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Articles by Vani Nalliappan
Page 3 of 13
How to change the size of a Dash Graph in Python Plotly?
Dash is a Python framework for building interactive web applications with Plotly graphs. You can easily control the size of graphs in a Dash app by using the style parameter in dcc.Graph() component to set custom height and width dimensions. Basic Setup First, import the required libraries and create sample data ? import dash from dash import dcc, html import pandas as pd import plotly.express as px # Create sample data df_bar = pd.DataFrame({ "Season": ["Summer", "Winter", "Autumn", "Spring"], "Rating": [3, 2, 1, 4] }) # ...
Read MoreHow to set the range of Y-axis in Python Plotly?
Plotly is a powerful Python library for creating interactive visualizations. One common requirement is controlling the Y-axis range to better display your data or focus on specific value ranges. Setting Y-axis Range with update_layout() The most straightforward way to set the Y-axis range is using the update_layout() method with the yaxis_range parameter − import plotly.graph_objs as go import numpy as np # Set random seed for reproducible results np.random.seed(3) # Generate X-axis data (0 to 18, step 2) x_values = list(range(0, 20, 2)) # Generate random Y-axis data y_values = np.random.randn(10) # ...
Read MoreHow to set the line color in Python Plotly?
Python Plotly provides several methods to customize line colors in graphs. In this tutorial, we'll explore how to set line colors using plotly.express and the update_traces() method. Plotly Express contains many methods to customize charts and render them in HTML format. The update_traces() method with the line_color parameter is the primary way to set line colors after creating a plot. Basic Line Color Setting Here's a complete example showing how to create a line plot and set its color ? import plotly.express as px import pandas as pd # Create sample data data = ...
Read MoreHow to plot multiple figures as subplots in Python Plotly?
Plotly is an open-source Python library for creating interactive charts. You can use the make_subplots feature available in Plotly to combine multiple figures into subplots within a single layout. In this tutorial, we will use plotly.graph_objects and plotly.subplots to create multiple subplots. The make_subplots() function allows you to specify the grid layout, while append_trace() adds individual plots to specific positions. Basic Subplot Creation Here's how to create a simple subplot layout with three scatter plots ? from plotly.subplots import make_subplots import plotly.graph_objects as go # Create subplot grid: 3 rows, 1 column fig = ...
Read MoreHow to open a URL by clicking a data point in Python Plotly?
In Python Plotly with Dash, you can create interactive scatter plots where clicking a data point opens a specific URL. This is achieved by storing URLs as custom data and using Dash callbacks to handle click events. Setting Up the Dashboard First, import the required libraries and create a Dash application − import webbrowser import dash from dash.exceptions import PreventUpdate from dash import dcc, html from dash.dependencies import Input, Output import plotly.express as px import pandas as pd # Create Dash app app = dash.Dash(__name__) Creating Data with URLs Create a DataFrame ...
Read MorePython Pandas – How to use Pandas DataFrame tail( ) function
The Pandas DataFrame tail() function returns the last n rows of a DataFrame. This is particularly useful when combined with filtering operations to examine the bottom portion of your filtered data. Syntax DataFrame.tail(n=5) Parameters: n (int, optional): Number of rows to select. Default is 5. Creating Sample Data Let's create a sample dataset to demonstrate the tail() function ? import pandas as pd # Create sample products data data = { 'id': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], ...
Read MorePython Pandas – How to use Pandas DataFrame Property: shape
The shape property in Pandas DataFrame returns a tuple containing the number of rows and columns. It's essential for understanding your dataset dimensions before performing data analysis operations. DataFrame.shape Property The shape property returns (rows, columns) as a tuple. You can access individual values using indexing ? # Basic syntax df.shape # Returns (rows, columns) df.shape[0] # Number of rows df.shape[1] # Number of columns Creating Sample Data Let's create a sample products dataset to demonstrate the shape ...
Read MorePython Pandas - Read data from a CSV file and print the 'product' column value that matches 'Car' for the first ten rows
When working with CSV data in Pandas, you often need to filter specific rows based on column values. This tutorial shows how to read a CSV file and filter rows where the 'product' column matches 'Car' from the first ten rows. We'll use the 'products.csv' file which contains 100 rows and 8 columns with product information. Sample Data Structure The products.csv file contains the following structure ? Rows: 100 Columns: 8 id product engine avgmileage price height_mm width_mm productionYear 1 2 ...
Read MoreWrite a program in Python to verify camel case string from the user, split camel cases, and store them in a new series
Camel case is a naming convention where the first letter is lowercase and each subsequent word starts with an uppercase letter (e.g., "pandasSeriesDataFrame"). This tutorial shows how to verify if a string is in camel case format and split it into a pandas Series. Understanding Camel Case Validation A valid camel case string must satisfy these conditions: Not all lowercase Not all uppercase Contains no underscores Solution Steps To solve this problem, we follow these steps: Define a function that accepts the input string Check if the string is in camel ...
Read MoreWrite a Python code to combine two given series and convert it to a dataframe
When working with Pandas Series, you often need to combine them into a single DataFrame for analysis. Python provides several methods to achieve this: direct DataFrame creation, concatenation, and joining. Method 1: Using DataFrame Constructor Create a DataFrame from the first series, then add the second series as a new column ? import pandas as pd series1 = pd.Series([1, 2, 3, 4, 5], name='Id') series2 = pd.Series([12, 13, 12, 14, 15], name='Age') df = pd.DataFrame(series1) df['Age'] = series2 print(df) Id Age 0 1 ...
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