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Competition - Bee friendly plants
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    Which plants are better for bees: native or non-native?

    📖 Background

    You work for the local government environment agency and have taken on a project about creating pollinator bee-friendly spaces. You can use both native and non-native plants to create these spaces and therefore need to ensure that you use the correct plants to optimize the environment for these bees.

    The team has collected data on native and non-native plants and their effects on pollinator bees. Your task will be to analyze this data and provide recommendations on which plants create an optimized environment for pollinator bees.

    💾 The Data

    You have assembled information on the plants and bees research in a file called plants_and_bees.csv. Each row represents a sample that was taken from a patch of land where the plant species were being studied.

    ColumnDescription
    sample_idThe ID number of the sample taken.
    bees_numThe total number of bee individuals in the sample.
    dateDate the sample was taken.
    seasonSeason during sample collection ("early.season" or "late.season").
    siteName of collection site.
    native_or_nonWhether the sample was from a native or non-native plot.
    samplingThe sampling method.
    plant_speciesThe name of the plant species the sample was taken from. None indicates the sample was taken from the air.
    timeThe time the sample was taken.
    bee_speciesThe bee species in the sample.
    sexThe gender of the bee species.
    specialized_onThe plant genus the bee species preferred.
    parasiticWhether or not the bee is parasitic (0:no, 1:yes).
    nestingThe bees nesting method.
    statusThe status of the bee species.
    nonnative_beeWhether the bee species is native or not (0:no, 1:yes).

    Source (data has been modified)

    💪 Challenge

    Provide your agency with a report that covers the following:

    • Which plants are preferred by native vs non-native bee species?
    • A visualization of the distribution of bee and plant species across one of the samples.
    • Select the top three plant species you would recommend to the agency to support native bees.

    These are in a slightly altered order!

    Predefining custom functions.

    def top_plants(df, n: int = 3):
        '''A function that selects and orders best plants for non-parasitic bees, including information about whether the plant is native or not.
        
        Arguments:
            df: the DataFrame with information about bees
            
        Returns:
            A dataframe containing the names and total bee counts for the top n species of plants and their nativity.
        '''
        
        # select the nonparasitic bee species and samples where the plant species is not none
        non_parasitic = df[(df["parasitic"] == 0) & (df['plant_species']!= 'None' )] 
        # group the samples by plant species and sum up bee counts for every species
        # sort the values in descending order              
        best_plants = non_parasitic.groupby("plant_species").agg({'count_per_species':'sum'}).sort_values('count_per_species',ascending=False)
        # include info on whether the species is native or not
        best_plants_and_kinds = best_plants.merge(df[['plant_species', 'native_or_non']].drop_duplicates(), on='plant_species', how='left')
        # rename the columns
        best_plants_and_kinds.columns = ['plant_species', 'total_bees', 'native_or_non']
    
        return best_plants_and_kinds
    def visualize_plants(df, title: str):
        '''A function that makes it easier to generate seaborn plots for the given data about plants and bees.
        
        Arguments:
            df: the DataFrame with the information about top plants
            title (str): a string to be used as the title of the plot
        '''
        # plot the plot
        sns.barplot(y='plant_species', x='total_bees', data=df).set(xlabel='Number of bees', ylabel='Plant species', title=title)
        # show the plot
        plt.show()

    Importing the data and libraries

    import pandas as pd
    import matplotlib.pyplot as plt
    import seaborn as sns
    data = pd.read_csv("data/plants_and_bees.csv")
    data

    One issue with the data is that every bee observation is its own entry, instead of there being a column with a count for a given species. First, that column has to be added, as bees_num cannot serve that purpose, being the total count of bees of different species in a sample.

    # obtain counts per sample, plant species, and bee species
    counts = data.groupby(['sample_id', 'plant_species', 'bee_species']).agg({'bee_species': 'count'})
    # rename the column to avoid errors and conflicts
    counts.columns = ['count_per_species']
    # merge the counts into the data, drop doubles as initially the number of identical entries was an indication of how many
    # individuals there were, and now that information is stored in a separate column
    data = data.merge(counts, on=['sample_id', 'plant_species', 'bee_species']).drop_duplicates()
    # display the data
    data

    A visualization of the distribution of bee and plant species across one of the samples.

    # we need to select a sample that features some plant species
    vis_candidates = data[data['plant_species'] != 'None']
    # selecting the most diverse sample in terms of bee species
    most_diverse_sample = vis_candidates.groupby('sample_id').agg({'bee_species':'count'}).sort_values('bee_species', ascending=False)
    
    print('Top 1 most diverse sample:')
    print(most_diverse_sample.head(1))
    # subsetting for just the best sample, subsetting relevant columns
    best_sample = vis_candidates.set_index("sample_id").loc[17473][['count_per_species', 'plant_species', 'bee_species']]
    best_sample
    # visualize the bee species per type of plant with an indication of their number
    
    best_sample.plot(
        kind='scatter', 
        y='bee_species', 
        x='plant_species',  
        xlabel='Bee species', 
        ylabel='Plant species', 
        legend=False, 
        title='Bee and plant species in sample 17473', 
        rot=45, 
        c='g', 
        s=best_sample['count_per_species']*25
    )
    
    plt.show()