Q032 - Quante sono le persone di staff retribuite e quali contratti hanno?


In [1]:
# -*- 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'})

In [2]:
# Load csv file first
data = pd.read_csv("data/lab-survey.csv", encoding="utf-8")

In [3]:
# Check data
#data[0:4] # Equals to data.head()

In [4]:
# For each subquestion, plot the data
subquestions = ["D32[SQ001]","D32[SQ002]","D32[SQ003]","D32[SQ004]","D32[SQ005]"]
subquestions_value = [u"Collaborazione con o senza Partita IVA (Freelance)", 
                      u"Contratto a tempo determinato", 
                      u"Contratto a tempo indeterminato", 
                      u"Contratto a progetto", 
                      u"Borsa di studio",]

In [5]:
space = {}
for k,i in enumerate(subquestions):
    current_series = data[i].value_counts(dropna=False)
    old_dict = current_series.to_dict()
    new_dict = {}
    zero_value = 0.0
    nan_value = 0.0
    for i in old_dict.keys():
        if np.isnan(i):
            nan_value = old_dict[i]
        elif i == 0 or i == 0.0:
            zero_value = old_dict[i]
        else:
            new_dict[i] = old_dict[i]
    new_dict[0.0] = zero_value + nan_value
            
    gradou = pd.Series(new_dict)
    space[i] = gradou.order()

In [6]:
%%capture output

# Save the output as a variable that can be saved to a file
for k,i in enumerate(space):
    print ""
    print subquestions_value[k].encode('utf-8')
    print
    print "Data:"
    print space[i]
    print ""
    print "Data %:"
    print space[i] / space[i].sum() * 100
    print ""
    print "Data: statistics:"
    print space[i].describe()

In [7]:
# Save+show the output to a text file
%save Q032-Staff.py str(output)
shutil.move("Q032-Staff.py", "text/Q032-Staff.txt")


The following commands were written to file `Q032-Staff.py`:

Collaborazione con o senza Partita IVA (Freelance)

Data:
2     1
3     1
1     8
0    60
dtype: int64

Data %:
2     1.428571
3     1.428571
1    11.428571
0    85.714286
dtype: float64

Data: statistics:
count     4.000000
mean     17.500000
std      28.524843
min       1.000000
25%       1.000000
50%       4.500000
75%      21.000000
max      60.000000
dtype: float64

Contratto a tempo determinato

Data:
3      1
10     1
2      2
0     66
dtype: int64

Data %:
3      1.428571
10     1.428571
2      2.857143
0     94.285714
dtype: float64

Data: statistics:
count     4.00000
mean     17.50000
std      32.33677
min       1.00000
25%       1.00000
50%       1.50000
75%      18.00000
max      66.00000
dtype: float64


In [8]:
# Swap nan for a more understandable word
space2 = {}
for i in space:
    old_dict = space[i].to_dict()
    new_dict = {}
    for k in old_dict:
        if isinstance(k, numpy.float64) and np.isnan(k):
            new_dict["Nessuna risposta"] = old_dict[k]
        elif type(k) is float and np.isnan(k):
            new_dict["Nessuna risposta"] = old_dict[k]
        else:
            new_dict[k] = old_dict[k]

    gradou = pd.Series(new_dict)
    space2[i] = gradou.order()

In [12]:
for k,i in enumerate(space2):
    # Plot the data 01
    plt.figure(figsize=(8,6))
    plt.xlabel(subquestions_value[k], fontsize=16)
    plt.ylabel('Lab', fontsize=16)
    plt.title(u"Quante sono le persone di staff retribuite e quali contratti hanno?", fontsize=18, y=1.02)
    my_colors = seaborn.color_palette("husl", len(space2[i])) # Set color palette
    space2[i].plot(kind="bar",color=my_colors)
    plt.savefig(u"svg/Q032-"+subquestions_value[k]+"01.svg")
    plt.savefig(u"png/Q032-"+subquestions_value[k]+"01.png")
    plt.savefig(u"pdf/Q032-"+subquestions_value[k]+"01.pdf")



In [10]:
# Plot the data 02
for k,i in enumerate(space2):
    # Reorder value_counts by index natural order
    space1 = space2[i].sort_index()
    plt.figure(figsize=(8,6))
    plt.title(u"Quante sono le persone di staff retribuite e quali contratti hanno? "+subquestions_value[k], fontsize=18, y=1.02)
    plt.xlabel(subquestions_value[k], fontsize=16)
    plt.ylabel('Lab', fontsize=16)

    # Plot the data
    my_colors = seaborn.color_palette("husl", len(space1)) # Set color palette
    space2[i].plot(kind='bar',color=my_colors)
    plt.savefig(u"svg/Q032-"+subquestions_value[k]+"02.svg")
    plt.savefig(u"png/Q032-"+subquestions_value[k]+"02.png")
    plt.savefig(u"pdf/Q032-"+subquestions_value[k]+"02.pdf")



In [11]:
for k,i in enumerate(space2):
    # Check histogram
    plt.figure(figsize=(8,6))
    plt.title(u"Quante sono le persone di staff retribuite e quali contratti hanno? "+subquestions_value[k], fontsize=18, y=1.02)
    plt.xlabel(subquestions_value[k], fontsize=16)
    plt.ylabel('Lab', fontsize=16)
    space2[i].hist(bins=60)
    plt.savefig(u"svg/Q032-"+subquestions_value[k]+"03.svg")
    plt.savefig(u"png/Q032-"+subquestions_value[k]+"03.png")
    plt.savefig(u"pdf/Q032-"+subquestions_value[k]+"03.pdf")



In [11]: