Install & Import Packages

Introduce & Install Packages

In our package development process, we leverage a variety of powerful Python libraries to streamline data processing, spatial analysis, and visualization workflows. Below is a brief overview of each package and its specific role in the overall analytical pipeline:

Introduce Packages

Pandas

Pandas is a fundamental library for data manipulation, primarily used for handling tabular data. It offers powerful data structures like DataFrame and Series, enabling efficient data cleaning, transformation, and analysis.

Numpy

Numpy is a core library for numerical computations, providing support for multi-dimensional arrays and a broad range of mathematical operations. It is often used for array manipulation, linear algebra, and scientific calculations.

Matplotlib

Matplotlib is a foundational 2D plotting library used for creating a variety of basic visualizations, such as line plots, bar charts, and scatter plots. It provides the essential tools for building customizable visual representations of data.

Seaborn

Seaborn is a data visualization library built on top of matplotlib, designed for creating more advanced and aesthetically pleasing statistical visualizations, such as heatmaps, violin plots, and pair plots.

Geopandas

Geopandas extends pandas to provide support for geospatial data operations, enabling the manipulation and analysis of geographic data structures, such as points, lines, and polygons. It is commonly used for spatial data analysis and map visualization.

Shapely

Shapely is a library for creating and manipulating geometric objects in Python. It enables complex spatial operations, such as union, intersection, and difference on geometric shapes like points, lines, and polygons.

Scipy

Scipy is a comprehensive library for scientific computing, providing tools for optimization, integration, interpolation, and statistical calculations. The cKDTree module in scipy.spatial is particularly useful for efficient nearest-neighbor searches.

Rtree

Rtree is a spatial indexing library designed to accelerate spatial data queries, making it a vital tool for geospatial analysis and GIS applications. It helps speed up spatial queries such as intersection and nearest-neighbor searches.

Fuzzywuzzy

Fuzzywuzzy is used for fuzzy string matching, helping to compute similarity scores between strings for tasks like text matching, text deduplication, and comparison, making it ideal for text-based data cleaning and analysis.

OS

OS is a standard library for interacting with the operating system, providing functions for file and directory manipulation, environment management, and system-level operations.

Warnings

Warnings is a built-in module that provides a way to handle and suppress warnings during program execution, helping to manage runtime warnings and enhance code robustness.

The combination of these libraries creates a robust and flexible analysis environment, capable of handling everything from tabular data manipulation to complex spatial geometric computations. Each library plays a unique and essential role in the analytical workflow, making data processing and spatial analysis more efficient.

Install Packages

To ensure the development environment has all the required libraries, use the command below to install the necessary packages. Uncomment the command and run it in Jupyter Notebook or Visual Studio Code if any of the packages are missing in your setup.

# Install the necessary packages
!pip install pandas numpy matplotlib seaborn geopandas shapely scipy rtree fuzzywuzzy

Import Packages

The code below imports the essential Python libraries needed to build a comprehensive environment for data processing, spatial analysis, and visualization in this project.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
import geopandas as gpd
import math

from shapely.geometry import LineString, MultiLineString, GeometryCollection
from shapely.geometry import box
from shapely.geometry import Point
from shapely.ops import unary_union
from shapely.ops import substring
from shapely.ops import linemerge
from shapely.ops import nearest_points
from shapely.ops import split
from shapely import wkt

from scipy.spatial import cKDTree
from rtree import index

from fuzzywuzzy import fuzz

import warnings
warnings.filterwarnings("ignore")

pd.set_option('display.max_columns', None)