n Pandas library in Python pd.qcut is a function for performing quantile-based discretization of continuous variables. Quantile-based discretization involves dividing a continuous variable into discrete intervals or bins based on the distribution of its values. This process ensures that each bin contains approximately the same number of observations, making it useful for creating categories or…
To grab random sample from a dataset in Python, you can use the pandas library. Assuming your dataset is stored in a pandas DataFrame, you can use the sample method to randomly select rows. Here’s an example: In this example, n=5 specifies the number of rows to sample, and random_state is set to ensure reproducibility.
You can use the subplot function in Matplotlib to create multiple plots arranged in a grid. To put two plots on top of each other, you can use the following approach: In this example, plt.subplot(2, 1, 1) creates the first subplot in a 2-row, 1-column grid, and plt.subplot(2, 1, 2) creates the second subplot beneath…
In pandas, you can give a specific sorting order to categorical values by creating a categorical variable with an ordered category. Here’s an example: In this example: This can be useful when you want to ensure that certain operations, such as sorting or plotting, take into account the natural order of the days of the…
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…
nivariate exploration refers to the analysis of a single variable in isolation. In data analysis, univariate exploration involves examining the distribution, central tendency, and variability of a single variable without considering its relationship with other variables. Common techniques used in univariate exploration include: Univariate exploration is often the first step in data analysis, providing insights…
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…
