Introduction / Background
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. |
species_num | The number of different bee species 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 plant. |
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)
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Read the CSV file into dataframe 'df'
df = pd.read_csv("data/plants_and_bees.csv")
dfData Cleaning
Meaning of variables and their type
df.info()Deteriming the count of Null and Not Null values
def plant_bees_info():
temp = pd.DataFrame(index=df.columns)
temp["Datatype"] = df.dtypes
temp["Not null values"] = df.count()
temp["Null values"] = df.isnull().sum()
temp["Percentage of Null values"] = (df.isnull().mean())*100
temp["Unique count"] = df.nunique()
return temp
plant_bees_info()Dropping the columns 'specialized_on' and 'status'
df = df.drop(['specialized_on','status'], axis = 1)
print(df.isnull().sum())Dropping all rows having null value in 'nonnative_bee' and 'parasitic' column
df_cleaned = df.dropna(subset=['nonnative_bee'])
df_cleaned = df.dropna(subset=['parasitic'])
df = df_cleaned
print(df.isnull().sum())Replacing the null values in 'nesting' by global constant 'No nesting'
‌
‌