Working with Python Data

1. Working with Data

Data analytics often starts with raw values. Python can store these values in lists, dictionaries, tuples, and sets.

sales = [12000, 18000, 9500, 22000]

print(sales)

A list is useful when you need an ordered collection of values.

2. Inspecting a Dataset

Before analyzing data, it is useful to inspect its size and individual values.

sales = [12000, 18000, 9500, 22000]

print(len(sales))
print(sales[0])

len() tells us how many values are in the list.

3. Loop Through Data

A for loop allows us to process every value in a dataset.

sales = [12000, 18000, 9500]

for sale in sales:
    print(sale)

4. Summarizing Data

Python provides useful functions such as sum(),min(), and max().

sales = [12000, 18000, 9500, 22000]

print(sum(sales))
print(min(sales))
print(max(sales))

5. Calculating an Average

A simple average can be calculated by dividing the total by the number of values.

sales = [10000, 20000, 30000]

average = sum(sales) / len(sales)

print(average)

6. Filtering Data

Conditions allow us to select only values that meet a specific rule.

sales = [50000, 120000, 75000, 150000]

for sale in sales:
    if sale >= 100000:
        print(sale)

7. Counting Matching Records

We can use a counter to count how many records satisfy a condition.

sales = [50000, 120000, 75000, 150000]

count = 0

for sale in sales:
    if sale >= 100000:
        count += 1

print(count)

8. Creating a Filtered Dataset

sales = [50000, 120000, 75000, 150000]

high_sales = []

for sale in sales:
    if sale >= 100000:
        high_sales.append(sale)

print(high_sales)

The new list contains only the records that satisfy the condition.

9. Working with Dictionary Records

Dictionaries are useful for representing individual records with named fields.

student = {
    "name": "Aman",
    "course": "Python",
    "score": 88
}

print(student["score"])

10. A Dataset as a List of Dictionaries

A common way to represent simple tabular data in Python is a list containing dictionaries.

students = [
    {"name": "Aman", "score": 88},
    {"name": "Riya", "score": 92},
    {"name": "Karan", "score": 76}
]

for student in students:
    print(student["name"], student["score"])

11. Filtering Records

students = [
    {"name": "Aman", "score": 88},
    {"name": "Riya", "score": 92},
    {"name": "Karan", "score": 76}
]

for student in students:
    if student["score"] >= 80:
        print(student["name"])

12. Building Totals with a Loop

sales = [10000, 20000, 15000]

total = 0

for sale in sales:
    total += sale

print(total)

The accumulator pattern is useful when you need to build a total step by step.

13. Combining Functions and Data

def average(values):
    return sum(values) / len(values)

scores = [80, 90, 70, 100]

result = average(scores)

print(result)

14. Basic Data Cleaning

Raw data can contain unwanted spaces or inconsistent text. Python string methods can help clean values before analysis.

city = "  Dehradun  "

clean_city = city.strip()

print(clean_city)

strip() removes whitespace from the beginning and end of a string.

15. Think Like a Data Analyst

A Python data workflow often follows a simple pattern:

1. Load or create the data.
2. Inspect the data.
3. Clean the data.
4. Filter records.
5. Calculate useful metrics.
6. Interpret the result.