This was an introduction to the Kotlin programming language, that was both tightly controlled by the host and encouraging of questions. There were several people that had either pointed questions or some experience with Kotlin (and the language(s) that the questioners were experienced with), which led to a fairly egalitarian discussion... at least compared to some of the other sessions.
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# Imports
import sys
import pandas as pd
import csv
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (20.0, 10.0)
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# %load util.py
#!/usr/bin/python
# Util file to import in all of the notebooks to allow for easy code re-use
# Calculate Percent of Attendees that did not speak
def percent_silent(df):
total = len(df)
silent = 0
for row in df.iteritems():
if row[1] == 0:
silent = silent + 1
percent = {}
percent['TOTAL'] = total
percent['SILENT'] = silent
percent['VERBOSE'] = total - silent
return percent
# Calculate Percent of Attendees that left
def percent_left(df):
total = len(df)
left = 0
for row in df.iteritems():
if row[1] == 0:
left = left + 1
percent = {}
percent['TOTAL'] = total
percent['LEFT'] = left
percent['STAYED'] = total - left
return percent
# Calculate Percent of Attendees along gender
def percent_gender(df):
total = len(df)
female = 0
for row in df.iteritems():
if row[1] == 1:
female = female + 1
percent = {}
percent['TOTAL'] = total
percent['FEMALE'] = female
percent['MALE'] = total - female
return percent
# Calculate Percent of Talking points by
def percent_talking_gender(df):
total = 0
male = 0
female = 0
for talks, gender in df.itertuples(index=False):
if talks > 0:
total = total + 1
if gender == 0:
male = male + 1
elif gender == 1:
female = female + 1
percent = {}
percent['TOTAL'] = total
percent['FEMALE'] = female
percent['MALE'] = male
return percent
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# Read
data = pd.read_csv('data/5_kotlin.csv')
# Display
data
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# Convert GENDER to Binary (sorry, i know...)
data.loc[data["GENDER"] == "M", "GENDER"] = 0
data.loc[data["GENDER"] == "F", "GENDER"] = 1
# Convert STAYED to 1 and Left/Late to 0
data.loc[data["STAYED"] == "Y", "STAYED"] = 1
data.loc[data["STAYED"] == "N", "STAYED"] = 0
data.loc[data["STAYED"] == "L", "STAYED"] = 0
# We should now see the data in numeric values
data
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# Run Describe to give us some basic Min/Max/Mean/Std values
data.describe()
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# Run Value_Counts in order to see some basic grouping by attribute
vc_talks = data['TALKS'].value_counts()
vc_talks
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vc_gender = data['GENDER'].value_counts()
vc_gender
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vc_stayed = data['STAYED'].value_counts()
vc_stayed
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# Now let's do some basic plotting with MatPlotLib
data.plot()
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data.plot(kind='bar')
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fig1, ax1 = plt.subplots()
ax1.pie(data['TALKS'], autopct='%1.f%%', shadow=True, startangle=90)
ax1.axis('equal')
plt.show()
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data_hostless = data.drop(data.index[[0]])
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data_hostless.head()
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data_hostless.describe()
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dh_vc_talks = data_hostless['TALKS'].value_counts()
dh_vc_talks
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dh_vc_gender = data_hostless['GENDER'].value_counts()
dh_vc_gender
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dh_vc_stayed = data_hostless['STAYED'].value_counts()
dh_vc_stayed
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data_hostless.plot()
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data_hostless.plot(kind='bar')
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fig1, ax1 = plt.subplots()
ax1.pie(data_hostless['TALKS'], autopct='%1.f%%', shadow=True, startangle=90)
ax1.axis('equal')
plt.show()
this is getting there...
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# Percentage of attendees that were silent during the talk
silent = percent_silent(data['TALKS'])
silent
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fig1, ax1 = plt.subplots()
sizes = [silent['SILENT'], silent['VERBOSE']]
labels = 'Silent', 'Talked'
explode = (0.05, 0)
ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.0f%%', shadow=True, startangle=90)
ax1.axis('equal')
plt.show()
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# Percentage of attendees that left early during the talk
left = percent_left(data['STAYED'])
left
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fig1, ax1 = plt.subplots()
sizes = [left['LEFT'], left['STAYED']]
labels = 'Left', 'Stayed'
explode = (0.1, 0)
ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.0f%%', shadow=True, startangle=90)
ax1.axis('equal')
plt.show()
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# Percentage of attendees that were Male vs. Female (see notes above around methodology)
gender = percent_gender(data['GENDER'])
gender
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fig1, ax1 = plt.subplots()
sizes = [gender['FEMALE'], gender['MALE']]
labels = 'Female', 'Male'
explode = (0.1, 0)
ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.0f%%', shadow=True, startangle=90)
ax1.axis('equal')
plt.show()
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# Calculate Percent of Talking points by GENDER
distribution = percent_talking_gender(data[['TALKS','GENDER']])
distribution
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fig1, ax1 = plt.subplots()
sizes = [distribution['FEMALE'], distribution['MALE']]
labels = 'Female Speakers', 'Male Speakers'
explode = (0.1, 0)
ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.0f%%', shadow=True, startangle=90)
ax1.axis('equal')
plt.show()
these numbers are damning...
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