Competition - Bee friendly plants

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.

Column	Description
`sample_id`	The ID number of the sample taken.
`bees_num`	The total number of bee individuals in the sample.
`date`	Date the sample was taken.
`season`	Season during sample collection ("early.season" or "late.season").
`site`	Name of collection site.
`native_or_non`	Whether the sample was from a native or non-native plot.
`sampling`	The sampling method.
`plant_species`	The name of the plant species the sample was taken from. None indicates the sample was taken from the air.
`time`	The time the sample was taken.
`bee_species`	The bee species in the sample.
`sex`	The gender of the bee species.
`specialized_on`	The plant genus the bee species preferred.
`parasitic`	Whether or not the bee is parasitic (0:no, 1:yes).
`nesting`	The bees nesting method.
`status`	The status of the bee species.
`nonnative_bee`	Whether 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.

🧑‍⚖️ Judging criteria

This is a community-based competition. The top 5 most upvoted entries will win.

The winners will receive DataCamp merchandise.

✅ Checklist before publishing

Rename your workspace to make it descriptive of your work. N.B. you should leave the notebook name as notebook.ipynb.
Remove redundant cells like the judging criteria, so the workbook is focused on your work.
Check that all the cells run without error.

⌛️ Time is ticking. Good luck!

import pandas as pd
data = pd.read_csv("data/plants_and_bees.csv")
data

# Start coding here

Data integrity First, we assess the completeness of the data - How many values are missing in each column? We already know that air samples have 'None' values in the plant species column. The first part of the EDA will be to check what otheer issues need attention before proceeding with visualizing the data.

data.info()

#Check unique values in the columns and count them
import numpy as np

for c in data.columns:
    
    print(f'Unique values in {c}: {len(data[c].unique())}')
    print(f'NA values: {np.sum(pd.Series(data[c].unique()).isna())}')
    
nan_c={c for c in data.columns if np.sum(pd.Series(data[c].unique()).isna())>=1}

Most columns appear to have no missing values. Missing values are present in 'specialized on', 'parasitc', 'nesting', 'status' and 'nonative_bee'. We should be able to impute some of those values from the information in the other samples.

#Create a function to check whether values can be imputed using information from other samples

def check_impute(c1, c2):
    
    """ Check whether missing values in specified columns can be imputed using other samples """
    
    paras={data.loc[i, c1]:data.loc[i, c2] for i in data.index if not pd.isnull(data.loc[i, c2])}

    #Check if all bee species from the bee_species column are in the dictionary
    print(f'Unique values for "{c1}": {len(data[c1].unique())}')
    print(f'Unique {c1} with non-NAN values for "{c2}"": {len(paras)}')

    #Get bee species that have nan values and check if they are in the dictionary
    nans={data.loc[i, c1] for i in data.index if pd.isnull(data.loc[i, c2]) and not data.loc[i, c1] \
         in paras}
    indictnan=[data.loc[i, c1] for i in data.index if pd.isnull(data.loc[i, c2]) and data.loc[i, c1] \
         in paras]

    print(f'{len(nans)} species with NAN values have no information on "{c2}". "{c2}" can be imputed from other samples for {len(indictnan)} values.')
    print('_'*10)

#see if missing data can be imputed with data from other samples on bee species
for c in nan_c:
    check_impute('bee_species', c)

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