Analysis and Visualization Summary of Environmental Data

Jun 14, 2024, 8:33 PM

Give a detailed analysis and visualization of the dataset

Analysis and Visualization Summary

  • Dataset Overview:
    • The dataset contains 47,370 entries with 5 columns: Day, Temperature, Humidity, Light intensity, and Time of Flight.
    • No missing values were found in the dataset.
  • Summary Statistics:
    • Temperature: Mean = 31.38°C, Min = 28.44°C, Max = 32.99°C.
    • Humidity: Mean = 68.64%, Min = 54.99%, Max = 78.79%.
    • Light Intensity: Mean = 213.12, Min = 0, Max = 9687.
    • Time of Flight: Mean = 474.40, Min = 0, Max = 1827.45.
  • Visualizations:
    • Temperature Distribution: Displayed as a histogram.
    • Humidity Distribution: Displayed as a histogram.
    • Light Intensity Distribution: Displayed as a histogram.
    • Time of Flight Distribution: Displayed as a histogram.

Visualize this for me

Show temperature and humidity correlation

Key Points:

  • The correlation between temperature and humidity was calculated.
  • The correlation coefficient is -0.73, indicating a strong negative correlation between the two variables.

Plot temperature vs. humidity scatter plot

  • A scatter plot has been generated showing the relationship between Temperature and Humidity using Plotly Express.
  • The plot visualizes the correlation between these two variables, if any.

Show light intensity vs. time of flight scatter plot

  • A scatter plot of Light Intensity vs. Time of Flight was generated using Plotly Express.
  • The plot visually represents the relationship between these two variables.

Show summary statistics for light intensity and time of flight

Summary Statistics

Light Intensity
  • Count: 47370
  • Mean: 213.12
  • Std Dev: 211.50
  • Min: 0.00
  • 25th Percentile: 172.00
  • Median: 217.00
  • 75th Percentile: 231.00
  • Max: 9687.00
Time of Flight
  • Count: 47370
  • Mean: 474.40
  • Std Dev: 74.41
  • Min: 0.00
  • 25th Percentile: 434.90
  • Median: 458.74
  • 75th Percentile: 482.30
  • Max: 1827.45

Show correlation between light intensity and time of flight

  • The correlation between light intensity and time of flight has been calculated.
  • The correlation coefficient is approximately 0.059.

Show time of flight over time

The user requested to plot the 'Time of Flight' over 'Time'. A line plot was generated using Plotly to visualize this.

Compare light intensity and time of flight trends

The conversation so far involved a request to compare the trends of light intensity and time of flight. A plot was generated using Plotly to visualize these trends over a period of days. The graph effectively shows the overlapping trends for better comparison.

Analyze peak values for light intensity

The analysis identified peak values for light intensity in the dataset. The peak light intensity value is 9687, which occurs on both Day 3 and Day 5. On both these days, the temperature is 32.36°C, humidity is 63.67%, and time of flight is 527.99.

boys hostel data (1) (1).csv

Missing file

Analyze and give a detailed insight about this data

The provided dataset contains the following columns:
  • Temperature: The temperature readings in degrees Celsius.
  • Humidity: The humidity readings in percentage.
  • CO2: The CO2 concentration readings in ppm (parts per million).
  • Light Intensity: The light intensity readings in lux.

Initial Insights

  1. Temperature: The temperature values range from 28.99°C to 29.05°C in the initial preview.
  2. Humidity: The humidity values are around 80.73% to 80.79%.
  3. CO2: The CO2 concentration varies significantly, with values ranging from 0.00 ppm to 671.08 ppm.
  4. Light Intensity: The light intensity values range from 4263 lux to 4294 lux.
Let's proceed with a detailed analysis of this data, including summary statistics and visualizations.

Summary Statistics

Here are the summary statistics for the provided dataset:
  • Temperature:
    • Mean: 29.01°C
    • Standard Deviation: 0.02°C
    • Minimum: 28.99°C
    • Maximum: 29.05°C
  • Humidity:
    • Mean: 58.04%
    • Standard Deviation: 12.81%
    • Minimum: 29.59%
    • Maximum: 80.79%
  • CO2:
    • Mean: 483.66 ppm
    • Standard Deviation: 123.10 ppm
    • Minimum: 0.00 ppm
    • Maximum: 671.08 ppm
  • Light Intensity:
    • Mean: 3764.83 lux
    • Standard Deviation: 4508.98 lux
    • Minimum: 0.00 lux
    • Maximum: 12751.00 lux

Observations

  1. Temperature: The temperature values are quite stable with a very small standard deviation.
  2. Humidity: The humidity values show more variability, ranging from 29.59% to 80.79%.
  3. CO2: There is a significant variation in CO2 concentration, with values ranging from 0.00 ppm to 671.08 ppm.
  4. Light Intensity: The light intensity values also show a wide range, from 0.00 lux to 12751.00 lux.
Would you like to see any specific visualizations or further analysis on this data?

Can you visualize the distribution of CO2 levels?

Can you analyze the correlation between temperature and humidity?

There is one non-numeric value in both the Temperature and Humidity columns. We need to handle these non-numeric values before proceeding with the correlation analysis.
The correlation coefficient between Temperature and Humidity is approximately 0.125. This indicates a very weak positive correlation between the two variables, suggesting that changes in temperature have a minimal impact on humidity levels in this dataset.

Can you provide a time series analysis of the temperature data?

Could you please provide the timestamp column or any other time-related column in your dataset? This will help in performing a time series analysis of the temperature data.

What is the seasonal pattern in the temperature data?

Could you please provide the timestamp column or any other time-related column in your dataset? This will help in identifying the seasonal patterns in the temperature data.

Can you visualize the temperature data over time?

Could you please provide the timestamp column or any other time-related column in your dataset? This will help in visualizing the temperature data over time.

Can you perform a time series analysis on another variable?

Could you please provide the timestamp column or any other time-related column in your dataset? This will help in performing a time series analysis on any variable of your choice.
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