In [ ]: In [ ]: In [ ]: In [ ]: In [ ]: In [ ]: In [ ]: Assign a name to this data (whatever name you want) Take a look at the first 15 rows returns statistics about the numerical columns in a dataset Get all the geographical locations whose geography_type is town Get the geographical location that has the max percentage of 'less_than_high_school_graduate' Get the average percentage of bachelor_s_degree_or_higher Make a countplot using seaborn (x = 'geography_type') Make a barplot using seaborn (assign geography as x axis, assign some_college_or_associate_s_degree as y axis) feel free to use plt.xticks(rotation=70) and plt.tight_layout() if you think location names are squeezing together
SQL
SQL stands for Structured Query Language, is a form of communication that uses queries structured in a specific format to store, manage & retrieve data from a relational database.
Queries
A query is a type of computer programming language that is used to retrieve data from a database. Databases are useful in a variety of ways. They enable the retrieval of records or parts of records, as well as the performance of various calculations prior to displaying the results. A search query is one type of query that many people perform several times per day. A search query is executed every time you use a search engine to find something. When you press the Enter key, the keywords are sent to the search engine, where they are processed by an algorithm that retrieves related results from the search index. Your query's results are displayed on a search engine results page, or SER.
please code this for python
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Assign a name to this data (whatever name you want)
Take a look at the first 15 rows
returns statistics about the numerical columns in a dataset
Get all the geographical locations whose geography_type is town
Get the geographical location that has the max percentage of 'less_than_high_school_graduate'
Get the average percentage of bachelor_s_degree_or_higher
Make a countplot using seaborn (x = 'geography_type')
Make a barplot using seaborn (assign geography as x axis, assign some_college_or_associate_s_degree as y axis) feel free to use plt.xticks (rotation=70) and
plt.tight_layout() if you think location names are squeezing together](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F2ae76079-938b-4b5d-8ceb-1dfbc10f88ce%2Fb7f575c2-760b-4dc1-b7f9-72ae9b730117%2Fyjgjp7_processed.png&w=3840&q=75)
![In [1]: #Numbers are percentage. We'll use Pandas and Matplotlib/Seaborn to analyze the data.
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This dataset contains data about the highest grade completed by residents of San Mateo County, California by city. Grade levels include less than high school
graduate, high school graduate, some college or associate's degree, and bachelor's degree or higher. This data was extracted from the United States Cenus
Bureau.
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Import all related packages
Get the data. Use this data
pd.read_json("https://data.smcgov.org/resource/mb6a-xn89.json").
Assign a name to this data (whatever name you want)
Take a look at the first 15 rows
returns statistics about the numerical columns in a dataset
Get all the geographical locations whose geography_type is town
Get the geographical location that has the max percentage of 'less_than_high_school_graduate'](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F2ae76079-938b-4b5d-8ceb-1dfbc10f88ce%2Fb7f575c2-760b-4dc1-b7f9-72ae9b730117%2F9i1of9n_processed.png&w=3840&q=75)
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