Competition - Optimizing Pollinator Bee-Friendly Spaces: Unraveling Plant Preferences for Native and Non-Native Species

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)

⌛️ Time is ticking. Good luck!

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

df.shape

print ('---------------------------------------------------------------------------------------------------------')
df.info()
print ('---------------------------------------------------------------------------------------------------------')

print ('---------------------------------------------------------------------------------------------------------')
print (df.nunique())
print ('---------------------------------------------------------------------------------------------------------')

df.isnull().sum()

Numeric and categorical validation

Numerical validation

Let's determine which data are numerical and which are categorical. The date, by definition, can be of a different data type such as categorical, ordinal, or both.

numeric = ['sample_id', 'bees_num', 'time']
for column in numeric:
  print(df[column].value_counts())

df [numeric].describe()

Categorical validation

Here are some data that have been assumed to be categorical, but could have been interpreted as boolean, which would help us in building machine learning models if required for the task.

That being said, the categorical data are: season, site, native_or_non (could be boolean), sampling, plant_species, bee_species, sex, specialized_on, nesting, status, parasitic, and nonnative_bee.

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