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Matplotlib allows you to customize the background color of your plots using the set_facecolor() method. This is useful for creating visually appealing plots or matching specific design requirements. Basic Face Color Change The simplest way to change the face color is using the set_facecolor() method on the axes object ? import matplotlib.pyplot as plt import numpy as np # Create data points x = np.linspace(-10, 10, 100) y = np.sin(x) # Create figure and axes fig, ax = plt.subplots(figsize=(8, 4)) # Plot the data ax.plot(x, y, color='yellow', linewidth=3) # Set the face ... Read More
A masked surface plot allows you to hide or display only specific portions of 3D surface data based on certain conditions. This is useful when you want to exclude invalid data points or highlight specific regions of your surface. Setting Up the Environment First, we need to import the required libraries and configure the plot settings ? import matplotlib.pyplot as plt import numpy as np # Set figure size and layout plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True Creating the Coordinate Grid We'll create a coordinate grid using meshgrid to define our ... Read More
To set the border color of dots in matplotlib scatterplots, use the edgecolors parameter in the scatter() method. This parameter controls the color of the dot borders, while linewidth controls their thickness. Basic Syntax plt.scatter(x, y, edgecolors='color_name', linewidth=width) Example with Red Border Here's how to create a scatterplot with red borders around the dots ? import numpy as np import matplotlib.pyplot as plt # Generate sample data N = 10 x = np.random.rand(N) y = np.random.rand(N) colors = np.random.rand(N) # Create scatterplot with red borders plt.scatter(x, y, s=500, c=colors, ... Read More
A Pandas DataFrame allows you to add prefixes to all column names using the add_prefix() method. This is useful for distinguishing columns when merging DataFrames or organizing data. Syntax DataFrame.add_prefix(prefix) Parameters: prefix − String to add before each column name Creating a DataFrame First, let's create a DataFrame with car data ? import pandas as pd # Create DataFrame dataFrame = pd.DataFrame({ "Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000], ... Read More
Matplotlib provides powerful date formatting capabilities for time series visualization. To display year ticks every 12 months with proper month labeling, we use YearLocator and MonthLocator with custom formatters. Understanding Date Locators and Formatters Matplotlib's date handling uses two key components: Locators − Determine where ticks are placed on the axis Formatters − Control how dates are displayed as text For year ticks every 12 months, we set major ticks for years and minor ticks for individual months. Complete Example Here's how to create a time series plot with year ticks showing ... Read More
To reverse the column order in a Pandas DataFrame, use the slice notation [::-1] with dataFrame.columns. This creates a new DataFrame with columns in reverse order without modifying the original data. Syntax dataFrame[dataFrame.columns[::-1]] Step-by-Step Process Import Required Library import pandas as pd Create a DataFrame import pandas as pd # Create a DataFrame with 4 columns dataFrame = pd.DataFrame({ "Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000], "Reg_Price": [7000, ... Read More
In Pandas, you can extract only capital words from a DataFrame using regular expressions. The re module provides pattern matching capabilities to identify words containing uppercase letters. Setting Up the Data First, let's create a sample DataFrame with mixed case words ? import re import pandas as pd # Create sample data with mixed case words data = [['computer', 'mobile phone', 'ELECTRONICS', 'electronics'], ['KEYBOARD', 'charger', 'SMARTTV', 'camera']] df = pd.DataFrame(data, columns=['Col1', 'Col2', 'Col3', 'Col4']) print("Original DataFrame:") print(df) Original DataFrame: ... Read More
When working with numerical data in Pandas, you may encounter infinite values. You can identify and display True for infinite values using the isin() method or np.isinf() function. Creating a DataFrame with Infinite Values First, let's create a DataFrame containing some infinite values using np.inf − import pandas as pd import numpy as np # Create DataFrame with infinite values data = {"Reg_Price": [7000.5057, np.inf, 5000, np.inf, 9000.75768, 6000, 900, np.inf]} dataFrame = pd.DataFrame(data) print("DataFrame...") print(dataFrame) DataFrame... Reg_Price 0 7000.506 1 ... Read More
When working with Pandas DataFrames, you may need to identify rows containing infinity values. This is useful for data cleaning and analysis. Python provides np.isinf() and any() methods to check and display row indexes with infinity values. Required Libraries First, import the necessary libraries ? import pandas as pd import numpy as np Creating DataFrame with Infinity Values Create a DataFrame containing infinity values using np.inf ? import pandas as pd import numpy as np # Create dictionary with infinity values data = {"Reg_Price": [7000.5057, np.inf, 5000, np.inf, 9000.75768, 6000, ... Read More
In Pandas, you can count the observations (rows) within groups using the groupby() method combined with count(). This is useful for analyzing the frequency of categories in your data. Creating a Sample DataFrame Let's start by creating a DataFrame with product information ? import pandas as pd # Create a DataFrame with product data dataFrame = pd.DataFrame({ 'Product Name': ['Keyboard', 'Charger', 'SmartTV', 'Camera', 'Graphic Card', 'Earphone'], 'Product Category': ['Computer', 'Mobile Phone', 'Electronics', 'Electronics', 'Computer', 'Mobile Phone'], 'Quantity': [10, 50, 10, 20, 25, 50] ... Read More