2. Subsetting

Subsetting

• To access portions of the data

individuals = homelessness["individuals"] # Select the individuals column

print(individuals.head())

Subsetting multiple columns

• The outer square brackets are responsible for subsetting the DataFrame
• The inner square brackets are creating a list of column name

state_fam = homelessness[["state", "family_members"]] # Select the state and family_members columns

print(state_fam.head())

Subsetting rows

• The most common way is to create a logical condition to filter

ind_gt_10k = homelessness[homelessness["individuals"] > 10000] # Filter for rows where individuals is greater than 10000

print(ind_gt_10k)

fam_lt_1k_pac = homelessness[(homelessness["family_members"] < 1000) & (homelessness["region"]=="Pacific")] # and region is Pacific

print(fam_lt_1k_pac)

Subsetting based on text data

• Subset rows based on text data

mountain_reg = homelessness[homelessness["region"]=="Mountain"] # Filter for rows where region is Mountain

print(mountain_reg)

Subsetting based on dates

• International standard date format: Year then month then day

students[students[“date_of_birth”] > “2015-01-01”]

Subsetting based on multiple conditions

• To subset the rows that meet multiple conditions, can combine conditions using logical operators, such as “and” operator
• Only rows that meet both of these conditions will be subsetted

south_mid_atlantic = homelessness[(homelessness["region"] == "South Atlantic") | (homelessness["region"] == "Mid-Atlantic")] # Subset for rows in South Atlantic or Mid-Atlantic regions

print(south_mid_atlantic)

Subsetting using .isin()

• Filter on multiple values of a categorical variable
• Takes in a list of values to filter for

canu = ["California", "Arizona", "Nevada", "Utah"] # The Mojave Desert states

mojave_homelessness = homelessness[homelessness["state"].isin(canu)] # Filter for rows in the Mojave Desert states

print(mojave_homelessness)