Perfect Plots: Slope Chart

22/10/2019 admin Data plots 0

Feel free to read the code on GitHub

 

In [1]:
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
import numpy as np

# Import Data
df = pd.read_csv('c:/2/gdppercap.txt')
df
Out[1]:
  continent 1952 1957
0 Africa 1252.572466 1385.236062
1 Americas 4079.062552 4616.043733
2 Asia 5195.484004 4003.132940
3 Europe 5661.057435 6963.012816
4 Oceania 10298.085650 11598.522455
 

To make vector of data: series 1, series 2 and labels

In [2]:
lebel = df.continent.to_list()
lebel
Out[2]:
['Africa', 'Americas', 'Asia', 'Europe', 'Oceania']
In [3]:
series1=np.round(df['1952'].to_list(), decimals=1)
series1
Out[3]:
array([ 1252.6,  4079.1,  5195.5,  5661.1, 10298.1])
In [4]:
series2=np.round(df['1957'].to_list(), decimals=1)
series2
Out[4]:
array([ 1385.2,  4616. ,  4003.1,  6963. , 11598.5])
 

Needed parameters

In [5]:
lebel = df.continent.to_list()
series1=np.round(df['1952'].to_list(), decimals=1)
series2=np.round(df['1957'].to_list(), decimals=1)
title = 'Slopechart: Comparing GDP Per Capita between 1952 vs 1957'
ylabel ='Mean GDP Per Capita'
xlabel =["1952", "1957"]
 

Definition of the trigger

In [6]:
def SlopeChart(series1, series2, title, xlabel, ylabel, lebel):
    
# Labels formatting
    left_label = [str(c) + ', '+ str(round(y)) for c, y in zip(lebel, series1)]
    right_label = [str(c) + ', '+ str(round(y)) for c, y in zip(lebel, series2)]
    klass = ['red' if (y1-y2) < 0 else 'green' for y1, y2 in zip(series1, series2)]

# lines color
    def newline(p1, p2, color='black'):
        ax = plt.gca()
        l = mlines.Line2D([p1[0],p2[0]], [p1[1],p2[1]], color='red' if p1[1]-p2[1] > 0 else 'green', marker='o', markersize=6)
        ax.add_line(l)
        return l

    fig, ax = plt.subplots(1,1,figsize=(14,14), dpi= 380)

# Vertical Lines
    ax.vlines(x=1, ymin=500, ymax=13000, color='black', alpha=0.7, linewidth=1, linestyles='dotted')
    ax.vlines(x=3, ymin=500, ymax=13000, color='black', alpha=0.7, linewidth=1, linestyles='dotted')

# Points
    ax.scatter(y=series1, x=np.repeat(1, df.shape[0]), s=10, color='black', alpha=0.7)
    ax.scatter(y=series2, x=np.repeat(3, df.shape[0]), s=10, color='black', alpha=0.7)

# Line Segmentsand Annotation
    for p1, p2, c in zip(series1, series2, df['continent']):
        newline([1,p1], [3,p2])
        ax.text(1-0.05, p1, c + ', ' + str(round(p1)), horizontalalignment='right', verticalalignment='center', fontdict={'size':14})
        ax.text(3+0.05, p2, c + ', ' + str(round(p2)), horizontalalignment='left', verticalalignment='center', fontdict={'size':14})

# 'Before' and 'After' Annotations
    ax.text(1-0.05, 13000, 'BEFORE', horizontalalignment='right', verticalalignment='center', fontdict={'size':18, 'weight':700})
    ax.text(3+0.05, 13000, 'AFTER', horizontalalignment='left', verticalalignment='center', fontdict={'size':18, 'weight':700})

# Decoration
    ax.set_title(title, fontdict={'size':22})
    ax.set(xlim=(0,4), ylim=(0,14000), ylabel=ylabel)
    ax.set_xticks([1,3])
    ax.set_xticklabels(xlabel)
    plt.yticks(np.arange(500, 13000, 2000), fontsize=12)

# Lighten borders
    plt.gca().spines["top"].set_alpha(.0)
    plt.gca().spines["bottom"].set_alpha(.0)
    plt.gca().spines["right"].set_alpha(.0)
    plt.gca().spines["left"].set_alpha(.0)
    plt.show()
 

