Russia-Ukraine War Data Analysis Project using Python
Naveen
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![](https://www.nomidl.com/wp-content/uploads/2022/11/IMAGE_1645764544-1024x576.jpg)
In this article I will take you through the task of Analyzing the Russia-Ukraine war Dataset using Python.
The dataset that I am using for the task of analysis the Ukraine and Russia War is downloaded from Kaggle. You can download russia-ukraine equipment dataset from here and russia-ukraine personnel losses dataset from here.
Now let’s import the necessary Python libraries and the dataset to get started with this task:
![](https://www.nomidl.com/wp-content/uploads/2022/11/image-40.png)
Let’s load our dataset with the help of pandas’ read_csv function, and let’s check our top 5 rows.
![](https://www.nomidl.com/wp-content/uploads/2022/11/image-41.png)
Now, we will perform some basic EDA like checking the columns in our dataset.
![](https://www.nomidl.com/wp-content/uploads/2022/11/image-42.png)
We will look the data types of our columns with the info() function and description of our dataset.
![](https://www.nomidl.com/wp-content/uploads/2022/11/image-43.png)
![](https://www.nomidl.com/wp-content/uploads/2022/11/image-44.png)
![](https://www.nomidl.com/wp-content/uploads/2022/11/image-45.png)
We will be looking into our dataset if we have null values and we will drop the POW column from dataset as it is of no use in our analysis.
![](https://www.nomidl.com/wp-content/uploads/2022/11/image-46.png)
![](https://www.nomidl.com/wp-content/uploads/2022/11/image-47.png)
We will merge our ‘fuel tank’ and ‘military auto’ columns into ‘vehicle and fuel tanks’ and ‘mobile SRBM system’ column into ‘cruise missiles’ column, and we will drop the unnecessary columns from our dataset.
![](https://www.nomidl.com/wp-content/uploads/2022/11/image-48.png)
![](https://www.nomidl.com/wp-content/uploads/2022/11/image-49.png)
Now, we will be visualizing the Top 10 Greatest Loss Directions, Troop Losses and Per Day Troop Losses one by one with the help of bar plot, line plot and histogram.
![](https://www.nomidl.com/wp-content/uploads/2022/11/image-50.png)
![](https://www.nomidl.com/wp-content/uploads/2022/11/image-51.png)
![](https://www.nomidl.com/wp-content/uploads/2022/11/image-52.png)
Now we will be shifting our greatest losses direction column in the beginning of our dataframe and also looking at the equipment losses.
![](https://www.nomidl.com/wp-content/uploads/2022/11/image-53.png)
![](https://www.nomidl.com/wp-content/uploads/2022/11/image-54.png)
![](https://www.nomidl.com/wp-content/uploads/2022/11/image-55.png)
![](https://www.nomidl.com/wp-content/uploads/2022/11/image-56.png)
![](https://www.nomidl.com/wp-content/uploads/2022/11/image-57.png)
Conclusion
In this article we have analysed Russia-Ukraine war dataset using python. I hope you liked it, if you have any questions let me know in the comment section below.