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Articles by Shriansh Kumar
211 articles
Plotting Geospatial Data using GeoPandas
GeoPandas is a powerful Python library built on top of Pandas that extends its capabilities to include geospatial data support. Geospatial data describes information related to various locations on Earth's surface, making it valuable for map visualization, urban planning, trade analysis, and network planning. In this article, we'll explore how to plot geospatial data using GeoPandas. What is GeoPandas? GeoPandas extends Pandas functionality to handle geometric data types and perform spatial operations. It combines the data manipulation capabilities of Pandas with the geospatial functionality of libraries like Shapely and Fiona. This makes it an excellent choice for working ...
Read MoreParallelizing a Numpy Vector Operation
NumPy is a powerful Python library for storing and manipulating large, multi-dimensional arrays. Although NumPy is already fast and efficient, we can further enhance its performance using parallelization. Parallelizing means splitting tasks into multiple processes to achieve better performance. Python provides several ways to parallelize NumPy vector operations, including the multiprocessing and numexpr modules. Using Multiprocessing The multiprocessing module allows running multiple processes concurrently. It provides the Pool() method for creating and executing multiple tasks simultaneously. Example The following example shows how to square each element of a vector using parallelization ? import numpy ...
Read MorePlotting A Square Wave Using Matplotlib, Numpy And Scipy
A square wave is a type of non-sinusoidal waveform widely used in electric and digital circuits to represent signals. These circuits use square waves to represent binary states like input/output or on/off. Python provides several ways to plot square waves using Matplotlib, NumPy, and SciPy libraries, which offer built-in methods for data visualization and signal processing. Required Libraries Overview Matplotlib The most widely used Python library for plotting, providing low-level control over graph elements like axes, labels, legends, colors, and markers. NumPy A powerful library for storing and manipulating large, multi-dimensional arrays. We'll use it to generate ...
Read MorePlot Line Graph from NumPy Array
A line graph is a common way to display the relationship between two dependent datasets. Its general purpose is to show change over time. To plot a line graph from the NumPy array, we can use matplotlib which is the oldest and most widely used Python library for plotting. Also, it can be easily integrated with NumPy which makes it easy to create line graphs to represent trends and patterns in the given datasets. Basic Line Graph from NumPy Array Here's how to create a simple line graph using NumPy arrays and matplotlib ? import numpy ...
Read MorePrint Full Numpy Array without Truncation
NumPy is a powerful Python library for handling large, multi-dimensional arrays. However, when printing large NumPy arrays, the interpreter often truncates the output to save space and shows only a few elements with ellipsis (...). In this article, we will explore how to print a full NumPy array without truncation. Understanding the Problem To understand the truncation issue, consider this example: import numpy as np # Create a large array with 1100 elements array = np.arange(1100) print(array) [ 0 1 2 ... 1097 1098 ...
Read MorePlot the Size of each Group in a Groupby object in Pandas
Pandas is a powerful Python library for data analysis that allows grouping of data using groupby(). Visualizing the size of each group helps understand data distribution patterns. Python provides libraries like Matplotlib, Seaborn, and Plotly to create informative plots from grouped data. Sample Dataset Let's start by creating a sample dataset to demonstrate plotting group sizes ? import pandas as pd # Creating sample data data = {'Group_name': ['A', 'A', 'B', 'B', 'B', 'C'], 'Values': [10, 12, 30, 14, 50, 16]} df = pd.DataFrame(data) print(df) ...
Read MorePercentile Rank of a Column in a Pandas DataFrame
The percentile rank shows what percentage of values in a dataset are less than or equal to a given value. In pandas, we can calculate percentile ranks using the rank() method or scipy's percentileofscore() function. What is Percentile Rank? If a student scores in the 80th percentile, it means their score is greater than or equal to 80% of all other scores in the dataset. Using rank() Method The most common approach is using pandas' rank() method with pct=True parameter ? import pandas as pd # Create sample DataFrame data = {'Name': ['Ram', ...
Read MoreHighlight Pandas DataFrame\'s Specific Columns using Apply()
While presenting or explaining data using Pandas DataFrames, we might need to highlight important rows and columns to make them more appealing, explainable and visually stunning. One way of highlighting specific columns is by using the built-in apply() method with Pandas styling. Understanding apply() with Pandas Styling The apply() method is used to apply a user-defined function to each column or row of the Pandas DataFrame. To highlight specific columns, we first define a custom function that sets the required conditions for highlighting, then use the apply() method along with the style module. Syntax df.style.apply(function_name) ...
Read MoreHierarchical Data in Pandas
Hierarchical data represents multiple levels of nested groups or categories, such as company departments with employees, or products with categories and subcategories. Pandas provides powerful tools like MultiIndex, set_index(), and groupby() to effectively represent and analyze hierarchical data structures. Understanding MultiIndex in Pandas A MultiIndex creates a hierarchical index structure with multiple levels, allowing you to organize data in a tree-like format within a DataFrame. Creating Hierarchical Data with set_index() The set_index() method converts regular columns into a hierarchical index ? import pandas as pd # Creating sample hierarchical data data = { ...
Read MorePlot a Vertical line in Matplotlib
Python's Matplotlib library provides powerful tools for creating visual representations in the form of plots and graphs. One useful feature is plotting vertical lines to add reference lines or highlight specific points on plots. The built-in methods axvline(), vlines(), and plot() allow you to create vertical lines with customizable parameters such as position, color, and linestyle. Using axvline() Method The axvline() method is the simplest way to plot a vertical line in Matplotlib. It automatically spans the entire y-axis of the plot, making it ideal for reference lines. Syntax axvline(x=position, color='color', linestyle='style', alpha=transparency) ...
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