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Python for Data Analytics - Quick Cheatsheet with Code 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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Python for Data Analytics - Quick Cheatsheet with Code 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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Why Telegram?

Telegram has no known backdoors and, even though it is come in for criticism for using proprietary encryption methods instead of open-source ones, those have yet to be compromised. While no messaging app can guarantee a 100% impermeable defense against determined attackers, Telegram is vulnerabilities are few and either theoretical or based on spoof files fooling users into actively enabling an attack.

Telegram auto-delete message, expiring invites, and more

elegram is updating its messaging app with options for auto-deleting messages, expiring invite links, and new unlimited groups, the company shared in a blog post. Much like Signal, Telegram received a burst of new users in the confusion over WhatsApp’s privacy policy and now the company is adopting features that were already part of its competitors’ apps, features which offer more security and privacy. Auto-deleting messages were already possible in Telegram’s encrypted Secret Chats, but this new update for iOS and Android adds the option to make messages disappear in any kind of chat. Auto-delete can be enabled inside of chats, and set to delete either 24 hours or seven days after messages are sent. Auto-delete won’t remove every message though; if a message was sent before the feature was turned on, it’ll stick around. Telegram’s competitors have had similar features: WhatsApp introduced a feature in 2020 and Signal has had disappearing messages since at least 2016.

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