The headings have been provided in CamelCase, rather than Snake_case
I will manually access and edit these, using the dataframe.column function
It would be nice to add an explanation for your decision. For example, after reading this suddenly I am curious, why snake_case? Why can’t we keep it in CamelCase? It is totally fine to state that you personally prefer snake_case over CamelCase. Or alternatively, you can state that snake_case is the naming convention preferred by Python in PEP 8 – Style Guide for Python Code. Adding this information will answer the curious question of the reader.
The top and bottom five values, look out of place, so I will convert values >= 10000000 and <= 100, to Null and then drop the rows with Null values
It would be nice to add your opinion on why do you think the values are out of place.
The new largest and smallest values seem like sensible prices of cars, although you would have to be very rich to afford, the most expensive in the list!
Why the new values are sensible?
However, to improve the quality of the data slightly, I’ve removed rows with registration years outside of 1950 - 2016
How does removing the rows outside of 1950 - 2016 will improve data quality?
This will allow us to identify any lin between price and mileage
Typo
autos.loc[:,'unrepaired_damage'].unique()
array(['nein', 'ja'], dtype=object)
It would be good to add an English translation of both nein and ja.
Additionally, please add a conclusion at the end that summarises and adds all of your findings in one place.
As a summary, the most important thing to note is that the reader doesn’t have any context or time to know what you were thinking when you are doing something. So it is very important to explain the reasoning behind certain decisions so that your actions can immediately make sense.
Overall, I liked your project. Hope my feedback is helpful.