Slope Chart realization

In [7]:
SlopeChart(series1, series2, title, xlabel, ylabel, lebel)
In [8]:
df2 = pd.read_csv('c:/1/WorldHappinessReport.csv')
df2.head(5)
Out[8]:
  Unnamed: 0 Country Region Happiness Rank Happiness Score Economy (GDP per Capita) Family Health (Life Expectancy) Freedom Trust (Government Corruption) Generosity Dystopia Residual Year
0 0 Afghanistan Southern Asia 153.0 3.575 0.31982 0.30285 0.30335 0.23414 0.09719 0.36510 1.95210 2015.0
1 1 Albania Central and Eastern Europe 95.0 4.959 0.87867 0.80434 0.81325 0.35733 0.06413 0.14272 1.89894 2015.0
2 2 Algeria Middle East and Northern Africa 68.0 5.605 0.93929 1.07772 0.61766 0.28579 0.17383 0.07822 2.43209 2015.0
3 3 Angola Sub-Saharan Africa 137.0 4.033 0.75778 0.86040 0.16683 0.10384 0.07122 0.12344 1.94939 2015.0
4 4 Argentina Latin America and Caribbean 30.0 6.574 1.05351 1.24823 0.78723 0.44974 0.08484 0.11451 2.83600 2015.0
In [9]:
kot = df2.pivot_table(index='Region',columns=['Year'], values='Happiness Rank', aggfunc='mean')
kot
Out[9]:
Year 2015.0 2016.0 2017.0
Region      
Australia and New Zealand 9.500000 8.500000 9.000000
Central and Eastern Europe 79.000000 78.448276 75.068966
Eastern Asia 64.500000 67.166667 63.600000
Latin America and Caribbean 46.909091 48.333333 50.772727
Middle East and Northern Africa 77.600000 78.105263 76.421053
North America 10.000000 9.500000 10.500000
Southeastern Asia 81.222222 80.000000 73.750000
Southern Asia 113.142857 111.714286 109.857143
Sub-Saharan Africa 127.900000 129.657895 127.871795
Western Europe 29.523810 29.190476 27.142857
In [10]:
PKP = kot.reset_index()
labelPKP = PKP['Region'].to_list()
labelPKP
Out[10]:
['Australia and New Zealand',
 'Central and Eastern Europe',
 'Eastern Asia',
 'Latin America and Caribbean',
 'Middle East and Northern Africa',
 'North America',
 'Southeastern Asia',
 'Southern Asia',
 'Sub-Saharan Africa',
 'Western Europe']
In [11]:
PKP.columns
Out[11]:
Index(['Region', 2015.0, 2016.0, 2017.0], dtype='object', name='Year')
In [12]:
PKP.columns = ['Region', '2015', '2016', '2017']
In [13]:
series1 = np.round(PKP['2015'].to_list(), decimals=1)
series1
Out[13]:
array([  9.5,  79. ,  64.5,  46.9,  77.6,  10. ,  81.2, 113.1, 127.9,
        29.5])
In [14]:
series2 = np.round(PKP['2017'].to_list(), decimals=1)
series2
Out[14]:
array([  9. ,  75.1,  63.6,  50.8,  76.4,  10.5,  73.8, 109.9, 127.9,
        27.1])
In [15]:
def SlopeChart(series1, series2, title, xlabel, ylabel, lebelPKP):
    
# Labels formatting
    left_label = [str(c) + ', '+ str(round(y)) for c, y in zip(labelPKP, series1)]
    right_label = [str(c) + ', '+ str(round(y)) for c, y in zip(labelPKP, series2)]
    klass = ['red' if (y1-y2) < 0 else 'green' for y1, y2 in zip(series1, series2)]

# lines color
    def newline(p1, p2, color='black'):
        ax = plt.gca()
        l = mlines.Line2D([p1[0],p2[0]], [p1[1],p2[1]], color='red' if p1[1]-p2[1] > 0 else 'green', marker='o', markersize=6)
        ax.add_line(l)
        return l

    fig, ax = plt.subplots(1,1,figsize=(14,14), dpi= 380)

# Vertical Lines
    ax.vlines(x=1, ymin=0, ymax=120, color='black', alpha=0.7, linewidth=1, linestyles='dotted')
    ax.vlines(x=3, ymin=0, ymax=120, color='black', alpha=0.7, linewidth=1, linestyles='dotted')

# Points
    ax.scatter(y=series1, x=np.repeat(1, PKP.shape[0]), s=10, color='black', alpha=0.7)
    ax.scatter(y=series2, x=np.repeat(3, PKP.shape[0]), s=10, color='black', alpha=0.7)

# Line Segmentsand Annotation
    for p1, p2, c in zip(series1, series2, labelPKP):
        newline([1,p1], [3,p2])
        ax.text(1-0.05, p1, c + ', ' + str(round(p1)), horizontalalignment='right', verticalalignment='center', fontdict={'size':14})
        ax.text(3+0.05, p2, c + ', ' + str(round(p2)), horizontalalignment='left', verticalalignment='center', fontdict={'size':14})

# 'Before' and 'After' Annotations
    ax.text(1-0.05, 140, 'BEFORE', horizontalalignment='right', verticalalignment='center', fontdict={'size':18, 'weight':700})
    ax.text(3+0.05, 140, 'AFTER', horizontalalignment='left', verticalalignment='center', fontdict={'size':18, 'weight':700})

# Decoration
    ax.set_title(title, fontdict={'size':22})
    ax.set(xlim=(0,4), ylim=(0,150), ylabel=ylabel) ## Skala osi Y
    ax.set_xticks([1,3])
    ax.set_xticklabels(xlabel)
    plt.yticks(np.arange(20, 150, 40), fontsize=12)  ## Podziałaka osi Y

# Lighten borders
    plt.gca().spines["top"].set_alpha(.0)
    plt.gca().spines["bottom"].set_alpha(.0)
    plt.gca().spines["right"].set_alpha(.0)
    plt.gca().spines["left"].set_alpha(.0)
    plt.show()
In [16]:
labelPKP
series1
series2
title = 'Changing the the level of happiness: 2015 vs. 2017'
ylabel ='Less points - more happiness'
xlabel =["2015", "2017"]
In [17]:
SlopeChart(series1, series2, title, xlabel, ylabel, labelPKP)

Source IPYNB file