How do I learn data analysis with Python?

Python has become one of the most popular programming languages for data analysis and data science. Its versatility, large collection of powerful libraries, and ease of use make it a great choice for analysing data and building machine learning models. This comprehensive guide will teach you how to set up your Python environment, learn core programming concepts, analyse and visualise data with popular libraries like Pandas and Matplotlib, and take your data analysis skills to the next level.

Python has become one of the most popular programming languages for data analysis and machine learning. Which is why so many companies opt to place staff on a data analyst course focused specifically on data analysis skills.

Setting Up Your Python Environment

To start analysing data in Python, you first need to set up your working environment. The easiest way to get started is to install a Python distribution like Anaconda that comes bundled with the most popular data science libraries.

What is Anaconda?

Anaconda is an open source Python distribution created by Continuum Analytics. It includes Python, common scientific computing packages like NumPy and SciPy, data science tools like Jupyter Notebook, and over 250+ other Python packages for data science. Anaconda makes it easy to get started with Python data analysis without having to deal with environment configuration and dependency conflicts.

Installing Anaconda and Jupyter Notebook

Follow these steps to install Anaconda on your operating system:

1. Go to the Anaconda download page and get the latest version of Anaconda for Python 3.x. Make sure to download the 64-bit graphical installer for your OS.
2. Run the Anaconda installer and follow the prompts. Make sure to install for "Just Me" unless you want to install for all users on the system. Agree to the license terms and select an install location with write permissions.
3. Allow the installer to configure your PATH environment variable so you can run conda commands. Test it out by opening a new terminal and typing conda list to verify it works.
4. Once Anaconda is installed, launch Jupyter Notebook by typing jupyter notebook on the command line. This will open up a browser window with the Notebook dashboard, allowing us to start coding in Python!

With Anaconda and Jupyter Notebook installed, you now have an excellent environment for data analysis in Python!

Importing Key Data Analysis Libraries

The major libraries we'll use for data analysis are:

• NumPy - Provides arrays and vectorized operations ideal for numerical data
• Pandas - Offers easy-to-use data structures and tools for data manipulation and analysis
• Matplotlib - Enables plotting and visualizing data with Python

We can import these libraries right in a Jupyter Notebook cell:

import numpy as np
import pandas as pd 
import matplotlib.pyplot as plt

This imports NumPy as np, Pandas as pd, and Matplotlib's Pyplot module as plt for easy usage.

Learning Python Basics

Before diving into data analysis, it's important to learn some core Python programming concepts. Python makes it easy to get started with its readable syntax, concise code, and robust standard library.

Python Syntax Overview

Python uses simple, English-like syntax with indented code blocks representing scope. Here are some key syntax elements:

• # denotes comments
• No need to end statements with semicolons
• Parentheses () call functions and enclose tuples
• Brackets [] access array elements and denote lists
• Braces {} contain dictionaries and sets
• = assigns values to variables

Variables and Data Types

You can store data in Python variables like so:

name = "John"
age = 25 
weights = [150, 152, 148] #list

Common Python data types include:

• Integers - Whole numbers like 2, 4, -5
• Floats - Decimals like 1.5, -3.14
• Strings - Text like "John Doe"
• Lists - Ordered arrays like [1, 2, 3]
• Tuples - Immutable lists like (1, 2, 3)
• Dictionaries - Key-value pairs like {"name":"John", "age":30}

Control Flow: Loops and Conditionals

Control flow statements allow you to iterate over sequences and make comparisons:

For Loops

for num in [1, 2, 3]:
  print(num)

While Loops

count = 0
while count < 5:
  print(count)
  count += 1

If Statements

age = 20 
if age >= 18:
  print("You are eligible to vote")
else:
  print("You are not eligible to vote yet")

Functions in Python

Functions group code you want to reuse. Here's a basic function that prints a greeting:

def greet(name):
  print("Hello " + name)
  
greet("Bob") #call function

You can return values from functions like so:

def square(x):
  return x * x

result = square(5)
print(result) #25

With these basic programming concepts, you can now write simple Python scripts!

Analyzing Data with Pandas

The Pandas library makes it extremely easy to work with structured data in Python. Learn how to load, view, subset, and manipulate your data for analysis.

Introducing Pandas Objects

Pandas provides two core data structures - Series and DataFrames:

• Series - 1D array with indices and values. Like a column in a spreadsheet.
• DataFrame - 2D tabular data structure with rows and columns. Like a spreadsheet or SQL table.

We can create a Series from a list:

import pandas as pd

s = pd.Series([5, 2, -1, 3]) 
print(s)

#0    5  
#1    2
#2   -1
#3    3

And build a DataFrame from a dictionary of Series:

data = {"Name":["John", "Mary"], "Age":[30, 25]} 
df = pd.DataFrame(data)
print(df)

#   Name  Age
#0  John   30    
#1  Mary   25

Loading Datasets into Pandas

Pandas makes loading external datasets easy with read_csv(), read_json(), read_excel(), and other I/O functions.

