Python Environment Setup

A practical installation guide for this course

Python environment setup

This guide will help you set up a professional Python environment for financial data science.

Required Software

2. Alternative: Miniconda + Manual Setup

For advanced users who prefer minimal installation:

# install miniconda
# Then install required packages
conda install pandas numpy matplotlib seaborn plotly
conda install scikit-learn tensorflow jupyter
pip install yfinance pandas-datareader quantlib-python

Required Python Libraries

Core Data Science Stack

# Data manipulation and analysis
import pandas as pd
import numpy as np

# Visualization
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

# Statistical analysis
import scipy.stats as stats
import statsmodels.api as sm

Financial Libraries

# Financial data acquisition
import yfinance as yf
import pandas_datareader as pdr

# Quantitative finance
import quantlib as ql

# Time series and econometrics
from arch import arch_model

Machine Learning & AI

# Traditional ML
from sklearn import *
import xgboost as xgb
import lightgbm as lgb

# Deep learning
import tensorflow as tf
from tensorflow.keras import layers

# Natural Language Processing
from textblob import TextBlob
from transformers import pipeline

Installation commands

Option 2: Pip installation

# Install all packages via pip
pip install pandas numpy matplotlib seaborn plotly
pip install scikit-learn jupyter jupyterlab
pip install yfinance pandas-datareader quantlib-python
pip install arch statsmodels scipy
pip install xgboost lightgbm tensorflow
pip install textblob transformers vaderSentiment

Verification Script

Run this script to verify your installation:

# Environment verification script
import sys
import importlib

required_packages = [
    'pandas', 'numpy', 'matplotlib', 'seaborn', 'plotly',
    'sklearn', 'yfinance', 'arch', 'statsmodels', 
    'tensorflow', 'xgboost', 'textblob'
]

print("Python environment check")
print("=" * 40)
print(f"Python Version: {sys.version}")
print()

missing_packages = []

for package in required_packages:
    try:
        module = importlib.import_module(package)
        version = getattr(module, '__version__', 'Unknown')
        print(f"✓ {package}: {version}")
    except ImportError:
        print(f"✗ {package}: NOT installed")
        missing_packages.append(package)

if missing_packages:
    print(f"\n❌ Missing packages: {', '.join(missing_packages)}")
    print("Please install missing packages before proceeding.")
else:
    print("\n✅ All required packages installed successfully!")
    print("You're ready to start.")

Development Environment Setup

Jupyter Lab Configuration

# Recommended Jupyter Lab extensions
# Run these commands in your terminal:

# install extensions
pip install jupyterlab-git
pip install jupyterlab_code_formatter
pip install jupyterlab-variableInspector

# Configure Jupyter for finance
jupyter lab --generate-config

# Add to ~/.jupyter/jupyter_lab_config.py:
c.ServerApp.open_browser = True
c.ServerApp.port = 8888
c.NotebookApp.notebook_dir = '/path/to/your/fds/projects'

API Setup

Financial Data APIs

1. Yahoo Finance (Free)

import yfinance as yf

# Test Yahoo Finance connection
ticker = yf.Ticker("AAPL")
data = ticker.history(period="1mo")
print("Yahoo Finance connection successful!")

2. Alpha Vantage (Free tier available)

  1. Sign up at https://www.alphavantage.co/
  2. Get your free API key
  3. Store in environment variable:
import os
os.environ['ALPHA_VANTAGE_API_KEY'] = 'your_api_key_here'

# Test connection
import pandas_datareader as pdr
data = pdr.get_data_alphavantage('AAPL', api_key=os.environ['ALPHA_VANTAGE_API_KEY'])

3. Quandl (Optional)

# install and setup Quandl
pip install quandl
import quandl
quandl.ApiConfig.api_key = "your_quandl_api_key"

Troubleshooting

Common Issues

ImportError: No module named ‘package_name’

# Solution: install the missing package
pip install package_name

# Or if using conda:
conda install package_name

SSL Certificate Errors (yfinance)

# Add this to your Python scripts
import ssl
ssl._create_default_https_context = ssl._create_unverified_context

Jupyter Kernel Issues

# Register your conda environment as Jupyter kernel
conda activate fds
python -m ipykernel install --user --name=fds

Memory Issues with Large Datasets

# Optimise pandas memory usage
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('mode.chained_assignment', None)

# Use chunking for large files
for chunk in pd.read_csv('large_file.csv', chunksize=10000):
    process(chunk)

Performance optimisation

Getting Help

Course Support

  • Discussion Board: Post technical questions
  • Office Hours: By appointment
  • Email: b.quinn1@ulster.ac.uk

Online Resources

Emergency Contacts

  • IT Support: 028 9536 7188
  • Blackboard Help: blackboardhelpdesk@ulster.ac.uk

This setup guide will be updated throughout the semester. Check back for the latest versions and troubleshooting tips.