral-pythonprogramming-activity-7141826568485605376-tf7V
👍21👏6
👍12👏5
Python for Data Analysts
Complete Python Course with Building 10 Real-world Applications 👇👇 https://www.linkedin.com/posts/sql-analysts_python-viral-pythonprogramming-activity-7141826568485605376-tf7V
Sent to all who liked and commented on the post 😄
❤8👍6
Master Python with this Free Guide
👇👇
https://www.linkedin.com/posts/sql-analysts_master-python-with-this-free-guide-tips-activity-7142572820852641793-l0UX?utm_source=share&utm_medium=member_android
👇👇
https://www.linkedin.com/posts/sql-analysts_master-python-with-this-free-guide-tips-activity-7142572820852641793-l0UX?utm_source=share&utm_medium=member_android
👍10
Forwarded from Python Projects & Resources
Master Python Programming with this Free Notion Guide
👇👇
https://www.linkedin.com/posts/sql-analysts_python-pythonprojects-freecourses-activity-7143810292228026368-wnhL?utm_source=share&utm_medium=member_android
👇👇
https://www.linkedin.com/posts/sql-analysts_python-pythonprojects-freecourses-activity-7143810292228026368-wnhL?utm_source=share&utm_medium=member_android
👍11
Python Libraries every Data Scientist should know
Join for more -> https://t.iss.one/addlist/KBNT2WWRIEs0NzIx
All the best 👍👍
Join for more -> https://t.iss.one/addlist/KBNT2WWRIEs0NzIx
All the best 👍👍
👍25❤10🥰1
👍10❤4
Python is a popular programming language in the field of data analysis due to its versatility, ease of use, and extensive libraries for data manipulation, visualization, and analysis. Here are some key Python skills that are important for data analysts:
1. Basic Python Programming: Understanding basic Python syntax, data types, control structures, functions, and object-oriented programming concepts is essential for data analysis in Python.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large multidimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
3. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data and perform tasks such as filtering, grouping, joining, and reshaping data.
4. Matplotlib and Seaborn: Matplotlib is a versatile library for creating static, interactive, and animated visualizations in Python. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive statistical graphics.
5. Scikit-learn: Scikit-learn is a popular machine learning library in Python that provides tools for building predictive models, performing clustering and classification tasks, and evaluating model performance.
6. Jupyter Notebooks: Jupyter Notebooks are an interactive computing environment that allows you to create and share documents containing live code, equations, visualizations, and narrative text. They are commonly used by data analysts for exploratory data analysis and sharing insights.
7. SQLAlchemy: SQLAlchemy is a Python SQL toolkit and Object-Relational Mapping (ORM) library that provides a high-level interface for interacting with relational databases using Python.
8. Regular Expressions: Regular expressions (regex) are powerful tools for pattern matching and text processing in Python. They are useful for extracting specific information from text data or performing data cleaning tasks.
9. Data Visualization Libraries: In addition to Matplotlib and Seaborn, data analysts may also use other visualization libraries like Plotly, Bokeh, or Altair to create interactive visualizations in Python.
10. Web Scraping: Knowledge of web scraping techniques using libraries like BeautifulSoup or Scrapy can be useful for collecting data from websites for analysis.
By mastering these Python skills and applying them to real-world data analysis projects, you can enhance your proficiency as a data analyst and unlock new opportunities in the field.
1. Basic Python Programming: Understanding basic Python syntax, data types, control structures, functions, and object-oriented programming concepts is essential for data analysis in Python.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large multidimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
3. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data and perform tasks such as filtering, grouping, joining, and reshaping data.
4. Matplotlib and Seaborn: Matplotlib is a versatile library for creating static, interactive, and animated visualizations in Python. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive statistical graphics.
5. Scikit-learn: Scikit-learn is a popular machine learning library in Python that provides tools for building predictive models, performing clustering and classification tasks, and evaluating model performance.
6. Jupyter Notebooks: Jupyter Notebooks are an interactive computing environment that allows you to create and share documents containing live code, equations, visualizations, and narrative text. They are commonly used by data analysts for exploratory data analysis and sharing insights.
7. SQLAlchemy: SQLAlchemy is a Python SQL toolkit and Object-Relational Mapping (ORM) library that provides a high-level interface for interacting with relational databases using Python.
8. Regular Expressions: Regular expressions (regex) are powerful tools for pattern matching and text processing in Python. They are useful for extracting specific information from text data or performing data cleaning tasks.
