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Easy Python scenarios for everyday data tasks



Scenario 1: Data Cleaning
Question:
You have a DataFrame containing product prices with columns Product and Price. Some of the prices are stored as strings with a dollar sign, like $10. Write a Python function to convert the prices to float.

Answer:
import pandas as pd
data = {
'Product': ['A', 'B', 'C', 'D'],
'Price': ['$10', '$20', '$30', '$40']
}
df = pd.DataFrame(data)

def clean_prices(df):
df['Price'] = df['Price'].str.replace('$', '').astype(float)
return df
cleaned_df = clean_prices(df)
print(cleaned_df)

Scenario 2: Basic Aggregation
Question:
You have a DataFrame containing sales data with columns Region and Sales. Write a Python function to calculate the total sales for each region.

Answer:
import pandas as pd
data = {
'Region': ['North', 'South', 'East', 'West', 'North', 'South', 'East', 'West'],
'Sales': [100, 200, 150, 250, 300, 100, 200, 150]
}
df = pd.DataFrame(data)

def total_sales_per_region(df):
total_sales = df.groupby('Region')['Sales'].sum().reset_index()
return total_sales

total_sales = total_sales_per_region(df)
print(total_sales)

Scenario 3: Filtering Data
Question:
You have a DataFrame containing customer data with columns ‘CustomerID’, Name, and Age. Write a Python function to filter out customers who are younger than 18 years old.

Answer:
import pandas as pd
data = {
'CustomerID': [1, 2, 3, 4, 5],
'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
'Age': [17, 22, 15, 35, 40]
}
df = pd.DataFrame(data)

def filter_customers(df):
filtered_df = df[df['Age'] >= 18]
return filtered_df
filtered_customers = filter_customers(df)
print(filtered_customers)

5 essential Python functions for handling missing data:

🔹 isna(): Detects missing values in your DataFrame. Identifies NaNs

🔹 notna(): Detects non-missing values. Filters out the NaNs.

🔹 interpolate(): Fills missing values using interpolation

🔹 bfill(): Backward fill. Fills missing values with the next valid observation.

🔹 ffill(): Forward fill. Fills missing values with the previous valid observation.

7 level of writing Python Dictionary



Level 1: Basic Dictionary Creation

Level 2: Accessing and Modifying values

Level 3: Adding and Removing key Values Pairs

Level 4: Dictionary Methods

Level 5: Dictionary Comprehensions

Level 6: Nested Dictionary

Level 7: Advanced Dictionary Operations

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Must practise the following questions for your next Python interview:

1. How would you handle missing values in a dataset?

2. Write a python code to merge datasets based on a common column.

3. How would you analyse the distribution of a continuous variable in dataset?

4. Write a python code to pivot an dataframe.

5. How would you handle categorical variables with many levels?

6. Write a python code to calculate the accuracy, precision, and recall of a classification model?

7. How would you handle errors when working with large datasets?

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How to create simple pivot table in Python? DataAnalytics

🔹 Step 1: Import pandas
🔹 Step 2: Load your DataFrame
🔹 Step 3: Pivot the DataFrame
🔹 Step 4: Display the pivoted data

How to Use Python’s range() Function

The range() function generates a sequence of numbers, commonly used for looping a specific number of times or creating numeric lists.

The first number is included, but the last number is excluded.

For example, range(5, 10) will generate numbers from 5 to 9, but not 10.

Creating Beautiful Box Plots with Seaborn in Python

A box plot is a simple way to visualise the distribution of a dataset and identify potential outliers. It displays the minimum, first quartile (Q1), median (Q2), third quartile (Q3), and maximum of the data, as well as any outliers. For more details on box plots you can watch my latest video on Insta

🔹 Step 1: Import Seaborn and load your dataset

🔹 Step 2: Create a basic box plot