How does Python for Data Science help in analysing real-world datasets?

Python has become one of the most popular programming languages used in data science. With its versatile libraries and frameworks for manipulating, visualizing and modelling data, Python provides data scientists with the tools to glean powerful insights from real-world data.

Python data science

This hands-on guide will demonstrate how Python's data science libraries allow you to efficiently access, clean, explore and build predictive models using real-world datasets. You'll learn:

How to access and load data from various sources into Python
Techniques for cleaning, formatting and preprocessing real-world data
Exploratory data analysis methods like summary statistics and visualizations
Building machine learning models to make predictions from data
Case studies applying Python data science libraries to solve real problems

Whether you're just getting started with JBI Trainings Advanced Python Course for data science or looking to level up your skills, this guide will have you equipped to harness the power of Python for analysing real-world data.

An Introduction to Python for Data Science

Python has cemented itself as a must-have skill for aspiring data professionals. Here's a quick primer on Python and its robust ecosystem of libraries for data science tasks:

What is Python?

Python is a general-purpose, high-level programming language that was created by Guido van Rossum in 1991. Some key qualities that make Python well-suited for data science:

Interpreted - No need to compile code, fast development workflow
Dynamically typed - No need to declare types, easy to work with different data
Readable syntax - Code is simple and intuitive, easy for beginners to learn

Why Use Python for Data Science?

Python has a vibrant open-source community that has developed powerful libraries for data manipulation, analysis and modelling, including:

Numpy - Foundational library for numeric Python
Pandas - Data analysis toolkit, great for working with tabular data
Matplotlib - Comprehensive 2D plotting for creating visualizations
Scikit-learn - Leading machine learning library for predictive modelling

Python code is relatively easy to write, learn and debug. The availability of skilled Python programmers makes it a great choice for data science teams. Python can integrate with other languages like R, SQL, C and JavaScript. All these qualities make Python an ideal first programming language for aspiring data scientists.

Python 2 vs Python 3

Python 3, released in 2008, was a major upgrade that is incompatible with earlier Python 2 code. Python 3 has become the standard version for data science. This guide covers Python 3, which has better Unicode support, modern features and active community support.

Now that you have a basic understanding of Python and its use in data science, let's look at how to work with real-world datasets using Python libraries.

Accessing and Importing Real-World Datasets

Many interesting real-world datasets are available that we can analyze using Python. Here are some places you can get free datasets:

Kaggle Datasets - One of the largest hubs of public datasets on topics like finance, healthcare, social media etc.
UCI Machine Learning Repository - Archive of experimental datasets for machine learning research.
Data.gov - US Government open datasets ranging from agriculture to climate science.
Google Data Search - Search engine for public datasets hosted on BigQuery, AWS, Kaggle etc.
Web Scraping -Harvesting your own datasets from the web using Python libraries like Beautiful Soup.

Once you've identified a dataset to work with, how do you get it into a Python environment? Here are some approaches:

CSV Files - Comma-separated values file, can be loaded using Pandas read_csv()
Excel Files - Use Pandas read_excel() to import Excel data into a dataframe
SQL Databases - Use SQLAlchemy library to connect and query SQL databases
JSON Format - Load JSON objects and files using json module
Web APIs - Request data programmatically from API endpoints like Twitter, Wikipedia etc.
Cloud Storage - Access public datasets on AWS, GCP, Azure with cloud SDKs

Let's look at a quick example of loading a CSV dataset:


import pandas as pd

dataset = pd.read_csv('path/to/file.csv')

This reads the CSV file into a Pandas dataframe ready for analysis. Let's now learn how to clean and prepare real-world data for exploration.

Cleaning and Preprocessing Real-world Data

Real-world data often requires preprocessing and tidying before analysis. Here are some common data cleaning steps in Python:

Handling Missing Data

Use Pandas dropna() and fillna() to deal with missing values.


# Drop rows with missing values
dataset.dropna()

# Fill missing values with a placeholder
dataset.fillna(0)

Data Formatting

Format strings, change data types and normalize formats with Pandas to_datetime, to_numeric etc.

  
# Convert string to proper date format
dataset['date'] = pd.to_datetime(dataset['date'])

# Normalize mixed data formats
dataset['sales'] = pd.to_numeric(dataset['sales'])

Fixing Duplicates

Identify and remove duplicate rows with dataset.duplicated().

Data Standardization

Apply functions like sklearn.preprocessing.scale() to standardize columns.

Proper cleaning and formatting ensures your data is ready for exploration and modelling. Let's now dive into exploratory data analysis using Python.

Exploratory Data Analysis with Python

Exploratory Data Analysis (EDA) allows us to summarize dataset characteristics, spot anomalies, identify patterns and relationships before applying predictive models. Here are useful Python EDA techniques:

Summary Statistics

Pandas dataframe.describe() provides summary stats like mean, std deviation, min/max values etc.

  

dataset.describe()

Data Visualization

Use Matplotlib and Seaborn to visualize distributions as histograms, scatter plots, heatmaps etc.


# Scatter plot
plt.scatter(x, y)

# Histogram 
plt.hist(data)

Grouping and Aggregating

Pandas groupby and agg enable aggregating metrics by specific columns.


# Average sales per store location  
sales_by_store = dataset.groupby('store_id').agg({'sales': 'mean'})

Correlations

Find linear correlations between columns using dataset.corr()


dataset.corr()

Thorough EDA provides intuition about trends, relationships and anomalies that can guide the application of predictive models.

Building Machine Learning Models with Scikit-learn

Once you've explored and understood the datasets, Scikit-learn provides a robust set of machine learning algorithms to build models that can make predictions from data.