9. Data Visualization Libraries: In addition to Matplotlib and Seaborn, data analysts may also use other visualization libraries like Plotly, Bokeh, or Altair to create interactive visualizations in Python.
10. Web Scraping: Knowledge of web scraping techniques using libraries like BeautifulSoup or Scrapy can be useful for collecting data from websites for analysis.
By mastering these Python skills and applying them to real-world data analysis projects, you can enhance your proficiency as a data analyst and unlock new opportunities in the field.
👍48❤13🥰1
7 must know SQL concepts with resources to learn 👇👇
https://www.instagram.com/reel/C3szNi4NLh3/?igsh=enpicm5wN2swNTBv
https://www.instagram.com/reel/C3szNi4NLh3/?igsh=enpicm5wN2swNTBv
👍7❤2
Working on creating a YouTube video on how to become a successful data analyst in 2024 from last few days for you guys. Excited for the video?
Anonymous Poll
93%
Yes 😀
7%
No need 😞
❤22👍5
For data analysts working with Python, mastering these top 10 concepts is essential:
1. Data Structures: Understand fundamental data structures like lists, dictionaries, tuples, and sets, as well as libraries like NumPy and Pandas for more advanced data manipulation.
2. Data Cleaning and Preprocessing: Learn techniques for cleaning and preprocessing data, including handling missing values, removing duplicates, and standardizing data formats.
3. Exploratory Data Analysis (EDA): Use libraries like Pandas, Matplotlib, and Seaborn to perform EDA, visualize data distributions, identify patterns, and explore relationships between variables.
4. Data Visualization: Master visualization libraries such as Matplotlib, Seaborn, and Plotly to create various plots and charts for effective data communication and storytelling.
5. Statistical Analysis: Gain proficiency in statistical concepts and methods for analyzing data distributions, conducting hypothesis tests, and deriving insights from data.
6. Machine Learning Basics: Familiarize yourself with machine learning algorithms and techniques for regression, classification, clustering, and dimensionality reduction using libraries like Scikit-learn.
7. Data Manipulation with Pandas: Learn advanced data manipulation techniques using Pandas, including merging, grouping, pivoting, and reshaping datasets.
8. Data Wrangling with Regular Expressions: Understand how to use regular expressions (regex) in Python to extract, clean, and manipulate text data efficiently.
9. SQL and Database Integration: Acquire basic SQL skills for querying databases directly from Python using libraries like SQLAlchemy or integrating with databases such as SQLite or MySQL.
10. Web Scraping and API Integration: Explore methods for retrieving data from websites using web scraping libraries like BeautifulSoup or interacting with APIs to access and analyze data from various sources.
Give credits while sharing: https://t.iss.one/pythonanalyst
ENJOY LEARNING 👍👍
1. Data Structures: Understand fundamental data structures like lists, dictionaries, tuples, and sets, as well as libraries like NumPy and Pandas for more advanced data manipulation.
2. Data Cleaning and Preprocessing: Learn techniques for cleaning and preprocessing data, including handling missing values, removing duplicates, and standardizing data formats.
3. Exploratory Data Analysis (EDA): Use libraries like Pandas, Matplotlib, and Seaborn to perform EDA, visualize data distributions, identify patterns, and explore relationships between variables.
4. Data Visualization: Master visualization libraries such as Matplotlib, Seaborn, and Plotly to create various plots and charts for effective data communication and storytelling.
5. Statistical Analysis: Gain proficiency in statistical concepts and methods for analyzing data distributions, conducting hypothesis tests, and deriving insights from data.
6. Machine Learning Basics: Familiarize yourself with machine learning algorithms and techniques for regression, classification, clustering, and dimensionality reduction using libraries like Scikit-learn.
7. Data Manipulation with Pandas: Learn advanced data manipulation techniques using Pandas, including merging, grouping, pivoting, and reshaping datasets.
8. Data Wrangling with Regular Expressions: Understand how to use regular expressions (regex) in Python to extract, clean, and manipulate text data efficiently.
9. SQL and Database Integration: Acquire basic SQL skills for querying databases directly from Python using libraries like SQLAlchemy or integrating with databases such as SQLite or MySQL.
10. Web Scraping and API Integration: Explore methods for retrieving data from websites using web scraping libraries like BeautifulSoup or interacting with APIs to access and analyze data from various sources.
Give credits while sharing: https://t.iss.one/pythonanalyst
ENJOY LEARNING 👍👍
👍40❤16