Work, Men, Women
3. Work, Men, Women#
Data sources:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
%%bash
ls datasets/work
GENDER_EMP_12112018223032946.csv
men_working_time_AVE_HRS_12112018221342527.csv
women_working_time_AVE_HRS_12112018221357215.csv
working_time_df = pd.read_csv( 'datasets/work/men_working_time_AVE_HRS_12112018221342527.csv' )
gender_working_df = pd.read_csv('datasets/work/GENDER_EMP_12112018223032946.csv')
men_df = working_time_df[ working_time_df['Sex'] == 'Men' ]
women_df = working_time_df[ working_time_df['Sex'] == 'Women' ]
both_df = working_time_df[ working_time_df['Sex'] == 'All persons' ]
men_time = men_df[ ['Time', 'Value'] ].groupby('Time').mean()
women_time = women_df[ ['Time', 'Value'] ].groupby('Time').mean()
men_gender_working_df = gender_working_df[ gender_working_df['Sex'] == 'Men' ][ ['Indicator', 'Time','Value'] ]
women_gender_working_df = gender_working_df[ gender_working_df['Sex'] == 'Women' ][ ['Indicator', 'Time','Value'] ]
women_reported = women_gender_working_df.groupby('Indicator').mean()/60
/tmp/ipykernel_1945/2608765999.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
women_reported = women_gender_working_df.groupby('Indicator').mean()/60
men_reported = men_gender_working_df.groupby('Indicator').mean()/60
/tmp/ipykernel_1945/2445414382.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
men_reported = men_gender_working_df.groupby('Indicator').mean()/60
women_reported
| Value | |
|---|---|
| Indicator | |
| Time spent in paid work, by sex | 3.542957 |
| Time spent in total work, by sex | 7.979086 |
| Time spent in unpaid work, by sex | 4.436452 |
men_reported
| Value | |
|---|---|
| Indicator | |
| Time spent in paid work, by sex | 5.30172 |
| Time spent in total work, by sex | 7.47672 |
| Time spent in unpaid work, by sex | 2.17500 |
with plt.xkcd():
plt.figure( figsize=(10,6.18) )
N = 2
unpaid = ( men_reported.loc['Time spent in unpaid work, by sex'].values[0] , women_reported.loc['Time spent in unpaid work, by sex'].values[0] )
paid = ( men_reported.loc['Time spent in paid work, by sex'].values[0], women_reported.loc['Time spent in paid work, by sex'].values[0] )
ind = [0.2, 0.8] # the x locations for the groups
width = 0.35 # the width of the bars: can also be len(x) sequence
unpaid_plt = plt.bar(ind, unpaid, width, color='k', alpha=0.45)
paid_plt = plt.bar(ind, paid, width, bottom=unpaid, color='g', alpha=0.45)
plt.yticks(np.arange(0, 10, 1))
plt.ylabel('Hours')
plt.title('Time Spent in Paid and Unpaid Work Per Day')
plt.xlim([0,1])
plt.xticks(ind, ('Men', 'Women'))
plt.legend(( paid_plt[0], unpaid_plt[0]), ('Paid' , 'Unpaid',), loc='center' )
plt.text(
0.5, 8.5,
'Source: OECD.Stat',
ha='center')
plt.show()
findfont: Font family ['xkcd', 'xkcd Script', 'Humor Sans', 'Comic Neue', 'Comic Sans MS'] not found. Falling back to DejaVu Sans.
findfont: Font family ['xkcd', 'xkcd Script', 'Humor Sans', 'Comic Neue', 'Comic Sans MS'] not found. Falling back to DejaVu Sans.
with plt.xkcd():
# Based on "The Data So Far" from XKCD by Randall Monroe
# http://xkcd.com/373/
fig, ax = plt.subplots( figsize=(10, 6.18) )
ax.plot( men_time.index.values, men_time.Value.values/5 )
ax.plot( women_time.index.values, women_time.Value.values/5 )
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.set_ylabel('Hours')
ax.set_xlabel('Year')
ax.set_ylim([0,8])
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
plt.title("Weekly Hours Worked on the Main Job")
fig.text(
0.5, -0.05,
'Source: OECD.Stat',
ha='center')
#ax.margin([0.5, 0.5, 0.5, 0.5])
plt.show()