Importing Scikit-learn


from sklearn import svm, tree, linear_model, ensemble

Splitting Training and Test Sets

Use train_test_split to create separate subsets to train and test models.


from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y)

Applying Models

Fit models like linear regression, random forests, SVM on the training set.


# Linear regression
model = linear_model.LinearRegression()
model.fit(X_train, y_train)

# Random forest classifier 
rf_model = ensemble.RandomForestClassifier()
rf_model.fit(X_train, y_train)

Evaluating Models

Evaluate models on the test set using metrics like accuracy, precision, recall, F1-score.


from sklearn.metrics import accuracy_score

predictions = rf_model.predict(X_test)  
accuracy = accuracy_score(y_test, predictions)

Scikit-learn makes it easy to apply machine learning models to make predictions and classifications from real-world data.

Now let's apply what we've learned by walking through end-to-end case studies.

Case Study 1 - Analysing Healthcare Data

Let's demonstrate a real-world application of Python's data science libraries by analyzing a healthcare dataset.

We'll use the freely available Diabetes 130-US hospitals for years 1999-2008 Data Set from UCI Machine Learning Repository.

This dataset contains over 100,000 patient records spanning 130 US hospitals from 1999-2008. Features include patient demographics, medical history, diagnoses, tests, medications and outcomes.

Let's use Python to analyze this rich dataset and derive insights that could improve healthcare services.

Data Import and Exploration

We load the CSV dataset into a Pandas dataframe:

  
import pandas as pd

df = pd.read_csv('diabetes_data.csv')

Now we can start exploring the data:


# Check dataframe info
df.info()

# Summary statistics  
df.describe()   

# Data visualization
plt.hist(df['time_in_hospital'])

Data Cleaning

We prepare the data for modelling by handling missing values, formatting and transforming columns:


# Fill NA values
df['test_result'] = df['test_result'].fillna('Unknown')

# Normalize gender column
df['gender'] = df['gender'].map({'Female':0, 'Male':1})

Developing Prediction Model

We'll train a random forest model to predict whether a patient will be readmitted based on their test results and medical history:


from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

# Select features and target
X = df[['test_result', 'age']]   
y = df['readmitted']

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y)

# Train model
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Evaluate on test data 
accuracy = model.score(X_test, y_test)

This model could help hospitals identify patients at high risk of readmission so they can improve patient care. The wide range of data also allows deriving many other insights to enhance healthcare services.

Case Study 2 - Analysing Social Media Data

Let's look at another real-world use case - analyzing tweets to identify user sentiment and trends.

We'll use the freely available Twitter Sentiment Analysis Dataset from Kaggle, containing 1.6 million tweets labelled for sentiment (positive/negative).

Here's how to analyze this social media data with Python:

Load the Data

We load the CSV file into a Pandas dataframe:

   
import pandas as pd

df = pd.read_csv('tweets_data.csv')

EDA

We check distribution of sentiment labels:


# Profile of sentiment column
df['sentiment'].value_counts() 

# Distribution plot
df['sentiment'].plot(kind='hist')

Text Preprocessing

We clean tweet text to prepare it for modelling:


import re
import string

# Remove links, users, hashtags
df['text'] = df['text'].apply(remove_urls_users_hashtags)

# Remove punctuation  
df['text'] = df['text'].translate(str.maketrans('', '', string.punctuation))

# Lemmatize text
from textblob import Word
df['text'] = df['text'].apply(lambda x: " ".join([Word(word).lemmatize() for word in x.split()]))

Sentiment Classification Model

We'll train a logistic regression model on the cleaned text to predict tweet sentiment:


from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

# Split data
X_train, X_test, y_train, y_test = train_test_split(df['text'], df['sentiment'], test_size=0.3)

# Train model
logit = LogisticRegression()
logit.fit(X_train, y_train)

# Evaluate  
accuracy = logit.score(X_test, y_test)

This model identifies tweet sentiment, helping analyze public perceptions, trends and reactions on social media platforms like Twitter.

Key Takeaways on Using Python for Real-World Data Science

We've explored a broad range of techniques to ingest, prepare, analyze, visualize and model real-world datasets using Python.

Here are some of the key takeaways:

Python provides easy access to free datasets for experimentation and learning.
Data preprocessing is critical before starting exploration and modelling.
Pandas offers powerful data manipulation and cleaning capabilities.
Matplotlib enables diverse visualizations for exploratory analysis.
Scikit-learn provides a robust set of machine learning algorithms for modelling.
Following case study examples helps cement concepts learnt.

Python's extensive libraries make practically any type of dataset and data problem accessible to aspiring data scientists.

Whether analyzing social media trends, customer engagement, financial indicators

or healthcare outcomes, Python empowers you to unlock meaningful insights from real-world data across industries. Its versatility and simplicity will ensure Python remains fundamental to data science workflows.

More Resources

To dive deeper, here are useful resources to build your Python data science skills:

Datasets: Kaggle, UCI Machine Learning Repository Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn Learning: DataCamp, DataQuest, Coursera (Python for Data Science course) Documentation: Official Python Documentation, Scikit-learn User Guide Community: StackOverflow, Reddit (r/Python, r/LearnPython)

Conclusion

This guide provided you with the techniques and tools to access, prepare, analyze and model real-world data using Python's robust data science libraries like Pandas, Matplotlib and Scikit-learn.

You learned how to:

Obtain interesting datasets from open portals and web scraping
Clean and preprocess raw data for analysis
Conduct exploratory data analysis with summary statistics and visualizations
Develop machine learning models to extract powerful insights from data
Follow case study examples to apply these techniques to real problems

Python's versatility, readability and vibrant ecosystem make it an essential skill for unlocking value from real-world data across industries.

So get out there, find interesting datasets to analyze and start honing your Python data science skills today!

You might like to read our article on Is Python good for finance? or How does Python for Data Science help in analysing real-world datasets?

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