Pandas Function Operations

Data is from the United States Census Bureau. Counties are political and geographic subdivisions of states in the United States. This dataset contains population data for counties

Solution 1:

Here is the answer that worked for me:

defanswer_five():
    return census_df.groupby(["STNAME"],sort=False).sum()["COUNTY"].idxmax()

First part created aggregated df

census_df.groupby(["STNAME"],sort=False).sum()

Second part takes the col you need

["COUNTY"].idxmax()

and returns value corresponding to index with max, check here

Solution 2:

Just a correction to your entire code.

First, according to the source, SUMLEV of 50 means the row is a county. Two ways to answer this.

Thought process (think of it like in Excel): You want to count the number of "county rows" in each state group. First, you create the mask/condition to select all SUMLEV == 50 ("county rows"). Then group them by STNAME. Then use .size() to count the number of rows in each grouping.

# this is it!defanswer_five():
    mask = (census_df.SUMLEV == 50)
    max_index = census_df[mask].groupby('STNAME').size().idxmax()
    return max_index

# not so elegantdefanswer_five():
    census_df['Counts'] = 1
    mask = (census_df.SUMLEV == 50)
    max_index = census_df[mask].groupby('STNAME')['Counts'].sum().idxmax()
    return max_index

You are welcome. https://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.GroupBy.size.html

Solution 3:

Actually you can just count the number in states level instead of looking into County details.

And this should work:

census_df[census_df['SUMLEV']==50].groupby(['STNAME']).size().idxmax()

Solution 4:

def answer_five():
    new_df = census_df[census_df['SUMLEV'] == 50]
    x = new_df.groupby('STNAME')
    return x.count()['COUNTY'].idxmax()


answer_five()

Solution 5:

It's the change from .max() to idxmax() that returns the correct value for the STNAME rather than a large integer.