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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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The S&P 500 slumped 1.8% on Monday and Tuesday, thanks to China Evergrande, the Chinese property company that looks like it is ready to default on its more-than $300 billion in debt. Cries of the next Lehman Brothers—or maybe the next Silverado?—echoed through the canyons of Wall Street as investors prepared for the worst.

Spiking bond yields driving sharp losses in tech stocks

A spike in interest rates since the start of the year has accelerated a rotation out of high-growth technology stocks and into value stocks poised to benefit from a reopening of the economy. The Nasdaq has fallen more than 10% over the past month as the Dow has soared to record highs, with a spike in the 10-year US Treasury yield acting as the main catalyst. It recently surged to a cycle high of more than 1.60% after starting the year below 1%. But according to Jim Paulsen, the Leuthold Group's chief investment strategist, rising interest rates do not represent a long-term threat to the stock market. Paulsen expects the 10-year yield to cross 2% by the end of the year. A spike in interest rates and its impact on the stock market depends on the economic backdrop, according to Paulsen. Rising interest rates amid a strengthening economy "may prove no challenge at all for stocks," Paulsen said.

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