Susan Wanjiku/

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.

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)

⌛️ Time is ticking. Good luck!

# import necessary libraries
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# read the 'plants_and_bees csv' into dataframe data
data = pd.read_csv("data/plants_and_bees.csv")


# get information about the dataset

Define a Function that checks the column name,data type, count of Null, Non-Null values and Unique values

# Create an empty DataFrame 'df' with column names based on the columns of the original DataFrame 'data'
def unique_data():
    df = pd.DataFrame(index=data.columns)
    df['Data_type'] = data.dtypes
    df['Null_Values'] = data.isnull().sum()
    df['Not_Null_Values']= data.count()
    df['Unique_Values'] =data.nunique()
    return df 



# Checking for missing values and calculating the percentage of missing values for each column.
data.isnull().sum() / len(data) * 100

# Dropping columns that have more than 90% of their data missing.
data = data.drop(['specialized_on', 'status'], axis=1)

Handling Missing Values in 'parasitic' and 'nonnative_bee' column

# Replace missing values in 'parasitic' and 'nonnative_bee' columns with 0
data['parasitic'] = data['parasitic'].replace(np.nan, 0)
data['nonnative_bee'] = data['nonnative_bee'].replace(np.nan, 0)

# Check for missing values after replacing

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