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Python for Data Science: Essential Libraries and Tools
Python is one of the most popular programming languages, and it’s easy to see why. It’s simple to learn, easy to read and can be used in many different fields. In this blog, we’ll talk about one of its key uses - data science. Python is a favorite in this field because of its powerful libraries, helpful community, detailed guides, and regular updates that keep it relevant.
If you’re thinking about starting a career in data science or switching to this field, it’s important to understand the problems data scientists solve, how they work, and the tools and libraries they use to get the job done.
What is Data Science?
Data science is a field that combines statistics and computing to find useful information and insights from data.
It is used in many areas, like machine learning, predicting trends, understanding images, processing language, and creating recommendations. While every data science project is different because of the problem it solves, the industry it’s in, or the type of data it uses, most projects follow a similar step-by-step process.
Data Science Lifecycle
Here are the five main stages of the data science lifecycle:
1. Data Collection: First, you gather data based on the problem you want to solve. This data can come from different sources like web scraping, APIs, databases, files, or even live data streams.
2. Data Cleaning & Preparation: Next, the data is cleaned to make it usable. This means removing duplicates, fixing missing information, and standardizing formats so everything is consistent.
3. Exploratory Data Analysis (EDA): Once the data is ready, you study it using charts and statistics. This helps you find patterns, spot unusual data, understand relationships, and get deeper insights.
4. Modeling and Evaluation: After exploring the data, machine learning models are created and trained to solve the problem. These models are fine-tuned, tested for accuracy, and evaluated to ensure the best one is chosen for making predictions or decisions.
5. Model Deployment: Finally, the chosen model is used in real-life settings to provide predictions or insights.
Python makes it easier for data scientists to work efficiently through all these steps because its libraries and tools work perfectly together.
Essential Python libraries and tools for Data Science
Python has a variety of libraries, ranging from basic to advanced, that are useful at each step of the data science lifecycle. Here are some key ones:
Here are brief descriptions of some of the most essential libraries and tools, including Pandas, NumPy, Matplotlib, Seaborn, scikit-learn, and jupyter notebook:
1. Pandas: Used for gathering, cleaning, and analyzing data. It’s great for working with structured data like spreadsheets or semi-structured data like JSON files.
2. NumPy: Helps with handling numbers, arrays, and mathematical calculations. It’s essential for tasks that need numerical computing.
3. Matplotlib: A simple library for creating basic graphs like line, bar, and pie charts.
4. Seaborn: Built on Matplotlib, this library creates more detailed and advanced graphs like heatmaps or violin plots.
5. scikit-learn: A powerful library for building machine learning models like regression, classification, or clustering. It also includes tools for preparing data and evaluating models.
6. Jupyter Notebook: A tool where you can write code, explain your process with text, and visualize your results all in one place.
These tools and libraries make Python a perfect fit for every stage of data science projects, from data cleaning to deploying machine learning models.
Problem-Solving with Python: Practical Example
We will examine a real-world problem to illustrate how Python libraries and tools can be effectively utilized at each stage of the data science lifecycle.
Problem Statement
We have a dataset with details about houses, like their size in square feet, the number of bedrooms and bathrooms, the neighborhood, and the year they were built. Our goal is to use this information to predict the sale price of a house.
Goal
We want to build a regression model (because the price is a continuous value) that can predict a house's price based on its features. We will check how well the model is working using two metrics: Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE).
Solution
The housing price data is stored in a CSV file. To work with this data and build our model, we will use a Jupyter notebook hosted online, along with Python libraries.
Price Prediction of Houses
This notebook demonstrates a workflow for predicting house prices
based on various features using Python libraries.
Data Collection
Pandas
provides utilities to read structured data, manipulate and analyze
it
In this scenario, we have a csv file which can be read into pandas
dataframe.
import pandas as pdfile_path ='housing_price_dataset.csv'housing_data = pd.read_csv(file_path)
Data Cleaning and
Preprocessing
Let's look at the first 10 rows of the data using the DataFrame
head() method.
housing_data.head(10)
SquareFeet
Bedrooms
Bathrooms
Neighborhood
YearBuilt
Price
0
2126
4
1
Rural
1969
215355.283618
1
2459
3
2
Rural
1980
195014.221626
2
1860
2
1
Suburb
1970
306891.012076
3
2294
2
1
Urban
1996
206786.787153
4
2130
5
2
Suburb
2001
272436.239065
5
2095
2
3
Suburb
2020
198208.803907
6
2724
2
1
Suburb
1993
343429.319110
7
2044
4
3
Rural
1957
184992.321268
8
2638
4
3
Urban
1959
377998.588152
9
1121
5
2
Urban
2004
95961.926014
From this we can infer that dataset has 6 columns:
Let's check the datatype of each column and make sure no placeholder
is used by replacing it wih NaN.
We can use NumPy library to replace specific
placeholder values with NaN
import numpy as nphousing_data.replace(["N/A", "none", ""], np.nan, inplace=True)missing_values = housing_data.isnull().sum()missing_values
0
SquareFeet
0
Bedrooms
0
Bathrooms
0
Neighborhood
0
YearBuilt
0
Price
0
0 for all columns indicates that there are no missing (null)
values
Exploratory Data Analysis
Pandas DataFrame provides methods like info()to learn about the
datatypes of the columns and describe() to view summary statistics for
numerical columns.
Let's
visualize the data using Matplotlib and
Seaborn to explore the correlation between features and
prices
import matplotlib.pyplot as pltimport seaborn as sns
Distrbution of House Prices
plt.figure(figsize=(10, 5))sns.histplot(data=housing_data, x='Price')plt.title('Distribution of House Prices')
Text(0.5, 1.0, 'Distribution of House Prices')
Bedrooms vs House Prices
plt.figure(figsize=(10, 5))sns.boxplot(data=housing_data, x='Bedrooms', y='Price')plt.title('Bedrooms vs. House Prices')
Text(0.5, 1.0, 'Bedrooms vs. House Prices')
SquareFeet vs House Prices
plt.figure(figsize=(10, 5))sns.lineplot(data=housing_data, x='SquareFeet', y='Price')plt.title('Square Feet vs. House Prices')
Text(0.5, 1.0, 'Square Feet vs. House Prices')
House Age vs House Prices
import datetimecurrent_year = datetime.datetime.now().yearhousing_data['HouseAge'] = current_year - housing_data['YearBuilt']plt.figure(figsize=(10, 5))sns.scatterplot(data=housing_data, x='HouseAge', y='Price')plt.title('House Age vs. Price')
Text(0.5, 1.0, 'House Age vs. Price')
Correlation Matrix
Correlation Matrix is used to show relationship between the features
with numerical data. Since, Neighbourhood is a categorical field, we
need to convert it into numerical column by using One-Hot Encoding
Random Forest Regressor Performance:
Mean Absolute Error: 359.56808451895427
Root Mean Squared Error: 595.4858304451691
Random Forest Regressor model is performing better than Linear
Regression model as it has lower MAE and RMSE.
Conclusion
Python is a widely used programming language, especially in data science, because it’s simple to learn and work with, yet incredibly powerful. Its flexibility and the support from a large, active community make it a favorite choice for many. Python comes with essential libraries like Pandas, NumPy, and Scikit-learn that help throughout the entire data science journey, from handling and analyzing data to building and deploying machine learning models.
I am a software engineer with 7 years of experience, mainly working with Python and its web frameworks. I am passionate about developing scalable web applications and solving complex problems. Have deep interest in parallel and multicore computing.
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