Categorical variables

Cleaning Data in Python

Adel Nehme

Content Developer @DataCamp

What type of errors could we have?

I) Value inconsistency

Inconsistent fields: 'married', 'Maried', 'UNMARRIED', 'not married'..
_Trailing white spaces: _'married ', ' married '..

II) Collapsing too many categories to few

Creating new groups: 0-20K, 20-40K categories ... from continuous household income data
Mapping groups to new ones: Mapping household income categories to 2 'rich', 'poor'

III) Making sure data is of type category(seen in Chapter 1)

Value consistency

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

# Get marriage status column
marriage_status = demographics['marriage_status']
marriage_status.value_counts()

unmarried    352
married      268
MARRIED      204
UNMARRIED    176
dtype: int64

Value consistency

# Get value counts on DataFrame
marriage_status.groupby('marriage_status').count()

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

Value consistency

# Capitalize

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

UNMARRIED    528
MARRIED      472

# Lowercase

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

unmarried    528
married      472

Value consistency

Trailing spaces: 'married ', 'married', 'unmarried', ' unmarried'..

# Get marriage status column
marriage_status = demographics['marriage_status']
marriage_status.value_counts()

 unmarried   352
unmarried    268
married      204
married      176
dtype: int64

Value consistency

# Strip all spaces
demographics = demographics['marriage_status'].str.strip()
demographics['marriage_status'].value_counts()

unmarried    528
married      472

Collapsing data into categories

Create categories out of data: income_group column from income column.

# 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
..

Collapsing data into categories

Create categories out of data: income_group column from income column.

# Using cut() - create category ranges and names
ranges = [0,200000,500000,np.inf]
group_names = ['0-200K', '200K-500K', '500K+']
# Create income group column
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

Collapsing data into categories

Map categories to fewer ones: reducing categories in categorical column.

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

operating_system column should become: 'DesktopOS', 'MobileOS'

# Create mapping dictionary and replace
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)

Let's practice!

Cleaning Data in Python