This homework uses knowledge of Numpy, Matplotlib, and Pandas to process electricity consumption data from file data.csv for 200 users (IDs 1-200). The dataset includes columns: CONS_NO (user ID), DATA_DATE (date, e.g., 2015/1/1), and KWH (electricity consumption). Tasks are as follows: Transpose da...
Data Preparation Begin by creating a duplicate of the original dataset to prevent contamination. Identify missing values using visualizations like heatmaps, and remove redundant fields. import numpy as np import pandas as pd # Find symmetric difference between two lists list_a = ["tom",&qu...
Counting Negative Values in Rows or Columns import pandas as pd # Create sample data df = pd.DataFrame({ 'x': [1, -3, 0, 1, 3], 'y': [-1, 0, 1, 5, 1], 'z': [0, -2, 0, -9, 0] }) # Count negatives per row (use axis=0 for columns) negatives_per_row = (df < 0).astype(int).sum(axis=1) print(negatives_...
Data discretization is the process of partitioning continuous attributes into a finite number of intervals, effectively mapping infinite numeric spaces into discrete categories. This transformation is fundamental in data preprocessing, especial when dealing with algorithms that require categorical i...
Here are several common methods for iterating over Pandas DataFrames: Iterator Methods (items, iterrows, itertuples): Iterate through all elements row-by-row or column-by-column. Best suited for element-level operations. Simple columns and index Traversal: Iterate over each row or column. Best suite...
Concatenation with pd.concat Use pd.concat to stack multiple DataFrames vertically or horizontally. Its suitable for simple aggregation of datasets along an axis. import pandas as pd # Sample data sets data_primary = pd.DataFrame({ 'identifier': ['X', 'Y', 'Z'], 'metric_A': [10, 20, 30] }) data_seco...
Problem Overview This challenge focuses on forecasting electricity consumption for multiple households using historical time-series data. Given sequences of past power usage labeled by household ID and day index (dt), the objective is to predict future target values — representing actual electricity...
Pandas and NumPy are fundamental libraries in Python for data analysis and scientific computing. They provide powerful tools that streamline workflows and enhance productivity. This article highlights 12 key functions from these libraries that can significantly improve enalysis efficiency. At the en...
Import required libraries and configure plotting settings: import numpy as np import matplotlib.pyplot as plt import pandas as pd import cv2 from moviepy.editor import VideoFileClip, AudioFileClip, afx # Configure Chinese font rendering in plots plt.rcParams['font.serif'] = ['YouYuan'] plt.rcParams[...
Install required dependencies: pip install pandas openpyxl Read and process student data from an Excel roster (e.g., student_roster.xlsx). The file must contain columns for unique identifiers and full names. Data Validatoin and Processing Load the Excel file using Pandas and verify required columns...
