please code this for python In [ ]:In [ ]:In [ ]:In [ ]:In [ ]:In [ ]:In [ ]:In [ ]:Assign a name to this data (whatever name you want)Take a look at the first 15 rowsreturns statistics about the numerical columns in a datasetGet all the geographical locations whose geography_type is townGet the geographical location that has the max percentage of 'less_than_high_school_graduate'Get the average percentage of bachelor_s_degree_or_higherMake 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) andplt.tight_layout() if you think location names are squeezing together In [1]: #Numbers are percentage. We'll use Pandas and Matplotlib/Seaborn to analyze the data.In [ ]:In [ ]:In [ ]:In [ ]:This dataset contains data about the highest grade completed by residents of San Mateo County, California by city. Grade levels include less than high schoolgraduate, high school graduate, some college or associate's degree, and bachelor's degree or higher. This data was extracted from the United States CenusBureau.In [ ]:Import all related packagesGet the data. Use this datapd.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 rowsreturns statistics about the numerical columns in a datasetGet all the geographical locations whose geography_type is townGet the geographical location that has the max percentage of 'less_than_high_school_graduate'

As, per guidelines we are supposed to answer first three sub-parts but for we have provided…

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