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# -*- coding: UTF-8 -*-
# Render our plots inline
%matplotlib inline
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import seaborn
import shutil
pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier, overridden by seaborn
pd.set_option('display.max_columns', None) # Display all the columns
plt.rcParams['font.family'] = 'sans-serif' # Sans Serif fonts for all the graphs
# Reference for color palettes: http://web.stanford.edu/~mwaskom/software/seaborn/tutorial/color_palettes.html
# Change the font
matplotlib.rcParams.update({'font.family': 'Source Sans Pro'})
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# Load csv file first
data = pd.read_csv("data/lab-survey.csv", encoding="utf-8")
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# Check data
#data[0:4] # Equals to data.head()
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%%capture output
# Save the output as a variable that can be saved to a file
# Get the distribution of data
space = data["D33"].value_counts(dropna=False)
print "Data:"
print space
print ""
print "Data %:"
print data["D33"].value_counts(normalize=True, dropna=False) * 100
print ""
print "Data: statistics:"
print data["D33"].describe()
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# Save+show the output to a text file
%save Q033-NumeroVolontari.py str(output)
shutil.move("Q033-NumeroVolontari.py", "text/Q033-NumeroVolontari.txt")
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# Swap nan for a more understandable word
old_dict = space.to_dict()
new_dict = {}
for i in old_dict:
if isinstance(i, numpy.float64) and np.isnan(i):
new_dict["Nessuna risposta"] = old_dict[i]
elif type(i) is float and np.isnan(i):
new_dict["Nessuna risposta"] = old_dict[i]
else:
new_dict[i] = old_dict[i]
spaceu = pd.Series(new_dict)
space = spaceu.order()
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# Plot the data 01
plt.figure(figsize=(8,6))
plt.xlabel(u'Numero volontari', fontsize=16)
plt.ylabel('Lab', fontsize=16)
plt.title(u"Quanti sono i volontari che lavorano alla gestione del laboratorio?", fontsize=18, y=1.02)
my_colors = seaborn.color_palette("husl", len(space)) # Set color palette
space.plot(kind="bar",color=my_colors)
plt.savefig(u"svg/Q033-NumeroVolontari01.svg")
plt.savefig(u"png/Q033-NumeroVolontari01.png")
plt.savefig(u"pdf/Q033-NumeroVolontari01.pdf")
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# Plot the data 02
# Reorder value_counts by index (age) natural order
space1 = space.sort_index()
plt.figure(figsize=(8,6))
plt.title(u'Quanti sono i volontari che lavorano alla gestione del laboratorio?', fontsize=18, y=1.02)
plt.xlabel(u'Numero volontari', fontsize=16)
plt.ylabel('Lab', fontsize=16)
# Plot the data
my_colors = seaborn.color_palette("husl", len(space1)) # Set color palette
space1.plot(kind='bar',color=my_colors)
plt.savefig(u"svg/Q033-NumeroVolontari02.svg")
plt.savefig(u"png/Q033-NumeroVolontari02.png")
plt.savefig(u"pdf/Q033-NumeroVolontari02.pdf")
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# Check histogram
plt.figure(figsize=(8,6))
plt.title(u'Quanti sono i volontari che lavorano alla gestione del laboratorio?', fontsize=18, y=1.02)
plt.xlabel(u'Numero volontari', fontsize=16)
plt.ylabel('Lab', fontsize=16)
space.hist(bins=60)
plt.savefig(u"svg/Q033-NumeroVolontari02.svg")
plt.savefig(u"png/Q033-NumeroVolontari02.png")
plt.savefig(u"pdf/Q033-NumeroVolontari02.pdf")
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