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# Author : Apolinario (Sam) Ortega - founder of invbat.com-A.I + chatbot <admin@invbat.com)
# Date created: 6/13/2020
# license : BSD 3 clause
# comment : Lesson 18 is my attempt to understand how pandas implement the .pivot_table method
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
# comment # Do shift + enter
# comment : array must all be of the same length. Otherwise you will get error message
tips = pd.DataFrame({"Server_Name": ["Abby", "Abby", "Abby", "Nina", "Jarah",
"Nina", "Jay", "Adiel", "Sam","Abby", "Jana", "Excel", "Nina", "Jarah",
"Nina", "Jay", "Adiel", "Sam", "Abby", "Abby", "Abby", "Nina", "Jarah",
"Nina", "Jay", "Adiel", "Sam","Abby", "Jana", "Excel", "Nina", "Jarah",
"Nina", "Jay", "Adiel", "Sam"],
"Table_Size": ["one", "one", "one", "two", "two","one", "one", "two", "two", "one", "one",
"one", "two", "two", "one", "one", "two", "two", "one", "one", "one", "two", "two",
"one", "one", "two", "two", "one", "one", "one", "two", "two",
"one", "one", "two", "two"],
"Time": ["dinner", "lunch", "lunch", "dinner", "lunch", "dinner", "lunch", "dinner",
"dinner" , "dinner", "lunch", "lunch", "dinner", "lunch", "dinner", "lunch", "dinner",
"lunch", "dinner", "lunch", "lunch", "dinner", "lunch", "dinner", "lunch", "dinner",
"dinner" , "dinner", "lunch", "lunch", "dinner", "lunch", "dinner", "lunch", "dinner",
"lunch"],
"Day": ["Tuesday", "Tuesday", "Friday", "Saturday", "Saturday", "Saturday", "Sunday", "Sunday",
"Thurday" , "Tuesday", "Wednesday", "Friday", "Monday", "Monday", "Tuesday", "Thursday", "Friday",
"Saturday", "Sunday", "Sunday", "Thursday", "Tuesday", "Saturday", "Saturday", "Friday", "Friday",
"Monday" , "Monday", "Sunday", "Sunday", "Wednesday", "Monday", "Monday", "Saturday", "Friday",
"Sunday"],
"Sex": ["female", "male", "male", "female", "female", "female", "male", "male",
"male" , "female", "male", "male", "female", "female", "female", "male", "male","male",
"female", "male", "male", "female", "female", "female", "male", "male",
"male" , "female", "male", "male", "female", "female", "female", "male", "male","male"],
"Tips": [5, 5.5, 6, 5.25, 4.75, 4.25, 5.85, 6.45, 7.50 , 5, 5.5,
6, 5.25, 4.75, 4.25, 5.85, 6.45, 7.50, 5, 5.5, 6, 5.25, 4.75, 4.25, 5.85, 6.45, 7.50 , 5, 5.5,
6, 5.25, 4.75, 4.25, 5.85, 6.45, 7.50],
"Total_Bills": [50.24, 42.75, 58.25, 59.25, 68.20, 61.50, 87.35, 95.45, 96.30,
50.24, 42.75, 58.25, 59.25, 68.20, 61.50, 87.35, 95.45, 96.30, 50.24, 42.75, 58.25, 59.25, 68.20, 61.50, 87.35, 95.45, 96.30,
50.24, 42.75, 58.25, 59.25, 68.20, 61.50, 87.35, 95.45, 96.30]})
print('')
print('Shown below is not a real data set for restaurant X')
tips
# comment # Do shift + enter
# comment # Assuming you have this Excel spreadsheet. Now I want you to use .pivot_table to create specific analyis
# for example I want to know total tips received by each server name by table size and time of meal. Next Iteration
# comment : Show me how to use .pivot_table method to create a targeted analysis
print('')
print('Tip Summary Analysis for each Server Name by table size and time')
table = pd.pivot_table(tips, values='Tips', index=['Server_Name', 'Time'],
columns=['Table_Size'], aggfunc=np.sum)
table
# comment # Do shift + enter
# Now I want to analysis the total bills generated by each server name by table size and meal time. Next Iteration
# comment : Show me how to use .pivot_table method to create a targeted analysis
print('')
print('Total Bills Summary Analysis for each Server Name by table size and time')
table = pd.pivot_table(tips, values='Total_Bills', index=['Server_Name', 'Time'],
columns=['Table_Size'], aggfunc=np.sum)
table
# comment # Do shift + enter
# Now I want to know the tips received by server name by table size and sex. Next Iteration
# comment : Show me how to use .pivot_table method to create a targeted analysis
print('')
print('Total Bills Summary Analysis for each Server Name by table size and sex')
table = pd.pivot_table(tips, values='Tips', index=['Server_Name', 'Sex'],
columns=['Table_Size'], aggfunc=np.sum)
table
# comment # Do shift + enter
# comment: Show the plot of tips versus total bills
tips.plot(figsize=(12,4))
plt.legend(loc='upper right')
plt.title('Total Bills vs Tip ', size=14)
plt.xlabel('Tips', size =12)
plt.ylabel('Total Bills', size=12)
# comment # Do shift + enter
# comment:
sns.relplot(x="Tips", y="Total_Bills", hue="Time", size="Sex",
sizes=(40, 400), alpha=.5, palette="muted",
height=6, data=tips)
print('')
print('Total Bills vs Tips analyze by sex and time of meal')
print('Color hue orange is for lunch and blue for dinner')
print('Big diameter is for female and smaller for male')
# comment # Do shift + enter
# comment
sns.relplot(x="Total_Bills", y="Tips", col="Time",
hue="Sex", style="Table_Size", size="Server_Name",
palette="YlGnBu", markers=["D", "o"], sizes=(10, 305),
edgecolor=".2", linewidth=.5, alpha=.75,
data=tips);
# comment # Do shift + enter
# Below the column or fieldname use for filtering the analysis is the meal time (col = "time")
# Below the diamond marker (D) represent single table and circle represent two table
# The bigger the size of the marker means bigger tips
# Who among the server name got more total and least tips? Answer is Abby and Excel
# comment : I want to see the distribution of tip at what meal type and what days
g = sns.catplot(x="Tips", y="Day", hue="Time",
height=3.5, aspect=1.5,
kind="box", legend=False, data=tips);
g.add_legend(title="Meal Type")
g.set_axis_labels("Tip ($)", "")
g.set(xlim=(0, 12), yticklabels=["Monday", "Tuesday", "Wednesday","Thursday", "Friday", "Saturday", "Sunday"])
g.despine(trim=True)
g.fig.set_size_inches(9.5, 5.5)
g.ax.set_xticks([2, 4, 6, 8, 10, 12], minor=True);
plt.setp(g.ax.get_yticklabels(), rotation=30);
# comment # Do shift + enter
# comment : I want to see the correlation of Tips to Total Bills
ax = sns.regplot(x="Tips", y="Total_Bills", data=tips,
x_estimator=np.mean, logx=True)
# comment # Do shift + enter
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