Categorische variabelen

Data opschonen in Python

Adel Nehme

Content Developer @DataCamp

Welke fouten kunnen we hebben?

I) Waarde-inconsistentie

Inconsistente velden: 'married', 'Maried', 'UNMARRIED', 'not married'..
Spaties aan het einde: 'married ', ' married '..

II) Te veel categorieën terugbrengen tot weinig

Nieuwe groepen maken: categorieën 0-20K, 20-40K ... uit continue inkomensdata per huishouden
Groepen mappen naar nieuwe: inkomenscategorieën mappen naar 2: 'rich', 'poor'

III) Zeker weten dat het datatype category is (zie hoofdstuk 1)

Waardconsistentie

Hoofdlettergebruik: 'married', 'Married', 'UNMARRIED', 'unmarried'..

# Haal de kolom marriage_status op
marriage_status = demographics['marriage_status']
marriage_status.value_counts()

unmarried    352
married      268
MARRIED      204
UNMARRIED    176
dtype: int64

Waardconsistentie

# Aantallen op DataFrame
marriage_status.groupby('marriage_status').count()

                 household_income  gender
marriage_status                          
MARRIED                       204     204
UNMARRIED                     176     176
married                       268     268
unmarried                     352     352

Waardconsistentie

# Omzetten naar hoofdletters

marriage_status['marriage_status'] = marriage_status['marriage_status'].str.upper()
marriage_status['marriage_status'].value_counts()

UNMARRIED    528
MARRIED      472

# Omzetten naar kleine letters

marriage_status['marriage_status'] = marriage_status['marriage_status'].str.lower()
marriage_status['marriage_status'].value_counts()

unmarried    528
married      472

Waardconsistentie

Spaties aan randen: 'married ', 'married', 'unmarried', ' unmarried'..

# Haal de kolom marriage_status op
marriage_status = demographics['marriage_status']
marriage_status.value_counts()

 unmarried   352
unmarried    268
married      204
married      176
dtype: int64

Waardconsistentie

# Verwijder alle spaties
demographics = demographics['marriage_status'].str.strip()
demographics['marriage_status'].value_counts()

unmarried    528
married      472

Data samenvoegen tot categorieën

Categorieën maken van data: kolom income_group uit kolom income.

# Using qcut()
import pandas as pd
group_names = ['0-200K', '200K-500K', '500K+']
demographics['income_group'] = pd.qcut(demographics['household_income'], q = 3, 
                                       labels = group_names)
# Print income_group column
demographics[['income_group', 'household_income']]

     category  household_income
0   200K-500K  189243
1       500K+  778533
..

Data samenvoegen tot categorieën

Categorieën maken van data: kolom income_group uit kolom income.

# Using cut() - maak categoriebereiken en namen
ranges = [0,200000,500000,np.inf]
group_names = ['0-200K', '200K-500K', '500K+']
# Maak income_group-kolom
demographics['income_group'] = pd.cut(demographics['household_income'], bins=ranges, 
                                      labels=group_names)
demographics[['income_group', 'household_income']]

     category  Income
0      0-200K  189243
1       500K+  778533

Data samenvoegen tot categorieën

Categorieën mappen naar minder: categorieën in een categorische kolom verminderen.

Kolom operating_system is: 'Microsoft', 'MacOS', 'IOS', 'Android', 'Linux'

Kolom operating_system wordt: 'DesktopOS', 'MobileOS'

# Maak mapping-dict en vervang
mapping = {'Microsoft':'DesktopOS', 'MacOS':'DesktopOS', 'Linux':'DesktopOS',
           'IOS':'MobileOS', 'Android':'MobileOS'}
devices['operating_system'] = devices['operating_system'].replace(mapping)
devices['operating_system'].unique()

array(['DesktopOS', 'MobileOS'], dtype=object)

Laten we oefenen!

Data opschonen in Python