Coffee preferences

Introduction

We are investigating the relationship between coffee preferences and age, specifically preferred coffee style, amount consumed, coffee expertise and overall favourite coffee type from the tasing. The dataset we are using comes from a Great American Coffee Taste Test, during which thousands of people simultaneously blind-tasted the same four coffees.

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

Investigating the Relationship Between Age and Coffee Bitterness

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

df = pd.read_csv("../../data_sources/coffee_survey.csv")

sns.catplot(data = df, x = "coffee_a_bitterness", y = "age", kind = "bar")

df = df[df["coffee_a_bitterness"] != "Missing"]

Pie chart, distribution of coffee styles

style_counts = df["style"].value_counts()

plt.pie(style_counts, labels=style_counts.index, autopct="%1.1f%%", startangle=140)

plt.axis("equal")

plt.title("Distribution of Coffee Styles")

plt.show()

Investigating the Relationship Between Age and Amount of Coffee Consumed

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly.express as px


custom_order = ['<18 years old', '18-24 years old', '25-34 years old',
              '35-44 years old', '45-54 years old', '55-64 years old',
              '>65 years old']
df["age"] = pd.Categorical(df["age"], categories=custom_order, ordered=True)

custom_order = ['Less than 1', '1', '2', '3', '4', 'More than 4']
df["cups"] = pd.Categorical(df["cups"], categories=custom_order, ordered=True)

fig = sns.displot(data = df,
            x = "age",
            hue_order = ['<18 years old', '18-24 years old', '25-34 years old',
                         '35-44 years old', '45-54 years old', '55-64 years old',
                         '>65 years old'],
            binwidth = 1,
            hue = "age",
            col = "cups")
            



axs = fig.axes[0]

for ax in axs:
    ax.tick_params(axis="x", rotation=30)


figu = px.bar(df, x="age", y="cups", color="cups", barmode="group", 
                          category_orders={"age":['<18 years old', '18-24 years old', '25-34 years old',
                          '35-44 years old', '45-54 years old', '55-64 years old',
                          '>65 years old'],"cups":['Less than 1', '1', '2', '3', '4', 'More than 4']},
             pattern_shape="cups", pattern_shape_sequence=[".", "x", "+", "-", "/","|"])
figu.show()

Investigating the Relationship Between Expertise and Coffee Preference

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
custom_order = ['Coffee A', 'Coffee B', 'Coffee C', 'Coffee D']
df["prefer_overall"] = pd.Categorical(df["prefer_overall"], categories = custom_order, ordered = True)
sns.catplot(data = df, x = "prefer_overall", y = "expertise", kind = "bar")
plt.xlabel("Coffee Preference")
plt.ylabel("Expertise")
plt.title("Coffee Preference and Expertise")
plt.show()