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Python for Data Science: Essential Libraries and Tools

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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:

 

Stage

Basic Libraries

Advanced Libraries

Tools

Data Collection

PandasRequests

ScrapyBeautifulSoup

Jupyter NotebookIPythonAnaconda

Data Cleaning & Preprocessing

PandasNumPy

PyjanitorDask

Exploratory Data Analysis

PandasMatplotlibSeaborn

PlotlyBokehHoloviews

Modeling and Evaluation

Scikit-learnXGBoost

LightGBMTensorFlowPyTorchYellowbrickHyperoptOptuna

Model Deployment

FlaskFastAPI

 MLflowTensorFlow Serving

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 pd

file_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)
SquareFeetBedroomsBathroomsNeighborhoodYearBuiltPrice
0212641Rural1969215355.283618
1245932Rural1980195014.221626
2186021Suburb1970306891.012076
3229421Urban1996206786.787153
4213052Suburb2001272436.239065
5209523Suburb2020198208.803907
6272421Suburb1993343429.319110
7204443Rural1957184992.321268
8263843Urban1959377998.588152
9112152Urban200495961.926014

From this we can infer that dataset has 6 columns:

  • SquareFeet: Size of the house in square feet
  • Bedrooms: Number of Bedrooms
  • Bathrooms: Number of Bathrooms
  • Neighbourhood: Categorical values: Rural, Suburb, Urban
  • YearBuilt: Year the house was constructed
  • Price: Sale Price of the house

Check for any missing values

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 np

housing_data.replace(["N/A", "none", ""], np.nan, inplace=True)
missing_values = housing_data.isnull().sum()
missing_values
0
SquareFeet0
Bedrooms0
Bathrooms0
Neighborhood0
YearBuilt0
Price0

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.

housing_data.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50000 entries, 0 to 49999
Data columns (total 6 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   SquareFeet    50000 non-null  int64  
 1   Bedrooms      50000 non-null  int64  
 2   Bathrooms     50000 non-null  int64  
 3   Neighborhood  50000 non-null  object 
 4   YearBuilt     50000 non-null  int64  
 5   Price         50000 non-null  float64
dtypes: float64(1), int64(4), object(1)
memory usage: 2.3+ MB
housing_data.describe()
SquareFeetBedroomsBathroomsYearBuiltPrice
count50000.00000050000.00000050000.00000050000.00000050000.000000
mean2006.3746803.4987001.9954201985.404420224827.325151
std575.5132411.1163260.81585120.71937776141.842966
min1000.0000002.0000001.0000001950.000000-36588.165397
25%1513.0000003.0000001.0000001967.000000169955.860225
50%2007.0000003.0000002.0000001985.000000225052.141166
75%2506.0000004.0000003.0000002003.000000279373.630052
max2999.0000005.0000003.0000002021.000000492195.259972

Let's visualize the data using Matplotlib and Seaborn to explore the correlation between features and prices

import matplotlib.pyplot as plt
import 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 datetime

current_year = datetime.datetime.now().year
housing_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

Scikit-learn provides utility for this purpose.

One-Hot Encoding using Sci-kit Learn (sklearn)
from sklearn.preprocessing import OneHotEncoder

one_hot_encoder = OneHotEncoder(drop='first', sparse_output=False)
encoded_neighborhood = one_hot_encoder.fit_transform(housing_data[['Neighborhood']])
encoded_df = pd.DataFrame(encoded_neighborhood, columns=one_hot_encoder.get_feature_names_out(['Neighborhood']))

housing_data_encoded = pd.concat([housing_data, encoded_df], axis=1).drop('Neighborhood', axis=1)
housing_data_encoded.head(3)
SquareFeetBedroomsBathroomsYearBuiltPriceHouseAgeNeighborhood_SuburbNeighborhood_Urban
02126411969215355.283618550.00.0
12459321980195014.221626440.00.0
21860211970306891.012076541.00.0
plt.figure(figsize=(10, 5))
sns.heatmap(housing_data_encoded.corr(), annot=True, cmap='coolwarm')
plt.title('Correlation Matrix')
Text(0.5, 1.0, 'Correlation Matrix')

Feature Engineering

Let's create some new features to unlock some hidden relationships

Price Per SquareFeet

housing_data_encoded['PricePerSqFt'] = housing_data_encoded['Price'] / housing_data_encoded['SquareFeet']

Mean Price by Neighbourhood

mean_price_by_neighborhood_suburb = housing_data_encoded.groupby('Neighborhood_Suburb')['Price'].transform('mean')
housing_data_encoded['NeighborhoodSuburbMeanPrice'] = mean_price_by_neighborhood_suburb

mean_price_by_neighborhood_urban = housing_data_encoded.groupby('Neighborhood_Urban')['Price'].transform('mean')
housing_data_encoded['NeighborhoodUrbanMeanPrice'] = mean_price_by_neighborhood_urban

Model Training and Evaluation

sklearn provides a wide range of models and utilities for building and evaluating machine learning models.

from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error, mean_squared_error

X = housing_data_encoded.drop('Price', axis=1)
y = housing_data_encoded['Price']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
from sklearn.linear_model import LinearRegression
linear_regression_model = LinearRegression()
linear_regression_model.fit(X_train, y_train)

y_pred = linear_regression_model.predict(X_test)


mae = mean_absolute_error(y_test, y_pred)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print("Linear Regression Performance:")
print(f'Mean Absolute Error: {mae}')
print(f'Root Mean Squared Error: {rmse}')
Linear Regression Performance:
Mean Absolute Error: 10917.728454376049
Root Mean Squared Error: 15339.472689214223
from sklearn.ensemble import RandomForestRegressor
random_forest_regressor_model = RandomForestRegressor(random_state=42)

random_forest_regressor_model.fit(X_train, y_train)
y_pred = random_forest_regressor_model.predict(X_test)

mae = mean_absolute_error(y_test, y_pred)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print("Random Forest Regressor Performance:")
print(f'Mean Absolute Error: {mae}')
print(f'Root Mean Squared Error: {rmse}')
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.

Palwisha's profile picture
Palwisha Akhtar

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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