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from sklearn.feature_extraction.text import CountVectorizer

# normalize to lower case
df_lower  = covid1_df['covid_prepare'].str.lower().to_frame()
df_lower['covid_prepare'].values
#Convert all including nan to strings
doc = df_lower['covid_prepare'].map(str).values
#delete nans
doc = np.delete(doc, np.where(doc == 'nan'))

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def createPropertyTypeCol(rental_df):
    """This function adds a column called property_type_class to the dataframe 
    Args:
        rental_df ([type]): [The dataframe has a column called property_type]

    Returns:
        [type]: [Dataframe with a more concise list of property types]
    """

    # Property types Private room and Shared Room identified

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def createRentalMap(df, fixed_radius, nbd_or_grp):

    # Initialize a Folium map. Center it to the mean of latitude and longitude of the entire dataset
    this_map = folium.Map(
        location=[df["latitude"].mean(), df["longitude"].mean()],
        tiles="CartoDB positron",
        zoom_start=13,
    )

    def plotDot(point_row, fixed_radius, nbd_or_grp):

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import pandas as pd
import datetime

# The historical data setup
assets = ['Automobiles', 'Building'] * 2
countries = ['Andorra', 'Andorra', 'Brunei', 'Brunei']
rates =  [12.5, 4.5, 14.0, 6.5]
effective_from =  ['2019-01-01'] * 4
effective_until = [None] * 4
depreciation_rate_df = pd.DataFrame(list(zip(assets, countries, rates, effective_from, effective_until)),

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--Oracle Merge
merge into depreciation_rate d
using
(select asset,
        country,
        effective_from,
        lead(effective_from) over (partition by asset, country order by effective_from)  - 1/86400 as effective_until
 from depreciation_rate) n
on (d.asset = n.asset
    and d.country = n.country

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#Create a GroupBy object
df_groups = df.groupby(['Destination City Name', 'Origin State', 'Origin City', 'Unique Carrier'])


new_df = df_groups.agg(Total_Flights = ('Flight Date','count'))
new_df['Count Delayed Flights'] = df_groups.apply(lambda x :  x[x['Arrival Delay'] > 0]['Arrival Delay'].count() )

new_df['Delayed Per Origin'] = new_df.groupby(['Destination City Name', 'Origin State', 'Origin City'])['Count Delayed Flights'].transform('sum')

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with cf as (select
		unique_carrier,
	 	origin_city, 
		origin_state, 
		destination_city_name,
		destination_state,
		sum (case when arrival_delay > 0 then 1 else 0 end) as count_delayed_flights,
		count(arrival_delay) as total_flight_count
	    from "ArrivalInfo" 
	    group by 

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# 'dataset' holds the input data for this script
import pandas as pd

df_groups = dataset.groupby(['Destination City Name', 'Origin State', 'Origin City', 'Unique Carrier'])

final_df = df_groups.agg(Total_Flights = ('Flight Date','count'))
final_df['Count of Delayed Flights'] = df_groups.apply(lambda x :  x[x['Arrival Delay'] > 0]['Arrival Delay'].count() )
final_df = final_df.reset_index()

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#'MetAreaPopAndTeams_df' and 'NHLData_df' hold the input data for this script
import pandas as pd
MetAreaPopAndTeams_df['Team Name Map'] =  MetAreaPopAndTeams_df['NHL Teams'].apply(lambda teamname: NHLData_df[NHLData_df['Team'].str.contains(teamname)]['Team'].any(0))

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--Option 1
select  metropolitanarea,
        nhl_team,
        team,
        win,
        loss 
from cities_expanded c,
     nhl_data n
where n.team like '%' || nhl_team || '%';