Feel free to read the code on GitHub
An old Chinese proverb says: one picture says more than one thousands words.
One good plot can rescue entire presentation. One poor picture can drown down all good speech. After plenty of shame appointment and boring presentations I decided to improve my tools of visualisation.
In [1]:
import squarify
import pandas as pd
import matplotlib.pyplot as plt
df1 = pd.read_csv('c:/11/freeFormResponses.csv', skiprows = 1)
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headers = ['Duration (in seconds)', 'Gender', 'Gender2','Age','Country','Education', 'Major_undergraduate','Recent_role', 'Recent_role2', 'Industry','Industry2' ,'Years_of_experience', 'compensation$USD']
df = pd.read_csv('c:/11/multipleChoiceResponses.csv', usecols=[0,1,2,3,4,5,6,7,8,9,10,11,12], header=None, names=headers, skiprows=2)
df.head(4)
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In [3]:
df.drop(['Gender2','Recent_role2','Industry2'], axis=1, inplace=True)
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df.isnull().sum()
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In [5]:
df.dtypes
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Very important is reduction of the class or join some similar groups if it is not bad for the project.
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df['Gender']=df['Gender'].replace('Prefer to self-describe', 'Prefer not to say')
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df.Education.value_counts(dropna = False)
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We can get assumption if somebody didn’t answer he didn’t want to give information: 'I prefer not to answer’.
In [8]:
import numpy as np
df['Education']=df['Education'].replace(np.NaN, 'I prefer not to answer')
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df.Education.value_counts(dropna = False)
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df.Education.isnull().sum()
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df.Major_undergraduate.value_counts(dropna = False)
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Rozumiem, że NaN i 'Other’ jest wtedy, gdy ktoś nie chce zadeklarować swojej specjalizacji:’I never declared a major’
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df['Major_undergraduate']=df['Major_undergraduate'].replace(np.NaN, 'I never declared a major')
df['Major_undergraduate']=df['Major_undergraduate'].replace('Other', 'I never declared a major')
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df.Major_undergraduate.value_counts(dropna = False, normalize=True).plot(kind='barh')
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In [14]:
df.Recent_role.value_counts(dropna=False)
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In [15]:
df['Recent_role']=df['Recent_role'].replace(np.NaN, 'Other')
Poland in data
Because I am from Poland, most interesting data for me is information from my country. I separate data about Poland from original data.
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PL= df[df.Country=='Poland']
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Z5 = PL.pivot_table(index=['Major_undergraduate'], values='Age',aggfunc='count').sort_values('Age', ascending=False)
Z5.head(10)
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The Treemap
I came across this publication and decided to do Treemap by this way.
https://www.machinelearningplus.com/plots/top-50-matplotlib-visualizations-the-master-plots-python/
To prepare perfect pie plot first I will need to pull vectors of data from the pivot table.
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PPL=Z5.reset_index()
PPL.head(5)
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Cut out too long descriptions
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PPL['Major_undergraduate']= PPL['Major_undergraduate'].str.split('(').apply(lambda x: x[0])
PPL['Major_undergraduate']
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Adds numbers of occurrences to the descriptions
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label = PPL['Major_undergraduate'].to_list()
label = PPL.apply(lambda x: str(x[0]) + "n (" + str(x[1]) + ")", axis=1)
label
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To pull vectors of data from the pivot table
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PPL.reset_index()
label
sizes = PPL['Age'].to_list()
colors = ['#ff0000','#434343','#666666','#999999','#b7b7b7','#cccccc','#d9d9d9','#efefef','#ffffff','#f3f3f3']
In [22]:
import squarify
import matplotlib.pyplot as plt
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# Plot
plt.figure(figsize=(12,8), dpi= 380)
squarify.plot(sizes=sizes, label=label, color=colors, alpha=0.9)
plt.title('Data Scientist society in Poland (2018)', fontdict={'fontsize': 30, 'fontweight': 'medium', 'color':'#d0e0e3','alpha':0.8, 'y':1.02})
plt.axis('off') # brak numerów na osiach
plt.show()
Trigger to create Treemap
Components to create perfect pie plot: labels, sizes, colors, title
To prepare perfect treemap first I will need to pull vectors of data from the pivot table.
To pull vectors of data from the pivot table
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PPL.reset_index()
label = label = PPL['Major_undergraduate'].to_list()
label = PPL.apply(lambda x: str(x[0]) + "n (" + str(x[1]) + ")", axis=1)
sizes = PPL['Age'].to_list()
title = 'Data Scientist society in Poland (2018)'
# https://yagisanatode.com/2019/08/06/google-apps-script-hexadecimal-color-codes-for-google-docs-sheets-and-slides-standart-palette/
#colors = ['#274e13','#6aa84f','#93c47d', '#b6d7a8','#d9ead3','#b7b7b7','#38761d'] #green
#colors = ['#0c343d','#134f5c','#45818e','#76a5af','#a2c4c9','#d0e0e3'] #cyan
#colors = ['#7f6000','#bf9000','#f1c232','#ffd966','#ffe599','#fff2cc'] #yelow
#colors = ['#4c1130','#a64d79','#c27ba0','#d5a6bd','#ead1dc','#741b47',] #magenta
#colors = ['#e6b8af','#b6d7a8','#e06666','#747574','#ffd966','#ffcc99','#ea9999']
#colors = ['#93c47d','#b6d7a8','#d9ead3','#d0e0e3','#a2c4c9','#76a5af']
colors = ['#c27ba0','#d5a6bd','#ead1dc','#ffffff','#a64d79','#d9d2e9','#b4a7d6'] #purple
#colors = ['#cfe2f3','#9fc5e8','#6fa8dc'] #blue
#colors = ['#d9ead3','#b6d7a8','#93c47d','#6aa84f']
#colors = ['#ff0000','#434343','#666666','#999999','#b7b7b7','#cccccc','#d9d9d9','#efefef','#ffffff','#f3f3f3'] #=> niemieckie czasopismo
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import squarify
import matplotlib.pyplot as plt
def Tmap(sizes, labels, colors, title):
plt.figure(figsize=(12,8), dpi= 380)
squarify.plot(sizes=sizes, label=label, color=colors, alpha=0.9)
plt.title(title, fontdict={'fontsize': 30, 'fontweight': 'medium', 'color':'#d0e0e3','alpha':0.9, 'y':1.02})
plt.axis('off') # brak numerów na osiach
plt.show()
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Tmap(sizes, label, colors, title)