For example, we can load the built-in Titanic dataset:

titanic = pd.read_csv("titanic.csv")

Viewing, Selecting, and Filtering Data

Pandas provides nice indexing and slicing syntax for viewing subsets of your data:

df.head() #first 5 rows
df.loc[0:2, ['Name', 'Age']] #rows 0-2, Name and Age columns  
df[df['Age'] > 25] #rows with Age > 25

Grouping, Aggregating, and Sorting

Common aggregation functions allow you to summarize and process data in Pandas:

df['Age'].mean() #average age
df.groupby('Sex').sum() #sum by sex   
df.sort_values('Fare', ascending=False) #sort by fare

As you can see, Pandas gives you extremely powerful, expressive tools for working with structured datasets in Python!

Visualizing Data with Matplotlib

Python's Matplotlib library provides simple yet highly customizable plotting capabilities to help you visualize your data.

Basic Plotting with Pyplot

The Pyplot module in Matplotlib provides a MATLAB-style interface for building plots:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4]
y = [2, 4, 6, 8]

plt.plot(x, y) #plot line chart
plt.show()

We can also make scatter plots, bar charts, histograms, and more:

plt.scatter(x, y) #scatter plot  
plt.bar(x, y) #bar chart
plt.hist(y) #histogram

Customizing Plots

Matplotlib makes it easy to customize every element of a plot:

plt.title("Sales Over Time")  
plt.xlabel("Month")
plt.ylabel("Revenue") 
plt.legend(['Store 1', 'Store 2'])
plt.style.use('dark_background')

This adds a title, axis labels, legend, and custom styling.

Visualizing Time Series Data

Pandas has built-in timeseries support that integrates nicely with Matplotlib:

ts = pd.DataFrame(np.random.randn(1000), 
                  index=pd.date_range('1/1/2000', periods=1000)) 
ts = ts.cumsum()
ts.plot()

This plots a random walk timeseries dataset that is indexed by dates.

Matplotlib empowers you to gain insights from your data through impactful and customizable visualizations.

Analyzing and Modeling Data

While Pandas and Matplotlib help you explore and visualize data, Numpy and SciPy provide foundational analytics capabilities, and Scikit-Learn enables advanced machine learning modeling.

Statistical Analysis with NumPy

NumPy (Numeric Python) provides Python's core mathematical and statistical operations for arrays:

import numpy as np

data = np.random.randn(10) #random data 

data.mean() #mean
data.std() #standard deviation
data.median() #median   
np.percentile(data, 75) #75th percentile

NumPy vectorizes these operations so they are fast and efficient.

Machine Learning with Scikit-Learn

The Scikit-Learn library contains many commonly used machine learning algorithms:

from sklearn.linear_model import LinearRegression

X = df[['Age']]  #predictors
y = df['Sales'] #target 

model = LinearRegression()
model.fit(X, y)

model.predict([[30]]) #predict sales for 30 year old

Here we built a simple linear regression model to predict sales based on age. Scikit-Learn makes it easy to build and evaluate predictive models.

Evaluating Model Performance

We can evaluate model performance using evaluation metrics:

from sklearn.metrics import mean_squared_error, accuracy_score

y_true = [1, 1, 0, 1] 
y_pred = [0, 1, 0, 1]

accuracy_score(y_true, y_pred) #accuracy
mean_squared_error(y_true, y_pred) #MSE

Metrics like accuracy, MSE, precision, recall, etc. are provided to measure model performance.

Here are some next steps for advancing your data analysis skills:

• Learn SQL to query and analyze databases
• Use web scraping techniques like Beautiful Soup to collect your own data from websites
• Apply natural language processing (NLP) to analyze text and sentiment
• Move beyond linear regression to advanced algorithms like random forests, SVMs, neural networks with libraries like Keras

With practice, you'll be well on your way to becoming a data analysis expert in Python!

Frequently Asked Questions

How long does it take to learn Python for data analysis?

It depends on your prior programming experience, but expect it to take at least 2-3 months of consistent practice to become comfortable with Python data analysis. Focus on getting exposure to key concepts versus trying to master everything.

What math do I need to know for Python data analysis?

Basic high school math around algebra, statistics, and probability is sufficient. Python libraries like Numpy and Pandas handle most of the advanced math behind the scenes.

What are some good project ideas for learning Python analysis?

Some beginner project ideas: exploratory data analysis on public datasets (titanic, iris flowers), build a simple linear regression model to predict home prices, analyze your own CSV data like budget expenses in Pandas, build a dashboard to visualize COVID-19 data using Matplotlib.

What are some other helpful Python data analysis libraries?

Seaborn for statistical data visualization, Scrapy for web scraping, SQLAlchemy for SQL support, StatsModels for statistical modeling, Keras for deep learning. The Python ecosystem has tons of great libraries for data tasks!

Check out our Blog for up to date information in the latest Python developments including articles on  Unlocking the Power of AI with Python and How many days required to learn Python for data analysis?