uswtdb_v7_1_20240814.csv

Missing file

The dataset has been successfully loaded. Here are the first few rows of the dataset:

```
case_id faa_ors faa_asn usgs_pr_id eia_id t_state \
0 3123319 40-097765 2021-WTW-7879-OE NaN 65511.0 OK
1 3123340 40-097520 2021-WTW-7901-OE NaN 65511.0 OK
2 3123283 40-098169 2021-WTW-8718-OE NaN 65511.0 OK
3 3123452 40-097545 2021-WTW-7884-OE NaN 65511.0 OK
4 3123519 40-097550 2021-WTW-7881-OE NaN 65511.0 OK
t_county t_fips p_name p_year ... t_ttlh t_retrofit \
0 Ellis County 40045.0 25 Mile Creek 2022.0 ... 180.1 0
1 Ellis County 40045.0 25 Mile Creek 2022.0 ... 180.1 0
2 Ellis County 40045.0 25 Mile Creek 2022.0 ... 180.1 0
3 Ellis County 40045.0 25 Mile Creek 2022.0 ... 180.1 0
4 Ellis County 40045.0 25 Mile Creek 2022.0 ... 180.1 0
t_retro_yr t_offshore t_conf_atr t_conf_loc t_img_date t_img_src \
0 NaN 0 3 3 1/4/2023 Maxar
1 NaN 0 3 3 1/4/2023 Maxar
2 NaN 0 3 3 1/4/2023 Maxar
3 NaN 0 3 3 1/4/2023 Maxar
4 NaN 0 3 3 1/4/2023 Maxar
xlong ylat
0 -99.740372 36.489838
1 -99.733086 36.451591
2 -99.762627 36.515823
3 -99.707520 36.490849
4 -99.724426 36.491375
[5 rows x 28 columns]
```

- Summary statistics
- Distribution of a specific column
- Correlation between two columns
- Time series analysis
- Geospatial visualization

Please specify your requirements.

The trend graph shows the increase in the number of wind turbines (

`p_tnum`

) from 1982 to 2024, categorized by state (`t_state`

) and manufacturer (`t_manu`

). Here are some key observations:-
**Overall Growth**: There is a clear upward trend in the number of wind turbines over the years, indicating a growing adoption of wind energy across various states. -
**State-wise Trends**: Different states show varying levels of growth. Some states have seen a rapid increase in the number of turbines, while others have more gradual growth. -
**Manufacturer Influence**: The graph also highlights the role of different manufacturers in the growth of wind turbines. Some manufacturers have a significant presence in certain states, contributing to the overall increase in turbine numbers. -
**Recent Years**: The most recent years (2020-2024) show a sharp increase in turbine installations, which could be due to advancements in technology, favorable policies, or increased investment in renewable energy. -
**Peaks and Valleys**: There are periods of rapid growth followed by slower growth or plateaus, which could be influenced by economic factors, policy changes, or other external factors.

Overall, the graph provides a comprehensive view of the growth of wind turbines in the US, highlighting the contributions of different states and manufacturers over the years.

As of recent years (2020-2024), the manufacturer that dominates the market in terms of the number of wind turbines is **GE Wind** with a total of **1,026,462 turbines**. The next leading manufacturers are:

**Vestas**with**411,191 turbines****Siemens Gamesa Renewable Energy**with**71,795 turbines****Nordex**with**49,132 turbines****Siemens**with**8,732 turbines**

These manufacturers have significantly contributed to the recent growth in wind turbine installations.

The histogram shows the distribution of wind turbine heights (

`t_ttlh`

). Here are some key observations:-
**Most Common Heights**: The majority of wind turbines have heights ranging between 80 to 100 meters. This indicates a standardization in turbine heights, likely due to optimal performance and design considerations. -
**Variation in Heights**: There is a noticeable variation in turbine heights, with some turbines being significantly taller or shorter than the common range. This could be due to different design requirements, technological advancements, or site-specific factors. -
**Outliers**: There are a few outliers with very tall heights, which might represent newer, more advanced turbines designed to capture more wind energy at higher altitudes.

Overall, the distribution shows a concentration around certain height ranges, with some variation and a few outliers.

To predict the growth of wind turbines in the US, we can perform a time series analysis using the historical data available. This involves fitting a model to the historical data and using it to forecast future growth. Here are the steps we will take:

**Data Preparation**: Aggregate the number of turbines by year.**Model Selection**: Choose an appropriate time series model (e.g., ARIMA, Prophet) for forecasting.**Model Training**: Train the model on the historical data.**Forecasting**: Use the trained model to predict future growth.

Let's start by preparing the data.

To predict the growth of wind turbines in the US, we can perform a time series analysis using the historical data available. This involves fitting a model to the historical data and using it to forecast future growth. Here are the steps we will take:

**Data Preparation**: Aggregate the number of turbines by year.**Model Selection**: Choose an appropriate time series model (e.g., ARIMA, Prophet) for forecasting.**Model Training**: Train the model on the historical data.**Forecasting**: Use the trained model to predict future growth.

Let's start by preparing the data.

To predict the growth of wind turbines in the US, we can perform a time series analysis using the historical data available. This involves fitting a model to the historical data and using it to forecast future growth. Here are the steps we will take:

**Data Preparation**: Aggregate the number of turbines by year.**Model Selection**: Choose an appropriate time series model (e.g., ARIMA, Prophet) for forecasting.**Model Training**: Train the model on the historical data.**Forecasting**: Use the trained model to predict future growth.

Let's start by preparing the data.

Could you please provide the dataset file 'uswtdb_v7_1_20240814.csv' so that I can proceed with the analysis?

Could you please provide the dataset file 'uswtdb_v7_1_20240814.csv' so that I can proceed with the analysis?