you will explore and analyze a public dataset of your choosing. Since this is "open-ended" in nature, you are free to expand upon the requirements below. However, you must meet the minimum requirments as indicated in each section. You must use Pandas as the primary tool to process your data. The preferred method for this analysis is in a .ipynb file in google colab.

Your data should need some "work", or be considered "dirty". You must show your skills in data cleaning/wrangling. Link of dataset: https://www.kaggle.com/dgomonov/new-york-city-airbnb-open-data

1. Introduction In this section, please describe the dataset you are using. Include a link to the source of this data. You should also provide some explanation on why you choose this dataset.

2. Data Exploration

Import your dataset into your .ipynb, create dataframes, and explore your data.

Include:

• Summary statistics means, medians, quartiles,
• Missing value information
• Any other relevant information about the dataset.

3. Data Wrangling

Make a subset of your original data and perform the following.

1. Modify multiple column names.
2. Look at the structure of your data - are any variables improperly coded? Such as strings or characters? Convert to correct structure if needed.
3. Fix missing and invalid values in data.
4. Create new columns based on existing columns or calculations.
5. Drop column(s) from your dataset.
6. Drop a row(s) from your dataset.
7. Sort your data based on multiple variables.
8. Filter your data based on some condition.
9. Convert all the string values to upper or lower cases in one column.
10. Check whether numeric values are present in a given column of your dataframe.
11. Group your dataset by one column, and get the mean, min, and max values by group.
    1. Groupby()
    2. agg() or .apply()
12. Group your dataset by two columns and then sort the aggregated results within the groups.

Conclusions

After exploring your dataset, provide a short summary of what you noticed from this dataset. What would you explore further with more time?