hen dealing with missing data of type categorical, several methods can be used to impute the missing values. Here are some common approaches: The choice of imputation method depends on the nature of the data, the underlying patterns, and the goals of the analysis. Always consider the context of the data and the potential impact…
leaning a dataset involves handling missing values, correcting errors, and preparing the data for analysis. Here are common steps to clean a dataset using Python and pandas: Always document the steps taken during the cleaning process for transparency and reproducibility. Additionally, it’s crucial to thoroughly understand the context of the data and the goals of…
n Pandas, pd.set_option(‘display.float_format’, …) is used to set the formatting options for floating-point numbers when they are displayed in the console or output. It allows you to customize how floating-point numbers are presented, including the number of decimal places, scientific notation, and other formatting details. In this example, the pd.set_option(‘display.float_format’, ‘{:,.2f}’.format) line sets the floating-point…
lotly is a data visualization library that allows users to create interactive and visually appealing plots and dashboards. It supports a wide range of chart types, including scatter plots, line charts, bar charts, pie charts, 3D plots, geographic maps, and more. Plotly is known for its interactive features, allowing users to explore and interact with…
eaborn is a data visualization library for Python that is built on top of Matplotlib. It provides a high-level interface for creating attractive and informative statistical graphics. Seaborn is particularly well-suited for visualizing complex datasets with multiple variables. Key features of Seaborn include: To use a library in your Python code, you typically need to…
andas is a powerful open-source data manipulation and analysis library for Python. It provides data structures for efficiently storing, manipulating, and analyzing structured data, such as tabular data and time series. Key features of Pandas include: To use Pandas, you typically start by importing it into your Python script or Jupyter Notebook: After importing, you…
In Google Colab, you can use np.save to save NumPy arrays to your Google Drive. Here are the steps: Mount Google Drive Start by mounting your Google Drive. Run the following code and follow the instructions to authorize and mount your Google Drive:
hen you create a subset of a NumPy array and modify its values, it can affect the original array if the subset is actually a view of the original array rather than a copy. NumPy provides views to enhance performance and memory efficiency by avoiding unnecessary data copying. Understanding whether you’re working with a view…
umPy is a powerful numerical library in Python that provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these elements. It is a fundamental package for scientific computing in Python and is widely used in various domains such as data science, machine learning, signal processing, and…
Suppose we have a file that contains historical weather temperature between years 1948 to 2024 in which we want to look at how January temperatures have changed over time.
