Python for Data Analysts
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Python Basics for Data Analytics ๐๐
Python is one of the most in-demand languages for data analytics due to its simplicity, flexibility, and powerful libraries. Here's a detailed guide to get you started with the basics:
๐ง 1. Variables Data Types
You use variables to store data.
name = "Alice" # String
age = 28 # Integer
height = 5.6 # Float
is_active = True # Boolean
Use Case: Store user details, flags, or calculated values.
๐ 2. Data Structures
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List โ Ordered, changeable
fruits = ['apple', 'banana', 'mango']
print(fruits[0]) # apple
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Dictionary โ Key-value pairs
person = {'name': 'Alice', 'age': 28}
print(person['name']) # Alice
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Tuple Set
Tuples = immutable, Sets = unordered unique
โ๏ธ 3. Conditional Statements
score = 85
if score >= 90:
print("Excellent")
elif score >= 75:
print("Good")
else:
print("Needs improvement")
Use Case: Decision making in data pipelines
๐ 4. Loops
For loop
for fruit in fruits:
print(fruit)
While loop
count = 0
while count < 3:
print("Hello")
count += 1
๐ฃ 5. Functions
Reusable blocks of logic
def add(x, y):
return x + y
print(add(10, 5)) # 15
๐ 6. File Handling
Read/write data files
with open('data.txt', 'r') as file:
content = file.read()
print(content)
๐งฐ 7. Importing Libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
Use Case: These libraries supercharge Python for analytics.
๐งน 8. Real Example: Analyzing Data
import pandas as pd
df = pd.read_csv('sales.csv') # Load data
print(df.head()) # Preview
# Basic stats
print(df.describe())
print(df['Revenue'].mean())
๐ฏ Why Learn Python for Data Analytics?
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Easy to learn
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Huge library support (Pandas, NumPy, Matplotlib)
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Ideal for cleaning, exploring, and visualizing data
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Works well with SQL, Excel, APIs, and BI tools
Python Programming: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
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