<jupyter_start><jupyter_text>Critical Thinking: From the above correlation table. What does the unemployment rate tell you about its correlation with the number of males or females?  ANSWER:<jupyter_code># Use the describe function to get a quick glance at calc_census_pd.<jupyter_output><empty_output><jupyter_text>Do you see anything strange about male % or female % in the describe above?  ANSWER:<jupyter_code># Create two variables called "male_zipcode_outliers" and "female_zipcode_outliers"
    # Set them to queries where male or female is are the outliers based on the described data from previous task.
    # Example: anything greater than 0.95 is an outlier


# Show all rows for either "male_zipcode_outliers" and "female_zipcode_outliers"<jupyter_output><empty_output><jupyter_text>What is a possible cause of some outliers with larger populations? Hint: Look up the zipcode for larger population of either male or female outliers. What information do these zipcodes have in common? ANSWER:   Heatmap of population<jupyter_code># Create a variable "zip_lng_lat_data" and using pandas import the zip_codes_states.csv from Resources folder.
    # https://www.gaslampmedia.com/download-zip-code-latitude-longitude-city-state-county-csv/
    
    # HINT: When loading zipcodes they may turn into integers and lose their 0's. 
    # To correct this check out dtype in the documentation:
        # https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html


# Show the first 5 rows of zip_lng_lat_data

# Get the longitude and latitude based calc_census_pd by merging them on their zip code columns.


# Show the first 5 rows of merged_table

# Configure gmaps with API key

# Define locations as a dataframe of latitude and longitude from merged_table.
    # HINT: You'll need to drop the NaN before storing into locations or population


# Define population as the population from merged_table
    # HINT: You'll need to drop the NaN before storing into locations or population


# Create a population Heatmap layer
    # Note you may need to run the following in your terminal to show the gmaps figure.
        # jupyter nbextension enable --py --sys-prefix widgetsnbextension
        # jupyter nbextension enable --py --sys-prefix gmaps


# Recommended settings for heatmap layer: max_intensity=2000000 and point radius = 1



# Adjust heat_layer setting to help with heatmap dissipating on zoom<jupyter_output><empty_output>