Production and Sale of Greenhouse Flowers and Plants - Open Canada

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Production and Sales of Greenhouse flowers and plants

The annual production and sales of different types of flowers and plants from 2007 to 2019 in Canada is compiled with its metadata file here. The metadata contains detailed variable descriptions. The dataset and supporting documentation can are available from Statistics Canada.

Variables include years from 2007 to 2019, province of production, flower and plant types, and production count and sales.

Libraries

Python

import requests, zipfile, io
import tempfile as tf
import pandas as pd
import seaborn as sb
import matplotlib.pyplot as plt

R

library(tidyverse)
library(GGally)

Organizing Dataset

The following code is used to obtain and organize the dataset and separate it into two by output type (sales and production).

Python

# Download the zip file of plant sales and production

temp = tf.TemporaryFile()
url = "https://www150.statcan.gc.ca/n1/tbl/csv/32100246-eng.zip"
r = requests.get(url)
temp = zipfile.ZipFile(io.BytesIO(r.content))
file_list = temp.namelist()
print(file_list)

['32100246.csv', '32100246_MetaData.csv']

plants = pd.read_csv(temp.open(file_list[0]))

# Rename a couple of columns
plants = plants.rename(columns = {"GEO" : "location", "REF_DATE" : "year"})

R

# Download the zip file of plant sales and production
temp <- tempfile()
download.file("https://www150.statcan.gc.ca/n1/tbl/csv/32100246-eng.zip",temp)
(file_list <- as.character(unzip(temp, list = TRUE)$Name))

[1] "32100246.csv"          "32100246_MetaData.csv"

plants <- read_csv(unz(temp, file_list[1])) 
unlink(temp) # Delete temp file

# Rename a couple of columns
plants <- plants |> 
  rename(year = REF_DATE, location = GEO)  |> # personal preference for these names
  rename_all(make.names)# R friendly naming that replaces spaces with '.'

Greenhouse plants production and sales in Canada

The following code is used to plot greenhouse plant production and sales in Canada

Python

# Subset a dataset of plants production in Canada 

sb.set_style("darkgrid")

sb.lineplot(data = plants[(plants["location"] == "Canada") & 
                          (plants["Output"] == "Production (number)")],
                          x = "year",
                          y = "VALUE",
                          hue = "Flowers and plants")
plt.ylabel("Production (number)")
plt.title("Greenhouse plants production in Canada")                         
plt.legend(title = "Flowers and Plants",
           bbox_to_anchor=(1.05, 1),
           loc='upper left',
           borderaxespad=0)
plt.show();

plt.clf()
sb.lineplot(data = plants[(plants["location"] == "Canada") & 
                          (plants["Output"] == "Sales")],
                          x = "year",
                          y = "VALUE",
                          hue = "Flowers and plants")
plt.ylabel("Production (number)")
plt.title("Greenhouse plants sales in Canada")                         
plt.legend(title = "Flowers and Plants",
           bbox_to_anchor=(1.05, 1),
           loc='upper left',
           borderaxespad=0)
plt.show();

R

# Subset a dataset of plants production in Canada 
plants |> subset(location == "Canada" & Output =="Production (number)") |>
ggplot( aes(x = year, y = VALUE, group = Flowers.and.plants)) +
  labs(y = "Production (number)", title = "Greenhouse plants production in Canada") +
  geom_line(aes(color = Flowers.and.plants))

plants |> subset(location == "Canada" & Output =="Sales") |>
ggplot( aes(x = year, y = VALUE, group = Flowers.and.plants)) +
  labs(y = "Sales ($ dollars)", title = "Greenhouse plants sales in Canada") +
  geom_line(aes(color = Flowers.and.plants))

• So, the following code is used to discover the plant production in different provinces.

Bedding plants (vegetables & ornamental) production

Python

# Select the datasets of two different bedding plants (vegetable & ornamental) in 10 different provinces

plt.clf()
sb.lineplot(data = plants[(plants["location"] != "Canada") & 
                          (plants["Output"] == "Production (number)") &
                          (plants["Flowers and plants"] == 
                          "Total vegetable bedding plants [115143421]")],
                          x = "year",
                          y = "VALUE",
                          hue = "location")
plt.ylabel("Number Produced")
plt.title("Total vegetable bedding plants production")                         
plt.legend(title = "Province",
           bbox_to_anchor=(1.05, 1),
           loc='upper left',
           borderaxespad=0)
plt.show();

plt.clf()
sb.lineplot(data = plants[(plants["location"] != "Canada") & 
                          (plants["Output"] == "Production (number)") &
                          (plants["Flowers and plants"] == "Total ornamental bedding plants [115143411]")],
                          x = "year",
                          y = "VALUE",
                          hue = "location")
plt.ylabel("Number Produced")
plt.title("Total ornamental bedding plants production")                         
plt.legend(title = "Province",
           bbox_to_anchor=(1.05, 1),
           loc='upper left',
           borderaxespad=0)
plt.show();

R

# Select the datasets of two different bedding plants (vegetable & ornamental) in 10 different provinces

plants |> subset(location != "Canada" & Output =="Production (number)" & Flowers.and.plants =="Total vegetable bedding plants [115143421]") |>
ggplot( aes(x = year, y = VALUE, group = location)) +
  geom_line(aes(color = location)) +
  labs(y = "Number produced")+
  ggtitle("Total vegetable bedding plants production")

plants |> subset(location != "Canada" & Output =="Production (number)" & Flowers.and.plants =="Total ornamental bedding plants [115143411]") |>
ggplot( aes(x = year, y = VALUE, group = location)) +
  geom_line(aes(color = location)) +
  labs(y = "Number produced")+
  ggtitle("Total ornamental bedding plants production")

Examining all production counts within Canada

Python

plants_2 = plants[(plants["location"] == "Canada") & 
                (plants["Output"] == "Sales")][["year",
                                               "Flowers and plants",
                                                "VALUE"]]

plants_2 = pd.pivot_table(plants_2,
                          values = "VALUE",
                          columns = "Flowers and plants",
                          index = "year").reset_index()
sb.pairplot(data = plants_2);
                    

R

plants |> subset(location == "Canada" & Output =="Production (number)") |> 
  select(year, Flowers.and.plants, VALUE)|>
  pivot_wider(names_from= Flowers.and.plants,values_from=VALUE) |>
  ggpairs()