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!
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ENJOY LEARNING 👍👍
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
Like for more ❤️
ENJOY LEARNING 👍👍
❤5
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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✅ 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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Share with credits: https://news.1rj.ru/str/sqlspecialist
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❤2
🎓 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
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 —
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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
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🚀 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
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
❤4
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
2️⃣ Numerical Operations with NumPy
3️⃣ Data Visualization with Matplotlib & Seaborn
4️⃣ Exploratory Data Analysis (EDA)
5️⃣ Working with Databases (SQL + Python)
React with ❤️ for more
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
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)
React with ❤️ for more
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤12
𝗦𝗤𝗟 𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
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Looking to master SQL for Data Analytics or prep for your dream tech job? 💼
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💥 Start learning today and build the skills top companies want!✅️
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Career Path for a Data Analyst
Education: Start by earning a bachelor's degree in fields like math, stats, economics, or computer science.
Skills Growth: Learn programming (Python/R), data tools (SQL/Excel), and visualization. Master data analysis basics.
Entry-Level Role: Begin as a Junior Data Analyst. Learn data cleaning, organization, and basic analysis.
Specialization: Deepen your expertise in a specific industry. Explore advanced analytics and visualization tools.
Advanced Analytics: Move up to Senior Data Analyst. Tackle complex projects and predictive modeling.
Machine Learning: Explore machine learning and data modeling techniques. Familiarize yourself with algorithms, and learn how to implement predictive and classification models.
Domain Expertise: Develop expertise in a particular industry, such as healthcare, finance, e-commerce, etc. This knowledge will enable you to provide more valuable insights from data.
Leadership Roles: As you gain experience, you can move into roles like Data Analytics Manager or Data Science Manager, where you'll oversee teams and projects.
Continuous Learning: Stay updated with the latest tools, techniques, and industry trends. Attend workshops, conferences, and online courses to keep your skills relevant.
Networking: Build a strong professional network within the data analytics community. This can open up opportunities and help you stay informed about industry developments.
Remember, your career path can be personalized based on your interests and strengths. Continuous learning and adaptability are key in the ever-evolving field of data analysis :)
Education: Start by earning a bachelor's degree in fields like math, stats, economics, or computer science.
Skills Growth: Learn programming (Python/R), data tools (SQL/Excel), and visualization. Master data analysis basics.
Entry-Level Role: Begin as a Junior Data Analyst. Learn data cleaning, organization, and basic analysis.
Specialization: Deepen your expertise in a specific industry. Explore advanced analytics and visualization tools.
Advanced Analytics: Move up to Senior Data Analyst. Tackle complex projects and predictive modeling.
Machine Learning: Explore machine learning and data modeling techniques. Familiarize yourself with algorithms, and learn how to implement predictive and classification models.
Domain Expertise: Develop expertise in a particular industry, such as healthcare, finance, e-commerce, etc. This knowledge will enable you to provide more valuable insights from data.
Leadership Roles: As you gain experience, you can move into roles like Data Analytics Manager or Data Science Manager, where you'll oversee teams and projects.
Continuous Learning: Stay updated with the latest tools, techniques, and industry trends. Attend workshops, conferences, and online courses to keep your skills relevant.
Networking: Build a strong professional network within the data analytics community. This can open up opportunities and help you stay informed about industry developments.
Remember, your career path can be personalized based on your interests and strengths. Continuous learning and adaptability are key in the ever-evolving field of data analysis :)
❤2
𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍
𝗦𝗤𝗟:- https://pdlink.in/3TcvfsA
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