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.

𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲 𝗼𝗻 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗯𝘆 𝗗𝗲𝗲𝗽𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴.𝗔𝗜 & 𝗢𝗽𝗲𝗻𝗔𝗜😍

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Lists 🆚 Tuples 🆚 Dictionaries

What's the difference?

Lists are mutable.
Tuples are immutable.
Dictionaries are associative.

When should you use each?

Lists:
⟶ When you want to add or remove elements
⟶ When you want to sort elements
⟶ When you want to slice elements

Tuples:
⟶ When you want a constant object
⟶ When you want to send multiple in a function
⟶ When you want to return multiple from a function

Dictionaries:
⟶ When you want to map keys to values
⟶ When you want to loop over the keys
⟶ When you want to validate if key exists

Now, pick your weapon of mass data analysis and become a Python pro!

Python Interview Q&A: https://topmate.io/coding/898340

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ENJOY LEARNING 👍👍

𝟱 𝗠𝘂𝘀𝘁-𝗙𝗼𝗹𝗹𝗼𝘄 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝗳𝗼𝗿 𝗔𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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7 Must-Have Tools for Data Analysts in 2025:

✅ SQL – Still the #1 skill for querying and managing structured data
✅ Excel / Google Sheets – Quick analysis, pivot tables, and essential calculations
✅ Python (Pandas, NumPy) – For deep data manipulation and automation
✅ Power BI – Transform data into interactive dashboards
✅ Tableau – Visualize data patterns and trends with ease
✅ Jupyter Notebook – Document, code, and visualize all in one place
✅ Looker Studio – A free and sleek way to create shareable reports with live data.

Perfect blend of code, visuals, and storytelling.

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🎓 𝗟𝗲𝗮𝗿𝗻 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝗳𝗿𝗼𝗺 𝗛𝗮𝗿𝘃𝗮𝗿𝗱, 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱, 𝗠𝗜𝗧 & 𝗚𝗼𝗼𝗴𝗹𝗲😍

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Free Resources for Python

Codebasics python tutorials (first 16) — 
https://www.youtube.com/playlist?list=PLeo1K3hjS3uv5U-Lmlnucd7gqF-3ehIh0

Practice Python course
https://dabeaz-course.github.io/practical-python/Notes/Contents.html

Codebasics python HINDI tutorials —
 https://www.youtube.com/playlist?list=PLPbgcxheSpE1DJKfdko58_AIZRIT0TjpO

Useful Python resources for beginners
https://news.1rj.ru/str/programming_guide/8

Python 3 Book for beginners
https://news.1rj.ru/str/pythondevelopersindia/272

🚀 Essential Python snippets to explore data:
 
1.   .head() - Review top rows
2.   .tail() - Review bottom rows
3.   .info() - Summary of DataFrame
4.   .shape - Shape of DataFrame
5.   .describe() - Denoscriptive stats
6.   .isnull().sum() - Check missing values
7.   .dtypes - Data types of columns
8.   .unique() - Unique values in a column
9.   .nunique() - Count unique values
10.   .value_counts() - Value counts in a column
11.   .corr() - Correlation matrix

𝗔𝗱𝗱 𝗗𝗲𝗹𝗼𝗶𝘁𝘁𝗲 𝘁𝗼 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 — 𝗡𝗼 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗡𝗲𝗲𝗱𝗲𝗱!😍

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Python for Data Analytics - Quick Cheatsheet with Cod e Example 🚀

1️⃣ Data Manipulation with Pandas

import pandas as pd  
df = pd.read_csv("data.csv")  
df.to_excel("output.xlsx")  
df.head()  
df.info()  
df.describe()  
df[df["sales"] > 1000]  
df[["name", "price"]]  
df.fillna(0, inplace=True)  
df.dropna(inplace=True)  

2️⃣ Numerical Operations with NumPy

import numpy as np  
arr = np.array([1, 2, 3, 4])  
print(arr.shape)  
np.mean(arr)  
np.median(arr)  
np.std(arr)  

3️⃣ Data Visualization with Matplotlib & Seaborn

import matplotlib.pyplot as plt  
plt.plot([1, 2, 3, 4], [10, 20, 30, 40])  
plt.bar(["A", "B", "C"], [5, 15, 25])  
plt.show()  
import seaborn as sns  
sns.heatmap(df.corr(), annot=True)  
sns.boxplot(x="category", y="sales", data=df)  
plt.show()  

4️⃣ Exploratory Data Analysis (EDA)

df.isnull().sum()  
df.corr()  
sns.histplot(df["sales"], bins=30)  
sns.boxplot(y=df["price"])  

5️⃣ Working with Databases (SQL + Python)

import sqlite3  
conn = sqlite3.connect("database.db")  
df = pd.read_sql("SELECT * FROM sales", conn)  
conn.close()  
cursor = conn.cursor()  
cursor.execute("SELECT AVG(price) FROM products")  
result = cursor.fetchone()  
print(result)

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𝗦𝗤𝗟 𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

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It's an educational goldmine.

If you do, you’re sleeping on a goldmine of knowledge and community. WhatsApp channels are a great way to practice data science, make your own community, and find accountability partners.

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