OHS public data – matplotlib visualisations3

Agnieszka
Lately the wheather makes me “where is my mind” mood. No sun, no snow, no green, so I decided to upload picture from my archives, this little, emaciated fawn I photographed in october 2017.

Today I searched for real public data from Statistics Poland site in OH&S area (occupational safety and health) and downloaded report titled:

Accidents at work and related health problems
with work in the second quarter of 2020 (dated:15/12/2020)

For first table with data, I found on second page, I created simple pie diagram as follow:

import pandas as pd
import matplotlib.pyplot as plt

OHS_file = pd.read_table(
    'OHS_2Q2020.txt', sep=",", skiprows=[0,3,4,5], 
    names=['Gender','ValuesInMln','OneAilment%',
           'MoreThanOne%'],
)
df = OHS_file.sort_values('ValuesInMln',ascending=False)
print(df)
A = list(df['ValuesInMln'])
print(A)
File Data (selected part)
# Pie chart
labels = 'Man', 'Woman'
sizes = A
explode = (0, 0.05)  

fig1, ax1 = plt.subplots()
ax1.pie(sizes, explode=explode, labels=labels, 
         autopct='%1.1f%%',
         shadow=True, startangle=90)
ax1.axis('equal')  
plt.title("Feeling at least one" 
work-related health condition by Gender")
plt.show()
Data Visualisation – simple pie chart
import pandas as pd
import matplotlib.pyplot as plt

OHS_file = pd.read_table(
    'OHS_2Q2020_accidents.txt', sep=","
)
df1 = OHS_file[0:4]
print(df1)

X = list(df1['DaysAtWork'])
print("X:",X)
A = list(df1['Volume[%]'])
print("A:",A)
Data File
# Pie chart
labels = X
sizes = A
explode = (0, 0, 0, 0.1)

fig1, ax1 = plt.subplots()
ax1.pie(sizes, explode=explode, labels=labels, 
         autopct='%1.1f%%',
         shadow=True, startangle=90)
ax1.axis('equal')
plt.title("Accidents at work-the examination 
by days incapacity for work")
plt.legend()
plt.show()
Data Visualisation – pie chart


Employed persons by gender and groups of workplace factors adverse effects on physical health

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

OHS_factors = pd.read_table(
    'OHS_2Q2020_factors.txt', sep=","
)
print(OHS_factors[0:5])

factors = list(OHS_factors['Factor'])
Factors = factors[0:5]
print("X: ", Factors)

man = list(OHS_factors['Man'])
Men = man[0:5]
print("Men:",Men)

woman = list(OHS_factors['Woman'])
Women = woman[0:5]
print("Women:",Women)
results of data reading
ohs_2q2020_factorsDownload

Horizontal bar chart

def survey(results, category_names):
    labels = list(results.keys())
    data = np.array(list(results.values()))
    data_cum = data.cumsum(axis=1)
    category_colors = plt.get_cmap('RdYlGn')(
        np.linspace(0.15, 0.85, data.shape[1]))

    fig, ax = plt.subplots(figsize=(9, 2.5))
    ax.invert_yaxis()
    ax.xaxis.set_visible(False)
    ax.set_xlim(0, np.sum(data, axis=1).max())

   for i, (colname, color) in enumerate(zip(category_names, 
                                             category_colors)):
        widths = data[:, i]
        starts = data_cum[:, i] - widths
        ax.barh(labels, widths, left=starts, height=0.5,
                label=colname, color=color)
        xcenters = starts + widths / 2

        r, g, b, _ = color
        text_color = 'white' if r * g * b < 0.5 else 'darkgrey'
        for y, (x, c) in enumerate(zip(xcenters, widths)):
            ax.text(x, y, str(int(c)), ha='center', va='center',
                    color=text_color)
    ax.legend(ncol=len(category_names), bbox_to_anchor=(0, 1),
              loc='lower left', fontsize='small')

    return fig, ax

survey(results, category_names)
plt.show()

Data Visualisation – bar chart