Unlocking Weather Patterns: A Ridgeline Plot Visualization of Seattle’s Temperature from 1950 to 2010

Unlocking Weather Patterns: A Ridgeline Plot Visualization of Seattle’s Temperature from 1950 to 2010


Visualizing weather patterns over time offers valuable insights into climate change, helping researchers, policymakers, and the general public understand the ongoing transformations in our environment. In this comprehensive guide, we’ll delve deep into the process of crafting a Ridgeline Plot to visualize the distribution of average temperatures in Seattle from 1950 to 2010. We’ll use Python, along with powerful libraries such as Plotly, NumPy, and Pandas.

Data Preparation and Processing

Importing Libraries

Start by importing the necessary Python libraries:

import plotly.graph_objects as go
import numpy as np
import pandas as pd

Loading and Organizing Data

Retrieve and organize the data for further processing:

# Load data
temp = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2016-weather-data-seattle.csv')
# Extract and store the year information
temp['year'] = pd.to_datetime(temp['Date']).dt.year

Data Filtering

Filter the dataset to include only the selected years for analysis:

# Selected years
year_list = [1950, 1960, 1970, 1980, 1990, 2000, 2010]
# Filter data
temp = temp[temp['year'].isin(year_list)]

Data Grouping and Aggregation

Group and aggregate the data to create a distribution of temperatures for each selected year:

# Group and aggregate
temp = temp.groupby(['year', 'Mean_TemperatureC']).agg({'Mean_TemperatureC': 'count'}).rename(columns={'Mean_TemperatureC': 'count'}).reset_index()

Data Structuring for Visualization

Preparing Data Arrays

Prepare and normalize the data arrays for each year:

array_dict = {}
for year in year_list:
array_dict[f'x_{year}'] = temp[temp['year']==year]['Mean_TemperatureC']
array_dict[f'y_{year}'] = temp[temp['year']==year]['count']
array_dict[f'y_{year}'] = (array_dict[f'y_{year}'] - array_dict[f'y_{year}'].min()) / (array_dict[f'y_{year}'].max() - array_dict[f'y_{year}'].min())

Crafting the Ridgeline Plot

Initiating the Plot

Create a Plotly Figure and add traces for each year:

fig = go.Figure()
for index, year in enumerate(year_list):
fig.add_trace(go.Scatter(x=[-20, 40], y=np.full(2, len(year_list)-index), mode='lines', line_color='white'))
fig.add_trace(go.Scatter(x=array_dict[f'x_{year}'], y=array_dict[f'y_{year}'] + (len(year_list)-index) + 0.4, fill='tonexty', name=f'{year}'))
fig.add_annotation(x=-20, y=len(year_list)-index, text=f'{year}', showarrow=False, yshift=10)

Finalizing and Displaying the Plot

Adjust the layout and display the visualization:

fig.update_layout(title='Average temperature from 1950 until 2010 in Seattle', showlegend=False, xaxis=dict(title='Temperature in degree Celsius'), yaxis=dict(showticklabels=False))

Prompts for Further Exploration

1. How does the temperature distribution change over the decades?
2. Is there a noticeable trend of increasing or decreasing temperatures?
3. How does the variation in temperatures within a year compare across different years?
4. Can this visualization technique be applied to other climate variables, like precipitation or humidity?
5. How can this visualization be enhanced to provide more insights or better clarity?
6. What other visualization tools or libraries can be used to achieve similar or better results?
7. How can the code be optimized for larger datasets or for a different range of years?
8. What are the implications of the observed temperature changes for Seattle’s climate and environment?
9. Can this analysis be expanded to compare temperature distributions in different cities or regions?
10. How can the data preprocessing steps be improved or modified for different datasets or requirements?
11. What additional data can be incorporated into this analysis to provide deeper insights?
12. How does the chosen color scheme affect the interpretation of the visualization?
13. What considerations should be taken into account when selecting years for analysis?
14. How can interactive elements be added to the visualization to make it more user-friendly?
15. What are the limitations of this analysis and visualization approach, and how can they be addressed?


The article provided an in-depth guide on crafting a Ridgeline Plot to visualize Seattle’s temperature distribution from 1950 to 2010. Through detailed code snippets and explanations, readers can replicate or build upon this analysis for their projects, gaining insights into weather patterns and contributing to our understanding of climate change.

End-to-End Codes

# Import required libraries
import plotly.graph_objects as go
import numpy as np
import pandas as pd

# Load and process the data
temp = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2016-weather-data-seattle.csv')
temp['year'] = pd.to_datetime(temp['Date']).dt.year
selected_years = [1950, 1960, 1970, 1980, 1990, 2000, 2010]
filtered_temp = temp[temp['year'].isin(selected_years)]
aggregated_temp = filtered_temp.groupby(['year', 'Mean_TemperatureC']).size().reset_index(name='count')

# Prepare data arrays for each year
data_dict = {}
for year in selected_years:
x_data = aggregated_temp[aggregated_temp['year'] == year]['Mean_TemperatureC']
y_data = aggregated_temp[aggregated_temp['year'] == year]['count']
normalized_y = (y_data - y_data.min()) / (y_data.max() - y_data.min())
data_dict[year] = (x_data, normalized_y)

# Create and configure the plot
fig = go.Figure()
for idx, year in enumerate(selected_years):
fig.add_trace(go.Scatter(x=[-20, 40], y=np.full(2, len(selected_years) - idx), mode='lines', line_color='white'))
x_data, normalized_y = data_dict[year]
fig.add_trace(go.Scatter(x=x_data, y=normalized_y + len(selected_years) - idx, fill='tonexty', name=f'{year}'))
fig.add_annotation(x=-20, y=len(selected_years) - idx, text=f'{year}', showarrow=False, yshift=10)

# Set layout and display the plot
fig.update_layout(title='Average Temperature from 1950 to 2010 in Seattle', showlegend=False,
xaxis=dict(title='Temperature in Degree Celsius'), yaxis=dict(showticklabels=False))

Essential Gigs